industrial electricity usage and stock returnszda/pred_eg.pdf · 2017-07-08 · industrial...

JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS Vol 52 No 1 Feb 2017 pp 37ndash69COPYRIGHT 2017 MICHAEL G FOSTER SCHOOL OF BUSINESS UNIVERSITY OF WASHINGTON SEATTLE WA 98195doi101017S002210901600079X

Industrial Electricity Usage and Stock Returns

Zhi Da Dayong Huang and Hayong Yun

AbstractThe growth rate of industrial electricity usage predicts future stock returns up to 1 yearwith an R2 of 9 High industrial electricity usage today predicts low stock returns in thefuture consistent with a countercyclical risk premium Industrial electricity usage tracksthe output of the most cyclical sectors Our findings bridge a gap between the asset pric-ing literature and the business cycle literature which uses industrial electricity usage togauge production and output in real time Industrial electricity growth compares favorablywith traditional financial variables and it outperforms Cooper and Priestleyrsquos output gapmeasure in real time

I IntroductionCan stock market returns be predicted This question is central to asset pric-

ing portfolio choice and risk management The general finding in the literatureis that price-based financial variables tend to predict stock returns better thanquantity-based macroeconomic indicators (Campbell (2003) Cochrane (2008)and Lettau and Ludvigson (2009) among others) This finding is discomfitingas expected returns should ultimately be linked to the business cycle In facta countercyclical risk premium is predicted by almost all leading asset pricingmodels whether they are consumption-based (Campbell and Cochrane (1999)Bansal and Yaron (2004) among others) or production-based models (Cochrane(1991) Zhang (2005) Li Livdan and Zhang (2009) and Liu Whited and Zhang(2009) among others) However many of the traditional business cycle vari-ables such as the growth rate of the gross domestic product (GDP) do not fore-cast stock returns (Pena Restoy and Rodriguez (2002)) One recent exception(Cooper and Priestley (2009)) finds that the deviation of log industrial production

Da zdandedu Mendoza College of Business University of Notre Dame Huang d huanguncgedu Bryan School of Business and Economics University of North Carolina at Greens-boro and Yun (corresponding author) yunhabusmsuedu Eli Broad College of Business MichiganState University We thank Hendrik Bessembinder (the editor) Tom Cosimano Bjorn Eraker WayneFerson Ravi Jagannathan Bill McDonald Stavros Panageas Jesper Rangvid Marco Rossi RamanUppal Annette Vissing-Jorgensen Jason Wei Xiaoyan Zhang and an anonymous referee for helpfulcomments We thank Manisha Goswami Steve Hayes Dongyoup Lee and Liang Tan for data supportAny errors are our own

37

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38 Journal of Financial and Quantitative Analysis

from its long-run trend also known as the output gap predicts stock market re-turns well (ldquoLogrdquo refers to natural logarithm throughout)

In this paper we propose a novel yet simple business cycle variable thatpredicts stock market returns well and even outperforms the output gap whenused in real time This variable is the growth rate of the aggregate industrial usageof electricity

Most modern industrial production activities involve the use of electricityCrucially because of technological limitations electricity cannot easily be storedAs a result industrial electricity usage can be used to track production and outputin real time1 Indeed since 1971 the Federal Reserve (ldquothe Fedrdquo) has been usingsurvey data on electric power when estimating key components of its monthlyindustrial production index The practice was discontinued in 2005 due to poorsurvey coverage2

Because electric utilities are highly regulated and are subject to extensivedisclosure requirements electricity usage data are accurately measured and re-ported For these reasons the business cycle literature has long used industrialelectricity usage as a proxy for capital services (Jorgenson and Griliches (1967)Burnside Eichenbaum and Rebelo (1995) (1996) and Comin and Gertler(2006)) Capacity utilization which is reflected in industrial electricity usage ap-pears to be the key missing ingredient that allows a relatively mild productivityshock to drive a much more volatile business cycle (King and Rebello (2000))Despite the importance of industrial electricity usage as a business cycle variableits predictive power for stock market returns has not been examined in the litera-ture Our paper fills this gap

Because monthly industrial electricity usage data are available in the UnitedStates in our sample period 1956ndash2010 we first conduct overlapping monthlypredictive regressions to maximize the power of the test To alleviate the impactof within-year seasonality in electricity usage we compute year-over-year growthrates For example we use the industrial electricity growth rate from January inyear tminus1 to January in year t to predict the excess stock return in February inyear t We then use the electricity growth rate from February in year tminus1 toFebruary in year t to predict the excess stock return in March in year t and so onStambaugh (1999) argues that predictive regressions potentially lead to overesti-mated t-values with a small sample in an overlapping regression because manypredictive variables are persistent To address this bias we follow Li Ng andSwaminathan (2013) closely and report p-values from simulation exercises Forcomparison purposes we also report the more standard Hodrick (1992) t-value

We find that this simple year-over-year industrial electricity usage growthrate has strong and significant predictive power for future stock market excessreturns in horizons ranging from 1 month up to 1 year At the annual hori-zon a 1 increase in the year-over-year industrial electricity usage growth rate

1As anecdotal evidence the Chinese premier relies on electricity consumption as a more accuratemeasure of economic growth in China ldquoAll other figures especially GDP statistics are lsquoman-madersquoand therefore unreliablerdquo See Wall Street Journal Dec 6 2010

2The survey was conducted by the regional Federal Reserve Banks of the electric utilities in theirdistrict it was not the Department of EnergyEnergy Information Administration survey that we usein this paper


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Da Huang and Yun 39

predicts an excess stock return that is 092 lower in the next year with an R2

of 864Compared with commercial and residential electricity usage industrial elec-

tricity usage is less affected by weather conditions Nevertheless to make sure ourresults are not driven by weather changes we orthogonalize industrial electricitygrowth on a weather-change variable and focus on the residual The weather-adjusted electricity usage growth rate produces very similar results suggestingthat any potential weather-effect remnants in our year-over-year electricity growthrate are not driving the predictive results

The in-sample predictive power of the industrial electricity usage growth ratecompares favorably to 10 well-known return predictors that are based on financialinformation These predictive variables include dividendndashprice ratio earningsndashprice ratio book-to-market ratio Treasury bill rates the default premium the termpremium net equity issuance inflation returns on long-term government bondsand stock variance These predictors are associated with much lower R2 valuesand their regression coefficients are in general insignificant with the inflation rateand the returns on long-term government bonds as the exceptions When we in-clude industrial electricity usage growth with the 10 predictors one at a timein the same predictive regression electricity growth drives out all the financialvariables except the inflation rate and the returns on long-term government bonds

We also compare industrial electricity growth to several predictors that arebased directly on industrial production The first is the year-over-year growthrate in monthly industrial production The second is the year-over-year change inmonthly capital utilization The next two are production growth from the fourthquarter of the previous year to the fourth quarter of this year and productiongrowth from the third quarter of this year to the fourth quarter of this year The lastpredictor is the in-sample output gap investigated by Cooper and Priestley (2009)who measure the gap as the deviation of log industrial production from its long-run trend using the full sample for regression These five measures are all highlycorrelated with industrial electricity growth At an annual frequency the correla-tions of industrial electricity growth with industrial-output growth from Decemberto December or fourth quarter to fourth quarter or third quarter to fourth quar-ter and capacity utilization are all above 60 the correlation with the in-sampleoutput gap is lower but still at 36 The high correlations are not surprising be-cause industrial-output-based measures just like industrial electricity usage arebusiness cycle variables as evidenced by their high correlations with the NationalBureau of Economic Research (NBER) expansion indicator

Which business cycle variable is the best predictor of future market returnsWe find the in-sample output gap to be the strongest predictor It has an R2 ofmore than 16 for predicting next-year market excess returns and the regres-sion slope coefficients are highly significant Nevertheless we find that indus-trial electricity usage growth comes in second and it outperforms the remainingindustrial-output-based measures including various versions of industrial-outputgrowth capacity utilization and the out-of-sample output gap which computesthe gap using backward rolling windows In addition even though the in-sampleoutput gap outperforms industrial electricity usage growth on a standalone basis


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when they are included in the same regression we find that industrial electricityusage growth remains significant This finding suggests that industrial electricityusage has incremental return predictive power

How can industrial electricity growth outperform the industrial-outputgrowth rate in predicting future stock returns We examine this question in detailby zooming in on industrial output from the 14 different industries that account formost of the total industrial output We first regress the output growth in each in-dustry on the electricity growth rate The regression coefficient therefore measuresthe outputrsquos sensitivity to electricity usage for each industry The industries withthe highest sensitivity to electricity usage are steel machinery fabricated prod-ucts and construction These industries are likely to be more capital-intensivewhich is consistent with the high sensitivity of their output to electricity usage3

The output growth rates of these four industries are highly cyclical One rea-son is that they produce capital goods used by other firms to make their own prod-ucts When demand is slack few firms will expand and purchase capital goodsThus capital goods producers bear the brunt of a slowdown but perform wellin good times Another reason is that these capital-intensive producers often havehigher operating leverage and therefore are more exposed to business cycle fluctu-ations Indeed we find the output growth of these four industries with high sensi-tivities to electricity usage to have strong predictive power for future stock returnsIn sharp contrast the output growth of the remaining industries which have mod-est or low sensitivity to electricity usage has little return predictive power Thisfinding suggests that industrial electricity usage appears to be a good measure ofoutput in the very cyclical industries which explains why it performs better thanthe total industrial output in forecasting stock returns

The predictability of stock returns is typically taken out of sample Welch andGoyal (2008) show that none of the existing predicting variables outperforms thehistorical mean in their out-of-sample experiment For this reason we evaluate theperformance of the industrial electricity growth rate and other return predictorsusing the out-of-sample test methodology of Campbell and Thompson (2008)Whereas most financial variables underperform the historical mean in the out-of-sample test the industrial electricity growth rate beats it and by the largestmargin When compared to the other industrial-output-based measures the onlyvariable that outperforms industrial electricity growth is the in-sample output gap

Because industrial electricity usage data are available only at an annual fre-quency in the United Kingdom and Japan we also conduct annual predictive re-gressions where the dependent variable is always excess stock returns in the nextcalendar year These annual regressions allow us to examine the performance ofindustrial electricity growth beyond the United States and also to compare it toother output measures Moreover annual regressions avoid the use of overlap-ping samples and are less subject to statistical inference bias Several interestingpatterns emerge from these annual-horizon analyses in all three countries

First the annual industrial electricity usage growth rate by itself remains agood predictor of future excess stock returns its regression R2 values are 1015

3See the discussion in the Federal Reserversquos ldquoIndustrial Production and Capacity Utilization The2005 Annual Revisionrdquo p A50 (httpswwwfederalreservegovpubsbulletin2006ip06 2pdf)


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Da Huang and Yun 41

in the United States 695 in Japan and 11 in the United Kingdom Secondindustrial electricity usage growth clearly outperforms the year-over-year out-put growth because when these two are combined electricity has much highert-values and lower p-values for all three countries Third when industrial elec-tricity usage is combined with various output growth measures as analyzed byMoller and Rangvid (2015) we find that industrial electricity usually outperformsother variables The only exception is that it underperforms the output growth ofthe third quarter of the current year to the fourth quarter of the current year in theUnited States Finally although Cooper and Priestleyrsquos (2009) output gap mea-sure forecasts stock market returns better on a standalone basis it does not driveout the electricity growth rate in the United States In fact industrial electricityusage growth rates often have higher t-values than the output gap does in head-to-head comparisons In other words industrial electricity usage contains valuableand incremental information that helps predict future stock returns

We could also compare industrial electricity usage growth to investmentgrowth rates using annual predictive regressions in the United States where quar-terly investment data are available Not surprisingly investment growth rates out-put growth rates and the industrial electricity growth rate are all highly correlatedat annual frequency We find that annual investment growth rates computed fromfourth quarter to fourth quarter and from third quarter to fourth quarter have pre-dictive power for the next yearrsquos excess stock returns These findings provide fur-ther empirical support for the investment-based asset pricing literature As arguedby Cochrane (1991) and more recently by Lin and Zhang (2013) under fairlygeneral assumptions investment today should negatively predict stock returns to-morrow Nevertheless industrial electricity usage growth still does a much betterjob than investment growth in predicting future excess stock returns in univariateregressions and it drives out investment growth in multivariate regressions Onepossible reason is that the standard investment data focus only on investment incapital stock When existing capital is utilized more intensively more investmentis also needed to maintain it Such a maintenance investment can be large it is es-timated to be 30 of the investment in new physical capital according to surveydata from Canada (see McGrattan and Schmitz (1999)) Although comprehensivemaintenance investment data are not directly available industrial electricity us-age is a good proxy because higher electricity use reflects more intensive capitalutilization and implies more maintenance investment

From a real-life investment point of view the industrial electricity usagegrowth rate is in our view a superior return predictor because it can be easilycalculated almost in real time In contrast the benchmark in-sample output mea-sure described by Cooper and Priestley (2009) requires estimation using a fullsample When we compare the industrial electricity usage growth rate to the out-of-sample output gap both lagged by 2 months so that investors can use them inreal time it is clear that the former outperforms the latter completely across allforecasting horizons

Our paper contributes to the long line of literature on stock return pre-dictability such as Campbell (2003) Cochrane (2008) Lamont (2000) Lettau andLudvigson (2001) Lustig and van Nieuwerburgh (2005) Lettau and Ludvigson(2010) Santos and Veronesi (2006) Rangvid (2006) Cooper and Priestley (2009)


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Belo and Yu (2013) and Rapach and Zhou (2013) among many others Fama andFrench (1989) suggest that financial variables correlate with the business cycleand can predict stock returns Also behavioral variables such as investor senti-ment (Baker and Wurgler (2006) Charoenrook (2003)) and consumer confidence(Fisher and Statman (2003) Ludvigson (2004)) can also predict stock returnsSeveral papers such as those by Campbell (2003) Cochrane (2008) and Lettauand Ludvigson (2009) show that price-based financial variables tend to predictstock returns better than quantity-based macroeconomic indicators In fact typ-ical business cycle indicators such as GDP do not forecast stock returns (Penaet al (2002)) We find that industrial electricity usage growth by overweight-ing the most business-cycle-sensitive industries predicts stock returns well Ourpaper thus contributes to the literature by linking financial markets and the realeconomy

The rest of the paper proceeds as follows Section II describes the data andprovides summary statistics for the main variables Sections III and IV present ourempirical results from monthly and annual regressions respectively Section Vexamines the predictive power in real time Section VI concludes

