Journal of Empirical Finance 16 (2009) 409–429

Contents lists available at ScienceDirect

Journal of Empirical Finance

j ourna l homepage: www.e lsev ie r.com/ locate / jempf in

The cross section of cashflow volatility and expected stock returns☆

Alan Guoming Huang⁎School of Accounting and Finance, and Center for Advanced Studies in Finance, University of Waterloo, Waterloo, Canada ON N2L 3G1

a r t i c l e i n f o

☆ An earlier version of this paper was titled “The Croof my dissertation. I am grateful to my committee mem(the editor), Changling Chen, Alan Douglas, Ranjini JUniversity of Colorado at Boulder, the University ofWatSciences and Humanities Research Council of Canada⁎ Tel.: +1 519 888 4567x36770.

E-mail address: aghuang@uwaterloo.ca.

0927-5398/$ – see front matter © 2009 Elsevier B.V.doi:10.1016/j.jempfin.2009.01.001

a b s t r a c t

Article history:Received 20 May 2008Received in revised 6 October 2008Accepted 14 January 2009Available online 23 January 2009

I show that historical cashflow volatility is negatively related to future returns cross-sectionally.The negative association is large; economically meaningful; long-lasting up to five years; robustto known return-informative effects of size, value, price and earnings momentums andilliquidity; and extends to both systematic and idiosyncratic cashflow volatilities. Using thestandard deviations of cashflow to sales and of cashflow to book equity as proxies for cashflowvolatility, the least volatile decile portfolio outperforms themost volatile decile portfolio by 13%a year relative to the Fama–French four factors. The cashflow volatility effect is closely related tothe idiosyncratic return volatility effect documented in Ang et al. [Ang, A., Hodrick, R.J., Xing, Y.and Zhang, X. “The cross-section of volatility and expected returns.” Journal of Finance, 51(2006), 259–299.]. However, in portfolios simultaneously sorted on both cashflow and returnvolatilities, and in cross sectional regressions of returns at the firm level, these two effectsneither drive out nor dominate each other. While the pricing of idiosyncratic cashflow volatilityrepresents an anomaly against the traditional asset pricing theories, the pricing of historicalcashflow uncertainty sheds light on potential fundamental risks embodied in the Fama–FrenchHML and SMB factors.

© 2009 Elsevier B.V. All rights reserved.

JEL Classifications:G12G14

Keywords:Cashflow volatilityExpected stock returnsIdiosyncratic return volatilityCross section

1. Introduction

There is a growing literature documenting a negative relationship between observed volatility and future stock returns. In afrequently cited paper, Ang et al. (2006) find that both systematic and idiosyncratic volatilities of stock returns are negativelyassociated with future returns cross-sectionally. Using a number of proxies for information uncertainty, such as firm age, size,analyst forecast dispersion and return volatility, both Jiang et al. (2005) and Zhang (2006) find that high information uncertainty,in the form of large analyst dispersion or return volatility, induces negative future returns. The negative association betweenanalyst forecast dispersion and future returns is also documented in Diether et al. (2002).

The literature also links idiosyncratic return volatility to earnings or cashflow volatility. Pastor and Veronesi (2003) argue thatlearning about firms' uncertainty in future profitability increases their idiosyncratic return volatility. Wei and Zhang (2006) findthat the rise in idiosyncratic stock volatility documented in Campbell et al. (2001) and Morck et al. (2000) from the 1970s to the1990s is largely attributable to a decreasing return-on-equity and an increasing volatility in return-on-equity. Moving a stepfurther, Irvine and Pontiff (in press) argue that cashflow shocks and increased economy-wide market competition are primarydrivers for the documented trend in return volatility. The Ang et al. (2006) results indicate a negative association between returnvolatility and future returns. If return volatility is positively associated with earnings or cashflow volatility, we should expect a

ss Section of Earnings Volatility and Expected Stock Returns.” This paper was developed from Chapter Threebers Eric Hughson, Chris Leach, Martin Boileau, Michael Stutzer and Jamie Zender, as well as Geert Bekaert

ha, Noah Stoffman, Ken Vetzal, Tony Wirjanto, two anonymous referees, and seminar participants at theerloo andMcMaster University for many helpful comments. I acknowledge financial support from the Social(SSHRC). I am solely responsible for all remaining errors.

All rights reserved.

410 A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

similar negative association between earnings/cashflow volatility and future stock returns. This paper links the above findings andexamines the impact of firms' cashflow volatility on returns.

I confirm the negative association between return and cashflow volatility by examining the cross-sectional relationship betweenhistorical cashflow volatility and ex post returns. The negative association is strong and consistent. My findings consist of three piecesof evidence: (1) the economic magnitude, (2) the pricing of systematic cashflow volatility and idiosyncratic cashflow volatility, and(3) the relationship of cashflow volatility with return volatility in explaining returns. I briefly discuss these results in order.

The magnitude of the negative association between historical cashflow volatility and future returns is large and long-lasting. Inmy benchmark cases I use two seasonality-adjusted measures of cashflow volatility: standard deviation of cashflow to sales andstandard deviation of industry-adjusted cashflow to book equity of the past sixteen quarters. I form ten decile portfolios based onincreasing values of cashflow volatility using all listed firms on the NYSE, NASDAQ and AMEX from 1980 to 2004. Between thevalue-weighted return on decile one and the value-weighted return on decile ten, the Jensen's alpha spreads relative to the Fama–French three factors of market, SMB, and HML and Carhart's (1997) pricemomentum factor (hereafter “Fama–French four factors”)are respectively 1.19% and 1.06% a month for the two volatility measures, or equivalently 13% a year. The value-weighted rawreturns have similar sizes of spread. Furthermore, the magnitude of the spread exists not only for one-month-ahead return, butalso for six-month and one-year-ahead buy-and-hold returns. It is only at an investment horizon of two years that the spreads startto decline, although the effect largely lasts up to year five.

The portfolio level relation between cashflow volatility and one-month to five-year returns survives at the firm level withmultiple controls of return-informative variables that include the Fama–French four factors of market, size, book to market andprice momentum, earnings momentum (Chan et al., 1996), illiquidity (Amihud, 2002), and earnings yield (Haugen and Baker,1996). In the Fama andMacBeth (1973) cross-sectional regression controlled for the Fama–French four factors, a firmwith averagecashflow/sales (cashflow/book equity) volatility experiences a monthly return of 0.10 (0.20)% less due to its volatility.

The pricing effect of cashflow volatility is due to both systematic volatility and idiosyncratic volatility. When total cashflow isdecomposed into systematic and idiosyncratic cashflow relative to the industry mean, I find that both systematic volatility andidiosyncratic volatility are priced similarly with the total volatility. In fact, these three volatilities move together: Firms with largetotal volatility tend to display both large idiosyncratic volatility and large systematic volatility. The indifference of the results to thecashflow decomposition is perhaps because cashflow is not directly traded, and therefore firms are not motivated to peg theircashflows to the industry mean according to their exposures to industry cashflow.

My final piece of evidence is that the cashflow volatility effect is different from the idiosyncratic return volatility effect at boththe portfolio and firm levels and that neither effect drives out the other, although they are highly correlated. At the portfolio level,high cashflow volatility portfolios display high idiosyncratic return volatility. However, in double sorting of portfolios on cashflowvolatility controlled for idiosyncratic return volatility, the cashflow volatility effect clusters in medium to high return volatilityfirms. Similarly, the return volatility effect clusters in medium to high cashflow volatility firms. The cashflow volatility effect isdifferent from the return volatility effect also in that in firms of the top 30% of cashflow volatility, only 30% of them rank in the top30% of idiosyncratic return volatility. Furthermore, controlling for return volatility, the overall Fama–French four-factor alphaspread between the least volatile cashflow quintile and themost volatile cashflow quintile is 0.70% amonth at 1% significance level.This spread size is similar to the unconditional spread when no control is imposed.

The cashflow volatility effect is also different from the return volatility effect at the firm level. In Fama–MacBeth cross sectionalregressions of stock returns, both effects co-exist. I also decompose cashflow volatility to a component related to and a componentorthogonal to contemporaneous and lagged return volatilities. The cashflow volatility effect remains for the orthogonal componentat the firm level. In sum, the evidence at the portfolio and firm levels points to a separate cashflow volatility effect that is noweakerthan the idiosyncratic return volatility effect.

My results survive a wide range of robustness checks. Bali and Cakici (2008) dispute Ang et al.'s (2006) findings onidiosyncratic volatility, emphasizing the sensitiveness of Ang et al.'s results to different estimationwindows of volatility, weightingschemes for portfolio returns, breakpoints for portfolio sorting, and size and liquidity controls. In light of Bali and Cakici's criticism,I check the robustness of my results against the following alternatives: (1) eight measures of cashflow volatility that also includemeasures based on accounting earnings, (2) two other estimation windows for cashflow volatility using either twelve or twentyquarters of past cashflows, (3) controlling for size, book-to-market equity, price momentum, earnings momentum, and liquidity,(4) two schemes of portfolio breakpoints based on either all CRSP stocks or only NYSE stocks, (5) sub-period breakdown by year,and (6) value-weighting versus simple average of returns for portfolios. In all of these cases, the results stand up well.

This paper contributes to the growing literature that historical volatility is negatively correlated with future stock returns. Thepricing of cashflow volatility itself has not received much attention in the empirical asset pricing literature. Earlier literaturefocuses on the cross-section pricing implication of the level of earnings or the change in earnings. For example, Basu (1977) andHaugen and Baker (1996) report that the earnings to price ratio (earnings yield) is positively related to future stock returns.1 Chanet al. (1996) confirm the long-standing post-earnings-announcement-drift phenomenon that firms who release positive earningssurprises tend to experience subsequent positive abnormal returns, and relabel it earnings momentum. To the best of myknowledge, Haugen and Baker (1996) is the only paper that includes volatilities in earnings and cashflow yields in a cross-sectionalregression of returns. In more than fifty firm attributes, these authors find that the variability in cashflow yield is negatively relatedto future stock returns, but do not find significant relationship between returns and volatilities in earnings and dividend yields.

1 However, Fama and French (1992) find that the usefulness of earnings yield is subsumed by size and book to market ratio.

411A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

Haugen and Baker (1996) provide no further discussion on this finding. This paper is different from Haugen and Baker (1996) inthat it shows that both earnings volatility and cashflow volatility (not just cashflow yield volatility) matter, and that the economicsignificance of the pricing of cashflow volatility is non-negligible. In addition, Haugen and Baker (1996) use market size as thescalar to calculate cashflow volatility, making it hard to separate their results from the familiar size effect.

That cashflow volatility, return volatility or analyst forecast dispersion is negatively correlated with future stock returnscontradicts the traditional notion that volatility connotes risk, and therefore should be compensated with higher returns. Inparticular, the pricing of idiosyncratic cashflow volatility presents yet another anomaly against the traditional asset pricingtheories. Some potential explanations can be offered for the results found in this and other similar papers. First, onemay argue thathistorical volatility is different from expected volatility in the sense that volatility is itself a mean-reverting process. Second, in afollow-up paper to Diether et al. (2002), Sadka and Scherbina (2007) argue for limits to arbitrage such as transaction costs (thatarbitrageurs cannot or would not short overpriced stocks that are high in forecast dispersion due to arbitrage limits). Jiang et al.(2005) resort to investor overconfidence (that investors are evenmore over confident on high-volatility firms and hence overpricethese firms). Finally, cashflow volatility may represent the cashflow uncertainty risk component in several interpretations of theFama–French's HML and SMB factors. Fama and French (1992, 1993, 1995, 1996) and Chen and Zhang (1998), among others,interpret HML as a measure of distress risk. Cashflow uncertainty captures distress risk at least partly—firms with volatilecashflows suffer fromhigher default probability and hence larger distress risk. Chan and Chen (1991) argue that small firms tend tohave high financial leverage and cashflow problems and are less likely to survive economic downturns. Thus it is also likely thatcashflow uncertainty is a component in SMB. Nonetheless, this paper focuses on presenting a robust empirical result and leaves forfuture research a credible theoretical explanation for the phenomenon.

The rest of the paper is organized as follows. Section 2 describes the data and defines cashflow volatility variables. Section 3details returns on and properties of portfolios sorted on cashflow volatility. Section 4 reports the firm-level results. Section 5provides robustness checks of longer-term returns and the decomposition of cashflow volatility into systematic and idiosyncraticvolatilities. Section 6 explores the relationship between idiosyncratic return volatility and cashflow volatility in explaining returns.Section 7 concludes.

2. Data and variable definitions

2.1. Cashflow volatility measures

My initial sample consists of all NYSE/NASDAQ/AMEX-listed firms for which I find data on the merged CRSP/Compustatmonthly stock returns and quarterly accounting items from 1973 to 2004. I select the survival-bias-free combined Compustat data,which include research files of extinct and acquired companies. Large-scale quarterly data exist on Compustat from 1973, andquarterly data items which are necessary for computing cashflow exist in large numbers from 1976. In a recent study that links thereturn volatility trend to the earnings volatility trend, Wei and Zhang (2006) use the same quarterly data from 1976 to 2002. Toensure that accounting information is known prior to trading, I match stock returns to accounting numbers of the prior fiscalquarter. I eliminate financial services companies (SIC code between 6000 and 6999) and observations with negative sharesoutstanding, negative assets and negative book equity.

I investigate the relationship between ex post returns and historical cashflow volatility. The nature of this study requires theestimation of cashflow volatility, for which the sample should ideally contain as many time-series observations as possible.Therefore, unlike many previous studies that use annual data, I use quarterly data to increase the number of observations.Matching quarterly Compustat data with monthly returns also implies that accounting information is impounded into stock pricesmore promptly than is the case when annual accounting data are matched with monthly returns. I compute cashflow volatility asthe rolling standard deviation of the standardized cashflow over the past sixteen quarters (four years). I require at least eight non-missing observations of cashflow within this estimation window. Although the choice of the estimation window of four years issomewhat arbitrary, I can report that virtually all of my results are robust to estimation windows of three years (twelve quarters)and five years (twenty quarters). Adjusted for the first four years needed for cashflow volatility calculation, my final sample periodcovers the period 1980–2004.