II Data

A Electricity and Weather DataMonthly industrial electricity usage data (millions of kilowatt-hours) in the

United States are manually collected from two sources published by the EnergyInformation Administration (EIA) Electric Power Statistics for data from 1955ndash1978 and Electric Power Monthly for data from 1979ndash20104 Because electric-ity consumption data can be revised by the EIA our hand collection of vintagedata minimizes any potential forward-looking bias which is an important concernwhen conducting return predictability tests The vintage data are usually availablewithin 2 months at most In other words Januaryrsquos electricity usage is availableby the end of March

A key concern with monthly electricity usage data is the strong within-year seasonal effects caused by such things as weather fluctuations For exam-ple Figure 1 shows normalized electricity usage (Graph A) and energy degreedays (EDDs) for each month (Graph B) EDDs are the sum of cooling degreedays (CDDs) and heating degree days (HDDs) which measure summer and win-ter weather variation respectively5 As shown in the figure industrial electricity

4EIA Form 826 describes the customers The residential sector consists of living quarters for pri-vate households The commercial sector consists of service-providing facilities such as businessesgovernments and institutional living quarters The industrial sector consists of facilities for producinggoods such as manufacturing (North American Industry Classification System (NAICS) codes 31ndash33) agriculture forestry and hunting (NAICS code 11) mining including oil and gas extraction(NAICS code 21) natural gas distribution (NAICS code 2212) and construction (NAICS code 23)Other customers include public street and highway lighting public authorities railroads and railwaysand irrigation as well as interdepartmental sales Total electricity usage accounts for the amount usedby ultimate customers and hence excludes resold or wasted amounts It also excludes direct use whichis electricity used in power plants for generating electricity

5Summer (winter) weather is measured by monthly cooling (heating) degree days (CDDs orHDDs) which we obtain from NOAA The daily CDD (HDD) values capture deviations in daily


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Da Huang and Yun 43

FIGURE 1Normalized Electricity Consumption and Weather Monthly (US)

Figure 1 shows normalized electricity usage and weather conditions Electricity usage data are obtained from the EnergyInformation Administration (EIA) Weather data are obtained from the National Oceanic and Atmospheric Administration(NOAA) Graph A shows normalized residential (circle dots) commercial (square dots) and industrial (triangle dots)electricity usage Normalized electricity usage is the average monthly consumption divided by the annual consumptionover the sample period (1956ndash2010) for each month Graph B plots the normalized average energy degree days (EDDs)for each month over the same period EDDs are the sum of normalized cooling degree days (CDDs) and normalizedheating degree days (HDDs) which measure summer and winter weather variation respectively

005

0 0

60

070

080

090

1

1 2 3

Graph A Normalized Residential Commercial and Industrial Electricity Usage

Graph B Normalized EDDs

4 5 6 7 8 9 10 11 12

Month

Nor

mal

ized

Ele

ctric

ity C

onsu

mpt

ion

1 2 3 4 5 6 7 8 9 10 11 12

Month

00

51

15

2

Nor

mal

ized

ED

D

mean temperatures above (below) 65 F the benchmark at which energy demand is low As an exam-ple if the average temperature is 75 F the corresponding CDD value for the day is 10 and the HDDis 0 If the average temperature is 55 F the corresponding CDD value for the day is 0 and the HDDis 10 Monthly CDD (HDD) values are the sum of the daily CDD (HDD) values in each month CDDand HDD values are computed from mean temperatures for the United Kingdom and Japan Meantemperatures are obtained from the Met Office Hadley Centre for the United Kingdom and from theJapan Meteorological Agency for Japan


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usage the focus of our paper is stable within the year and weather fluctuation isless likely to affect industrial electricity consumption To further alleviate the sea-sonality effect we compute year-over-year growth rates in industrial electricityusage between the same months in two successive years and thus identify dif-ferences in demand due to changes in economic conditions rather than seasonalweather effects One may argue that year-over-year electricity usage growth isstill subject to residual weather effects (for instance if Dec 2014 is unusuallycold compared with other Decembers) To that end we also orthogonalize year-over-year electricity growth rates on weather changes measured with EDDs Wefind that residual electricity usage growth performs similarly if not slightly betterin predicting stock returns

Annual industrial electricity consumption data for Japan and the UnitedKingdom are obtained from the International Energy Agencyrsquos Energy Balancesof Organization for Economic Cooperation and Development (OECD) countries

B Output MeasuresWe consider several output growth measures Monthly industry produc-

tion data are obtained from the Federal Reserve Bank of St Louisrsquos EconomicData (FRED) Web site (httpsfredstlouisfedorg) With the monthly date wecan compute year-over-year output growth as the year-over-year growth rate inmonthly industrial production similar to the industrial electricity usage growthrate Quarterly industrial production data are obtained from the Board of Gover-nors of the Federal Reserve System (for the United States) the Office for NationalStatistics (for the United Kingdom) and the Ministry of Economy (for Japan)We compute two alternative annual output growth rates from these quarterly dataOutput Q4ndashQ4 refers to the log difference of the industrial production index inthe fourth quarter of a given year and in the fourth quarter of the previous yearThe year-over-year growth rate alleviates seasonality in the output data OutputQ3ndashQ4 refers to the log difference of the industrial production index in the fourthquarter of the current year and in the third quarter of a given year Moller andRangvid (2015) show that output growth rates from the third to the fourth quarterof the current year predict the stock market returns of next year well The indus-trial production index is subject to later revisions and we use the final revisednumbers instead of the vintage data as originally announced This means that out-put growth rates are computed using more updated information than the electricitygrowth rates

We collect industrial production data for 14 industries from FRED from Jan1972 to Dec 2010 The purpose is to investigate how sectoral industrial produc-tion growth rates relate to the growth rate of aggregate industrial electricity usageand to provide explanations for the industrial electricity usage growth ratersquos abil-ity to forecast future stock returns We follow Kenneth Frenchrsquos industrial classi-fication and focus on those 17 industries Because industrial production data forbanking retail and other industries are not available we are left with 14 indus-tries steel machinery durables fabricated products construction clothes con-sumer products chemicals utilities cars oil mines transportation and food Wecompute the sectoral growth rates of industrial production as changes in the logindex level of industrial production each month relative to the level a year ago


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Da Huang and Yun 45

We compute the output gap measure following Cooper and Priestley (2009)6

In the United States we regress the log of monthly industrial production on atime trend and the square of the time trend The residual is the estimated outputgap To avoid using forward-looking data we also follow Cooper and Priestley(2009) to compute an out-of-sample output gap using expandingndashrolling-windowregressions In particular at the end of month t in year j we estimate the outputgap regression using data from Jan 1927 up to that month and compute the out-of-sample output gap using the residual in that month For the next month wereestimate the output gap regression using all data from Jan 1927 up to montht+1 to compute the out-of-sample output gap in month t+1

In the United Kingdom and Japan to match the frequency of the availableelectricity data we use annual industry production data to compute the annualoutput gap The sample period for the output gap calculation covers 1956ndash2010for the United States 1970ndash2008 for the United Kingdom and 1980ndash2008 forJapan

Another related measure is the capacity utilization index reported in the Fed-eral Reserve Boardrsquos G17 release This index is constructed using potential outputfrom a survey of plants and actual output and it measures the proportion of firmcapacity that is being used We compute the growth of capacity utilization as thechange in the log index level of capacity utilization in each month relative to itslevel a year ago These data are seasonally adjusted and available for 1968ndash2010

Investment growth Q3ndashQ4 (Q4ndashQ4) is the growth rate of the fourth-quarterper capita investment relative to that of the current yearrsquos third quarter or theprevious yearrsquos fourth quarter Investment data are obtained from the Fed

C Other DataExcess returns are value-weighted returns in excess of the T-bill rate and are

obtained from the Web site of Kenneth French (httpmbatuckdartmouthedupagesfacultykenfrenchdata libraryhtml) We also consider the forecastingvariables investigated by Welch and Goyal (2008) Campbell and Thompson(2008) and Ferreira and Santa-Clara (2011) The details of these variables areas follows The dividendndashprice ratio is the difference between the log of divi-dends and the log of prices The earningsndashprice ratio is the difference betweenthe log of earnings and the log of prices The book-to-market ratio is the ratio ofbook value to market value for the Dow Jones Industrial Average The Treasurybill rate is the secondary market rate on 3-month T-bills The default spread is thedifference of yields on BAA- and AAA-rated corporate bonds The term spreadis the difference of yields on long-term government bonds and 3-month T-billsThe net stock issue is the ratio of 12-month moving sums of net issues by stockslisted on the New York Stock Exchange (NYSE) divided by the total end-of-yearmarket capitalization of NYSE stocks Inflation is the change in the log of theConsumer Price Index The long-term rate of return on government bonds is takenfrom Ibbotsonrsquos SBBI Yearbook Stock return variance is computed as the sum ofsquared daily returns of the Standard amp Poorrsquos (SampP) 500 We take these data from

6We verify that the output gap we computed closely replicates the one used by Cooper and Priestley(2009) using a short overlapping sample up to 2005 when their data end


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Amit Goyalrsquos Web site (wwwhecunilchagoyal) more details of data construc-tion are provided by Welch and Goyal (2008)

The NBER expansion is the fraction of months spent in expansion in eachyear monthly NBER expansion data are obtained from the NBER Web site(wwwnberorgcycleshtml)

D Summary StatisticsPanel A of Table 1 presents summary statistics for our main variables of

interest at an annual frequency The sample covers 1956ndash2010 in the United States(55 years) 1970ndash2008 in the United Kingdom (39 years) and 1980ndash2008 in Japan(29 years)

The December-to-December annual industrial electricity growth rate in theUnited States has a mean of 109 and a standard deviation of 569 The an-nual industrial electricity growth rates have lower means and are less volatile inthe United Kingdom and Japan possibly due to a shorter and more recent sam-ple period The weather-adjusted electricity growth rate in the United States asa regression residual has a mean of 0 by construction7 Its standard deviation of528 is only slightly smaller suggesting that the bulk of the variation in the rawindustrial electricity growth rate is unrelated to weather change Similar patternsare observed in the United Kingdom and Japan as well Orthogonalizing indus-trial electricity growth on weather fluctuation hardly changes its volatility Theautocorrelations for industrial electricity growth rates are relatively lowminus00645in the United States 01086 in the United Kingdom and 00309 in Japan

The average annual (Q4ndashQ4) industry production growth is highest in theUnited States (266) followed by Japan (204) and is the lowest in the UnitedKingdom (089) The growth rate is most volatile in Japan (523) followedby the United States (453) then the United Kingdom (376) In the UnitedStates not surprisingly the December-to-December output growth rate has aboutthe same mean as the Q4ndashQ4 output growth rate but it is more volatile

Panel A also shows that the in-sample output gap does not have a mean of0 in all three countries because it is estimated in a regression using all availabledata over a longer sample period in each country Because the output gap measuresdeviation from long-term trends it is more autocorrelated than the annual growthrates of both industrial electricity usage and production For example the annualautocorrelation of the output gap is 06044 in the United States 07832 in theUnited Kingdom and 07059 in Japan

In the United States where quarterly investment data are available we findinvestment growth rates (Q3ndashQ4 and Q4ndashQ4) to have similar means to the corre-sponding output growth rates but they tend to be much more volatile

December-to-December capital utilization in the United States has a meanof minus00034 with a standard deviation of 00466 More months are in expan-sion periods than contraction periods as shown by the mean which is 08333There is substantial variation in EDD growth Whereas the mean is only 00002the standard deviation is 00380 We find similar patterns of EDD growth in the

7Specifically we regress December-to-December industrial electricity growth on the December-to-December change in EDD and use the residuals from the regression


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DaH

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47

TABLE 1Sample Description

Table 1 shows summary statistics (Panel A) and correlations (Panel B) for the sample The summaries include the number of observations (N ) the mean the standard deviation (Std Dev) the 10thpercentile (P10) the median the 90th percentile (P90) and the autocorrelation (Auto) The top panels of both are for the US sample Excess return is the annual value-weighted return in excess of theT-bill rate and is obtained from Kenneth Frenchrsquos Web site (httpmbatuckdartmouthedupagesfacultykenfrenchdata_libraryhtml) The industrial electricity usage growth rate (EG) is the December-to-December year-on-year log difference of per capita industrial electricity usage which is obtained from Electric Power Statistics (1956ndash1978) and Electric Power Monthly (1979ndash2010) both from theEIA EG_DEC-DEC (Residual) for the United States is the residual of EG_DEC-DEC regressed on the growth of EDDs in each December where EDD is the sum of CDDs and HDDs CDD is defined asCDD=max

[0minus Tmax+Tmin

2 minus65] HDD is defined as HDD=max

[065minus Tmax+Tmin

2

] Tmax (Tmin) is the daily maximum (minimum) temperature Monthly CDD (HDD) is the sum of daily CDD (HDD) values and

captures deviations in mean temperatures below 65F the benchmark at which energy demand is low Weather data (CDD and HDD) are obtained from the NOAA for the United States For the United Kingdomand Japan CDD and HDD values are computed from mean temperatures where mean temperatures are obtained from the Met Office Hadley Centre for the United Kingdom and the Japan MeteorologicalAgency for Japan EDD_GROWTH_ANNUAL is the growth rate in annual EDD OUTPUT_GROWTH_DEC-DEC is the log difference of the December and the prior yearrsquos December industrial productionindex OUTPUT_GROWTH_Q3ndashQ4 (OUTPUT_GROWTH_Q4ndashQ4) is the log difference of the fourth quarter and third quarter (prior yearrsquos fourth quarter) industrial production index which is obtained from theBoard of Governors of the Federal Reserve System OUTPUT_GAP is the residual of regressing the log of industrial production on time and time-squared following the procedures of Cooper and Priestley(2009) CAPACITY_UTILIZATION_DEC-DEC is the log difference of December-to-December capacity utilization obtained from the Fed INVESTMENT_GROWTH_Q3ndashQ4 (INVESTMENT_GROWTH_Q4ndashQ4)is the growth rate of fourth quarter per capita investment relative to that in current yearrsquos third quarter (previous yearrsquos fourth quarter) Investment data are obtained from the Fed NBER_EXPANSION is thefraction of the month spent in expansion in each year monthly NBER expansion data are obtained from the NBER Web site (wwwnberorgcycleshtml) The middle (bottom) rows of both panels are forthe United Kingdom (Japan) sample for 1970ndash2008 (1980ndash2008) Excess return is the annual MSCI log difference from the prior year in excess of the annual risk-free rate Risk-free rates for Japan and theUnited Kingdom are from Datastream Industrial electricity usage growth (EG_ANNUAL) is the log difference of the annual per capita industrial electricity usage from the prior year industrial electricity usagedata are obtained from the OECD database EG_ANNUAL(RESIDUAL) is the residual from regressing EG_ANNUAL on EDD_GROWTH_ANNUAL OUTPUT_GROWTH_Q3ndashQ4 (OUTPUT_GROWTH_Q4ndashQ4)is the log difference of the fourth quarter and third quarter (prior yearrsquos fourth quarter) industrial production index which is obtained from the Office for National Statistics (UK) and the Ministry of Economy(Japan) The output gap is computed following the procedures of Cooper and Priestley (2009) Monthly industrial production data are obtained from the FRED Web site (httpalfredstlouisfedorgseriesseid=INDPRO) and are available from 1927 We regress the log of industrial production on time trend and trend-squared The residual is the estimated output gap

Panel A Summary Statistics

United States N Mean Std Dev P10 Median P90 Auto

R e (t +1) 55 00448 01767 minus01795 00896 02365 minus01214EG_DEC-DEC 55 00109 00569 minus00596 00128 00640 minus00645EG_DEC-DEC (RESIDUAL) 55 00000 00528 minus00825 00155 00629 00017OUTPUT_GROWTH_DEC-DEC 55 00267 00500 minus00546 00331 00845 00866OUTPUT_GROWTH_Q4ndashQ4 55 00266 00453 minus00528 00299 00741 02190OUTPUT_GROWTH_Q3ndashQ4 55 00074 00191 minus00219 00095 00264 minus02153OUTPUT_GAP 55 00018 00621 minus00950 00091 00730 06044CAPACITY_UTILIZATION_DEC-DEC 43 minus00034 00466 minus00748 00020 00499 00900INVESTMENT_GROWTH_Q4ndashQ4 55 00218 00949 minus01193 00460 01237 00227INVESTMENT_GROWTH_Q3ndashQ4 55 00009 00436 minus00579 00049 00480 minus01572NBER_EXPANSION 55 08333 02996 02500 10000 10000 01368EDD_GROWTH_ANNUAL 55 00002 00380 minus00496 00122 00465 minus03834

(continued on next page)

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48JournalofFinancialand

Quantitative

Analysis

TABLE 1 (continued)Sample Description

Panel A Summary Statistics (continued)

United Kingdom N Mean Std Dev P10 Median P90 Auto

R e (t +1) 39 00292 02474 minus03045 00869 01912 minus01915EG_ANNUAL 39 00100 00396 minus00410 00090 00569 01086EG_ANNUAL (RESIDUAL) 39 00000 00368 minus00520 00028 00401 00775OUTPUT_GROWTH_Q4ndashQ4 39 00089 00376 minus00316 00108 00557 minus00622OUTPUT_GROWTH_Q3ndashQ4 39 00026 00155 minus00135 00039 00178 minus00739OUTPUT_GAP 39 00045 00459 minus00750 00220 00548 07832EDD_GROWTH_ANNUAL 39 minus00020 00537 minus00784 minus00048 00723 minus02564

Japan N Mean Std Dev P10 Median P90 Auto

R e (t +1) 29 minus00412 02154 minus02628 minus00239 03317 00795EG_ANNUAL 29 minus00005 00532 minus00564 00121 00596 00309EG_ANNUAL (RESIDUAL) 29 00000 00572 minus00931 00073 00641 00322OUTPUT_GROWTH_Q4ndashQ4 29 00204 00523 minus00701 00333 00750 00017OUTPUT_GROWTH_Q3ndashQ4 29 00251 00187 minus00077 00261 00458 01943OUTPUT_GAP 29 00008 00542 minus00587 minus00058 01037 07059EDD_GROWTH_ANNUAL 29 minus00040 00798 minus01013 minus00171 00918 minus02673



DaH

uangandYun

49


Panel B Correlations

United States R e (t +1) EG_DEC-DEC EG_DEC-DEC (RESIDUAL) OUTPUT_GROWTH_DEC-DEC OUTPUT_GROWTH_Q4ndashQ4 OUTPUT_GROWTH_Q3ndashQ4

EG_DEC-DEC minus03421EG_DEC-DEC (RESIDUAL) minus03358 09266OUTPUT_GROWTH_DEC-DEC minus03002 06847 07197OUTPUT_GROWTH_Q4ndashQ4 minus02751 06174 06326 09455OUTPUT_GROWTH_Q3ndashQ4 minus03667 06308 06382 07958 06587OUTPUT_GAP minus04495 03612 03773 0383 04346 02839CAPACITY_UTILIZATION minus01998 06471 06383 09249 08672 07559INVESTMENT_GROWTH_Q3ndashQ4 minus01964 05131 04449 05414 0382 07228INVESTMENT_GROWTH_Q4ndashQ4 minus02039 06157 06002 08699 08859 05633NBER_EXPANSION minus01933 06093 06151 07993 07894 06351EDD_GROWTH_ANNUAL minus00692 02921 00639 00323 00303 0056

United States OUTPUT_GAP CAPACITY_UTILIZATION INVESTMENT_GROWTH_Q3ndashQ4 INVESTMENT_GROWTH_Q4ndashQ4 NBER_EXPANSION

CAPACITY_UTILIZATION 02389INVESTMENT_GROWTH_Q3ndashQ4 01371 05116INVESTMENT_GROWTH_Q4ndashQ4 03307 08179 04789NBER_EXPANSION 04672 08 04482 07125EDD_GROWTH_ANNUAL minus0035 02208 01703 00731 00587

United Kingdom R e (t +1) EG_DEC-DEC EG_DEC-DEC (RESIDUAL) OUTPUT_GROWTH_Q4ndashQ4 OUTPUT_GROWTH_Q3ndashQ4 OUTPUT_GAP

EG_DEC-DEC minus02352EG_DEC-DEC (RESIDUAL) minus03346 08684OUTPUT_GROWTH_Q4ndashQ4 minus01072 05105 05676OUTPUT_GROWTH_Q3ndashQ4 minus02216 04122 04341 06301OUTPUT_GAP minus02521 02048 03698 00349 minus01671EDD_GROWTH_ANNUAL minus00608 01659 0 00034 00514 minus00988

Japan R e (t +1) EG_DEC-DEC EG_DEC-DEC (RESIDUAL) OUTPUT_GROWTH_Q4ndashQ4 OUTPUT_GROWTH_Q3ndashQ4 OUTPUT_GAP

EG_ANNUAL minus02811EG_ANNUAL (RESIDUAL) minus02945 09817OUTPUT_GROWTH_Q4ndashQ4 minus00373 05663 0538OUTPUT_GROWTH_Q3ndashQ4 minus01494 06557 06319 08087OUTPUT_GAP minus05002 03037 03209 02243 03336EDD_GROWTH_ANNUAL 00402 01956 0 02027 01887 minus00577



FIGURE 2Time Series of Electricity Usage and Output Growth Annual (USJapanUK)

Figure 2 shows the time-series plot of industrial electricity usage and output growth measured by industrial productiongrowth rates Industrial electricity usage data are obtained from the EIA (the United States) and the OECD database (theUnited Kingdom and Japan) The industrial production index is obtained from the Board of Governors of the FederalReserve System (the United States) the Office for National Statistics (the United Kingdom) and the Ministry of Economy(Japan) The industrial electricity usage growth rate is computed by the log difference of per capita annual industrialelectricity consumption Output growth is computed as the log difference of industrial production Graphs AndashC comparethe industrial electricity usage growth rate (circle dots) Q3ndashQ4 output growth (rectangular dots) and Q4ndashQ4 outputgrowth (triangle dots) for the United States (Graph A) the United Kingdom (Graph B) and Japan (Graph C) Electricityis measured in 1000 MWh and the industrial production index is seasonally adjusted and referenced relative to a baseyear (2007 = 100 for the United States 2008 = 100 for the United Kingdom and 2005 = 100 for Japan)

ndash020

ndash010

000

010

020

Gro

wth

Rat

eG

row

th R

ate

Gro

wth

Rat

e

1950 1960 1970 1980

Graph B United Kingdom

Graph C Japan

1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

ndash010

ndash005

000

005

010

ndash010

ndash015

ndash005

000

005

010

Industrial Electricity Usage GrowthQ3ndashQ4 Output GrowthQ4ndashQ4 Output Growth

Graph A United States




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Da Huang and Yun 51

United Kingdom and Japan where the mean is small but the standard deviationis large

Panel B of Table 1 reports correlations among the key variables Several in-teresting patterns emerge First industrial electricity usage growth rates closelytrack the growth rates in industry production in all three countries In the UnitedStates the correlations between industrial electricity usage growth rates and out-put growth rates are above 60 Similarly higher correlations are observed inthe United Kingdom and Japan Figure 2 provides a visualization of these highcorrelations which support our view that industrial electricity usage is trackingcapital services in real time Given the high correlations with output measures itis not surprising that the industrial electricity growth rate is a good business cycleindicator For example in the United States the correlation between the industrialelectricity growth rate and the NBER expansion indicator is 61 The importantdifference is that the industrial electricity usage growth rate is observed almost inreal time whereas NBER expansionrecession dates are often released with sig-nificant delays The correlations between the industrial electricity growth rate andinvestment growth rates are also high (above 50)

Second as highlighted in Figure 1 industrial electricity usage is partiallydriven by weather change The correlation between December-to-December in-dustrial electricity usage growth and annual EDD growth is 2921 in theUnited States Orthogonalizing December-to-December industrial electricity us-age growth on December-to-December EDD growth greatly alleviates the weathereffect The residual has a much lower correlation 639 with annual EDDgrowth yet it remains highly correlated with other output measures and the busi-ness cycle indicator Moreover it is highly correlated (9266) with the raw elec-tricity growth rate We find similar patterns in Japan and the United KingdomIn both countries the growth rates of raw annual industrial electricity usage arepositively correlated with changes in annual EDD (the correlations are 1659(Japan) and 1956 (United Kingdom)) The residuals from regressing annual in-dustrial electricity usage growth on annual EDD growth in these two countriesby construction are uncorrelated with annual EDD growth

Finally in all three countries we observe evidence that supports a counter-cyclical risk premium Industry output measures in year t are negatively correlatedwith stock market excess returns in year t+1 consistent with the notion that therisk premium increases during a recession For the remainder of the paper we willformally analyze the predictive power of the industrial electricity usage growthrate especially relative to various measures of industry output growth

III Monthly Predictive RegressionsBecause monthly industrial electricity consumption data are available in the

United States we first conduct predictive regressions at a monthly frequency inorder to maximize the power of the test To alleviate the impact of seasonalitywe use a year-over-year growth rate in industrial electricity usage For examplewe use the electricity growth rate from January of year tminus1 to January of year tto predict excess stock returns in February of year t Then we use the electricitygrowth rate from February of year tminus1 to February of year t to predict excess


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stock returns in March of year t and so on As a result the monthly predictiveregressions will be overlapping

A The In-Sample Predictability of Electricity GrowthIn this subsection we conduct the standard overlapping in-sample forecast-

ing exercise For each month from 1956 to 2010 we use a year-over-year in-dustrial electricity usage growth rate (January to January February to Februaryetc) to predict excess as well as actual stock market returns in the next month3 months 6 months 9 months and 12 months Due to the overlapping natureof such a regression we present the Hodrick (1992) t-value (Hodrick-t) In ad-dition to persistent regressors the evaluation of predictive regressions needs toproperly account for the effect of a short sample and estimation with overlap-ping data To consider persistent predictors overlapping regressions and a shortsample simultaneously we compute the p-values of coefficients through simula-tion following Li et al (2013)

We illustrate our simulation procedure using a bivariate regression wherethe predictive variables are industrial electricity growth (EG) and the output gap(GAP) We denote the excess return r e

Define a 3times1 column vector Z t=[r et EGt GAPt ]

prime We first estimate a first-order vector autoregression (VAR(1)) Z t+1= A0+ A1 Z t+u t+1 We impose thenull hypothesis of no return predictability by setting the slope coefficients of ther e

t equation to 0 and the intercept of the equation to the empirical mean of r et

The fitted VAR is then used to generate T observations of the simulated variables[r e

t EGt GAPt ]prime The initial observations are drawn from a multivariate normal

distribution of the three variables with the mean and the covariance matrix setto their empirical counterparts Once the initial observations are chosen the sub-sequent T minus1 simulated observations are generated from the fitted VAR with theshocks bootstrapped from the actual VAR residuals (sampling without replace-ment) These simulated data are then used to run a bivariate return predictiveregression to produce regression coefficients

We repeat the process 50000 times to obtain the empirical distribution of theregression coefficients (under the null of no predictability) and the R2 which inturn produces the p-values associated with our actual estimated coefficients andthe p-value associated with the R2 The results are reported in Table 2

Panel A of Table 2 indicates that the simple year-over-year industrial elec-tricity usage growth rate has strong predictive power for future excess stock mar-ket returns In particular an increase in industrial electricity usage today predictslower future excess stock returns consistent with a countercyclical risk premiumThe regression slope coefficients for electricity growth are statistically highly sig-nificant Their magnitudes increase with the forecast horizon At the annual hori-zon a 1 increase in the year-over-year electricity growth rate predicts an excessstock return that is 092 lower with an R2 of 864 The p-values at all horizonsstrongly reject the null hypothesis that these predicting coefficients are zeros Inaddition the p-values for the R2 are also highly significant suggesting that it isvery unlikely to observe our R2 when the industrial electricity usage growth ratehas no return predictive power


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Da Huang and Yun 53

TABLE 2Overlapping Monthly Predictive Regressions United States (Jan 1956ndashDec 2010)

In Table 2 for each month t the industrial electricity usage growth rate is calculated as the per capita year-over-yeargrowth rate of industrial electricity usage We then regress future 1-month 3-month 6-month 9-month and 12-monthcumulative excess returns on the industrial electricity usage growth rate and report coefficient estimates (b) Hodrick t -values (Hodrick-t ) following Hodrick (1992) p-values following Li et al (2013) adjusted R 2 (R 2) and adjusted R 2 underthe null of no predictability (R 2-0) following Cooper and Priestley (2009) Panel A presents results from forecasting excessmarket returns using the raw industrial electricity usage growth rate Panel B presents results from forecasting excessmarket returns using the weather-adjusted industrial electricity usage growth rate Panels C and D repeat the analysisusing the risk-free rate The sample is monthly from Jan 1956 to Dec 2010 The US stock returns and 1-month T-bill rates are obtained from Center for Research in Security Prices (CRSP) tape provided by Wharton Research DataServices Monthly industrial electricity consumption data are obtained from Electric Power Statistics (1956ndash1978) andElectric Power Monthly (1979ndash2010) both from the EIA