I choose cashflow to proxy for firms' economic earnings. Direct use of accounting earnings may disguise firms' operationalprofit due to the pervasive earnings management documented in the accounting literature and may subsequently underestimatethe volatility of operational profit (e.g., Healy, 1985; Dechow et al., 1995). I define cashflow from operations, CF, as the sum ofearnings before extraordinary items, depreciation and amortization, and change in working capital, where following Fama andFrench (1992), I define accounting earnings as income before extraordinary items minus preferred dividends.2

For the purpose of cross-sectional aggregation, cashflow needs to be standardized by firm size. The question is, which variableshould be used as the scalar? Some choices of the scalar in previous studies are book equity (e.g., Shroff, 1999; Wei and Zhang,2006), shares outstanding (e.g., Allayannis et al., 2005; Waymire, 1985), and the absolute value of the variable's own mean (e.g.,Barnes, 2001; Minton and Schrand, 1999; Minton et al., 2002). My focus is on operating variables. I therefore exclude sharesoutstanding as the scalar. Similarly, I do not use market equity as the scalar because market equity contains information about

2 In their analysis using annual data, Fama and French (1992) define accounting earnings as earnings before extraordinary items plus preferred dividendsminus deferred taxes. However, in the Compustat quarterly data file a substantial amount of deferred taxes observations are missing. Including deferred taxeswould then result in a severe loss of observations. That said, I can report that including deferred taxes in the earnings definition does not change my results.

412 A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

return and may be different from operating variables (see, e.g., Berk, 1995). Following Wei and Zhang (2006), I use book equity asthe scalar. In addition, I also use sales as the scalar.

I choose sales for two reasons. First, sales is also used in prior studies as a measure of firm size (e.g., Berk, 1997). Second, usingsales as the scalar has the advantage of addressing seasonality in cashflow. There is substantial evidence in the accountingliterature suggesting that operating variables exhibit significant seasonality (e.g., Brown, 1993). The data confirms its existence.The full-sample lag-four autocorrelation between the current quarter cashflow and the cashflow of a year ago is high at 0.32. Salespositively co-movewith earnings, which is confirmed inmy sample: the contemporaneous correlation between cashflow and salesis 0.44. Scaling cashflow by sales greatly reduces the autocorrelation in cashflow. The lag-four autocorrelation of cashflow to salesreduces to only 0.02.

I also deseasonalize cashflow to book equity. I adjust it by its industry mean; that is, I use the excess cashflow to book equityover and above the mean cashflow to book equity of the industry defined by the first two-digit SIC code.3 This method accounts forthe seasonality common to the industry that the firm is in. The lag-four autocorrelation of the adjusted cashflow to book equity is0.20, again substantially lower than the autocorrelation of CF.

Based on the analysis above, I construct these two cashflow volatility measures: standard deviation of cashflow to sales, labeledas CFSALES, and standard deviation of seasonality-adjusted cashflow to book equity, labeled as CFBE. To mitigate the effect ofextreme observations, I follow the literature (e.g., Minton et al., 2002) and winsorize the volatility measures at the 1st and 99thpercentiles over the full sample period. Such winsorization greatly reduces the excess kurtosis and the upper bound of thevolatility measures. For example, the maximum of CFSALES before the winsorization is 3.6×104, and after the winsorizationreduces to only 66.3. I winsorize monthly stock returns similarly.4 It is noteworthy to point out that since most of my resultsdepend on the ranking of cashflow volatility of a firm, winsorization of volatility would not affect those results.

2.2. Return informative variables

I control for the following return-informative variables when studying the effect of cashflow volatility on returns: size, book tomarket equity, price momentum, idiosyncratic return volatility, earnings momentum, illiquidity, and earnings yield. I select thesevariables based on three considerations. First, size, book to market, price momentum and illiquidity are now well acknowledgedfactors affecting stock returns.5 Second, earnings momentum and earnings yield are profitability-related variables that maysubsume cashflow volatility in predicting stock returns.6 Finally, I control for idiosyncratic return volatility to differentiate thispaper from Ang et al. (2006).

I define size (ME) as the beginning of the periodmarket equity (lagged one-monthmarket equity), book tomarket equity (BEME)as book equity to market equity, and earnings yield (EY) as earnings to market equity. Following Ang et al. (2006), I define pricemomentum (PMOM) as the past twelve-month return and idiosyncratic return volatility (IRV) relative to the Fama–French three-factormodel. Specifically, monthly IRV is defined as

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffivar �it

� �qin the following regression of daily excess stock returns for firm i:

whereearnin

ILLIQ it

3 I th4 The

by mon5 See

(2002)6 See

momen

Rit − Rf

t = αi + βiMKTMKTt + βi

SMBSMBt + βiHMLHMLt + �

it ; ð1Þ

where Rti is the stock return at day t during the month, Rf is the riskfree rate, MKT, SMB and HML are the daily Fama–French three

factors of market, size and value, respectively, and ε is the residual. Eq. (1) is estimated with the previous one-month daily returnsand is updated every month for each stock. I follow Chan et al. (1996) to define earnings momentum and Amihud (2002) to defineilliquidity. Earnings momentum at month t is defined as the standardized unexpected earnings (SUE) in the followingexpression:

SUEit =eiq − eiq − 4

σ it

; ð2Þ

eqi is the most recent quarterly earnings, eq−4

i is earnings four quarters ago, and σti is the standard deviation of unexpected

gs, eqi −eq−4i , over the preceding sixteen quarters. Illiquidity (ILLIQ) for month-t is defined as:

=previous within‐month mean of jdaily return j i

daily volumei

cross sectional average of the numerator:

The ILLIQ ratio gives the absolute percentage price change per dollar of daily trading volume, or the daily price impact of the orderflow, standardized by the cross sectional mean.

ank an anonymous referee for suggesting this measure.subsequent return informative variables are also winsorized analogously. The results remain virtually the same if these variables are winsorized monthth., e.g., Fama and French (1992) for the size and value (book to market) effects, Jegadeesh and Titman (1993) for the price momentum effect, and Amihudfor the illiquidity effect., e.g., Haugen and Baker (1996) for the relationship between earnings yield and returns, and Chan et al. (1996) for the relationship between earningstum and returns.

Table 1Time-series averages of cross-sectional summary statistics.

Number of observations Mean Std dev Minimum Median Maximum

CF 3,453.5 21.6 184.7 −3,753.3 1.4 4,398.8ME 4,276.7 896.8 5,116.3 0.3 83.2 160,874.8BEME 4,276.7 2.30 39.96 0.00 0.64 2283.28PMOM (%) 4,057.0 16.14 63.60 −85.42 4.90 351.43SUE 3,283.0 0.15 1.35 −3.41 0.13 3.88ILLIQ 4,107.4 0.98 3.49 0.00 0.05 36.77EY 4,257.7 0.00 3.41 −115.00 0.01 118.90IRV (%) 4,266.0 3.27 2.34 0.03 2.66 14.42CFSALES 2,948.7 1.55 6.81 0.03 0.19 62.70CFBE 2,973.6 0.16 0.15 0.03 0.11 0.76RET (%) 4,277.9 1.08 15.45 −41.81 −0.06 66.54

This table presents the time-series averages of cross-sectional summary statistics of the variables. The data covers all NYSE, NASDAQ, and AMEX-listed firms fromthe quarterlyfiles of COMPUSTATandmonthly returnfiles of CRSP from January 1980 to December 2004. CF= cashflow fromoperation (inmillion $);ME=marketvalue of equity (inmillion $); BEME=book tomarket equity,measured as book equity of last quarter tomarket equity at the beginning of themonth; PMOM=pricemomentum, measured as the past 12-month return; SUE = standardized unexpected earnings; ILLIQ = the illiquidity measure in Amihud (2002); EY = earningsyield, measured as earnings of last quarter divided by beginning-of-the-month market equity; IRV = idiosyncratic volatility of the previous month's daily returnsrelative to the Fama–French three factors; CFSALES= standard deviation of cashflow to sales over the past 16 quarters; CFBE= standard deviation of the industry-adjusted cashflow to book equity over the past 16 quarters; and RET =monthly stock return. PMOM, SUE, ILLIQ, IRV, CFSALES, CFBE and RET are winsorized at the1st and 99th percentiles over the full sample.

413A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

2.3. Summary statistics and correlation

Table 1 reports the time series averages of the cross-sectional summary statistics of the above variables. Two observations are inorder. First, the sample size is large. On average, the sample has about 4000 firm observations in returns and 3000 observations incashflow volatility per month. Second, the sample consists of more small firms than large firms. The mean firm size is significantlyhigher than its median. This skewness of firm size distribution is consistent with prior studies.

Table 2 presents the time series averages of the cross-sectional correlations of the variables in Table 1. A quick examinationreveals several unsurprising observations. First, returns are positively correlated with book tomarket, price momentum, SUE, ILLIQand earnings yield. Second, corroborating the findings of Ang et al. (2006) andWei and Zhang (2006), the correlation between IRVand return is negative, and the correlation between cashflow volatility and IRV is highly significant. Consistent with these twocorrelations, cashflow volatility is significantly negatively correlated with return. The cross-sectional correlation between returnand size (lagged market equity) is insignificant, suggesting a weak size effect during the sample period.

3. Portfolios sorted on cashflow volatility

3.1. Returns

In this section, I examine the raw and risk-adjusted returns on portfolios sorted on cashflow volatility. I form ten decileportfolios every month using all stocks in the sample based on increasing cashflow volatility breakpoints. Following Ang et al.

Table 2Time-series averages of cross-sectional correlations of variables.

CF ME BEME PMOM SUE ILLIQ EY IRV CFSALES CFBE RET

CF 1ME 0.58 1BEME 0.04 −0.04 1PMOM 0.03 0.04 −0.15 1SUE 0.09 0.07 −0.09 0.28 1ILLIQ −0.04 −0.07 0.12 −0.15 −0.05 1EY 0.08 0.04 −0.05 0.15 0.23 −0.16 1IRV −0.12 −0.16 0.03 −0.13 −0.11 0.45 −0.21 1CFSALES −0.03 −0.04 −0.05 −0.03 −0.05 0.03 −0.05 0.14 1CFBE −0.09 −0.12 −0.07 −0.05 −0.07 0.16 −0.18 0.32 0.19 1RET 0.007 0.000 0.027 0.015 0.092 0.014 0.037 −0.022 −0.015 −0.022 1

This table presents the time-series averages of cross-sectional correlations of the variables. The data covers all NYSE, NASDAQ, and AMEX-listed firms from thequarterly files of COMPUSTATandmonthly return files of CRSP from January 1980 to December 2004. CF= cashflow from operation; ME=market value of equity;BEME= book to market equity, measured as book equity of last quarter to market equity at the beginning of the month; PMOM= price momentum, measured as thepast 12-month return; SUE= standardized unexpected earnings; ILLIQ= the illiquidity measure in Amihud (2002); EY= earnings yield, measured as earnings of lastquarter divided by beginning-of-the-month market equity; IRV = idiosyncratic volatility of the previous month’s daily returns relative to the Fama–French threefactors; CFSALES = standard deviation of cashflow to sales over the past 16 quarters; CFBE = standard deviation of the industry-adjusted cash-flow to book equityover the past 16 quarters; and RET = monthly stock return. Underscored numbers are significant at the 5% level. Bold faced numbers are insignificant. All othernumbers are significant at the 1% level.

414 A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

(2006), the breakpoints are determined by the ranked values of all stocks and are updated month by month. I then calculate themonthly value-weighted return of each portfolio weighted by each stock's market equity at the beginning of the month, as well asthe monthly simple average return.

The first two rows of Panels A and B of Table 3 report the value-weighted and simple average returns of portfolios sorted onCFSALES and CFBE, respectively. The column labeled “D1–D10” shows the spread between the smallest decile portfolio (D1) andthe largest decile portfolio (D10), and the column labeled “D1:5–D6:10” shows the spread between the simple average of deciles 1to 5 and the simple average of deciles 6 to 10.

One common feature across both panels is a positive, large D1–D10 spread in both value-weighted and simple average returns.In Panel A, the D1–D10 spread in value-weighted return is 1.35% per month (with a t-statistics of 3.78), and in Panel B the spread is0.64% per month (with a t-statistics of 2.63). This translates into an annual return difference of about 9–15%. The return spreads forthe simple average returns are about the same magnitude: the D1–D10 spread for CFSALES (CFBE)-sorted portfolios is 1.25%(0.92%) a month. Furthermore, as shown in the column “D1:5–D6:10,” the first half of portfolios, which have lower cashflowvolatility, displays higher returns than the second half of the portfolios. The t-statistics for all D1:5–D6:10 return spreads aresignificant. For the rest of the paper I choose to present value-weighted returns. Using simple average returns instead would notchange the results.

The next two rows of Panels A and B, labeled “CAPM alpha” and “FF-4 alpha,” report respectively the alphas of the value-weighted portfolios relative to the CAPM and the Fama–French four-factor model. The return spreads are even stronger after Icontrol for these risk factors. In Panel A, the D1–D10 spread increases to 1.64% a month after controlled for the market returnand slightly decreases to 1.19% after controlled for the FF-4 factors. In Panel B, the D1–D10 spread increases to 0.87% and 1.06%respectively. All these D1–D10 alpha spreads are highly significant. The D1:5–D6:10 spreads increase similarly in both panelsand are highly significant as well. A further examination of individual portfolios reveals that although statistically significantalphas cluster in portfolios with large cashflow volatility, the value of alpha is generally decreasing in cashflow volatilitydecile. These results suggest that returns on cashflow volatility sorted portfolios are not attributable to the Fama–French riskfactors.

In the next row I report the Sharpe ratio of each portfolio. The Sharpe ratio is defined as the mean to the standard deviation ofthe excess return. This ratio can be interpreted as a measure of total risk-adjusted return. Confirming the results of previous rows,the Sharpe ratio decreases with cashflow volatility in both panels. The D1–D10 spread is 0.24 in Panel A and 0.13 in Panel B. These

Table 3Returns on the ten portfolios sorted on cashflow volatility.