Horizon

1 3 6 9 12

Panel A Predicting Excess Return with Electricity Growth

b minus00882 minus03088 minus05800 minus08272 minus09217Hodrick-t minus25202 minus32598 minus34255 minus35301 minus31787p-value 00057 00011 00014 00011 00038R 2 00101 00415 00687 00916 00864p-value (R 2) 00047 00009 00024 00023 00085R 2-0 00101 00247 00367 00416 00427

Panel B Predicting Excess Return with Weather-Adjusted Electricity Growth


Panel C Predicting Risk-Free Rate with Electricity Growth

b 00013 00065 00192 00401 00623Hodrick-t 06320 11027 16958 25141 31616p-value 02234 00978 00214 00019 00000R 2

minus00006 00011 00040 00090 00127p-value (R 2) 04428 04306 03939 03093 02854R 2-0 minus00006 minus00015 minus00022 minus00025 minus00026

Panel D Predicting Risk-Free Rate with Weather-Adjusted Electricity Growth



We also report the implied R2 (R2-0) that a variable obtains under the nullhypothesis of no predictability in returns following Boudoukh Richardson andWhitelaw (2008) and Cooper and Priestley (2009) Specifically the adjusted R2 iscomputed as

(1) R2-0 =

(1+

ρ(1minus ρkminus1)1minus ρ

)2

kR2

where ρ is the autocorrelation coefficient of the predictor variable k is the hori-zon and R2 is the empirical R2

The implied R2 provides us with an economic sense of how far the R2 ofa predicting variable deviates from the R2 this variable generates given its per-sistence and no predictability For instance given the persistence of electricitygrowth even if there is no predictability in returns the R2 will be 427 But


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electricity growthrsquos actual R2 is 864 which is twice the R2 generated underthe null hypothesis of no predictability8 It is evident that the actual R2 valuesachieved by electricity growth at various forecasting horizons are substantial im-provements over those produced under the null hypothesis of no predictabilityUsing the simulated distribution of the adjusted R2 we confirm that these im-provements are statistically significant with the associated p-values overwhelm-ingly under 1 at all horizons

The year-over-year electricity growth rate is still subject to weather effectsas is evident in Panel B of Table 1 To that end we try to orthogonalize theseyear-over-year electricity growth rates on weather fluctuation measured by theyear-over-year growth rate in monthly EDD so that we can focus on residualelectricity usage growth Panel B of Table 2 reports the predictive regression re-sults using weather-adjusted electricity growth rates The results are very similarOverall it is clear that seasonality and weather effects are not driving the returnpredictive power of industrial electricity data We confirm that weather-adjustedelectricity growth rates provide results similar to those of the raw growth rates inall other tests and in Japan and the United Kingdom as well For brevity in therest of the paper we present only results using the raw growth rates which areeasy to compute and do not suffer from forward-looking bias

Panels C and D of Table 2 repeat the analysis from Panels A and B withrisk-free rates and we find strong predictive power as well In particular a higherindustrial electricity growth today predicts higher risk-free rates for up to a yearsuggesting that industrial electricity usage growth is highly procyclical

B The In-Sample Predictability of Other PredictorsTo put the predictive power of industrial electricity growth into perspective

we examine 14 other monthly return predictors one at a time in the same monthlyoverlapping predictive regressions The first 10 predictors are well-known finan-cial variables dividendndashprice ratio earningsndashprice ratio book-to-market ratioTreasury bill rate default spread term spread net equity issuance inflation re-turn on long-term government bonds and stock variance We also consider fourother measures of output growth the in-sample output gap calculated using thefull sample the out-of-sample output gap computed using the expandingndashrollingsample the year-over-year change in capacity utilization and the year-over-yeargrowth rate of industrial production

Table 3 presents the performance of these alternative predictors by them-selves Among the financial variables judging by the p-values associated with theregression coefficients inflation and long-term bond returns have significant pre-dictive power but their R2 values are noticeably lower than those of the industrialelectricity growth rate at all horizons The performance of the financial ratios ap-pears weak in our sample for two reasons First our sample starts in 1956 ratherthan in 1926 Second our statistical inference corrects for the biases caused byhaving a persistent predictor in overlapping regressions Financial ratios whichtend to be more persistent naturally become weaker after this correction

8For predicting next-month excess returns (k=1) the two R2 values are identical because theregression does not use an overlapping sample


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Da Huang and Yun 55

TABLE 3Alternative Predicting Variables United States (Jan 1956ndashDec 2010)

In Table 3 we predict future 1-month 3-month 6-month 9-month and 12-month cumulative excess returns using thefollowing common predicting variables dividendndashprice ratio earningsndashprice ratio book-to-market ratio T-bill rate defaultspread term spread net equity issuance inflation rate of return on long-term government bonds stock variance in-sample output gap out-of-sample output gap following Cooper and Priestley (2009) capacity utilization and year-over-year output growth We report coefficient estimates (b) Hodrick t -value (Hodrick-t ) following Hodrick (1992) p-values(p-value) following Li et al (2013) adjusted R 2 (R 2) and adjusted R 2 under the null of no predictability (R 2-0) Datafor the first 10 predicting variables are from Welch and Goyal (2008) and are available at httpresearchivo-welchinfo The monthly output production gap is computed following Cooper and Priestley (2009) Capacity utilization is fromthe G17 release from the Fed The monthly overlapping output growth (year-over-year) is the year-over-year growth ofindustrial production The sample is from Jan 1956 to Dec 2010 with the exception of capacity utilization which goesfrom Jan 1968 to Dec 2010 The US stock returns and 1-month T-bill rates are obtained from CRSP tape provided byWharton Research Data Services Monthly industrial electricity usage is obtained from Electric Power Statistics (1956ndash1978) and Electric Power Monthly (1979ndash2010) both from the EIA

Horizon

1 3 6 9 12 1 3 6 9 12

DIVIDEND_PRICE_RATIO INFLATION

b 02254 07669 17795 26888 34087 minus09805 minus18017 minus37725 minus57593 minus72224Hodrick-t 12806 14871 17516 17950 17294 minus15875 minus10809 minus12980 minus14536 minus14789p-value 02778 02283 01777 01749 01892 00264 00743 00376 00189 00147R 2 00016 00094 00259 00397 00488 00041 00042 00102 00164 00200R 2-0 00016 00048 00092 00134 00174 00041 00052 00039 00028 00021

EARNINGS_PRICE_RATIO LONG-TERM_GOVERNMENT_BOND_RETURN

b 00599 01692 03729 05757 07479 01621 02055 06026 06192 07038Hodrick-t 07574 07380 08400 08916 08925 23834 16554 35713 31482 32345p-value 04788 04911 04664 04598 04676 00042 00319 00001 00003 00004R 2

minus00002 00016 00056 00096 00128 00090 00036 00188 00126 00124R 2-0 minus00002 minus00007 minus00013 minus00019 minus00025 00090 00032 00016 00011 00008

BOOK-TO-MARKET_RATIO STOCK_VARIANCE

b 00036 00145 00389 00599 00742 minus10935 minus07940 10351 25833 31431Hodrick-t 04535 06268 08528 08895 08359 minus21099 minus05885 03860 07907 08706p-value 06104 05620 04927 04830 05000 00057 01856 07392 09025 09076R 2

minus00011 00005 00054 00093 00111 00090 00002 minus00002 00039 00046R 2-0 minus00011 minus00033 minus00064 minus00094 minus00123 00090 00092 00058 00040 00030

T-BILL_RATE OUTPUT_GAP_IN_SAMPLE

b minus00995 minus02325 minus03581 minus04374 minus05081 minus01107 minus03302 minus06047 minus08854 minus10921Hodrick-t minus14800 minus11688 minus09105 minus07518 minus06646 minus44891 minus45422 minus42064 minus41379 minus38296p-value 00722 01185 01754 02179 02424 00013 00014 00024 00023 00035R 2 00025 00051 00057 00055 00056 00234 00652 01024 01447 01668R 2-0 00025 00074 00141 00202 00258 00234 00678 01289 01840 02336

DEFAULT_SPREAD OUTPUT_GAP_OUT_OF_SAMPLE


TERM_SPREAD CAPACITY_UTILIZATION

b minus00423 minus00504 minus00041 01876 03140 minus00575 minus02243 minus04992 minus07365 minus08442Hodrick-t minus05499 minus02238 minus00093 02941 03745 minus11408 minus16075 minus19301 minus20583 minus19408p-value 06775 05854 05254 04411 04164 01235 00658 00442 00437 00665R 2

minus00010 minus00013 minus00015 minus00007 00003 00014 00133 00331 00473 00474R 2-0 minus00010 minus00030 minus00059 minus00086 minus00111 00014 00039 00071 00097 00118

NET_EQUITY_ISSUANCE OUTPUT_GROWTH_YEAR-OVER-YEAR

b minus00376 minus00429 minus00760 minus01130 minus00863 minus00725 minus02351 minus04561 minus06902 minus07658Hodrick-t minus03284 minus01256 minus01108 minus01103 minus00651 minus23073 minus26507 minus27095 minus29196 minus26215p-value 03605 04468 04523 04535 04768 00140 00091 00090 00067 00164R 2

minus00013 minus00014 minus00014 minus00013 minus00015 00074 00268 00480 00733 00688R 2-0 minus00013 minus00036 minus00068 minus00095 minus00118 00074 00204 00364 00487 00581

The four industrial-output-based measures predict excess stock market re-turns better than the financial variables The strongest predictor among the four isthe in-sample output gap It significantly predicts stock returns at all horizons andthe accompanying adjusted R2 values are even higher than those of the industrial


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electricity usage growth rate However the in-sample output gap is computed us-ing future information which might not be available at the time of return predic-tion Therefore we also examine the out-of-sample output gap This new measuredoes not perform as well as the in-sample output gap Its regression coefficientsare marginally significant at short horizons and its adjusted R2 values are muchlower all are below 3

The year-over-year change in capacity utilization significantly predicts ex-cess stock returns beyond 1 month Its adjusted R2 values are much lower thanthose of the industrial electricity usage growth rate and are all below 5 Theyear-over-year growth rate of industrial production predicts returns highly signif-icantly at all horizons but its adjusted R2 values are still lower than those of theindustrial electricity usage growth rate It is interesting that industrial electricityusage predicts returns better than a direct measure of industrial output

In Table 4 we conduct bivariate predictive regressions by combining the in-dustrial electricity usage growth rate with the other 14 variables one at a time

TABLE 4Competing with Alternative Predicting Variables United States (Jan 1956ndashDec 2010)

In Table 4 we predict future 1-month 3-month 6-month 9-month and 12-month cumulative excess returns using both theindustrial electricity usage growth rate and one of the following predicting variables dividendndashprice ratio earningsndashpriceratio book-to-market ratio T-bill rate default spread term spread net equity issuance inflation return on long-termgovernment bonds stock variance in-sample output gap out-of-sample output gap following Cooper and Priestley(2009) capacity utilization and year-over-year output growth We report coefficient estimates (b1 and b2) Hodrickt -value (t (b1) and t (b2)) following Hodrick (1992) and p-values (p(b1) and p(b2)) following Li et al (2013) for elec-tricity and alternative predicting variables and the adjusted R 2 (R 2-0) Data for the first 10 predicting variables are fromWelch and Goyal (2008) and are available at httpresearchivo-welchinfo The monthly output production gap is com-puted following Cooper and Priestley (2009) with revised industrial production data obtained from the Fed Capacityutilization is from the G17 release of the Fed The monthly overlapping output growth (year-over-year) is the year-over-year growth of revised industrial production The sample is from Jan 1956 to Dec 2010 with the exception of capacityutilization which goes from Jan 1968 to Dec 2010 The US stock returns and 1-month T-bill rates are obtained fromCRSP tape provided by Wharton Research Data Services Monthly industrial electricity usage is obtained from ElectricPower Statistics (1956ndash1978) and Electric Power Monthly (1979ndash2010) both from the EIA

Horizon

1 3 6 9 12 1 3 6 9 12


b1 minus00889 minus03117 minus05892 minus08462 minus09489 minus00854 minus03038 minus05684 minus08074 minus08960b2 02319 07944 18500 28241 35810 minus09127 minus15508 minus32594 minus49436 minus63061t (b1) minus25613 minus33142 minus35101 minus36532 minus33248 minus24329 minus32027 minus33529 minus34482 minus31016t (b2) 13244 15508 18362 19027 18349 minus14574 minus09137 minus11301 minus12728 minus13166p(b1) 00084 00014 00016 00015 00040 00078 00016 00017 00017 00043p(b2) 02780 02329 01806 01712 01833 00370 01061 00622 00362 00279R 2 00119 00518 00970 01356 01406 00134 00443 00760 01033 01014


b1 minus00921 minus03206 minus06051 minus08666 minus09725 minus00828 minus03031 minus05606 minus08063 minus08980b2 00789 02353 04980 07577 09561 01510 01663 05258 04928 05650t (b1) minus26398 minus33734 minus35652 minus37012 minus33828 minus24046 minus32703 minus33651 minus34994 minus31464t (b2) 10071 10372 11353 11888 11590 21958 13699 31934 26193 27943p(b1) 00057 00007 00008 00007 00032 00093 00019 00024 00018 00049p(b2) 02503 02527 02335 02274 02381 00071 00662 00003 00032 00034R 2 00108 00460 00798 01093 01083 00177 00434 00827 00990 00939


b1 minus00901 minus03163 minus05994 minus08587 minus09618 minus01075 minus03316 minus05856 minus08194 minus09087b2 00051 00199 00496 00759 00928 minus13537 minus15951 minus03898 05137 08539t (b1) minus25688 minus33284 minus35401 minus36832 minus33629 minus31363 minus35893 minus36063 minus36277 minus32476t (b2) 06466 08633 10940 11377 10602 minus27464 minus11632 minus01545 01756 02681p(b1) 00073 00010 00015 00011 00034 00011 00003 00009 00012 00039p(b2) 04396 03765 03129 03030 03275 00011 00440 03627 05717 06147R 2 00094 00439 00785 01075 01048 00242 00466 00674 00904 00855



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Da Huang and Yun 57

TABLE 4 (continued)Competing with Alternative Predicting Variables United States (Jan 1956ndashDec 2010)