Decile 1 (S) 2 3 4 5 6 7 8 9 10(L) D1–D10 D1:5–D6:10

Panel A: Portfolios sorted on CFSALESValue-weightedret. (%)

1.22 1.15 1.06 1.23 1.15 1.01 0.99 0.72 0.93 −0.13 1.35[3.78]⁎

0.46[3.12]⁎

Simple averageret. (%)

1.63 1.53 1.48 1.42 1.41 1.33 1.14 0.98 0.67 0.39 1.25[3.70]⁎

0.59[3.71]⁎

CAPM alpha (%) 0.14[1.40]

0.02[0.17]

−0.06[−0.45]

0.14[1.34]

0.01[0.12]

−0.12[−1.1]

−0.14[−1.11]

−0.44[−3.31]⁎

−0.33[−1.58]

−1.51[−4.98]⁎

1.64[4.84]⁎

0.56[3.92]⁎

FF-4 alpha (%) 0.11[1.07]

0.00[−0.03]

0.09[0.67]

0.15[1.31]

0.07[0.67]

−0.14[−1.2]

−0.12[−0.91]

−0.31[−2.46]⁎⁎

−0.16[−0.82]

−1.08[−4.46]⁎

1.19[4.27]⁎

0.44[3.58]⁎

Sharpe ratio 0.16 0.13 0.12 0.16 0.13 0.11 0.10 0.04 0.07 −0.08 0.24 0.09# of firms 295 295 295 295 295 295 295 295 295 294Market weight(%)

17.75 17.11 16.88 12.77 9.87 8.82 7.11 4.77 3.29 1.63

Panel B: Portfolios sorted on CFBEValue-weightedret. (%)

1.07 0.98 1.23 1.09 1.19 1.07 1.31 1.12 0.68 0.43 0.64[2.63]⁎

0.19[1.81]⁎⁎⁎

Simple averageret. (%)

1.42 1.39 1.43 1.40 1.35 1.42 1.24 0.98 0.84 0.49 0.92[3.58]⁎

0.40[3.23]⁎

CAPM alpha (%) −0.02[−0.18]

−0.13[−1.36]

0.11[1.24]

−0.03[−0.3]

0.03[0.32]

−0.09[−0.73]

0.16[1.31]

−0.05[−0.39]

−0.57[−3.38]⁎

−0.89[−4.65]⁎

0.87[3.76]⁎

0.28[2.79]⁎

FF-4 alpha (%) 0.15[1.24]

−0.06[−0.65]

0.09[1.029]

−0.39[−0.34]

−0.03[−0.33]

−0.09[−0.72]

0.19[1.543]

−0.07[−0.54]

−0.58[−3.88]⁎

−0.91[−5.38]⁎

1.06[4.91]⁎

0.25[3.65]⁎

Sharpe ratio 0.12 0.11 0.16 0.12 0.14 0.11 0.16 0.12 0.03 −0.01 0.13 0.05# of firms 298 297 297 297 297 298 297 297 297 297Market weight(%)

23.12 18.25 14.84 12.29 9.75 7.14 6.52 3.68 2.55 1.86

This table reports returns on 10 monthly portfolios formed with all stocks based on increasing cashflow volatility breakpoints. The breakpoints are determined bythe ranked values of cashflow volatility of all stocks in the sample and are updatedmonth bymonth. CFSALES (CFBE) is the standard deviation of cash-flow to sales(industry-adjusted cashflow to book equity) over the past 16 quarters. CAPM alpha and FF-4 alpha are Jensen's alpha relative to, respectively, CAPM and Fama–French four-factors of market, SMB, HML andmomentum. Sharpe ratio is themean portfolio excess return divided by its standard deviation. “D1–D10” is the spreadbetween decile 1 and decile 10, and “D1:5–D6:10” is the spread between the mean of deciles 1–5 and the mean of deciles 6–10. Numbers in square brackets are t-statistics. ⁎, ⁎⁎, and ⁎⁎⁎ indicate significance at the 1%, 5% and 10% levels, respectively.

415A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

spreads are not trivial: As a benchmark for comparison, the Sharpe ratio of the S&P 500 index return for the same period is 0.15. Ifwe assume independence in portfolio return distribution, the D1–D10 spread in the Sharpe ratio indicates that a zero-investmentstrategy that goes long in decile 1 stocks and short in decile 10 stocks produces a total-risk adjusted return comparable to the S&P500 index!

The findings identified in Table 3 largely hold for individual years. Fig.1 depicts themean D1–D10 return spread averaged acrosseach year from 1980 to 2004. Panel (a) shows the D1–D10 spread of portfolios sorted on CFSALES, and Panel (b) shows the spread

Fig. 1. Monthly D1–D10 return spread averaged across year.

416 A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

on portfolios sorted on CFBE. Although the spread is not always positive, we observe that the frequency of years with positivespread roughly triples the frequency of years with negative spread, and that those negative spreads are generally small inmagnitude. These patterns suggest that the findings in Table 3 are not driven by one or multiple outlier years of disparately large,positive spreads.

3.2. Characteristics of cashflow volatility sorted portfolios

I now examine the properties of the decile portfolios sorted on cashflow volatility. I first look at themarket weight in each decileportfolio. The last two rows of Panels A and B of Table 3 provide the average number of firms in each decile and its market weight.By definition, the firm number is constant at around 300 for each decile. The portfolio market weight decreases with decile, withdecile 10 portfolio accounting for 1.63% of themarket weight for CFSALES and 1.86% for CFBE. By comparison, Ang et al. (2006) formfive portfolios sorted on idiosyncratic return volatility. Their smallest idiosyncratic return volatility quintile accounts for 1.9% of themarket share. Furthermore, the second half of the portfolios, which has significantly lower return than the first half of theportfolios, accounts for about 25% of the market share. Based on the weight of the second half of the cashflow volatility portfolios,the cashflow volatility effect uncovered in Table 3 does not seem to belong to only marginal firms.

I next show the standard return-informative variables of these deciles. Fig. 2 depicts the value-weighted means of size, book tomarket, price momentum, illiquidity, and earnings momentum for each decile portfolio. In both CFSALES and CFBE sortedportfolios, high cashflow volatility is associated with low market value and SUE, and high price momentum and illiquidity. Theassociation between book to market and cashflow volatility in these decile portfolios is sensitive to the proxy for cashflowvolatility, as shown in Panel (b) that the portfolio book to market is somewhat increasing in CFSALES but decreasing in CFBE.

Fig. 2 just reveals that the inverse relation between cashflow volatility and returns documented in Table 3 is largely unrelated tosome existing empirical regularities. From the figure, firms whose cashflow are highly volatile are small, illiquid and with a strongprice momentum. This set of firms is supposed to have higher returns. However, Table 3 shows that returns on these decileportfolios decrease with cashflow volatility. Hence, Fig. 2 suggests that the cashflow volatility effect is different from the size, pricemomentum and illiquidity effects.

The portfolio properties that may subsume the cashflow volatility effect is earnings momentum. This is because decreasingreturn is associated with both decreasing earnings momentum and increasing cashflow volatility—implying that earningsmomentum and cashflow volatility is inversely related, which is confirmed in Fig. 2. Book to market equity of the CFBE sortedportfolios may also be treated similarly, as high CFBE portfolios display low book to market ratios and thus low returns.Nonetheless, in the next section I formally show that the cashflow volatility effect is not driven out by any of these control variables.

3.3. Robustness to double sorts

To show that the results in Table 3 are not subsumed by a control variable, I construct 5×5 portfolios sorted first on the controlvariable and then on cashflow volatility. At the first step, I group stocks into 5 quintile portfolios based on the control variable. Atthe second step I further sort each such quintile portfolio into 5 quintile portfolios based on cashflow volatility. The sorting isupdated every month. If the cashflow volatility effect is capturing the effect due to the control variable, then the risk-adjustedreturn spread among various cashflow volatility quintile portfolios inside each control quintile should not be different from zero.

Panel A of Table 4 reports the FF-4 alphas on the 5×5 portfolios first sorted by size then by cashflow volatility. The column “V1–V5” shows the alpha spread between the portfoliowith the least volatile cashflow and the portfoliowith themost volatile cashflowinside each size quintile. In each size quintile, the highest cashflow volatility quintile has substantially lower FF-4 alpha than thelowest size quintile. The V1–V5 spread is statistically significant at below 1% level for all but the largest stocks. Hence, it is not smallstocks that are driving the results.

The overall effect of controlling for size is shown in the row labeled “Controlling for Size,” where I average across the five sizequintiles to produce cashflow volatility quintile portfolios. Each of these cashflow volatility quintile covers all sizes of firms. Aftercontrolling for size, the overall V1–V5 alpha spread is still high at 0.89% (0.94%) a month for CFSALES (CFBE) with a significancelevel of 1%. To put the overall significance of the size-controlled alpha spread into perspective, the last row of Panel A, labeled“Unconditional CFV alpha,” shows the unconditional FF-4 alpha spread of cashflow volatility quintile portfolios when no control isimposed. In comparison, the unconditional FF-4 alpha spread is 0.55% (0.74%) for CFSALES (CFBE). Thus, Panel A shows thatcontrolling for size does not weaken the overall significance of the cashflow volatility effect.

The rest of Table 4 repeats the same exercise for book-to-market, SUE, PMOM and ILLIQ. In all of these controls, the cashflowvolatility effect holds up well, with the overall V1–V5 alpha spreads in the same order of magnitude as the size-controlled spread.An examination of individual control quintile reveals that the cashflow volatility effect spreads to firms differing widely in book tomarket, price momentum and illiquidity, as for these controls, the cashflow volatility effect is present within almost all of thecontrol quintiles.

One noteworthy finding in Table 4 is that while the overall effect of cashflow volatility holds after controlling for SUE, the effectis concentrated in medium to low (often negative) SUE stocks, as shown in Panel C that the alpha spread is significantly positiveonly in the smallest quintile of SUE for CFSALES and in the smallest two quintiles of SUE for CFBE. In other words, low earningssurprise firms must have some property that is both related to cashflow volatility and able to induce lower future returns that highearnings surprise firms lack. One possible explanation is persistence of earnings surprise. In untabulated results, I find that amongthe lowest SUE firms, the persistence of earnings surprise increases with cashflow volatility. That is, among firms with negative

Fig. 2. Value-weighted properties of the 10 decile portfolios sorted on cashflow volatility.

417A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

past earnings surprises, those with past high cashflow volatility is more likely to continue to have negative earnings surprises inthe future than those with low cashflow volatility, leading to lower future returns of the high cashflow volatility firms. In contrast,firms in other SUE quintiles have stable or decreasing persistence of earnings surprise along cashflow volatility quintiles.

Table 4FF-4 alphas of 5×5 portfolios sorted first on a control variable and then on cashflow volatility.

Panel A: Controlling for size

Size quintile CFSALES quintile CFBE quintile

V1 (S) V2 V3 V4 V5 (L) V1–V5 V1 (S) V2 V3 V4 V5 (L) V1–V5

1 (S) 0.77[3.86]⁎

0.47[2.18]⁎⁎

0.04[0.19]

−0.22[−0.76]

−0.28[−0.83]

1.05[3.80]⁎

0.71[3.52]⁎

0.73[3.32]⁎

0.17[0.70]

−0.15[−0.55]

−0.60[−1.9]⁎⁎⁎

1.31[5.75]⁎

2 0.47[3.24]⁎

0.21[1.29]

−0.07[−0.41]

−0.29[−1.42]

−1.01[−3.6]⁎

1.49[5.40]⁎

0.49[3.12]⁎

0.22[1.35]

0.02[0.12]

−0.47[−2.39]⁎⁎

−1.02[−4.08]⁎

1.51[7.96]⁎

3 0.24[1.81]⁎⁎⁎

0.26[2.22]⁎⁎

0.16[1.18]

−0.11[−0.81]

−0.79[−3.57]⁎

1.03[3.93]⁎

0.31[2.97]⁎

0.20[1.70]⁎⁎⁎

0.00[−0.03]

−0.17[−1.26]

−0.58[−3.25]⁎

0.88[5.27]⁎

4 0.21[1.66]⁎⁎⁎

0.18[1.5]

0.08[0.66]

−0.09[−0.87]

−0.51[−2.75]⁎

0.72[2.76]⁎

0.21[1.96]⁎⁎

0.17[1.71]⁎⁎⁎

0.14[1.39]

−0.05[−0.44]

−0.68[−5.98]⁎

0.89[6.17]⁎

5 (L) 0.03[0.32]

0.03[0.26]

0.11[1.27]

−0.15[−1.47]

−0.14[−1.07]

0.17[0.99]

0.13[1.06]

−0.07[−0.73]

0.02[0.27]

−0.13[−1.44]

0.03[0.33]

0.10[0.63]

Controlling forsize

0.34[3.67]⁎

0.23[2.49]⁎

0.06[0.67]

−0.17[−1.53]

−0.55[−3.06]⁎

0.89[4.46]⁎

0.37[4.37]⁎

0.25[2.89]⁎

0.07[0.77]

−0.19[−1.8]⁎⁎⁎

−0.57[−4.1]⁎

0.94[8.78]⁎

UnconditionalCFV alpha

0.04[0.46]

0.10[1.12]

−0.05[−0.58]

−0.19[−1.82]⁎⁎⁎

−0.51[−2.32]⁎⁎

0.55[2.54]⁎⁎

0.04[0.52]

0.04[0.57]

−0.07[−0.79]

0.09[0.91]

−0.70[−5.56]⁎

0.74[4.70]⁎

Panel B: Controlling for book-to-market (BM)

BM quintile CFSALES quintile CFBE quintile

V1 (S) V2 V3 V4 V5 (L) V1–V5 V1 (S) V2 V3 V4 V5 (L) V1–V5

1 (S) 0.03[0.26]

0.15[1.20]

−0.27[−1.51]

−0.42[−1.82]⁎⁎⁎

−1.29[−4.28]⁎

1.32[3.91]⁎

0.07[0.53]

0.23[1.64]⁎⁎⁎

−0.05[−0.33]

−0.66[−3.57]⁎

−1.12[−5.19]⁎

1.19[4.78]⁎

2 0.02[0.14]

−0.15[−0.95]

−0.25[−1.57]

−0.56[−3.36]⁎

−0.60[−2.2]⁎⁎

0.62[1.99]⁎⁎

−0.13[−0.91]

0.02[0.13]

−0.31[−1.91]⁎⁎⁎

−0.02[−0.13]

−1.16[−5.4]⁎

1.03[4.40]⁎

3 0.00[0.02]

−0.28[−1.64]⁎⁎⁎

−0.24[−1.44]

−0.13[−0.76]

−0.80[−3.71]⁎

0.80[3.07]⁎

−0.19[−1.24]

−0.15[−0.9]

−0.37[−2.69]⁎

−0.08[−0.45]

−0.16[−0.77]

−0.04[−0.16]

4 0.26[1.60]

0.10[0.67]

−0.16[−1.09]

−0.05[−0.28]

−0.02[−0.11]

0.29[0.99]

0.17[1.19]

−0.06[−0.37]

0.11[0.71]

0.00[0.02]