Horizon

1 3 6 9 12 1 3 6 9 12


b1 minus00872 minus03060 minus05740 minus08178 minus09095 minus00467 minus01914 minus03618 minus04917 minus04819b2 minus00962 minus02181 minus03185 minus03583 minus04065 minus00959 minus02685 minus04853 minus07177 minus09237t (b1) minus25047 minus32447 minus34007 minus34961 minus31403 minus12375 minus18366 minus19121 minus18519 minus14691t (b2) minus14449 minus11133 minus08248 minus06279 minus05427 minus35481 minus33003 minus29763 minus29315 minus28512p(b1) 00064 00015 00019 00016 00051 00990 00349 00329 00378 00831p(b2) 00759 01335 02067 02625 02911 00028 00046 00074 00083 00095R 2 00124 00459 00730 00949 00896 00247 00780 01244 01711 01855


b1 minus00910 minus03173 minus05459 minus08026 minus08661 minus00781 minus02804 minus05315 minus07580 minus08387b2 minus00680 minus02067 08223 05837 13124 minus00292 minus00813 minus01349 minus01842 minus02160t (b1) minus24872 minus31878 minus30164 minus31007 minus26702 minus21562 minus28339 minus29491 minus29589 minus25727t (b2) minus01366 minus01433 03159 01598 02869 minus09910 minus09302 minus07730 minus07053 minus06243p(b1) 00115 00025 00046 00035 00092 00156 00038 00040 00037 00090p(b2) 06041 06073 04194 04812 04406 02135 02286 02670 02847 03100R 2 00086 00402 00680 00904 00860 00101 00437 00718 00957 00908


b1 minus00915 minus03153 minus05868 minus08274 minus09172 minus01632 minus04836 minus07458 minus09497 minus10919b2 minus00671 minus01337 minus01497 minus00036 01079 00911 02171 01827 01276 01467t (b1) minus25796 minus32793 minus34356 minus34992 minus31274 minus26813 minus31358 minus26656 minus23461 minus21079t (b2) minus08700 minus05970 minus03477 minus00058 01313 12091 11240 04950 02430 02229p(b1) 00054 00010 00015 00011 00050 00058 00029 00061 00093 00148p(b2) 07523 06769 06027 05050 04682 08884 08564 06891 06064 06024R 2 00098 00415 00681 00901 00852 00130 00477 00724 00895 00901



The results are consistent and striking When combined with each of the 14 ad-ditional predictive variables electricity growth rate remains significant across theboard for forecasting horizons up to 1 year In fact it drives out 8 of the 10 fi-nancial variables the inflation rate and long-term bond returns are the only ex-ceptions It is important to note that these financial variables are computed usingprice or return information whereas the industrial electricity usage growth rate isa quantity-based variable

Interestingly although the industrial electricity usage growth rate is highlycorrelated with the other four industrial-output-based measures it outperformsthree of these four measures in the bivariate regressions The only exception is thein-sample output gap Both the industrial electricity usage growth rate and the in-sample output gap are significant when they are included in the same regressionsuggesting that they are complementary

C An Anatomy of Industrial ProductionHow can industrial electricity usage presumably proxying industrial pro-

duction outperform a direct measure of industrial production based on the finalrelease from the Fed in predicting future stock returns


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We first examine the possibility that industrial electricity usage is a moretimely measure of industrial production The vintage industrial electricity usagegrowth rate in month t indeed positively and significantly predicts the future re-vision in the month-t industrial production (defined as final release minus initialvintage) with a coefficient of 00191 and a t-value of 235 However the magni-tude of the revision in industrial production is very small (05 on average) Inother words the vintage industrial production should be quite accurate already Inaddition even growth rates computed from the revised industrial production stillunderperform the industrial electricity usage growth rate computed using vintageelectricity data

We then zoom in on the industrial components of the total industrial outputWe focus on 17 industries as described on Kenneth Frenchrsquos Web site Becauseindustrial production is not available for banking retail and other industrieswe are left with 14 industries steel machinery durables fabricated productsconstruction clothes consumer products chemicals utilities cars oil minestransportation and food We first regress the year-over-year output growth in eachindustry on the aggregate industrial electricity usage growth rate The regressionbeta measures how sensitive the output of that industry is to industrial electricityusage These coefficients are reported in Panel A of Table 5

Four industries have betas that are equal to or larger than 1 steel (184) ma-chinery (127) fabricated products (103) and construction (100) These indus-tries are likely to be capital intensive which is consistent with the high sensitivityof their output to industrial electricity usage

The output growth rates of these four industries turn out to be highly cyclicalThis is evident in Figure 3 where we plot the average year-over-year industrialproduction growth rates of these industries with the solid line For comparison wealso plot the same growth rates of the four industries with the lowest sensitivity toelectricity usage (cars transportation chemicals and food) with the dashed lineThe shaded bars mark NBER recession dates It is clear that the solid line tracksbusiness cycles more closely We see large dips during recessions In contrast wedo not always see dips in the dashed line during recessions

There are at least two reasons why outputs from the steel machinery fabri-cated products and construction industries are more cyclical First they producecapital goods used by other firms to produce their own products When demand isslack few firms will expand and purchase capital goods As such capital goodsproducers bear the brunt of a slowdown but perform well in good times Anotherreason is that these capital-intensive producers often have higher operating lever-age and therefore are more exposed to business cycle fluctuations

Indeed we find the output growth of these four industries with high sensi-tivity to electricity usage to have strong predictive power for future stock returnsThese results are reported in Panel B of Table 5 Only the output growth rates ofthese four industries can significantly predict future excess stock returns The out-put growth rates of industries with medium or low sensitivity to electricity usagehave little return predictive power

Industrial electricity usage turns out to be a good measure of the output ofthe very cyclical industries in particular which explains why it performs betterthan total industrial output in predicting stock returns


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Da Huang and Yun 59

TABLE 5Industry Electricity Sensitivity United States (Jan 1972ndashDec 2010)

In Panel A of Table 5 we regress the year-over-year output growth of each industry on aggregate year-over-year percapita industrial electricity usage growth We report coefficient estimates (COEFF) and t -values following Newey andWest (1987) (NW_T) We consider the following industries steel machinery (Machn) durables fabricated products(FabPr) construction (Cnstr) clothes (Clths) consumer products (Cnsum) chemicals (Chems) utilities (Utils) cars oilmines transportation (Trans) and food In Panel B we predict future 1-month 3-month 6-month 9-month and 12-monthcumulative excess returns using the average of the year-over-year output growth of industries with high medium andlow electricity sensitivity We report coefficient estimates (b) Hodrick t -value (Hodrick-t ) following Hodrick (1992) p-values following Li et al (2013) the adjusted R 2 and the adjusted R 2 under the null of no predictability (R 2-0) Data onindustry-level output is the industrial production index from the G17 release of the Federal Reserve Board The sampleis from Jan 1972 to Dec 2010

Panel A Regressing Output Growth on Electricity Growth

High Electricity Sensitivity Low Electricity Sensitivity

Industry COEFF NW_T Industry COEFF NW_T

Steel 18391 94581 Cars 03044 14821Machn 12666 86210 Trans 02704 15114FabPr 10348 76708 Chems 02576 23998Cnstr 10045 66275 Food 01094 20445

Medium Electricity Sensitivity


Durbl 09976 67686 Utils 03604 86415Clths 08326 31260 Oil 03412 35443Cnsum 03909 61487 Mines 03396 55345

Panel B Predicting Return Using Output Growth

Horizon

1 3 6 9 12

Panel B1 High Electricity Sensitivityb minus00593 minus01833 minus03409 minus04521 minus04557Hodrick-t minus21364 minus23678 minus24300 minus23354 minus19752p-value 00239 00202 00262 00376 00877R 2 00095 00318 00535 00616 00472R 2-0 00095 00267 00489 00672 00823

Panel B2 Medium Electricity Sensitivityb minus00519 minus01800 minus02577 minus03284 minus02960Hodrick-t minus07370 minus09620 minus07533 minus06924 minus05027p-value 00512 00302 00852 01178 02111R 2

minus00004 00044 00042 00047 00020R 2-0 minus00004 minus00011 minus00019 minus00025 minus00029

Panel B3 Low Electricity Sensitivityb minus00385 minus00997 minus01998 minus03090 minus03595Hodrick-t minus07072 minus06564 minus06912 minus07325 minus06395p-value 02266 02536 02453 02363 02605R 2


D Out-of-Sample PredictabilityThe predictability of stock returns is often taken out of sample Welch and

Goyal (2008) show that none of the existing predicting variables outperformsthe historical mean in their out-of-sample experiment9 We generate our out-of-sample forecast with a sequence of expanding samples and we require 5 yearsof data for the initial forecast In particular we use information up to time t andestimate the following equation counting the fitted values from the estimation as

9This result is overturned when Campbell and Thompson (2008) put restrictions on the out-of-sample exercise These restrictions include proper signs on the coefficient estimates and a positivesign on the forecasted risk premium


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FIGURE 3Average Output Growth of Industries with HighLow Electricity Sensitivity

In Figure 3 we plot the average year-on-year output growth of industries with high (thick solid line) and low (thin dashedline) electricity sensitivity The industries with high electricity sensitivity are steel machinery fabricated products andconstruction The industries with low electricity sensitivity are cars transportation chemicals and food The sample isfrom Jan 1972 to Dec 2010

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03

04Output Growth of Electricity Sensitive (solid) and Insensitive (dashed) Industries

the forecasted equity premium for time t+1

(2) rt = α+βxtminus1+ εt

where rt is the excess return at time t xtminus1 is a predicting variable at time tminus1εt is the residual and α and β are coefficients We follow this process increasingthe sample by one additional observation each time and thereby generate a seriesof out-of-sample equity premium forecasts microt microt+1 microTminus1 The out-of-sampleR2 then compares the mean-squared errors for a specific predicting variable tothose achieved when using historical means

(3) R2= 1minus

Tminus1sumi=s0

(rt+1minus microi )2

Tminus1sumi=s0

(rt+1minus ri )2

where ri is the historical mean of returns up to time i s0 equals 60 months andT is the number of observations in the sample For robustness we also consider arolling method in which we always keep 10 years of data to estimate equation (6)This approach is different from the previous one in that the starting observation isnot fixed and at any point our sample for estimation contains 10 years of monthlydata

Table 6 reports the out-of-sample forecasting performance of electricitygrowth and the other 14 predictive variables examined in Tables 3 and 4


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Da Huang and Yun 61

TABLE 6Out-of-Sample Test United States (Jan 1956ndashDec 2010)

In Table 6 we use information up to time t and estimatert = α+βxtminus1 + εt

where xtminus1 is the industrial electricity usage growth rate and rt is the 1-month-ahead excess return We then count thefitted value microt from the estimation as the forecasted equity premium for time t +1

microt = α+ βxtminus1

We either increase the sample by one additional observation each time (fixed) or have a rolling sample of 10 yearsof data (rolling) and thereby generate a series of out-of-sample equity premium forecasts microt microt+1 microTminus1 FollowingCampbell and Thompson (2008) and Welch and Goyal (2008) the out-of-sample R 2 compares the mean-squared errorsfor a specific predicting variable to those achieved when using historical means

R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2

where ri is the historical mean of returns up to time i and T is the number of observations in our sample We requires0 to be 60 months when we expand the sample and 10 years when we roll the sample In each case we considerthree scenarios i) without any restrictions ii) with sign restrictions which sets premia estimates to the historical meanwhen coefficient signs are incorrect and iii) premia restrictions which set premia estimates to 0 when forecasted premiaare negative We compete with the following predicting variables pricendashdividend ratio pricendashearnings ratio market-to-book ratio T-bill rate default spread term spread net equity issuance inflation rate of return on long-term governmentbonds stock variance in-sample output gap out-of-sample output gap following Cooper and Priestley (2009) capacityutilization and year-over-year output growth The US stock returns and 1-month T-bill rates are obtained from CRSP tapeprovided by Wharton Research Data Services Monthly industrial electricity usage data are obtained from Electric PowerStatistics (1956ndash1978) and Electric Power Monthly (1979ndash2010) both from the EIA

Out-of-Sample R 2 from Predicting Next-Month Excess Returns

Positive PositiveSample No Restriction Negative Slope Premium No Restriction Negative Slope Premium

EG NET_STOCK_ISSUANCE

Fixed 00129 00129 00128 minus00148 minus00146 minus00141Rolling 00048 00175 00229 minus00119 minus00136 minus00042

PRICE_DIVIDEND_RATIO INFLATION

Fixed minus00033 minus00032 minus00026 minus00167 minus00168 minus00047Rolling minus00319 minus00170 minus00077 minus00145 minus00152 00036

LONG-TERM_GOVERNMENT_BOND_RETURNPRICE_EARNINGS_RATIO (Negative)


MARKET-TO-BOOK_RATIO STOCK_VARIANCE



Fixed minus00198 minus00198 minus00009 00192 00192 00179Rolling minus00251 minus00189 minus00007 00035 00084 00120

DEFAULT_SPREAD (Negative) OUTPUT_GAP_OUT_OF_SAMPLE


TERM_PREMIUM (Negative) CAPACITY_UTILIZATION

Fixed minus00147 00000 00020 minus00003 minus00003 00031Rolling minus00260 minus00068 minus00028 minus00163 00063 00162

OUTPUT_GROWTH_YEAR-OVER-YEAR

Fixed 00061 00061 00046Rolling minus00036 00042 00096

We consider three scenarios The first column in Table 6 is the simple out-of-sample R2 without any restrictions It shows that only the industrial elec-tricity usage growth rate the in-sample output gap and year-over-year outputgrowth outperform historical mean methodology in forecasting the future equitypremium out of sample Among the three variables when using an expandingin-sample window with a fixed starting point the in-sample output gap has the


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highest out-of-sample R-squared (192) followed by the industrial electricityusage growth rate (129) and year-over-year output growth (061) If how-ever a fixed-length rolling window is used electricity growth has the highest R2048

The second column of Table 6 requires the slope estimates to have the cor-rect signs If the sign restrictions are violated we set the risk premium forecastto the historical mean This method primarily prevents the poor performance ofterm premium because it often has the wrong sign The restriction howeverhardly improves the performance of price multiples and interest rate variablesAgain the industrial electricity usage growth rate the in-sample output gap andyear-over-year output growth outperform historical mean methodology in fore-casting the future equity premium out of sample The industrial electricity usagegrowth rate has the highest out-of-sample R2 175 when a fixed-length rollingwindow is used