0.04[0.17]

0.13[0.50]

5 (L) 0.47[2.57]⁎

0.81[4.43]⁎

0.72[3.26]⁎

0.43[1.85]⁎⁎⁎

0.26[1.04]

0.22[0.72]

0.73[3.84]⁎

0.76[3.85]⁎

0.64[3.32]⁎

0.48[2.10]⁎⁎

−0.10[−0.38]

0.83[2.44]⁎⁎

Controlling forBM

0.16[1.72]⁎⁎⁎

0.13[1.54]

−0.04[−0.51]

−0.15[−1.41]

−0.49[−3.18]⁎

0.65[3.33]⁎

0.13[1.55]

0.16[2.24]⁎⁎

0.00[0.04]

−0.05[−0.56]

−0.50[−4.08]⁎

0.63[4.43]⁎

Panel C: Controlling for SUE

SUE quintile CFSALES quintile CFBE quintile

V1 (S) V2 V3 V4 V5 (L) V1–V5 V1 (S) V2 V3 V4 V5 (L) V1–V5

1 (S) −0.59[−3.17]⁎

−0.38[−2.01]⁎⁎

−0.76[−3.97]⁎

−1.65[−7.79]⁎

−2.08[−7.37]⁎

1.49[4.49]⁎

−0.28[−1.67]⁎⁎⁎

−0.86[−4.69]⁎

−0.93[−4.63]⁎

−1.81[−7.7]⁎

−2.80[−11.8]⁎

2.52[9.02]⁎

2 −0.44[−2.59]⁎

−0.40[−2.64]⁎

−0.52[−2.99]⁎

−0.38[−2.03]⁎⁎

−0.64[−2.3]⁎⁎

0.21[0.62]

−0.47[−3.11]⁎

−0.20[−1.3]

−0.26[−1.4]

−0.93[−4.92]⁎

−1.54[−6.89]⁎

1.07[4.02]⁎

3 −0.08[−0.49]

0.12[0.69]

−0.03[−0.14]

−0.35[−2.02]⁎⁎

0.03[0.12]

−0.12[−0.38]

−0.02[−0.13]

−0.03[−0.23]

−0.14[−0.69]

0.25[1.34]

−0.33[−1.39]

0.31[1.08]

4 0.11[0.70]

0.17[1.00]

0.07[0.49]

0.49[2.73]⁎

−0.29[−1.08]

0.40[1.20]

0.11[0.68]

0.12[0.73]

0.20[1.28]

0.41[2.02]⁎⁎

0.17[0.82]

−0.06[0.23]

5 (L) 0.50[3.51]⁎

0.56[3.38]⁎

0.51[2.98]⁎

0.50[3.08]⁎

0.20[0.84]

0.30[1.04]

0.56[3.58]⁎

0.50[3.89]⁎

0.48[2.82]⁎

0.75[4.41]⁎

0.53[2.66]⁎

0.03[0.12]

Controlling forSUE

−0.10[−1.2]

0.01 [0.18] −0.14[−2.08]⁎⁎

−0.28[−2.86]⁎

−0.56[−3.19]⁎

0.46[2.20]⁎⁎

−0.02[−0.28]

−0.10[−1.58]

−0.13[−1.61]

−0.27[−3.1]⁎

−0.80[−6.73]⁎

0.77[5.36]⁎

Panel D: Controlling for price momentum (PMOM)

PMOMquintile

CFSALES quintile CFBE quintile

V1 (S) V2 V3 V4 V5 (L) V1–V5 V1 (S) V2 V3 V4 V5 (L) V1–V5

1 (S) 0.97[4.43]⁎

0.46[1.69]⁎⁎⁎

0.02[0.08]

−0.37[−1.11]

−1.07[−3.31]⁎

2.04[5.22]⁎

1.14[4.59]⁎

0.57[2.24]⁎⁎

−0.13[−0.46]

−0.65[−2.25]⁎⁎

−1.35[−3.69]⁎

2.49[6.07]⁎

2 0.48[2.84]⁎

0.44[2.32]⁎⁎

0.18[0.88]

0.10[0.47]

−0.91[−3.71]⁎

1.38[4.33]⁎

0.53[3.12]⁎

0.41[2.23]⁎⁎

0.14[0.721]

−0.20[−0.99]

−0.75[−3.16]⁎

1.28[4.20]⁎

3 0.08[0.56]

−0.02[−0.13]

0.07[0.47]

−0.24[−1.53]

−0.70[−3.53]⁎

0.77[3.26]⁎

0.04[0.30]

0.02[0.13]

−0.39[−2.54]⁎

0.03 [0.15] −0.49[−2.56]⁎

0.53[2.30]⁎⁎

4 0.08[0.52]

−0.02[−0.14]

−0.08[−0.61]

−0.25[−1.59]

−0.45[−2.31]⁎⁎

0.53[2.15]⁎⁎

0.02[0.16]

−0.19[−1.24]

−0.13[−0.81]

−0.03[−0.15]

−0.30[−1.65]⁎⁎⁎

0.32[1.37]

5 (L) −0.03[−0.18]

−0.07[−0.39]

−0.03[−0.16]

−0.48[−2.33]⁎⁎

−0.49[−1.88]⁎⁎⁎

0.46[1.38]

−0.14[−0.74]

−0.23[−1.4]

−0.24[−1.61]

0.12[0.66]

−0.61[−2.71]⁎

0.47[1.68]⁎⁎⁎

Controlling forPMOM

0.31[3.44]⁎

0.16[1.69]⁎⁎⁎

0.03[0.36]

−0.25[−2.22]⁎⁎

−0.72[−4.8]⁎

1.04[5.47]⁎

0.32[3.37]⁎

0.11[1.31]

−0.15[−1.73]⁎⁎⁎

−0.15[−1.52]

−0.70[−5.9]⁎

1.02[7.13]⁎

418 A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

Panel E: Controlling for illiquidity (ILLIQ)

ILLIQ quintile CFSALES quintile CFBE quintile

V1 (S) V2 V3 V4 V5 (L) V1–V5 V1 (S) V2 V3 V4 V5 (L) V1–V5

1 (S) 0.02[0.25]

0.05[0.43]

0.10[1.10]

−0.15[−1.39]

−0.15[−1.07]

0.18[0.98]

0.17[1.41]

−0.12[−1.24]

0.07[0.71]

−0.04[−0.44]

−0.11[−1.08]

0.28[1.68]⁎⁎⁎

2 0.19[1.47]

0.06[0.59]

0.05[0.46]

−0.33[−3.02]⁎

−0.42[−2.15]⁎⁎

0.60[2.26]⁎⁎

0.09[0.82]

−0.02[−0.24]

0.03[0.31]

−0.05[−0.51]

−0.59[−5.01]⁎

0.68[4.47]⁎

3 −0.03[−0.25]

0.18[1.40]

0.16[1.20]

−0.33[−2.35]⁎⁎

−0.83[−3.69]⁎

0.80[2.95]⁎

0.11[0.88]

0.05[0.40]

−0.01[−0.06]

−0.44[−3.69]⁎

−0.73[−4.27]⁎

0.84[4.41]⁎

4 0.15[0.99]

−0.05[−0.32]

−0.26[−1.63]⁎⁎⁎

−0.40[−2.14]⁎⁎

−1.27[−4.74]⁎

1.42[4.66]⁎

0.00[−0.02]

−0.10[−0.63]

−0.16[−1.06]

−0.54[−3.24]⁎

−0.93[−4.00]⁎

0.93[4.08]⁎

5 (L) 0.20[1.19]

−0.16[−0.84]

−0.41[−1.99]⁎⁎

−0.73[−3.03]⁎

−1.33[−4.34]⁎

1.53[5.07]⁎

−0.03[−0.17]

−0.05[−0.24]

−0.32[−1.67]⁎⁎⁎

−0.71[−3.02]⁎

−1.36[−4.88]⁎

1.33[5.29]⁎

Controlling forILLIQ

0.11[1.25]

0.02[0.22]

−0.07[−0.85]

−0.39[−3.88]⁎

−0.80[−4.72]⁎

0.90[4.45]⁎

0.07[0.90]

−0.05[−0.61]

−0.08[−0.97]

−0.36[−4.22]⁎

−0.75[−6.03]⁎

0.81[6.89]⁎

This table reports the FF-4 alphas of 5 by 5 portfolios formed with all stocks in the sample sorted first by a control variable and then by cashflow volatility. Based onincreasing value of the control variable, firms are first broken into 5 control quintiles. Each control quintile is then further broken into 5 quintiles based onincreasing value of CFSALES (the standard deviation of cashflow to sales over the past 16 quarters) or CFBE (the standard deviation of the industry- adjustedcashflow to book equity over the past 16 quarters). The breakpoints are determined by all stocks and are updated every month. In each panel, the detailed 5×5alphas are first reported. At the end of each panel, the five cashflow volatility quintiles are averaged across each control quintile so that each cashflow volatilityquintile contains all values of the control variable. The control variables are size, book to market equity, standardized unexpected earnings (SUE), past twelve-month return (PMOM), and illiquidity (ILLIQ). In Panel A, the row “Unconditional CFV alpha” reports the unconditional FF-4 alpha on the cashflow volatility-sortedquintile port- folios without any control. t-statistics are in square brackets. ⁎, ⁎⁎, and ⁎⁎⁎ indicate significance at the 1%, 5% and 10% levels, respectively.

Table 4 (continued)

419A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

In summary, the evidence in this section shows a strong, negative association between cashflow volatility and return at theportfolio level. This relationship cannot be accounted for by a number of known factors, including size, value, price momentum,earnings momentum and illiquidity. By following a mechanical, zero-cost investible strategy of going long in stocks in cashflowvolatility decile 1 and short in stocks in cashflow volatility decile 10, one could have generated value-weighted returns of 9–15% ayear or alpha returns of 13% a year during 1980–2004.

4. The cashflow volatility effect at the firm level

In this section, I explore the cashflow volatility effect at the firm level using regressions that allow for multiple controls. I followthe standard approach in Fama and MacBeth (1973) and Fama and French (1992) to run the two-step cross sectional regressions. Ikeep each stock's β, size and book-to-market equity as the original Fama and French (1992) variables. I augment this set ofvariables with previously mentioned return-informative variables and cashflow volatility. I therefore run the following cross-sectional regression for every month t:

Rit = αi

t + γ1;tβit + γ2;t ln MEð Þit + γ3;t ln BEMEð Þit + γ4;tPMOMi

t + γ5;tSUEit + γ6;tILLIQ

it + γ7;tEY

it + γ8;tCashflow Volatilityit + �

it ; ð3Þ

where sup-i indexes stock i, sub-t indexes time, R is the raw return, ME is lagged market equity, BEME is book-to-market equity,PMOM is price momentum, SUE is standardized unexpected earnings, ILLIQ is illiquidity, and EY is earnings yield. The time-seriesof regression coefficients are then averaged to compute the mean estimates and the associated t-statistics.

To map Fama and French (1992) accurately, I use their definitions of β, ME and BEME. Specifically, ln(ME) is measured as thelogarithm of market equity of June of the latest fiscal year, ln(BEME) is measured as the logarithm of book equity of the latest fiscalquarter divided by market equity of December of the same year, and β is estimated with the Fama–MacBeth two-pass procedure.Note that with the exception of β, the value of every other right hand side variable in Eq. (3) is knownprior to the stock transaction.I also replace the forward-looking β in Eq. (3) with historical beta measured from the previous four years of excess return, and findthat the results are similar.

Table 5 presents the results for different sets of regressors in Eq. (3). Model 0 provides the benchmark Fama and French (1992)results, with price momentum as an additional regressor. Models 11 and 12 add CFSALES and CFBE respectively. Models 21 and 22further add earnings momentum. Finally, models 31 and 32 use the full specification in Eq. (3).

Most tellingly, Table 5 shows consistent evidence that cashflow volatility negatively predicts expected stock returns at the firmlevel, regardless of model specification. In every regression that I estimate, cashflow volatility loads negatively with significant t-statistics. In models 11 and 12, the coefficient of cashflow volatility is highly significant when it is combined with the traditionalfour factors of beta, market, book-to-market and price momentum. The cashflow volatility effect is somewhat weakened with theaddition of SUE, ILLIQ and EY but still holds.

The loading of cashflow volatility is economically meaningful. I emphasize the results in models 11 and 12. In model 11, theloading of CFSALES is−0.06. Over the full sample, the mean of CFSALES is 1.74 and the standard deviation is 7.82. A−0.06 loadingwould give rise to an average return of 0.06×1.74 or 0.10% of stock return every month. And if CFSALES increases by one standarddeviation, return decreases by 0.06×7.82 or about 0.47% a month. The results with CFBE are similar: the loading of CFBE is−1.20.The full sample mean of CFBE is 0.17. This means on average, CFBE explains 0.20% of stock return a month.

Table 5Average slopes from Fama and French (1992) month-to-month regressions at the firm level.

Dependent variable: Monthly stock return

Model Intercept Beta ln(ME) ln(BEME) PMOM SUE ILLIQ EY CFSALES CFBE

0 1.66 [4.81]⁎ −0.01 [−0.04] −0.03 [−0.63] 0.59 [9.38]⁎ 0.004 [2.69]⁎11 1.68 [4.91]⁎ 0.00 [−0.01] −0.04 [−0.72] 0.58 [9.39]⁎ 0.004 [2.69]⁎ −0.06 [−3.01]⁎12 1.91 [5.89]⁎ 0.00 [0.01] −0.06 [−1.24] 0.53 [8.18]⁎ 0.003 [2.67]⁎ −1.20 [−3.11]⁎21 1.77 [5.19]⁎ 0.11 [0.37] −0.09 [−1.87]⁎⁎⁎ 0.65 [10.72]⁎ −0.004 [−2.87]⁎ 1.04 [37.50]⁎ −0.06 [−2.88]⁎22 1.92 [5.95]⁎ 0.11 [0.39] −0.11 [−2.27]⁎⁎ 0.62 [9.62]⁎ −0.004 [−2.88]⁎ 1.04 [37.51]⁎ −0.74 [−1.95]⁎⁎31 1.61 [4.83]⁎ 0.15 [0.54] −0.09 [−1.80]⁎⁎⁎ 0.60 [9.94]⁎ −0.004 [−3.47]⁎ 0.96 [35.22]⁎ 0.05 [4.20]⁎ 0.04 [11.41]⁎ −0.05 [−1.98]⁎⁎32 1.69 [5.27]⁎ 0.15 [0.54] −0.09 [−2.02]⁎⁎ 0.59 [9.08]⁎ −0.004 [−3.45]⁎ 0.96 [35.16]⁎ 0.05 [4.31]⁎ 0.04 [11.44]⁎ −0.45 [−1.66]⁎⁎⁎

This table presents estimates of coefficients from the Fama and French (1992) cross-sectional regressions of monthly raw returns (multiplied by 100) of all NYSE/NASDAQ/AMEX listed firms. The Fama andMacBeth (1973) procedure is used to estimate the beta of a stock. ln(ME) is the logarithm of market equity measured inthe latest June; ln(BE/ME) is the logarithm of book equity of the latest fiscal year divided by market equity of December of the same year; PMOM is past 12-monthstock return; SUE is standardized unexpected earnings; ILLIQ is the illiquidity measure in Amihud (2002); EY is earnings yield, measured as earnings of last quarterdivided by beginning-of-the-month market equity; and CFSALES (CFBE) is the standard deviation of cashflow to sales (industry-adjusted cashflow to book equity)over the past 16 quarters. The estimates are the averages of the time-series coefficients of the monthly regression slopes for January 1980 to December 2004.Numbers in square brackets are t-statistics. ⁎, ⁎⁎, and ⁎⁎⁎ indicate significance at the 1%, 5% and 10% levels, respectively.

420 A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

The results on the control variables are mostly unremarkable. First, the original Fama and French (1992) results hold: β isinsignificant, whereas book to market positively predicts returns. The slope on ln(ME) is negative but not significant in models 0–12, confirming the disappearing size effect after the 1980s. SUE, ILLIQ and EY are significantly positive, as expected. Perhaps apuzzling result among the control variables is that of PMOM in models 21–32. The coefficient on PMOM switches sign frompositive, as expected, in models 11–12 to negative in models 21–22 when SUE is added. This is just because the price momentumeffect is subsumed by earnings momentum (SUE), as pointed out by Chordia and Shivakumar (2006).

The cross-sectional regression results presented in Table 5must be interpreted with caution if one is looking for a fully specifiedasset pricingmodel. Since the regressand is raw return, if the model is correctly specified, one should expect the intercept to be therisk free rate. Although in all of the specifications the estimated intercepts are positive and significant, they are too large comparedto the risk free rate. Regression (3) is therefore, at best, an approximation to the unobservable “true”model. Nevertheless, I want toemphasize the strong, negative association between cashflow volatility and future return found in these regressions.

5. Robustness

In this section I provide robustness checks of the previous portfolio-sorting and firm-level regression results against twoalternatives: longer-term returns and idiosyncratic cashflow volatility. In untabulated results, I also use the following alternativecashflow volatility measures: the absolute coefficient of variation of cashflow (standard deviation divided by own absolute mean),standard deviation of accounting earnings to sales, standard deviation of seasonality-adjusted earnings to book equity, the absolutecoefficient of variation of earnings, and two principal components constructed from comprehensive sets of cashflow volatilitymeasures used. Most of my results hold with these other volatility measures. In addition, the portfolio sorting results are robust tobreakpoints determined by the NYSE stocks rather than by all stocks. These untabulated results are available upon request.

5.1. Longer term returns

Instead of one-month-ahead returns, I repeat the same exercises with buy-and-hold returns of 6-month, 1-year, 2-year and 5-year ahead. Table 6 provides the results.

Panels A and B show the annualized longer-term buy-and-hold returns on portfolios sorted on CFSALES and CFBE, respectively.By and large, we still observe a decreasing return and a decreasing FF-4 alpha in cashflow volatility. Let us examine the D1–D10spread first. The D1–D10 spread shows little sign of abating as the holding horizon is increased to two years. For CFSALES deciles,the annualized D1–D10 return and alpha spreads for up to 2-year ahead returns stay at where Table 3 shows: about 15% a year. Theannualized return and alpha spreads of the CFBE deciles are a bit smaller than those in Table 3 but remain at the same order ofmagnitude. The t-statistics of the D1–D10 return spread, which are adjusted for appropriate serial correlation for overlappingobservations of the buy-and-hold returns, and the Chi-square test statistics of the D1–D10 alpha spread, which are adjustedsimilarly, are all significant at the 5% significance level.7 For the 5-year horizon, the D1–D10 return spread is less than half of thesize of the one-month spread, but the alpha spread remains similar in size.

The D1:5–D6:10 spreads are also significantly positive most of the time. For CFSALES-sorted portfolios, the annualized D1:5–D6:10 return and alpha spreads stay relatively stable at about 20% for 6-month to 2-year buy-and-hold investments. The resultswith CFBE-sorted portfolios are weaker—for 6-month to 2-year buy-and-hold investments, the annualized D1:5–D6:10 return

7 The t-statistics are Newey–West adjusted. For 6-month return, the tests are adjusted with 5 lags; for 1-year return, the tests are adjusted with 11 lags, and soon until for 5-year return the tests are adjusted with 59 lags. I execute the tests on the spread in the FF-4 Alpha in a system of equations with the generalizedmethod of moments, and therefore report the Chi-square statistics associated with the Wald tests.

Table 6Annualized longer-term returns and cashflow volatility.

Panel A: Portfolio sorted on CFSALES

Return Decile

1 (S) 2 3 4 5 6 7 8 9 10 (L) D1–D10 D1:5–D6:10

6-month Ret (%) 15.13 13.39 14.74 15.56 13.40 13.41 11.97 9.97 10.63 −0.67 15.80[4.01]⁎

26.91[3.82]⁎

FF-4alpha (%)

1.95[2.94]⁎

−0.27[−0.41]

2.07[2.86]⁎

2.21[2.82]⁎

−0.68[−1.06]

−0.52[−0.77]

0.06[0.08]

−2.09[−2.47]⁎⁎

0.37[0.28]

−9.55[−6.25]⁎

11.51{12.62}⁎

17.00{4.72}⁎⁎

1-year Ret (%) 14.48 12.86 14.93 15.00 13.07 13.56 11.67 10.60 9.69 0.55 13.93[3.64]⁎

24.27[3.71]⁎

FF-4alpha (%)

1.96[2.94]⁎

0.00[−0.00]

2.80[4.66]⁎

2.30[3.51]⁎

−1.15[−2.03]⁎⁎

−0.50[−0.91]

−1.19[−1.94]⁎⁎

−1.61[−2.00]⁎⁎

0.53[0.43]

−8.68[−6.14]⁎

10.64{8.40}⁎

17.38{3.39}⁎⁎

2-year Ret (%) 14.42 12.66 14.17 14.50 13.56 12.85 12.04 10.93 9.08 2.18 12.24[5.10]⁎

22.23[4.79]⁎

FF-4alpha (%)

3.09[4.55]⁎

1.87[3.23]⁎

0.92[1.56]

3.51[5.37]⁎

0.63[1.08]

0.06[0.11]

−0.35[−0.65]

−1.08[−1.48]

−2.78[−2.2]⁎⁎

−10.90[−9.1]⁎

14.00{18.87}⁎

25.07{12.41}⁎

5-year Ret (%) 14.85 13.15 14.34 13.62 14.11 13.33 13.95 11.95 9.41 8.02 6.83[4.84]⁎

13.41[3.37]⁎

FF-4alpha (%)

5.65[6.86]⁎

5.21[6.73]⁎

2.54[3.44]⁎

0.56[0.66]

5.28[6.11]⁎

−1.57[−1.25]

−5.03[−3.72]⁎

−3.09[−3.44]⁎

−10.16[−7.33]⁎

−10.63[−5.66]⁎

16.27{21.14}⁎

49.70{46.30}⁎

Panel B: Portfolio sorted on CFBE

6-month Ret (%) 13.66 12.42 14.52 14.56 14.07 13.60 16.75 14.06 8.83 7.03 6.63 8.95[2.52]⁎⁎ [1.62]

FF-4alpha (%)

2.24 0.57 1.38 1.01 −0.65 −0.53 3.73 −1.78 −4.32 −7.59 9.83 15.04

[2.67]⁎ [1.00] [2.46]⁎⁎ [1.63]⁎⁎⁎ [−0.99] [−0.69] [4.14]⁎ [−2.47]⁎⁎ [−4.36]⁎ [−6.78]⁎ {22.14}⁎ {8.73}⁎1-year Ret (%) 13.31 13.00 13.76 13.84 13.69 13.76 15.64 13.27 9.35 7.92 5.40 7.67

[2.07]⁎⁎ [1.51]FF-4alpha (%)

1.88 2.22 2.09 0.62 −0.59 −1.08 4.20 −3.63 −4.11 −7.79 9.67 18.63

[2.68]⁎ [4.40]⁎ [3.88]⁎ [1.09] [−1.03] [−1.65]⁎⁎⁎ [5.34]⁎ [−5.27]⁎ [−4.08]⁎ [−6.06]⁎ {17.14}⁎ {9.08}⁎2-year Ret (%) 13.20 12.65 13.29 14.22 13.25 13.71 14.90 13.03 9.33 8.66 4.54

[2.02]⁎⁎6.99[1.62]

FF-4alpha (%)

2.26[3.47]⁎

2.31[4.46]⁎

3.30[5.82]⁎

3.32[5.53]⁎

−0.39[−0.61]

−0.96[−1.38]

5.60[6.35]⁎

−2.93[−3.38]⁎

−4.26[−4.11]⁎

−9.94[−6.77]⁎

12.20{28.02}⁎

23.30{13.10}⁎

5-year Ret (%) 13.51 14.17 13.76 14.13 13.88 13.19 15.85 14.37 11.29 9.93 3.58[2.70]⁎

4.81[2.50]⁎⁎

FF-4alpha (%)

4.82[6.42]⁎

2.32[3.46]⁎

3.25[4.60]⁎

2.57[3.90]⁎

1.94[2.54]⁎

1.82[1.81]⁎⁎⁎

11.35[9.20]⁎

−5.84[−3.20]⁎

−16.88[−9.45]⁎

−15.12[−6.13]⁎

19.94{48.82}⁎

39.59{17.57}⁎

Panel C: Firm level cross-sectional regressions of non-annualized returns

Dep. var. Intercept beta ln(ME) ln(BEME) PMOM SUE ILLIQ EY CFSALES CFBE

6-monthret. (%)

10.28[11.11]⁎

0.38[0.48]

−0.47[−3.97]⁎

2.81[14.25]⁎

0.004[1.51]

2.84[37.22]⁎

0.34[8.89]⁎

0.12[11.14]⁎

−0.37[−3.89]⁎

10.72[12.52]⁎

0.36[0.45]

−0.51[−4.51]⁎

2.74[12.85]⁎

0.004[1.50]

2.85[37.25]⁎

0.34[9.16]⁎

0.11[10.58]⁎

−2.83[−2.82]⁎

1-yearret. (%)

22.28[14.06]⁎

−0.34[−0.28]

−0.94[−5.19]⁎

4.75[15.82]⁎

−0.006[−1.31]

3.60[31.89]⁎

0.68[12.39]⁎

0.16[10.08}⁎

−0.72[−4.47]⁎

23.07[15.75]⁎

−0.37[−0.30]

−1.00[−5.76]⁎

4.68[13.68]⁎

−0.006[−1.31]

3.62[32.15]⁎

0.68[12.74]⁎

0.15[9.45]⁎

−6.26[−4.34]⁎

2-yearret (%)

44.91[15.38]⁎

−3.00[−1.95]⁎⁎

−1.26[−4.52]⁎

9.07[19.67]⁎

−0.049[−7.04]⁎

4.69[28.09]⁎

1.45[14.83]⁎

0.18[7.89]⁎

−0.62[−8.17]⁎

45.90[16.67]⁎

−3.18[−2.06]⁎⁎

−1.29[−4.84]⁎

9.12[16.96]⁎

−0.049[−6.95]⁎

4.75[28.62]⁎

1.47[15.15]⁎

0.17[7.36]⁎

−9.67[−4.41]⁎

5-yearret (%)

138.46[18.59]⁎

−11.56[−3.61]⁎

−2.43[−3.91]⁎

26.53[24.88]⁎

−0.120[−6.46]⁎

7.60[23.71]⁎

2.90[11.67]⁎

0.04[0.80]

−7.12[−5.77]⁎

137.99[19.18]⁎

−11.95[−3.71]⁎

−2.32[−3.81]⁎

27.43[23.87]⁎

−0.113[−5.84]⁎

7.76[24.01]⁎

2.91[11.86]⁎

0.04[0.73]

−12.88[−2.79]⁎

Panel A (B) shows the annualized compound longer-term returns on and the FF-4 alphas of the portfolios sorted on CFSALES (CFBE). For the FF-4 alphas, thecorresponding Fama–French factors are calculated from the monthly factor returns. CFSALES (CFBE) is the standard deviation of cashflow to sales (industry-adjusted cashflow to book equity) over the past 16 quarters. “D1–D10” is the spread between decile 1 and decile 10, and “D1:5–D6:10” is the spread between themean of deciles 1–5 and the mean of deciles 6–10. For the D1–D10 and D1:5–D6:10 return spreads, the t-statistics [in square brackets] are Newey–West adjustedfor the degree of serial correlation corresponding to the buy-and-hold horizon. The adjusted serial lags are 5 for 6-month returns until 59 for 5-year returns. The FF-4 alphas are calculated with generalized method of moments (GMM) with the similar serial error-correlation adjustments for a system of equations consisting ofthe ten portfolios. The Wald-statistics {in curly brackets} are reported for the D1–D10 and D1:5–D6:10 FF-4 alpha spreads. Panel C shows the firm-level cross-sectional regressions of Fama and French (1992) using, respectively, these (non-annualized) longer-term returns as the dependent variable. beta is the stock beta asmeasured in Fama and MacBeth (1973), ln(ME) is the logarithm of market equity, ln(BE/ME) is the logarithm of book equity, PMOM is past 12-month stock return,SUE is standardized unexpected earnings, ILLIQ is illiquidity, and EY is earnings yield. ⁎, ⁎⁎, and ⁎⁎⁎ indicate significance at the 1%, 5% and 10% levels, respectively.

421A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

422 A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

spreads stay at about 10% and only marginally significant, and the D1:5–D6:10 alpha spreads stay at around 18% and significant.Similar to the D1–D10 spread, in the 5-year horizon, the D1:5–D6:10 return spreads are smaller inmagnitude but the alpha spreadsare even larger. In sum, the cashflow volatility effect extends well to these longer-term returns at the portfolio level.