The third column in Table 6 requires risk premium forecasts to be positiveIf one is negative then it is set to 0 This column shows that the performance ofmost forecasting variables is improved but the industrial electricity usage growthrate remains the best predictor of the future equity premium out-of-sample whena fixed-length rolling window is used

Overall the industrial electricity usage growth rate and the in-sample out-put gap are the only two predictors that consistently beat the historical meanin predicting future stock excess returns Although in general the out-of-sampleR2 is small Campbell and Thompson (2008) provide an approach for evaluat-ing whether the small increase is economically meaningful for mean-variance in-vestors

A positive out-of-sample R2 means that the predictor outperforms the his-torical mean In that case a mean-variance investor can optimally allocate his orher assets between the stock market and a risk-free asset and can enhance his orher return by using this predictor (rather than the historical average) Campbelland Thompson (2008) show that this return enhancement is approximately out-of-sample R2γ where γ measures relative risk aversion

The industrial electricity usage growth rate has monthly out-of-sampleR2 values varying between 048 and 229 when we roll the sample and ofapproximately 128 when we fix the sample starting point in estimation If therelative risk aversion for an average mean-variance investor is 3 these numberstranslate into annual excess return gains of 192 916 and 512

IV Annual Predictive RegressionsBecause industrial electricity usage data are available only at an annual fre-

quency in the United Kingdom and Japan we also conduct annual predictive re-gressions where the dependent variable is always the calendar-year excess stockreturns These annual regressions allow us to examine the performance of elec-tricity growth beyond the United States and also against other output measuressome of which can be computed only at annual frequencies (eg Q3ndashQ4 growthin industry production) In addition annual regressions avoid the use of overlap-ping samples which may cause statistical inference bias Because sample size is


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Da Huang and Yun 63

further reduced with annual data we still compute simulated p-values as in theprevious section to address the statistical issues associated with having persistentpredictors in a small overlapping sample

Table 7 reports the results from in-sample univariate regressions on one pre-dictive variable at a time Panel A contains the results for the United StatesConsistent with the results shown in Table 2 December-to-December industrialelectricity growth strongly and negatively predicts future excess stock returnswith an R2 of more than 10

Consistent with the findings of Cooper and Priestley (2009) the output gapmeasure calculated using the full sample seems to be the best predictor so farNot only is it statistically significant in the monthly and annual samples but alsoit is associated with the highest R2 values (1849 in the annual regression)Consistent with the results of Moller and Rangvid (2015) growth rates in industryproduction especially those from the third quarter to the fourth quarter also sig-nificantly and negatively predict future excess stock returns The performance ofthe output growth rate from the fourth quarter to the fourth quarter rate is slightlyweaker Interestingly December-to-December output growth also performs well

TABLE 7International Annual Regressions

In Table 7 we predict 1-year future excess returns using the per capita industrial electricity usage growth rate andother macro predicting variables The annual industrial electricity usage growth rate (ELECTRICITY_GROWTH) in theUnited States is the log difference of the current Decemberrsquos per capita industrial electricity consumption and theprevious Decemberrsquos per capita industrial electricity consumption Annual output growth (OUTPUT_GROWTH_DEC-DEC) in the United States is the log difference of the current Decemberrsquos industrial production and the previousDecemberrsquos industrial production Other output growths are based on the growth rates of fourth-quarter industrial pro-duction (OUTPUT_GROWTH_Q4ndashQ4) relative to either the previous yearrsquos fourth-quarter industrial production or thecurrent yearrsquos third-quarter industrial production (OUTPUT_GROWTH_Q3ndashQ4) The output gap (in sample) is computedfollowing the procedures of Cooper and Priestley (2009) We use revised industry production data Investment growthrates are the growth rate of fourth-quarter per capita investment relative to those in the previous yearrsquos fourth quarter(INVESTMENT_GROWTH_Q4ndashQ4) or the current yearrsquos third quarter (INVESTMENT_GROWTH_Q3ndashQ4) Annual indus-trial electricity usage data for Japan and the United Kingdom are from the OECD database Population data are from theWorld Bank Risk-free rates for Japan and the United Kingdom are from Datastream Industrial electricity usage data arefrom the EIA (United States) and the OECD database (United Kingdom and Japan) The industrial production index isobtained from the Board of Governors of the Federal Reserve System (United States) the Office for National Statistics(United Kingdom) and the Ministry of Economy (Japan) In each regression we report the coefficient estimates (b)Hodrick t -value (Hodrick-t ) following Hodrick (1992) p-value and adjusted R 2 (R 2)

Variable b Hodrick-t p-Value R 2

Panel A United States (1956ndash2010)

ELECTRICITY_GROWTH minus11171 minus32836 00053 01015OUTPUT_GROWTH_DEC-DEC minus11008 minus26362 00169 00703OUTPUT_GROWTH_Q4ndashQ4 minus09987 minus21198 00311 00553OUTPUT_GROWTH_Q3ndashQ4 minus24157 minus23093 00236 00591OUTPUT_GAP minus13368 minus34993 00027 01849INVESTMENT_GROWTH_Q4ndashQ4 minus03949 minus16477 00841 00223INVESTMENT_GROWTH_Q3ndashQ4 minus08457 minus17694 00649 00214

Panel B Japan (1980ndash2008)

ELECTRICITY_GROWTH minus12033 minus17959 00565 00695OUTPUT_GROWTH_Q4ndashQ4 00225 00296 04378 minus00434OUTPUT_GROWTH_Q3ndashQ4 minus04989 minus02432 04804 minus00416OUTPUT_GAP minus22046 minus28126 00331 02224

Panel C United Kingdom (1970ndash2008)

ELECTRICITY_GROWTH minus24217 minus15827 00141 01100OUTPUT_GROWTH_Q4ndashQ4 minus10160 minus09386 01886 minus00022OUTPUT_GROWTH_Q3ndashQ4 minus39847 minus11229 00622 00371OUTPUT_GAP minus14227 minus18081 00976 00466


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with an R2 of 703 but this is less than that achieved by the industrial electricityusage growth rate

Panel B of Table 7 reports the results for Japan Again the industrial elec-tricity usage growth rate performs well The coefficient on electricity growth ismarginally significant according to the Hodrick t-value (minus179) but based on p-values from the simulations it has a p-value of 57 which is much stronger thanthe p-values of more than 40 that Q3ndashQ4 and Q4ndashQ4 output growth have Theseweaker results may arise because we are restricted to annual industrial electricityusage data in Japan over a much shorter sample period of 29 years The in-sampleoutput gap remains the strongest predictor in Japan with an R2 of 2224 and ithas a highly significant regression coefficient

Panel C of Table 7 reports the results for the United Kingdom Indus-trial electricity growth is the best predictor among the four with the highestR2 (11) The coefficient on electricity growth has a Hodrick t-value of minus158and a p-value of 14 from simulations Output growth measures perform mod-estly well The Q3ndashQ4 output growth produces marginally significant resultsMoreover the in-sample output gap comes with a marginally significant coeffi-cient and a lower R2 of 466

More directly we compare the industrial electricity growth rate with the pre-vious output measures one at a time in the same annual predictive regressionsThe results are presented in Table 8 and can be summarized as follows First whencombined industrial electricity usage growth rates always drive out December-over-December output growth possibly because industry electricity usage is amore timely measure of output Second when the industrial electricity usagegrowth rate is combined with Moller and Rangvidrsquos (2015) Q3ndashQ4 or Q4ndashQ4output growth we find that it usually outperforms as evidenced by its relativelylarger t-values and much smaller p-values the one exception is when it is com-pared to Q3ndashQ4 output growth in the United States Third the in-sample outputgap does not drive out the industrial electricity usage growth rate in the UnitedStates In fact the industrial electricity usage growth rate has higher t-values andlower p-values than the output gap These results suggest that industrial elec-tricity usage contains valuable and incremental information that helps to predictfuture stock returns Finally beyond the United States it is unclear whether thein-sample output gap is strictly a better predictor than the industrial electricityusage growth rate Although it outperforms the industrial electricity usage growthrate in Japan it does not in the United Kingdom

We could also compare industrial electricity usage growth to investmentgrowth rates using annual predictive regressions in the United States where quar-terly investment data are available Panel A of Table 7 shows that annual invest-ment growth rates computed from fourth quarter to fourth quarter and from thirdquarter to fourth quarter both have a marginally significant predictive power fornext-year excess stock returns (p-values = 008 and 006 respectively) Thesefindings provide further empirical support for the investment-based asset pricingliterature As argued by Cochrane (1991) and more recently by Lin and Zhang(2013) under fairly general assumptions investment today should negatively pre-dict stock returns tomorrow Nevertheless industrial electricity usage growth still


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Da Huang and Yun 65

does a much better job than investment growth in predicting future excess stockreturns in a univariate regression

Panel A of Table 8 shows that industrial electricity usage growth completelydrives out investment growth in multivariate regressions One possible reason isthat the standard investment data focus only on investment in capital stock Whenexisting capital is utilized more intensively more investment is needed to main-tain it This maintenance investment can be large it is estimated to be 30 ofthe investment in new physical capital according to survey data from Canada (seeMcGrattan and Schmitz (1999)) Although comprehensive maintenance invest-ment data are not directly available industrial electricity usage is a good proxybecause higher electricity use reflects more intensive capital utilization and thusimplies more maintenance investment

Even at an annual frequency the industrial electricity usage growth rate re-mains a robust predictor of future excess stock returns Horse-race tests suggestthat it compares favorably against growth rates in industry production and hasincremental predictive power over the output gap

TABLE 8Competing with Output Measures Annual (USJapanUK)

In Table 8 we combine the per capita industrial electricity usage growth rate with various measures of output growthreal per capita gross domestic investment growth or the output gap to predict 1-year-ahead excess stock returns An-nual output growth (OUTPUT_GROWTH_DEC-DEC) in the United States is the log difference of the current Decem-berrsquos industrial production and the previous Decemberrsquos industrial production Annual industrial electricity usage growthin the United States is the log difference of the current Decemberrsquos per capita industrial electricity consumption andthe previous Decemberrsquos per capita industrial electricity consumption Other output growth rates are based on thegrowth rates of fourth-quarter industrial production relative to either the previous yearrsquos fourth-quarter industrial pro-duction (OUTPUT_GROWTH_Q4ndashQ4) or the current yearrsquos third-quarter industrial production (OUTPUT_GROWTH_Q3ndashQ4) The output gap (in sample) is computed following the procedures of Cooper and Priestley (2009) We use re-vised industry production data Investment growth rates are the growth rate of fourth-quarter per capita investment rel-ative to those in the previous yearrsquos fourth quarter (INVESTMENT_GROWTH_Q4ndashQ4) or the current yearrsquos third quarter(INVESTMENT_GROWTH_Q3ndashQ4) Annual industrial electricity usage data for Japan and the United Kingdom are fromthe OECD database Population data are from the World Bank Industrial electricity usage data are from the EIA (UnitedStates) and the OECD database (United Kingdom and Japan) The industrial production index is obtained from the Boardof Governors of the Federal Reserve System (United States) the Office for National Statistics (United Kingdom) and theMinistry of Economy (Japan) We report coefficient estimates for industrial electricity growth (b1) and for alternative pre-dicting variables (b2) their Hodrick t -values (t (b1) and t (b2)) their p-values (p(b1) and p(b2)) and their adjustedR 2 values (R 2)

Competing Variable b1 b2 t (b1) t (b2) p(b1) p(b2) R 2

Panel A United States

OUTPUT_GROWTH_DEC-DEC minus08626 minus04257 minus21103 minus08063 00853 03115 00911OUTPUT_GROWTH_Q4ndashQ4 minus09234 minus03973 minus26341 minus07092 00571 03391 00899OUTPUT_GROWTH_Q3ndashQ4 minus06069 minus24080 minus13199 minus18132 01571 00842 01224OUTPUT_GAP minus11823 minus09366 minus35731 minus24414 00039 00202 01913INVESTMENT_GROWTH_Q4ndashQ4 minus11487 00310 minus26817 01055 00242 05707 00840INVESTMENT_GROWTH_Q3ndashQ4 minus10666 minus01279 minus25945 minus02217 00265 04177 00845

Panel B Japan

OUTPUT_GROWTH_Q4ndashQ4 minus11635 05780 minus12316 05953 01101 07655 minus00235OUTPUT_GROWTH_Q3ndashQ4 minus11267 13387 minus12762 05706 01310 07260 minus00296OUTPUT_GAP minus04660 minus16608 minus06605 minus20925 02668 00951 01114

Panel C United Kingdom

OUTPUT_GROWTH_Q4ndashQ4 minus27188 05220 minus16371 04229 00165 06658 00903OUTPUT_GROWTH_Q3ndashQ4 minus20974 minus18786 minus12261 minus04670 00376 02755 00979OUTPUT_GAP minus20417 minus07879 minus12246 minus09448 00347 02729 01054


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V Predictive Regressions in Real TimeSo far our comparison between the industrial electricity usage growth rate

and the output gap has not taken into account the data-reporting delay For ex-ample whereas we use vintage (first-release) data for electricity we use the finalrevised data for industrial production which could be available only 6 or 7 monthsafter the first release For better comparison in this section we recompute outputgap measures using vintage industrial production data

Historically and especially during the early parts of our sample even vin-tage data (for both electricity usage and industrial production) are released witha delay of up to 2 months In other words vintage electricity usage and vintageindustrial production data from month t sometimes reach investors only at the endof month t+2 To account for this reporting lag we follow the practice of Cooperand Priestley (2009) we lag the predictive variables by 2 months in our monthlypredictive regressions For example we use the industrial electricity usage growthrate from January of year tminus1 to January in year t and the output gap in Januaryof year t to predict excess stock returns in April of year t

Panel A of Table 9 reports the results from these predictive regressions Witha 2-month lag the predictive power of the industrial electricity usage growth ratebecomes weaker with the presence of the output gap It remains significant forpredicting excess stock returns up to the next 6 months

Panel B of Table 9 reports the results from using the in-sample output gapcomputed with vintage data and the same 2-month lag The in-sample output gapremains a significant return predictor at all horizons although the R2 values dropsignificantly from those reported in Table 3 For example at a 1-year horizon theR2 is 1339 compared with 2336 when revised data are used with no lag

However the in-sample output gap even with the 2-month lag cannot beused by an investor in real time because it is estimated using the full sample ofindustry production data from 1927 to 2010 In Panel C of Table 9 we examinethe out-of-sample output gap instead As in Panel A we use the out-of-sampleoutput gap in month t to predict excess stock returns in month t+3 We observeno significant predictability in this case