Panel C of Table 6 repeats the firm level cross-sectional regressions of Eq. (3) with these longer term returns. Consistent withthe theme of the portfolio-sorting results in Panels A and B, the loadings of both CFSALES and CFBE are significantly negative for allinvestment horizons. The economic significance of cashflow volatility remains in the same order of magnitude for return horizonswithin one year. Recall in Table 5 that uses monthly return, for the same regression specification as the ones presented in Panel C ofTable 6, the loading of CFSALES (CFBE) is −0.05 (−0.45), or equivalently, −0.60 (−5.40) annually. From Panel C of Table 6, theannualized, estimated loadingof CFSALES (CFBE) is−0.37×2or−0.74 (−2.83×2) for6-month return, and−0.72 (−6.26) for 1-yearreturn, which are similar in size as those for the one-month-return. The economic significance of cashflow volatility declines for 2-year and 5-year returns yet still remains strong: The annualized estimated loading of CFSALES (CFBE) is −0.31 (−4.84) for 2-yearreturn, and −1.43 (−2.58) for 5-year return.

In a nutshell, the cashflowvolatility effect remains strong and robust at least for future returnswithin one year. Longer horizons thanone yearmodestlyweaken the cashflow volatility effect, as the information uncertainty represented by cashflow volatility resolves overtime. The evidence presented in Table 6 suggests that such information resolution, if any, seems to happen at a very slow pace.

5.2. Idiosyncratic or systematic cashflow volatility?

My results so far indicate that total cashflow volatility is negatively correlated with future returns. It may be necessary todisentangle total cashflow volatility into systematic and firm-specific components for at least two reasons. First, if cashflowvolatility functions as a risk factor similar to risk factors in the APT model, then only the systematic volatility matters. On the otherhand, recent literature finds that idiosyncratic return volatility is priced as well (Ang et al., 2006).

Defining a systematic component of cashflow for an individual firm is challenging because there lacks a benchmark “market”cashflow. In order to define suchmarket cashflow, onemay need to consider a comprehensive set of variables that can describe themacro-economic and industry conditions. Nevertheless, managers, investors and analysts frequently compare firms' earnings withpeers in the same industry. Motivated by this observation, I take the industry cashflow as the “market” cashflow for the firm. I thendefine idiosyncratic and systematic cashflowaccording to firms' exposure to the industrymean. This approach is similar to defininga systematic return based on the CAPM; and hence the part of cashflow volatility from the exposure to the industry mean can bethought of as systematic. Since this way of defining systematic volatility appears ad hoc, from now on I label systematic volatilitysuch defined “industry volatility” for industry-exposed volatility.

Using this approach, I run the following parsimonious regression for each firm on a rolling four-year basis:8

8 Firmindustr

9 The

CFi;j;t = αi + βiCFj;t + �i;j;t ; ð4Þ

where CF is cashflow scaled by either sales or book equity, i indexes firm, j indexes the industry that the firm is in, and t indexestime. CFj,t is industry j's cashflow at time t, defined as the mean of cashflow of firms in that industry. In this regression, βi measuresfirm-i's exposure of cashflow to market cashflow, and εi,j,t has a natural interpretation of idiosyncratic cashflow at time t of firm i.

For each rolling period, I define idiosyncratic cashflow volatility asffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiVar �i;j;t

� �q, and industry cashflow volatility as

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiVar CFi;j;tb� �r

,

whereCFi;j;tb ubαi + βibCFj;t is thepredicted cashflow fromregression (4). Bydefinition, Var �i;j;t

� �+ Var CFi;j;tb� �

= Var CFi;j;t� �

: To avoid

having too many industries so that CFj,t is less able to represent market cashflow, I follow Kenneth French's 5-industry definition tobreak firms into five industries: consumer, manufacturing, high-tech, health, and others.9 Note that estimating Eq. (4) with rollingwindows provides ex antemeasures for idiosyncratic and industry cashflow volatilities.

Table 7 reports two sets of results: returns on 10 volatility sorted portfolios (Panels A and B), and firm-level cross sectionalregressions of Eq. (3) (Panel C). For Panels A and B, the results for industry volatility and idiosyncratic volatility are similar: Rawreturn and FF-4 alpha are both decreasing in either CFSALES or CFBE-based industry and idiosyncratic volatility deciles. The D1–D10spreads are all significant for both volatility measures, and the D1:5–D6:10 spreads are significant for most of the cases presented.

I make two further observations. First, the magnitude of spreads for industry and idiosyncratic volatilities is similar in size tothat of total volatility reported in Table 3. Recall in Table 3 the D1–D10 FF-4 alpha spread for total CFSALES (CFBE) is 1.19% (1.06%).Correspondingly, the D1–D10 spread for CFSALES (CFBE) based industry volatility is 0.71% (0.68%), and for CFSALES (CFBE) basedidiosyncratic volatility is 1.02% (0.90%). Second, idiosyncratic volatility has a stronger effect on returns than industry volatility.Panels A and B offer eight pairs of D1–D10 and D1:5–D6:10 spread comparison between industry and idiosyncratic volatilities. Inseven of them, idiosyncratic volatility sorted portfolios have higher spreads.

The significance of both industry and idiosyncratic volatilities is strengthened in Panel C, where cross sectional regressions ofreturns are run on industry and idiosyncratic volatilities. Both the coefficients on CFSALES and CFBE-based idiosyncratic volatilitiesare significantly negative, while the coefficient on CFSALES-based industry volatility is significantly negative. Again, idiosyncraticvolatility has a stronger effect on returns: CFBE-based industry volatility is not significant in the cross sectional regressions.

s differ in fiscal year end month and thus reporting time. Using each firm's current quarter cashflow as its “monthly” cashflow, I update the meany cashflow by month.industry definition is available on the web site: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/.

Table 7Pricing of industry cashflow volatility and idiosyncratic cashflow volatility.

Panel A: Returns on portfolio sorted on CFSALES

Volatility Decile

1 (S) 2 3 4 5 6 7 8 9 10 (L) D1–D10 D1:5–D6:10

Industry Ret (%) 1.20 1.23 1.21 1.01 1.10 1.22 0.89 0.94 0.84 0.41 0.79[2.48]⁎⁎

0.29[2.47]⁎⁎

FF-4alpha (%)

0.17[1.60]

0.15[1.30]

0.03[0.22]

−0.18[−1.58]

−0.02[−0.15]

0.17[1.39]

−0.24[−1.98]⁎⁎

−0.09[−0.7]

−0.19[−1.2]

−0.54[−2.22]⁎⁎

0.71[2.72]⁎

0.21[1.98]⁎⁎

Idio. Ret (%) 1.22 1.08 1.26 1.14 1.06 1.17 1.07 0.82 0.92 0.07 1.15[3.22]⁎

0.34[2.55]⁎

FF-4alpha (%)

0.09[0.86]

0.01[−0.09]

0.14[1.22]

0.14 [1] 0.06[−0.56]

0.02[0.20]

−0.03[−0.20]

−0.28[−2.1]⁎⁎

−0.17[−0.92]

−0.93[−4.04]⁎

1.02[3.77]⁎

0.37[2.86]⁎

Panel B: Returns on portfolio sorted on CFBE

Volatility Decile

1 (S) 2 3 4 5 6 7 8 9 10 (L) D1–D10 D1:5–D6:10

Industry Ret (%) 1.05 1.15 1.04 1.28 1.00 1.05 1.02 1.12 1.39 0.52 0.53[2.41]⁎⁎

0.08[0.83]

FF-4alpha (%)

−0.01[−0.09]

0.03[0.25]

0.02[0.12]

0.20[1.76]⁎⁎⁎

−0.06[−0.53]

−0.09[−0.78]

−0.07[−0.53]

0.03[0.24]

0.08[0.59]

−0.69[−4.87]⁎

0.68[3.68]⁎

0.18[2.10]⁎

Idio. Ret (%) 1.07 1.07 1.09 1.16 1.30 1.10 1.26 1.19 0.76 0.58 0.48[1.99]⁎⁎

0.16[1.53]

FF-4alpha (%)

0.17[1.31]

−0.01[−0.11]

0.00[0.04]

0.03[0.26]

0.03[0.28]

−0.09[−0.73]

0.17[1.27]

0.04[0.31]

−0.54[−3.36]⁎

−0.73[−4.55]⁎

0.90[4.22]⁎

0.28[3.05]⁎

Panel C: Firm level cross-sectional regressions

Intercept beta ln(ME) ln(BEME) PMOM SUE ILLIQ EY CFSALES CFBE

Industry 1.64[4.97]⁎

0.18[0.65]

−0.09[−1.93]⁎⁎⁎

0.59[9.68]⁎

−0.005[−3.49]⁎

0.96[34.72]⁎

0.06[4.17]⁎

0.04[10.90]⁎

−0.57[−2.47]⁎⁎

1.66[5.02]⁎

0.17[0.60]

−0.09[−1.96]⁎⁎

0.59[9.26]⁎

−0.004[−3.37]⁎

0.96[34.67]⁎

0.06[4.09]⁎

0.04[10.88]⁎

−0.74[−0.92]

Idiosyncratic 1.68[5.10]⁎

0.17[0.62]

−0.09[−2.04]⁎⁎

0.58[9.57]⁎

−0.005[−3.58]⁎

0.96[34.86]⁎

0.06[4.19]⁎

0.04[10.95]⁎

−0.22[−1.97]⁎⁎

1.70[5.37]⁎

0.17[0.60]

−0.09[−2.07]⁎⁎

0.59[8.84]⁎

−0.004[−3.33]⁎

0.96[34.68]⁎

0.06[4.20]⁎

0.04[10.95]⁎

−0.62[−1.79]⁎⁎⁎

Firm i's idiosyncratic and industry cashflows are defined, respectively, as the residual the predicted value from the regression CFi,j,t=αi+βiCFj,t+εi,j,t, where i indexesfirm and j indexes the industry firm i is in, and CFj,t is the mean industry cashflow at t. The regression is estimated with rolling four years of data for each firm. PanelA (B) shows the one-month-ahead returns on portfolios sorted on industry and idiosyncratic volatilities of CFSALES (CFBE), where CFSALES (CFBE) is the standarddeviation of cashflow to sales (industry-adjusted cashflow to book equity) over the past 16 quarters. Panel C shows the firm-level cross-sectional regressions ofFama and French (1992) using one-month-ahead raw return as the dependent variable. beta is the stock beta as measured in Fama and MacBeth (1973), ln(ME) isthe logarithm of market equity, ln(BE/ME) is the logarithm of book equity, PMOM is past 12- month stock return, SUE is standardized unexpected earnings, ILLIQ isilliquidity, and EY is earnings yield. Numbers in square brackets are t-statistics. ⁎, ⁎⁎, and ⁎⁎⁎ indicate significance at the 1%, 5% and 10% levels, respectively.

423A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

In sum, the evidence in this section suggests that the pricing of total cashflow volatility comes not only from idiosyncraticvolatility, but also from industry volatility. One possible reason for the presence of the pricing effect among total volatility and itscomponents is that they move together: firms having volatile cashflow are likely to have both high industry volatility and highidiosyncratic volatility. Thus ranking a firm on these volatility measures would tend to place the firm in the same portfolioregardless of which measure is used as the ranking variable. The evidence suggests that this is likely the case. Fig. 3 shows thetime series of the percentage of firms that are in the top 30% of total volatility that are also in the top 30% of industry volatility oridiosyncratic volatility. These two percentages, especially the latter, remain high all the time. Over the full sample period, onaverage, for firms in the top 30% of total CFSALES (CFBE) volatility, 60% (55%) of them are also in the top 30% of industryvolatility, and 79% (80%) of them are also in the top 30% of idiosyncratic volatility. The strong overlapping between totalcashflow volatility and its components at least partly explains the results in Table 7. The stronger mapping between totalvolatility and idiosyncratic volatility also explains why the idiosyncratic volatility results in Table 7 are closer to the totalvolatility results in Tables 3 and 5.

6. Relationship between cashflow volatility and idiosyncratic return volatility

This section examines the roles of idiosyncratic return volatility versus cashflow volatility in explaining future returns. Priorstudies relate the increasing trend in return volatility documented in Campbell et al. (2001) to earnings volatility (Wei and Zhang,2006) and cashflow volatility (Irvine and Pontiff, in press). If returns are based on firm fundamentals, it is possible that cashflowvolatility leads return volatility. For example, if past cashflows are best proxies for future cashflows and if the discount rate remains

Fig. 3. Percentage of firms that are in the top 30% of total cashflow volatility that are also in the top 30% of industry volatility (“Industry/Total”) or idiosyncraticvolatility (“Idio./Total”).

424 A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

stable, the discounted cashflow model will equate historical cashflow volatility to return volatility. In light of this argument, thecashflow volatility effect presented in this paper may just “rediscover” the return volatility effect documented in Ang et al. (2006).In this section, I show that although cashflow volatility is closely related to idiosyncratic return volatility, the cashflow volatilityeffect is not driven out by return volatility at both the portfolio and firm levels. Likewise, the idiosyncratic return volatility effectdocumented in Ang et al. (2006) is robust to the control of cashflow volatility.

Table 8Returns on portfolios sorted on idiosyncratic return volatility and cashflow volatility.