Panels D and E of Table 9 compare the electricity growth rate to the in-sample and out-of-sample output gaps both in real time It is evident now that theelectricity growth rate adds incremental forecast power to the in-sample outputgap and is strictly preferred to the out-of-sample output gap because the formerdrives out the latter completely across all forecasting horizons Unreported out-of-sample tests similar to those in Table 6 suggest that when implemented in real timeusing only data available at the time of prediction although the lagged industrialelectricity usage growth rate beats the historical mean the out-of-sample outputgap does not Going forward industrial electricity usage data are likely to beobserved in real time making this approach even more appealing to an averageinvestor


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Da Huang and Yun 67

TABLE 9In-Sample Real-Time Prediction Monthly (US)

In Table 9 we combine the monthly per capita industrial electricity usage growth rate with the monthly output gap ofCooper and Priestley (2009) to predict future 1-month 3-month 6-month 9-month and 12-month cumulative excessreturns We lag the per capita industrial electricity usage growth rate by 2 months to account for reporting lags Panel Auses the electricity usage growth rate The output gap estimates in Panel B use vintage industrial production data andthe full sample and the estimates in Panel C use vintage data and a rolling sample where we extend the sample by 1month for an estimate in each month We report the coefficient estimate (b) Hodrick-t p-value of the coefficient estimateand adjusted R 2 value (R 2) Panels D and E present results obtained from combining the electricity usage growth ratewith the output gap We report coefficient estimates for the industrial electricity usage growth rate (b1) and for alternativepredicting variables (b2) which are the output gap their Hodrick t -values (t (b1) and t (b2)) their p-values (p(b1) andp(b2)) and their adjusted R 2 values (R 2) The sample is monthly from Jan 1956 to Dec 2010

Horizon

1 3 6 9 12

Panel A Electricity Growth

b minus01126 minus03215 minus05703 minus07894 minus08246Hodrick-t minus33590 minus34953 minus34345 minus33634 minus28073p-value 00009 00005 00010 00014 00069R 2 00169 00438 00641 00805 00667

Panel B Output Gap (In-Sample)


Panel C Output Gap (Out-of-Sample)

b minus00174 minus00522 minus01009 minus01394 minus01362Hodrick-t minus08613 minus08894 minus08870 minus08374 minus06252p-value 02810 02799 02853 03038 03560R 2

minus00004 00014 00036 00049 00031

Panel D Electricity Growth + Output Gap (In-Sample)

b1 minus00820 minus02327 minus03932 minus05275 minus04942b2 minus00805 minus02315 minus04458 minus06321 minus07919t (b1) minus22487 minus22955 minus21488 minus20204 minus15278t (b2) minus29022 minus27619 minus26654 minus24901 minus23401p(b1) 00100 00096 00188 00240 00738p(b2) 00071 00079 00086 00116 00147R 2 00290 00761 01206 01545 01544

Panel E Electricity Growth + Output Gap (Out-of-Sample)

b1 minus01223 minus03480 minus06146 minus08600 minus09111b2 00149 00403 00654 01007 01227t (b1) minus34297 minus35599 minus34251 minus33135 minus27764t (b2) 07193 06662 05431 05561 05104p(b1) 00006 00007 00013 00013 00059p(b2) 07359 07182 06887 06943 06844R 2 00161 00438 00644 00818 00683

VI ConclusionStock return predictability has important implications for asset pricing port-

folio choice and risk management In this paper we show that a simple growthrate in industrial electricity usage does a remarkable job of predicting future stockreturns up to 1 year with an R2 of around 10 Specifically high industrial elec-tricity usage today predicts low stock returns in the future which is consistentwith the presence of a countercyclical risk premium Industrial electricity usageforecasts excess returns particularly well because it tracks the output of the verycyclical sectors such as steel and machinery Our findings thus bridge a gap be-tween the asset pricing literature and the traditional business cycle literature thatuses industrial electricity consumption to gauge production and output in realtime


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s


As an empirical return predictor the industrial electricity usage growth ratecompares favorably with traditional financial variables and output-based mea-sures and it beats the output gap measure of Cooper and Priestley (2009) whenused in real time

Our paper also illustrates the usefulness of electricity data in financial appli-cations The availability of high-frequency real-time electricity data over differentgeographic areas could lead to further applications in finance and economics10

We leave this for future studies

ReferencesBaker M and J Wurgler ldquoInvestor Sentiment and the Cross-Section of Stock Returnsrdquo Journal of

Finance 51 (2006) 1645ndash1680Bansal R and A Yaron ldquoRisks for the Long Run A Potential Resolution of Asset Pricing Puzzlesrdquo

Journal of Finance 59 (2004) 1481ndash1509Belo F and J Yu ldquoGovernment Investment and the Stock Marketrdquo Journal of Monetary Economics

60 (2013) 325ndash339Boudoukh J M Richardson and R Whitelaw ldquoThe Myth of Long-Horizon Predictabilityrdquo Review

of Financial Studies 21 (2008) 1577ndash1605Burnside C M Eichenbaum and S Rebelo ldquoCapacity Utilization and Returns to Scalerdquo NBER

Macroeconomics Annual 1995 (1995) 67ndash110Burnside C M Eichenbaum and S Rebelo ldquoSectoral Solow Residualsrdquo European Economic

Review 40 (1996) 861ndash869Campbell J Y ldquoConsumption-Based Asset Pricingrdquo In Handbook of the Economics of Finance

Vol IB G Constantinides M Harris and R Stulz eds Amsterdam Netherlands North-Holland(2003) 803ndash887

Campbell J Y and J H Cochrane ldquoBy Force of Habit A Consumption-Based Explanation ofAggregate Stock Market Behaviorrdquo Journal of Political Economy 107 (1999) 205ndash251

Campbell J Y and S Thompson ldquoPredicting the Equity Premium Out of Sample Can AnythingBeat the Historical Averagerdquo Review of Financial Studies 21 (2008) 1509ndash1531

Charoenrook A ldquoDoes Sentiment Matterrdquo Working Paper Vanderbilt University (2003)Cochrane J H ldquoProduction-Based Asset Pricing and the Link between Stock Returns and Economic

Fluctuationsrdquo Journal of Finance 46 (1991) 209ndash237Cochrane J H ldquoThe Dog That Did Not Bark A Defense of Return Predictabilityrdquo Review of

Financial Studies 21 (2008) 1533ndash1575Comin D and M Gertler ldquoMedium-Term Business Cyclesrdquo American Economic Review 96 (2006)

523ndash551Cooper I and R Priestley ldquoTime-Varying Risk Premiums and the Output Gaprdquo Review of Financial

Studies 22 (2009) 2801ndash2833Da Z W Yang and H Yun ldquoHousehold Production and Asset Pricesrdquo Management Science 62

(2016) 387ndash409Fama E and K French ldquoBusiness Conditions and Expected Returns on Stocks and Bondsrdquo Journal

of Financial Economics 25 (1989) 23ndash49Ferreira M A and P Santa-Clara ldquoForecasting Stock Market Returns The Sum of the Parts Is More

than the Wholerdquo Journal of Financial Economics 100 (2011) 514ndash537Ferson W and J Lin ldquoAlpha and Performance Measurement The Effects of Investor Heterogeneityrdquo

Journal of Finance 69 (2014) 1565ndash1596Fisher K and M Statman ldquoConsumer Confidence and Stock Returnsrdquo Journal of Portfolio Manage-

ment 30 (2003) 115ndash127Hodrick R J ldquoDividend Yields and Expected Stock Returns Alternative Procedures for Inference

and Measurementrdquo Review of Financial Studies 5 (1992) 357ndash386Jorgenson D W and Z Griliches ldquoThe Explanation of Productivity Changerdquo Review of Economic

Studies 34 (1967) 249ndash283

10As recent examples Da Yang and Yun (2016) use residential electricity consumption to measurehousehold production and Ferson and Lin (2014) use electricity usage across different states as theproxy for investor heterogeneity in a study of mutual fund performance


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otre Dam



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s

Da Huang and Yun 69

King R G and S Rebello ldquoResuscitating Real Business Cyclesrdquo In Handbook of MacroeconomicsJ Taylor and M Woodford eds Amsterdam Netherlands North-Holland (2000) 927ndash1007

Lamont O ldquoInvestment Plans and Stock Returnsrdquo Journal of Finance 55 (2000) 2719ndash2745Lettau M and S Ludvigson ldquoConsumption Aggregate Wealth and Expected Returnsrdquo Journal of

Finance 55 (2001) 815ndash849Lettau M and S Ludvigson ldquoEuler Equation Errorsrdquo Review of Economic Dynamics 12 (2009)

255ndash283Lettau M and S Ludvigson ldquoMeasuring and Modeling Variation in the Risk-Return Trade-Offrdquo

In Handbook of Financial Econometrics Y Ait-Sahalia and L Hansen eds Amsterdam Nether-lands North-Holland (2010) 617ndash690

Li E X N D Livdan and L Zhang ldquoAnomaliesrdquo Review of Financial Studies 22 (2009)4301ndash4334

Li Y D T Ng and B Swaminathan ldquoPredicting Market Returns Using Aggregate Implied Cost ofCapitalrdquo Journal of Financial Economics 110 (2013) 419ndash436

Lin X and L Zhang ldquoThe Investment Manifestordquo Journal of Monetary Economics 60 (2013)351ndash366

Liu L X T M Whited and L Zhang ldquoInvestment-Based Expected Stock Returnsrdquo Journal ofPolitical Economy 117 (2009) 1105ndash1139

Ludvigson S ldquoConsumer Confidence and Consumer Spendingrdquo Journal of Economic Perspectives18 (2004) 29ndash50

Lustig H and S van Nieuwerburgh ldquoHousing Collateral Consumption Insurance and Risk PremiaAn Empirical Perspectiverdquo Journal of Finance 60 (2005) 1167ndash1219

McGrattan E and J Schmitz ldquoMaintenance and Repair Too Big to Ignorerdquo Federal Reserve Bankof Minneapolis Quarterly Review 23 (1999) 2ndash13

Moller S V and J Rangvid ldquoEnd-of-the-Year Economic Growth and Time-Varying ExpectedReturnsrdquo Journal of Financial Economics 115 (2015) 136ndash154

Newey W K and K D West ldquoA Simple Positive Semi-Definite Heteroskedasticity and Autocorre-lation Consistent Covariance Matrixrdquo Econometrica 55 (1987) 703ndash708

Pena I J F Restoy and R Rodriguez ldquoCan Output Explain the Predictability and Volatility of StockReturnsrdquo Journal of International Money and Finance 21 (2002) 163ndash182

Rangvid J ldquoOutput and Expected Returnsrdquo Journal of Financial Economics 81 (2006) 595ndash624Rapach D and G Zhou ldquoForecasting Stock Returnsrdquo In Handbook of Economic Forecasting

G Elliott and A Timmermann eds Amsterdam Netherlands North-Holland (2013) 328ndash383Santos T and P Veronesi ldquoLabor Income and Predictable Stock Returnsrdquo Review of Financial

Studies 19 (2006) 1ndash44Stambaugh R F ldquoPredictive Regressionsrdquo Journal of Financial Economics 54 (1999) 375ndash421Welch I and A Goyal ldquoA Comprehensive Look at the Empirical Performance of Equity Premium

Predictionrdquo Review of Financial Studies 21 (2008) 1455ndash1508Zhang L ldquoThe Value Premiumrdquo Journal of Finance 60 (2005) 67ndash103


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s


Introduction

Data

Electricity and Weather Data

Output Measures

Other Data

Summary Statistics

Monthly Predictive Regressions

The In-Sample Predictability of Electricity Growth

The In-Sample Predictability of Other Predictors

An Anatomy of Industrial Production

Out-of-Sample Predictability

Annual Predictive Regressions

Predictive Regressions in Real Time

Conclusion

References


from its long-run trend also known as the output gap predicts stock market re-turns well (ldquoLogrdquo refers to natural logarithm throughout)

In this paper we propose a novel yet simple business cycle variable thatpredicts stock market returns well and even outperforms the output gap whenused in real time This variable is the growth rate of the aggregate industrial usageof electricity

Most modern industrial production activities involve the use of electricityCrucially because of technological limitations electricity cannot easily be storedAs a result industrial electricity usage can be used to track production and outputin real time1 Indeed since 1971 the Federal Reserve (ldquothe Fedrdquo) has been usingsurvey data on electric power when estimating key components of its monthlyindustrial production index The practice was discontinued in 2005 due to poorsurvey coverage2

Because electric utilities are highly regulated and are subject to extensivedisclosure requirements electricity usage data are accurately measured and re-ported For these reasons the business cycle literature has long used industrialelectricity usage as a proxy for capital services (Jorgenson and Griliches (1967)Burnside Eichenbaum and Rebelo (1995) (1996) and Comin and Gertler(2006)) Capacity utilization which is reflected in industrial electricity usage ap-pears to be the key missing ingredient that allows a relatively mild productivityshock to drive a much more volatile business cycle (King and Rebello (2000))Despite the importance of industrial electricity usage as a business cycle variableits predictive power for stock market returns has not been examined in the litera-ture Our paper fills this gap

Because monthly industrial electricity usage data are available in the UnitedStates in our sample period 1956ndash2010 we first conduct overlapping monthlypredictive regressions to maximize the power of the test To alleviate the impactof within-year seasonality in electricity usage we compute year-over-year growthrates For example we use the industrial electricity growth rate from January inyear tminus1 to January in year t to predict the excess stock return in February inyear t We then use the electricity growth rate from February in year tminus1 toFebruary in year t to predict the excess stock return in March in year t and so onStambaugh (1999) argues that predictive regressions potentially lead to overesti-mated t-values with a small sample in an overlapping regression because manypredictive variables are persistent To address this bias we follow Li Ng andSwaminathan (2013) closely and report p-values from simulation exercises Forcomparison purposes we also report the more standard Hodrick (1992) t-value

We find that this simple year-over-year industrial electricity usage growthrate has strong and significant predictive power for future stock market excessreturns in horizons ranging from 1 month up to 1 year At the annual hori-zon a 1 increase in the year-over-year industrial electricity usage growth rate

1As anecdotal evidence the Chinese premier relies on electricity consumption as a more accuratemeasure of economic growth in China ldquoAll other figures especially GDP statistics are lsquoman-madersquoand therefore unreliablerdquo See Wall Street Journal Dec 6 2010

2The survey was conducted by the regional Federal Reserve Banks of the electric utilities in theirdistrict it was not the Department of EnergyEnergy Information Administration survey that we usein this paper


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s

Da Huang and Yun 39








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otre Dam



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s










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otre Dam



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s

Da Huang and Yun 41






ownloaded from

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otre Dam



ww


s




II Data







ownloaded from

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wcam


otre Dam



ww


s

Da Huang and Yun 43



005

0 0

60

070

080

090

1

1 2 3



4 5 6 7 8 9 10 11 12

Month

Nor

mal

ized

Ele

ctric

ity C

onsu

mpt

ion

1 2 3 4 5 6 7 8 9 10 11 12

Month

00

51

15

2

Nor

mal

ized

ED

D



ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 45










ownloaded from

httpsww

wcam


otre Dam



ww


s













ownloaded from

httpsww

wcam


otre Dam



ww


s

DaH

uangandYun

47



[0minus Tmax+Tmin


[065minus Tmax+Tmin

2









Quantitative

Analysis









DaH

uangandYun

49















ndash020

ndash010

000

010

020

Gro

wth

Rat

eG

row

th R

ate

Gro

wth

Rat

e

1950 1960 1970 1980


Graph C Japan

1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

ndash010

ndash005

000

005

010

ndash010

ndash015

ndash005

000

005

010






ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 51








ownloaded from

httpsww

wcam


otre Dam



ww


s















ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 53



Horizon

1 3 6 9 12












(1) R2-0 =

(1+


)2

kR2




ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

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otre Dam



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s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

Da Huang and Yun 39








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wcam


otre Dam



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s










ownloaded from

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wcam


otre Dam



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s

Da Huang and Yun 41






ownloaded from

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wcam


otre Dam



ww


s




II Data







ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 43



005

0 0

60

070

080

090

1

1 2 3



4 5 6 7 8 9 10 11 12

Month

Nor

mal

ized

Ele

ctric

ity C

onsu

mpt

ion

1 2 3 4 5 6 7 8 9 10 11 12

Month

00

51

15

2

Nor

mal

ized

ED

D



ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



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s

Da Huang and Yun 45










ownloaded from

httpsww

wcam


otre Dam



ww


s













ownloaded from

httpsww

wcam


otre Dam



ww


s

DaH

uangandYun

47



[0minus Tmax+Tmin


[065minus Tmax+Tmin

2









Quantitative

Analysis









DaH

uangandYun

49















ndash020

ndash010

000

010

020

Gro

wth

Rat

eG

row

th R

ate

Gro

wth

Rat

e

1950 1960 1970 1980


Graph C Japan

1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

ndash010

ndash005

000

005

010

ndash010

ndash015

ndash005

000

005

010






ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 51








ownloaded from

httpsww

wcam


otre Dam



ww


s















ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 53



Horizon

1 3 6 9 12












(1) R2-0 =

(1+


)2

kR2




ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

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otre Dam



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s

Da Huang and Yun 63














ownloaded from

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otre Dam



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s








ownloaded from

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Da Huang and Yun 65









Panel B Japan





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s










ownloaded from

httpsww

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s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

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wcam


otre Dam



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s





























ownloaded from

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s

Da Huang and Yun 69





















ownloaded from

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s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References










ownloaded from

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s

Da Huang and Yun 41






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s




II Data







ownloaded from

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wcam


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s

Da Huang and Yun 43



005

0 0

60

070

080

090

1

1 2 3



4 5 6 7 8 9 10 11 12

Month

Nor

mal

ized

Ele

ctric

ity C

onsu

mpt

ion

1 2 3 4 5 6 7 8 9 10 11 12

Month

00

51

15

2

Nor

mal

ized

ED

D



ownloaded from

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otre Dam



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s








ownloaded from

httpsww

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otre Dam



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s

Da Huang and Yun 45










ownloaded from

httpsww

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otre Dam



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s













ownloaded from

httpsww

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DaH

uangandYun

47



[0minus Tmax+Tmin


[065minus Tmax+Tmin

2









Quantitative

Analysis









DaH

uangandYun

49















ndash020

ndash010

000

010

020

Gro

wth

Rat

eG

row

th R

ate

Gro

wth

Rat

e

1950 1960 1970 1980


Graph C Japan

1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

ndash010

ndash005

000

005

010

ndash010

ndash015

ndash005

000

005

010






ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 51








ownloaded from

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wcam


otre Dam



ww


s















ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 53



Horizon

1 3 6 9 12












(1) R2-0 =

(1+


)2

kR2




ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



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s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

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otre Dam



ww


s











ownloaded from

httpsww

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otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

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otre Dam



ww


s








ownloaded from

httpsww

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otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

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otre Dam



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s










ownloaded from

httpsww

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otre Dam



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s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



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s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

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otre Dam



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s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

Da Huang and Yun 41






ownloaded from

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otre Dam



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s




II Data







ownloaded from

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otre Dam



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s

Da Huang and Yun 43



005

0 0

60

070

080

090

1

1 2 3



4 5 6 7 8 9 10 11 12

Month

Nor

mal

ized

Ele

ctric

ity C

onsu

mpt

ion

1 2 3 4 5 6 7 8 9 10 11 12

Month

00

51

15

2

Nor

mal

ized

ED

D



ownloaded from

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ownloaded from

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Da Huang and Yun 45










ownloaded from

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ownloaded from

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wcam


otre Dam



ww


s

DaH

uangandYun

47



[0minus Tmax+Tmin


[065minus Tmax+Tmin

2









Quantitative

Analysis









DaH

uangandYun

49















ndash020

ndash010

000

010

020

Gro

wth

Rat

eG

row

th R

ate

Gro

wth

Rat

e

1950 1960 1970 1980


Graph C Japan

1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

ndash010

ndash005

000

005

010

ndash010

ndash015

ndash005

000

005

010






ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 51








ownloaded from

httpsww

wcam


otre Dam



ww


s















ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 53



Horizon

1 3 6 9 12












(1) R2-0 =

(1+


)2

kR2




ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References




II Data







ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 43



005

0 0

60

070

080

090

1

1 2 3



4 5 6 7 8 9 10 11 12

Month

Nor

mal

ized

Ele

ctric

ity C

onsu

mpt

ion

1 2 3 4 5 6 7 8 9 10 11 12

Month

00

51

15

2

Nor

mal

ized

ED

D



ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 45










ownloaded from

httpsww

wcam


otre Dam



ww


s













ownloaded from

httpsww

wcam


otre Dam



ww


s

DaH

uangandYun

47



[0minus Tmax+Tmin


[065minus Tmax+Tmin

2









Quantitative

Analysis









DaH

uangandYun

49















ndash020

ndash010

000

010

020

Gro

wth

Rat

eG

row

th R

ate

Gro

wth

Rat

e

1950 1960 1970 1980


Graph C Japan

1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

ndash010

ndash005

000

005

010

ndash010

ndash015

ndash005

000

005

010






ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 51








ownloaded from

httpsww

wcam


otre Dam



ww


s















ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 53



Horizon

1 3 6 9 12












(1) R2-0 =

(1+


)2

kR2




ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

Da Huang and Yun 43



005

0 0

60

070

080

090

1

1 2 3



4 5 6 7 8 9 10 11 12

Month

Nor

mal

ized

Ele

ctric

ity C

onsu

mpt

ion

1 2 3 4 5 6 7 8 9 10 11 12

Month

00

51

15

2

Nor

mal

ized

ED

D



ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 45










ownloaded from

httpsww

wcam


otre Dam



ww


s













ownloaded from

httpsww

wcam


otre Dam



ww


s

DaH

uangandYun

47



[0minus Tmax+Tmin


[065minus Tmax+Tmin

2









Quantitative

Analysis









DaH

uangandYun

49















ndash020

ndash010

000

010

020

Gro

wth

Rat

eG

row

th R

ate

Gro

wth

Rat

e

1950 1960 1970 1980


Graph C Japan

1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

ndash010

ndash005

000

005

010

ndash010

ndash015

ndash005

000

005

010






ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 51








ownloaded from

httpsww

wcam


otre Dam



ww


s















ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 53



Horizon

1 3 6 9 12












(1) R2-0 =

(1+


)2

kR2




ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 45










ownloaded from

httpsww

wcam


otre Dam



ww


s













ownloaded from

httpsww

wcam


otre Dam



ww


s

DaH

uangandYun

47



[0minus Tmax+Tmin


[065minus Tmax+Tmin

2









Quantitative

Analysis









DaH

uangandYun

49















ndash020

ndash010

000

010

020

Gro

wth

Rat

eG

row

th R

ate

Gro

wth

Rat

e

1950 1960 1970 1980


Graph C Japan

1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

ndash010

ndash005

000

005

010

ndash010

ndash015

ndash005

000

005

010






ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 51








ownloaded from

httpsww

wcam


otre Dam



ww


s















ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 53



Horizon

1 3 6 9 12












(1) R2-0 =

(1+


)2

kR2




ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

Da Huang and Yun 45










ownloaded from

httpsww

wcam


otre Dam



ww


s













ownloaded from

httpsww

wcam


otre Dam



ww


s

DaH

uangandYun

47



[0minus Tmax+Tmin


[065minus Tmax+Tmin

2









Quantitative

Analysis









DaH

uangandYun

49















ndash020

ndash010

000

010

020

Gro

wth

Rat

eG

row

th R

ate

Gro

wth

Rat

e

1950 1960 1970 1980


Graph C Japan

1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

ndash010

ndash005

000

005

010

ndash010

ndash015

ndash005

000

005

010






ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 51








ownloaded from

httpsww

wcam


otre Dam



ww


s















ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 53



Horizon

1 3 6 9 12












(1) R2-0 =

(1+


)2

kR2




ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References













ownloaded from

httpsww

wcam


otre Dam



ww


s

DaH

uangandYun

47



[0minus Tmax+Tmin


[065minus Tmax+Tmin

2









Quantitative

Analysis









DaH

uangandYun

49















ndash020

ndash010

000

010

020

Gro

wth

Rat

eG

row

th R

ate

Gro

wth

Rat

e

1950 1960 1970 1980


Graph C Japan

1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

ndash010

ndash005

000

005

010

ndash010

ndash015

ndash005

000

005

010






ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 51








ownloaded from

httpsww

wcam


otre Dam



ww


s















ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 53



Horizon

1 3 6 9 12












(1) R2-0 =

(1+


)2

kR2




ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

DaH

uangandYun

47



[0minus Tmax+Tmin


[065minus Tmax+Tmin

2









Quantitative

Analysis









DaH

uangandYun

49















ndash020

ndash010

000

010

020

Gro

wth

Rat

eG

row

th R

ate

Gro

wth

Rat

e

1950 1960 1970 1980


Graph C Japan

1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

ndash010

ndash005

000

005

010

ndash010

ndash015

ndash005

000

005

010






ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 51








ownloaded from

httpsww

wcam


otre Dam



ww


s















ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 53



Horizon

1 3 6 9 12












(1) R2-0 =

(1+


)2

kR2




ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References


Quantitative

Analysis









DaH

uangandYun

49















ndash020

ndash010

000

010

020

Gro

wth

Rat

eG

row

th R

ate

Gro

wth

Rat

e

1950 1960 1970 1980


Graph C Japan

1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

ndash010

ndash005

000

005

010

ndash010

ndash015

ndash005

000

005

010






ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 51








ownloaded from

httpsww

wcam


otre Dam



ww


s















ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 53



Horizon

1 3 6 9 12












(1) R2-0 =

(1+


)2

kR2




ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

DaH

uangandYun

49















ndash020

ndash010

000

010

020

Gro

wth

Rat

eG

row

th R

ate

Gro

wth

Rat

e

1950 1960 1970 1980


Graph C Japan

1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

ndash010

ndash005

000

005

010

ndash010

ndash015

ndash005

000

005

010






ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 51








ownloaded from

httpsww

wcam


otre Dam



ww


s















ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 53



Horizon

1 3 6 9 12












(1) R2-0 =

(1+


)2

kR2




ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References




ndash020

ndash010

000

010

020

Gro

wth

Rat

eG

row

th R

ate

Gro

wth

Rat

e

1950 1960 1970 1980


Graph C Japan

1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

1950 1960 1970 1980 1990 2000 2010

ndash010

ndash005

000

005

010

ndash010

ndash015

ndash005

000

005

010






ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 51








ownloaded from

httpsww

wcam


otre Dam



ww


s















ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 53



Horizon

1 3 6 9 12












(1) R2-0 =

(1+


)2

kR2




ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

Da Huang and Yun 51








ownloaded from

httpsww

wcam


otre Dam



ww


s















ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 53



Horizon

1 3 6 9 12












(1) R2-0 =

(1+


)2

kR2




ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References















ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 53



Horizon

1 3 6 9 12












(1) R2-0 =

(1+


)2

kR2




ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

Da Huang and Yun 53



Horizon

1 3 6 9 12












(1) R2-0 =

(1+


)2

kR2




ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

Da Huang and Yun 55



Horizon

1 3 6 9 12 1 3 6 9 12





















ownloaded from

httpsww

wcam


otre Dam



ww


s







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References







Horizon

1 3 6 9 12 1 3 6 9 12









ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

Da Huang and Yun 57


Horizon

1 3 6 9 12 1 3 6 9 12














ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

Da Huang and Yun 59











Horizon

1 3 6 9 12










ownloaded from

httpsww

wcam


otre Dam



ww


s




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References




1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

ndash04

ndash03

ndash02

ndash01

0

01

02

03





(3) R2= 1minus

Tminus1sumi=s0


Tminus1sumi=s0

(rt+1minus ri )2




ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

Da Huang and Yun 61






R 2= 1minus

sumTminus1

i=s0

(ri+1 minus microi

)2sumTminus1

i=s0

(ri+1 minus ri

)2






















ownloaded from

httpsww

wcam


otre Dam



ww


s











ownloaded from

httpsww

wcam


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ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


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ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References











ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

Da Huang and Yun 63














ownloaded from

httpsww

wcam


otre Dam



ww


s








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References








ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

Da Huang and Yun 65









Panel B Japan





ownloaded from

httpsww

wcam


otre Dam



ww


s










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References










ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

Da Huang and Yun 67



Horizon

1 3 6 9 12







minus00004 00014 00036 00049 00031








ownloaded from

httpsww

wcam


otre Dam



ww


s





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References





























ownloaded from

httpsww

wcam


otre Dam



ww


s

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

Da Huang and Yun 69





















ownloaded from

httpsww

wcam


otre Dam



ww


s


Introduction

Data


Output Measures

Other Data

Summary Statistics








Conclusion

References

industrial electricity usage and stock returnszda/pred_eg.pdf · 2017-07-08 · industrial...

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