Panel B: FF-4 alphas of 5 by 5 portfolios first sorted by cashflow volatility and then by IRV

CFVquintile

CFSALES for cashflow volatility CFBE for cashflow volatility

IRV quintile IRV quintile

V1 (S) V2 V3 V4 V5 (L) V1–V5 V1 (S) V2 V3 V4 V5 (L) V1–V5

1 (S) 0.01[0.06]

−0.07[−0.48]

0.28[1.51]

0.34[1.76]⁎⁎⁎

−0.20[−0.74]

0.21[0.66]

0.02[0.16]

0.04[0.28]

0.26[1.54]

0.48[2.31]⁎⁎

−0.08[−0.31]

0.10[0.34]

2 0.14[1.08]

0.31[1.90]⁎⁎⁎

0.18[0.94]

−0.25[−1.05]

−0.24[−0.78]

0.38[1.12]

0.05[0.46]

0.09[0.64]

0.19[1.16]

−0.07[−0.32]

−0.71[−2.42]⁎⁎

0.76[2.26]⁎⁎

3 0.04[0.33]

−0.01[−0.07]

0.06[0.29]

−0.01[−0.03]

−0.60[−2.05]⁎⁎

0.64[1.97]⁎⁎

0.04[0.32]

−0.10[−0.61]

−0.03[−0.14]

0.08[0.36]

−0.94[−2.91]⁎

0.99[2.72]⁎

4 −0.10[−0.73]

0.24[1.35]

−0.20[−0.81]

−0.79[−2.93]⁎

−1.47[−4.45]⁎

1.37[3.78]⁎

0.22[1.56]

0.11[0.60]

−0.18[−0.8]

−0.34[−1.34]

−0.68[−2.01]⁎⁎

0.89[2.31]⁎⁎

5 (L) −0.06[−0.29]

−0.25[−0.92]

−0.62[−2.09]⁎⁎

−1.12[−3.02]⁎

−2.37[−5.68]⁎

2.31[5.01]⁎

−0.25[−1.45]

−0.54[−2.41]⁎⁎

−0.92[−3.33]⁎

−1.54[−4.58]⁎

−2.46[−6.02]⁎

2.21[4.88]⁎

Controllingfor CFV

0.00[0.04]

0.04[0.46]

−0.06[−0.49]

−0.37[−2.32]⁎⁎

−0.98[−5.14]⁎

0.98[4.58]⁎

0.02[0.22]

−0.08[−0.83]

−0.13[−1.2]

−0.28[−1.81]⁎⁎⁎

−0.97[−4.96]⁎

0.99[4.31]⁎

Panel C: FF-4 alphas of 5 by 5 portfolios first sorted by IRV and then by cashflow volatility

IRVquintile

CFSALES quintile CFBE quintile

V1 (S) V2 V3 V4 V5 (L) V1–V5 V1 (S) V2 V3 V4 V5 (L) V1–V5

1 (S) −0.06[−0.52]

0.00[−0.04]

0.24[1.66]⁎⁎⁎

0.03[0.21]

−0.06[−0.4]

0.00[0.01]

0.00[−0.04]

−0.11[−0.95]

0.10[0.76]

0.17[1.27]

0.05[0.33]

−0.06[0.28]

2 0.17[1.04]

0.24[1.26]

0.01[0.04]

0.06[0.37]

−0.03[−0.18]

0.20[0.79]

0.29[1.78]⁎⁎⁎

0.25[1.39]

−0.01[−0.04]

−0.05[−0.28]

−0.06[−0.33]

0.34[1.50]

3 0.29[1.56]

0.20[0.91]

0.06[0.32]

−0.01[−0.06]

−0.39[−1.72]⁎⁎⁎

0.68[2.24]⁎⁎

0.48[2.24]⁎⁎

0.07[0.36]

−0.01[−0.05]

−0.02[−0.13]

−0.55[−2.52]⁎

1.03[3.49]⁎

4 −0.18[−0.7]

0.01[0.03]

−0.24[−0.89]

−0.52[−1.95]⁎⁎

−0.89[−2.87]⁎

0.71[1.81]⁎⁎⁎

−0.13[−0.54]

−0.42[−1.68]⁎⁎⁎

−0.06[−0.25]

−0.22[−0.81]

−1.09[−4.08]⁎

0.96[2.97]⁎

5 (L) −0.26[−0.78]

−0.72[−2.49]⁎

−1.23[−3.98]⁎

−1.36[−3.23]⁎

−2.10[−5.98]⁎

1.83[3.96]⁎

−0.67[−2.24]⁎⁎

−0.70[−2.16]⁎⁎

−0.66[−1.91]⁎⁎⁎

−1.77[−5.79]⁎

−1.94[−4.77]⁎

1.27[2.78]⁎

Controllingfor IRV

−0.01[−0.06]

−0.06[−0.49]

−0.23[−2.06]⁎⁎

−0.36[−2.5]⁎

−0.69[−4.21]⁎

0.69[3.32]⁎

−0.01[−0.06]

−0.18[−1.77]⁎⁎⁎

−0.13[−1.12]

−0.38[−3.66]⁎

−0.72[−5.47]⁎

0.71[4.54]⁎

Panel A reports 1-month-ahead returns on quintile portfolios sorted on idiosyncratic return volatility as in Ang et al. (2006). Panel B reports the FF-4 alphas of 5 by5 portfolios formed with all stocks in the sample first sorted by cashflow volatility (CFV) then by IRV. At the end of panel A, in the row labeled “Controlling for CFV,”the five IRV quintiles are averaged across each cashflow volatility quintile so that each IRV quintile contains all values of cashflow volatility. Panel C reports the FF-4alphas of 5 by 5 portfolios formedwith all stocks in the sample first sorted by IRV then by cashflow volatility (CFV). In the row labeled “Controlling for IRV,” the fiveIRV quintiles are averaged across each cashflow volatility quintile. Numbers in square brackets are t-statistics. ⁎, ⁎⁎, and ⁎⁎⁎ indicate significance at the 1%, 5% and10% levels, respectively.

Panel A: Returns on quintile portfolios sorted on idiosyncratic return volatility

Quintile portfolios, 1-month return

1 (S) 2 3 4 5 (L) 1–5

Return 1.19 1.13 1.12 0.61 −0.30 1.49 [3.54]⁎FF-4 alpha 0.08 [1.33] 0.07 [0.80] 0.09 [0.77] −0.28 [−1.85]⁎⁎⁎ −1.17 [−4.85]⁎ 1.25 [4.62]⁎

425A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

6.1. Returns on portfolios sorted on idiosyncratic return volatility and cashflow volatility

I first replicate returns on the portfolios sorted on idiosyncratic return volatility (IRV) of Ang et al. (2006) for my sample fromJanuary 1980 to December 2004. Panel A, Table 8 presents the one-month ahead returns on quintile portfolios sorted on IRV.

Panel A confirms the findings of Ang et al. (2006)—portfolios sorted on increasing idiosyncratic return volatility displaydecreasing returns. Consistent with Ang et al. (2006), the return (FF-4 alpha) spread between quintile 1 (the lowest returnvolatility) and quintile 5 (the highest return volatility) is positive and large at 1.49% (1.25%).

Panels B and C present the FF-4 alphas of 5×5 portfolios sorted by both IRV and cashflow volatility. What motivates this doublesorting is the close relationshipbetween idiosyncratic returnvolatility and cashflowvolatility, as argued in Irvine andPontiff, (inpress)and shown in Table 2, where the average cross sectional correlation between CFSALES (CFBE) and IRV is relatively high at 0.14 (0.32).Given this close relationship, it is then logical to ask: (1) Can cashflow volatility potentially explains the idiosyncratic return volatilityeffect, and (2) is the cashflowvolatility effect identified inTable 3 driven by idiosyncratic return volatility? Panel B of Table 8 addressesquestion (1) and Panel C addresses question (2).

Panel B of Table 8 presents one-month-ahead FF-4 alpha returns on 5×5 portfolios first sorted on cashflow volatility and then onIRV. In each rowcashflowvolatility is controlledwhile IRV has dispersion. Using eithermeasure of cashflowvolatility, the idiosyncraticreturn volatility effect clusters in firmswithmedium to high cashflow volatility. For example, controlled for CFSALES, IRV quintiles 3, 4and 5 have statistically significant FF-4 alpha spreads of 0.64%, 1.37% and 2.31% a month, respectively. The return volatility effect isparticularly strong infirmswith the largest cashflowvolatility—theV1–V5 FF-4 alpha spread (2.31% for CFSALES and 2.21% for CFBE) ismuch larger for cashflow volatility quintile 5 firms than the unconditional alpha spread of 1.25% in Panel A of Table 8.

Fig. 4. Percentage of firms that are in the top 30% of CFSALES that are also in the top 30% of IRV (“IRV/CFSALES”) or in the top 30% of CFBE that are also in the top 30%of IRV (“IRV/CFBE”).

426 A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

The last row of Panel B, labeled “Controlling for CFV,” shows the overall return volatility effect after controlling for cashflowvolatility. Here as in Table 4 I average across the five cashflow volatility quintiles to produce IRV quintile portfolios. Compared withan unconditional spread of 1.25% in Panel A, the overall V1–V5 spread after controlling for CFSALES (CFBE), although weakening to0.98% (0.99%), is still highly significant. Thus controlling for cashflow volatility only modestly weakens the idiosyncratic returnvolatility effect.

Panel C presents the one-month-ahead FF-4 alpha returns on 5×5 portfolios first sorted on IRV and then on cashflow volatility.Across each row idiosyncratic return volatility is controlled for cashflow volatility portfolios. The cashflow volatility effect holds formedium to high IRV stocks: controlled for IRV, CFSALES (CFBE) quintiles 3, 4 and 5 have statistically significant FF-4 alpha spreadsof 0.68% (1.03%), 0.71% (0.96%) and 1.83% (1.27%) a month. The overall alpha spread for CFSALES (CFBE) after controlling for IRV is0.69% (0.71%), as shown in the row labeled “Controlling for IRV.” By comparison, recall from Panel A of Table 4 that when no controlis imposed, the unconditional FF-4 alpha spread is 0.55% (0.74%) for CFSALES (CFBE). Thus, Panel C of Table 8 shows that controllingfor IRV does not weaken the overall significance of the cashflow volatility effect.

Table 9Cross-sectional regressions with idiosyncratic return volatility.

Dependent variable: Monthly stock return

Model Intercept Beta ln(ME) ln(BEME) PMOM SUE ILLIQ EY IRV CFSALES CFBE

1 2.08[6.00]⁎

0.11[0.42]

−0.08[−1.71]⁎⁎⁎

0.54[9.25]⁎

0.003[2.55]⁎⁎

−0.14[−4.24]⁎

−0.06[−2.90]⁎

2 2.23[6.62]⁎

0.11[0.42]

−0.09[−2.11]⁎⁎

0.51[8.05]⁎

0.003[2.54]⁎⁎

−0.13[−4.16]⁎

−0.82[−2.19]⁎⁎

3 1.94[5.72]⁎

0.25[0.97]

−0.12[−2.65]⁎

0.57[9.92]⁎

−0.005[−3.79]⁎

0.95[35.06]⁎

0.07[5.10]⁎

0.04[11.24]⁎

−0.13[−3.89]⁎

−0.05[−1.96]⁎⁎

4 1.96[5.93]⁎

0.26[0.98]

−0.12[−2.77]⁎

0.56[9.01]⁎

−0.004[−3.75]⁎

0.96[35.02]⁎

0.07[5.12]⁎

0.04[11.27]⁎

−0.13[−3.96]⁎

−0.35[−1.46]

This table presents estimates of coefficients from the Fama and French (1992) cross-sectional regressions of monthly raw returns (multiplied by 100) of all NYSE/NASDAQ/AMEX listed firms. The Fama andMacBeth (1973) procedure is used to estimate the beta of a stock. ln(ME) is the logarithm of market equity measured inthe latest June; ln(BE/ME) is the logarithm of book equity of the latest fiscal year divided by market equity of December of the same year; PMOM is past 12-monthstock return; SUE is standardized unexpected earnings; ILLIQ is the illiquidity measure in Amihud (2002); EY is earnings yield, measured as earnings of last quarterdivided by beginning-of-the-month market equity; IRV is idiosyncratic volatility of the past one month’s daily returns relative to the Fama–French three factors;and CFSALES (CFBE) is the standard deviation of cashflow to sales (industry-adjusted cashflow to book equity) over the past 16 quarters. The estimates are theaverages of the time-series coefficients of the monthly regression slopes for January 1980 to December 2004. Numbers in square brackets are t-statistics. ⁎, ⁎⁎, and⁎⁎⁎ indicate significance at the 1%, 5% and 10% levels, respectively.

Table 10Lead–lag relationship between cashflow volatility and cross-sectional regressions of returns.

Panel A: CFSALES conditioned on IRV

Stage 1: Regression of CFSALES on contemporaneous and lagged IRV

Intercept IRV Lagged 1 IRV Lagged 2 IRV Adj. R2

−0.07[−5.19]⁎

0.17[34.04]

0.15[28.21]⁎

0.17[34.91]⁎

0.02

Stage 2: Cross sectional regressions of monthly returns using CFSALES residual from stage 1

Model Intercept Beta ln(ME) ln(BEME) PMOM SUE ILLIQ EY IRV Res (CFSALES)

1 2.08[5.99]⁎

0.09[0.32]

−0.08[−1.72]⁎⁎⁎

0.54[9.11]⁎

0.003[2.62]⁎⁎

−0.15[−4.56]⁎

−0.06[−2.42]⁎⁎

2 1.92[5.68]⁎

0.24[0.94]

−0.11[−2.59]⁎⁎

0.57[9.89]⁎

−0.004[−3.72]⁎

0.95[34.96]⁎

0.07[5.11]⁎

0.04[11.18]⁎

−0.14[−4.19]⁎

−0.06[−2.16]⁎⁎

Panel B: CFBE conditioned on IRV

Stage 1: egression of CFBE on contemporaneous and lagged IRV

Intercept IRV Lagged 1 IRV Lagged 2 IRV Adj. R2

0.081[279.59]⁎

0.009[87.26]⁎

0.008[71.99]⁎

0.009[87.49]⁎

0.13

Stage 2: Cross sectional regressions of monthly returns using CFBE residual from stage 1

Model Intercept Beta ln(ME) ln(BEME) PMOM SUE ILLIQ EY IRV Res (CFBE)

1 2.16[6.26]⁎

0.09[0.35]

−0.09[−2.04]⁎⁎

0.50[7.89]⁎

0.003[2.48]⁎⁎

−0.14[−4.34]⁎

−0.69[−1.97]⁎⁎

2 1.95[5.84]⁎

0.26 [0.98] −0.12[−2.71]⁎

0.55[8.79]⁎

−0.004[−3.77]⁎

0.95[34.87]⁎

0.07[5.28]⁎

0.04[11.18]⁎

−0.13[−3.89]⁎

−0.31[−1.29]

Panel C: IRV conditioned on CFSALES

Stage 1:Regression of IRV on contemporaneous and lagged CFSALES

Intercept ΔCFSALES Lagged 1 CFSALES Adj. R2

3.17[1072.86]⁎

0.04[21.32]

0.05[120.40]⁎

0.02

Stage 2: Cross sectional regressions of monthly returns using IRV residual from stage 1

Model Intercept Beta ln(ME) ln(BEME) PMOM SUE ILLIQ EY CFSALES Res (IRV)

1 1.67[4.95]⁎

0.11[0.41]

−0.08[−1.81]⁎⁎⁎

0.53[8.94]⁎

0.003[2.64]⁎⁎

−0.05[−1.98]⁎⁎

−0.14[−4.26]⁎

2 1.53[4.65]⁎

0.26[1.01]

−0.12[−2.69]⁎

0.57[9.77]⁎

−0.004[−3.70]⁎

0.95[34.77]⁎

0.07[5.11]⁎

0.04[11.09]⁎

−0.05[−1.86]⁎⁎⁎

−0.13[−3.95]⁎

Panel D: IRV conditioned on CFBE

Stage 1:Regression of IRV on contemporaneous and lagged CFBE

Intercept ΔCFBE Lagged 1 CFBE Adj. R2

2.377[593.59]⁎

7.245[93.53]⁎

5.31[294.86]⁎

0.11

Stage 2: Cross sectional regressions of monthly returns using IRV residual from stage 1

Model Intercept Beta ln(ME) ln(BEME) PMOM SUE ILLIQ EY CFBE Res (IRV)

1 1.93[5.97]⁎

0.11[0.40]

−0.10[−2.16]⁎⁎

0.49[7.76]⁎

0.003[2.50]⁎⁎

−1.52[−3.48]⁎

−0.13[−4.08]⁎

2 1.66[5.23]⁎

0.26[1.01]

−0.12[−2.82]⁎

0.55[8.73]⁎

−0.004[−3.75]⁎

0.95[34.75]⁎

0.07[5.23]⁎

0.04[11.13]⁎

−0.94[−1.97]⁎⁎

−0.13[−4.04]⁎

Each panel runs a two-stage regression. In Panel A, the first stage regression regresses CFSALES (standard deviation of cashflow to sales) on contemporaneous IRV(idiosyncratic return volatility) and lagged one and two-month IRVs. In the second stage, the Fama and French (1992) cross-sectional regressions of monthly rawreturns (multiplied by 100) of all NYSE/NASDAQ/AMEX listed firms is run on beta, ln(ME) (logarithm of market equity), ln(BE/ME) (logarithm of book equity tomarket equity), PMOM (past 12- month stock return), SUE (standardized unexpected earnings), ILLIQ (illiquidity), EY (earnings yield), IRV , and residual ofCFSALES from the first-stage regression (Res(CFSALES)). The estimates are the averages of the time-series coefficients of the monthly regression slopes for January1980 to December 2004. In Panel B, CFSALES is replaced with CFBE (standard deviation of industry-adjusted cashflow to book equity). In Panel C, in the first stageregression IRV is regressed on lagged one-quarter CFSALES and contemporaneous change in CFSALES. The residual from the first stage is used in place of IRV in thesecond stage cross sectional regressions. In Panel D, CFSALES is replacedwith CFBE. Numbers in square brackets are t-statistics. ⁎, ⁎⁎, and ⁎⁎⁎ indicate significance atthe 1%, 5% and 10% levels, respectively.

427A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

428 A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

Nonetheless, Panels A and B of Table 8 show that the idiosyncratic return volatility effect clusters in firms with medium to highcashflow volatility and vice versa. A further examination on the individual 5×5 quintiles reveals that the Fama–French four factorsmisprice firmswith high IRV and/or high cashflow volatility themost. It is this mispricing that gives rise to the idiosyncratic returnvolatility and cashflow volatility effects. Given the overlapping of both effects in high IRV and cashflow volatility firms, it isreasonable to ask whether high IRV firms and high cashflow volatility firms are the same.

To answer this question, Fig. 4 plots the time-series of the percentage of the firms that are in the top 30% bracket in cashflowvolatility that are also in the top 30% of IRV. The percentage fluctuates from 10% to 40% over the sample period of 1980–2004.Unconditionally, the mean percentage of the firms that are in the top 30% CFSALES (CFBE) that are also in the top 30% of IRV is 27%(32%). Thus, although there is a significant overlapping between high IRV and cashflow volatility firms, there are more highcashflow volatility firms whose IRVs are not large. Collectively, the evidence presented in Table 8 and Fig. 4 suggests that thecashflow volatility effect is distinct from the return volatility effect at the portfolio level.

6.2. Firm-level robustness of cashflow volatility to return volatility

I now present evidence that the cashflow volatility effect is also robust to idiosyncratic return volatility at the firm level. Toshow this, I augment regression (3) with IRV and re-run the Fama–MacBeth cross sectional regression. Table 9 shows the results oftwo specifications: one with the Fama–French four-factor variables, and the other with all variables. CFSALES still loadssignificantly negatively in both specifications, with roughly the same magnitude as in Table 5. The coefficient on CFBE issignificantly negative in the first specification (model 2), and negative but not significant in the second specification (model 4). Inboth cases, the loading of CFBE is smaller than in Table 5. Note that in all of the regressions presented in the table, IRV loadssignificantly negative, a result consistent with Ang et al. (2009). Overall, Table 9 supports that the negative association betweencashflow volatility and future returns is robust to the control of idiosyncratic return volatility at the firm level.

6.3. Lead–lag relationship between cashflow and return volatilities and cross-sectional regressions

This section provides further support by decomposing total cashflow volatility into a component explained by return volatilityand a component orthogonal to return volatility. The idea here is to examine the effect of cashflow volatility after fully purging outthe effect of return volatility. In other words, if cashflow volatility is incorporated in return volatility, does cashflow volatility stillhas explanatory power on returns after that part of cashflow volatility incorporated in return volatility is removed?

To answer the above question, I run a two-stage regression that uses the cashflow volatility component orthogonal to IRV. In thefirst stage, cashflow volatility is regressed on contemporaneous IRV and lagged one- and two-month IRVs. The use of lagged IRVshas two purposes. First, I wish to study the lead–lag relationship between cashflow volatility and return volatility. Second, itreconciles, to some extent, the horizon difference that cashflow volatility is estimated with past sixteen quarters of data and eachIRV is estimated with past month's daily returns. In the second stage regression, the estimated cashflow volatility residual fromstage one is used in place of cashflow volatility in the cross-sectional regressions of returns of Eq. (3).

Panels A and B of Table 10 show results for CFSALES and CFBE, respectively. In the first stage, IRV and lagged IRVs are positivelyrelated to cashflow volatility with very high t-statistics. In the second stage, Panel A shows that the coefficient on the CFSALESresidual is significantly negative. Panel B shows the loading of the CFBE residual is significantly negative for the regressionwith theFF-4 variables and IRV, and negative but not significant for the regressionwith all control variables. These results are highly similarto those of Table 9. Again, the two-stage regression results indicate that although cashflow volatility is closely related to returnvolatility, its explanatory power on returns is distinct from that of return volatility.

For completeness, Panels C and D of Table 10 present the results for the two-stage regressions of IRV conditioned oncontemporaneous and lagged cashflow volatility.10 Observe that return volatility is significantly associated with contemporaneousand lagged cashflow volatilities, and that the cashflow volatility effect and the conditioned return volatility effect remain intact.Therefore, idiosyncratic return volatility effect is also different from the cashflow volatility effect.

7. Conclusion

I show that historical cashflow volatility is negatively related to future returns cross-sectionally. The negative association islarge, economically meaningful, long-lasting up to five years, robust to many known return-informative effects, and extends toboth systematic and idiosyncratic cashflow volatilities. Using the standard deviations of cashflow to sales and of cashflow to bookequity as proxies for cashflow volatility, the least volatile decile portfolio outperforms the most volatile decile portfolio by 9–15% ayear in value-weighted return or by 13% a year relative to the Fama–French four factors. The cashflow volatility effect is closelyrelated to the idiosyncratic return volatility effect documented in Ang et al. (2006). However, in portfolios simultaneously sorted onboth cashflowand return volatilities, and in cross sectional regressions of returns at the firm level that use either cashflow volatilityor the cashflow flow volatility component orthogonal to contemporaneous and lagged return volatilities, these two effects neitherdrive out nor dominate each other.

10 A slight difference is worth noting. At the first stage, IRV is run on the change in cashflow volatility and lagged one cashflow volatility. This is becausecashflow volatility is estimated with rolling quarterly data, and the use of change in cashflow volatility avoids the overlapping of regressors.

429A.G. Huang / Journal of Empirical Finance 16 (2009) 409–429

My findings add to a growing literature that shows that various proxies for information uncertainty are negatively associatedwith future stock returns. In particular, my findings echo the findings of the pricing of return volatility by Ang et al. (2006), whodocument that both systematic and idiosyncratic return volatilities are negatively related to returns. The pricing of idiosyncraticcashflow volatility represents an anomaly against the traditional asset pricing theories. Given that a commonality in the advocateddistress risk in HML and small-firm risk in SMB is cashflow uncertainty risk, the direct evidence of the pricing of historical cashflowuncertainty provided in this paper sheds light on potential fundamental risks embodied in the Fama–French HML and SMB factors.

The inadequacy of known return-informative variables to explain the existence of the pricing of cashflow volatility also calls foran unorthodox explanation. Potential explanations to these findings include those used in the information uncertainty literature,such as behavioral biases of overconfidence or underreaction, and limits to arbitrage such as transaction costs, etc. A rational modelthat incorporates the second moment of earnings may also help explain my findings. For instance, Chen (2003) extends theIntertemporal Capital Asset Pricing Model to incorporate a time-varying variance in investment opportunity set and shows thatincreasing precautionary savings motive due to the existence of this varying volatility can dampen asset returns.While models likethis lend support to the negative pricing of systematic volatility, they do not predict the pricing of idiosyncratic volatility. Thus, achallenge in proposing an explanation for my findings is that the explanation needs to explain not only the pricing of systematicvolatility, but also the pricing of idiosyncratic volatility. To answer this question, future research is warranted.

References

Ang, A., Hodrick, R.J., Xing, Y., Zhang, X., 2006. The cross-section of volatility and expected returns. Journal of Finance 51, 259–299.Ang, A., Hodrick, R.J., Xing, Y., Zhang, X., 2006. High idiosyncratic volatility and low returns: international and further U.S. evidence. Journal of Financial Economics

91, 1–23.Allayannis, G., Rountree, B. andWeston, J.P. Earnings volatility, cash flow volatility, and firmvalue.Working paper, (2005), University of Virginia and Rice University.Amihud, Y., 2002. Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets 5, 31–56.Bali, T.G., Cakici, N., 2008. Idiosyncratic volatility and the cross-section of expected returns. Journal of Financial and Quantitative Analysis 43, 3–18.Barnes, R., 2001. Earnings volatility and market valuation: an empirical investigation. Working paper. London Business School.Basu, S., 1977. Investment performance of common stocks in relation to their price-earnings ratios: a test of the efficient market hypothesis. Journal of Finance 32,

663–682.Berk, J.B., 1995. A critique of size related anomalies. Review of Financial Studies 8, 275–286.Berk, J.B., 1997. Does size really matter? Financial Analysts Journal 12–18 September/October.Brown, L.D., 1993. Earnings forecasting research: its implications for capital markets research. International Journal of Forecasting 9, 295–320.Carhart, M., 1997. On persistence in mutual fund performance. Journal of Finance 52, 57–82.Campbell, J.Y., Lettau, M., Malkiel, B.G., Xu, Y., 2001. Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk. Journal of Finance

56, 1–43.Chan, K.C., Chen, N., 1991. Structural and return characteristics of small and large firms. Journal of Finance 46, 1467–1484.Chan, L.K., Jegadeesh, N., Lakonishok, J., 1996. Momentum strategies. Journal of Finance 51, 1681–1713.Chen, J., 2003. Intertemporal CAPM and the cross-section of stock returns. Working paper. University of Southern California.Chen, N., Zhang, F., 1998. Risk and return of value stocks. Journal of Business 71, 501–535.Chordia, T., Shivakumar, L., 2006. Earnings and price momentum. Journal of Financial Economics 80, 627–656.Dechow, P., Sloan, R., Sweeney, A., 1995. Detecting earnings management. Accounting Review 70, 193–225.Diether, K., Malloy, C., Scherbina, A., 2002. Differences of opinion and the cross-section of stock returns. Journal of Finance 57, 2113–2141.Fama, E.F., French, K.R., 1992. The cross-section of expected stock returns. Journal of Finance 47, 427–465.Fama, E.F., French, K.R., 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3–56.Fama, E.F., French, K.R., 1995. Size and book-to-market factors in earnings and returns. Journal of Finance 50, 131–155.Fama, E.F., French, K.R., 1996. Multifactor explanations of asset pricing anomalies. Journal of Finance 51, 55–84.Fama, E.F., MacBeth, J.D., 1973. Risk, return, and equilibrium: empirical tests. Journal of Political Economy 81, 607–636.Haugen, R.A., Baker, N.L., 1996. Commonality in the determinants of expected stock returns. Journal of Financial Economics 41, 401–439.Healy, P., 1985. The effect of bonus schemes on accounting decisions. Journal of Accounting and Economics 7, 85–107.Irvine, P.J., Pontiff, J.,in press. Idiosyncratic return volatility, cash flows, and product market competition. Review of Financial Studies.Jegadeesh, N., Titman, S., 1993. Returns to buying winners and selling losers: implications for stock market efficiency. Journal of Finance 48, 65–91.Jiang, G., Lee, C.M., Zhang, Y., 2005. Information uncertainty and expected returns. Review of Accounting Studies 10, 185–221.Minton, B.A., Schrand, C., 1999. Does cash flow volatility affect firm value: its impact on discretionary investment and the costs of debt and equity financing. Journal

of Financial Economics 54, 432–460.Minton, B.A., Schrand, C.M., Walther, B.R., 2002. The role of volatility in forecasting. Review of Accounting Studies 7, 195–215.Morck, R., Yeung, B., Yu, W., 2000. The information content of stock markets: why do emerging markets have synchronous stock price movements? Journal of

Financial Economics 58, 215–260.Pastor, L., Veronesi, P., 2003. Stock valuation and learning about profitability. Journal of Finance 58, 1749–1790.Sadka, R., Scherbina, A., 2007. Analyst disagreement, mispricing and liquidity. Journal of Finance 62, 2367–2403.Shroff, P.K., 1999. The variability of earnings and non-earnings information and earnings prediction. Journal of Business Finance and Accounting 26, 863–882.Waymire, G., 1985. Earnings volatility and voluntary management forecast disclosure. Journal of Accounting Research 23, 268–295.Wei, S.X., Zhang, C., 2006. Why did individual stocks become more volatile? Journal of Business 79, 259–292.Zhang, X.F., 2006. Information uncertainty and stock returns. Journal of Finance 61, 105–136.