Annals of the
University of North Carolina Wilmington
International Masters of Business Administration
http://csb.uncw.edu/imba/
EXCHANGE RATE EXPOSURE AT THE INDUSTRY LEVEL
Brandon R. Bennett
A Thesis Submitted to the
University of North Carolina Wilmington in Partial Fulfillment
of the Requirements for the Degree of
Master of Business Administration
Cameron School of Business
University of North Carolina Wilmington
2014
Approved by
Advisory Committee
Peter Schuhmann Nivine Richie
Clay M. Moffett
Chair
Accepted by
Dean, Graduate School
ii
TABLE OF CONTENTS
ABSTRACT ................................................................................................................................... iv
LIST OF TABLES .......................................................................................................................... v
LIST OF FIGURES ....................................................................................................................... vi
INTRODUCTION .......................................................................................................................... 1
LITERATURE REVIEW ............................................................................................................... 3
DATA & METHODOLOGY ......................................................................................................... 6
Sample Selection and Data ......................................................................................................... 6
Sector Exchange Traded Funds .................................................................................................. 6
Indices – United States, World ................................................................................................... 6
Exchange rates – U.S. Dollar, Euro, Japanese Yen, Chinese Yuan ............................................ 7
Methodology ............................................................................................................................... 9
RESULTS ..................................................................................................................................... 10
Simple Regression – Euro / U.S. Dollar ................................................................................... 10
Simple Regression – U.S. Dollar / Chinese Renminbi ............................................................. 11
Simple Regression – U.S. Dollar / Japanese Yen ..................................................................... 12
Multiple Regression – Euro / U.S. Dollar and S&P500 ........................................................... 13
Multiple Regression – Euro / U.S. Dollar and MSCI World Index .......................................... 14
Multiple Regression – U.S. Dollar / Chinese Renminbi and S&P500...................................... 16
Multiple Regression – U.S. Dollar / Chinese Renminbi and MSCI World Index .................... 17
iii
Multiple Regression – U.S. Dollar / Japanese Yen and S&P500 ............................................. 19
Multiple Regression – U.S. Dollar / Japanese Yen and MSCI World Index ............................ 20
Simple Regression Summary .................................................................................................... 21
Multiple Regression Summary ................................................................................................. 22
CONCLUSION ............................................................................................................................. 26
REFERENCES ............................................................................................................................. 27
iv
ABSTRACT
This paper analyzes the relationship between industry stock returns and the U.S. Dollar
exchange rate against the Euro, Yen and Yuan using daily closing prices from January, 1999 to
August, 2013. Each currency pair and industry are examined individually with exchange rate
exposure being influenced by industry characteristics. Some industries move independently of
the exchange rate while others display correlation.
v
LIST OF TABLES
Table Page
1. Variable names and definitions ............................................................................................7
2. S&P500 components & weights ..........................................................................................8
3. MSCI World Index components & weights.........................................................................8
4. MSCI World Index country weights ....................................................................................8
5. Simple Regression – Euro / U.S. Dollar ............................................................................10
6. Simple Regression – U.S. Dollar / Chinese Renminbi ......................................................11
7. Simple Regression – U.S. Dollar / Japanese Yen ..............................................................12
8. Multiple Regression – Euro / U.S. Dollar and S&P500 ....................................................13
9. Multiple Regression – Euro / U.S. Dollar and MSCI World Index ...................................14
10. Multiple Regression – U.S. Dollar / Chinese Renminbi and S&P500...............................16
11. Multiple Regression – U.S. Dollar / Chinese Renminbi and MSCI World Index .............17
12. Multiple Regression – U.S. Dollar / Japanese Yen and S&P500 ......................................19
13. Multiple Regression – U.S. Dollar / Japanese Yen and MSCI World Index .....................20
vi
LIST OF FIGURES
Figure Page
1. Euro / U.S. Dollar and MSCI World Index – Technology ................................................23
2. Euro / U.S. Dollar and MSCI World Index – Health care .................................................23
3. Euro / U.S. Dollar and MSCI World Index – Industrials...................................................24
4. U.S. Dollar / Yen & MSCI World Index – Industrials ......................................................24
5. U.S. Dollar / Yen & MSCI World Index – Consumer staples ...........................................25
6. U.S. Dollar / Yen & MSCI World Index – Consumer discretionary .................................25
INTRODUCTION
President Nixon and the U.S. government terminated the gold exchange standard in 1971.
The dollar was no longer a fixed currency. In 1973, most major currencies followed suit and
allowed exchange rates to become free-floating. As time passed, dramatic increases in
globalization and reductions in foreign exchange controls spawned considerable interest in
exchange rate and stock price relationships. The literature offers mixed results with exchange
rates and stock prices being related, but other research suggests firms do not have significant FX
exposures. Observed insignificant FX exposure may be the result of firms successfully
managing their risk.
Floating exchange rates are especially interesting since the devaluation of the native or
home currency causes imported inputs to become more expensive. Thus devaluation would
benefit exporters, but consequently hurt the importers. Exposure to fluctuations in foreign
exchange rates is a major concern for corporations with international activities. Companies
attempt to measure FX risk and then implement strategies to protect earnings and assets
denominated in foreign currency. Understanding this risk is clearly important to managers.
Exchange rate risk (ERR) is the uncertainty of earnings due to changes in exchange rates
faced by firms that do business abroad. With world trade increasing and capital movements
expanding, exchange rates are one of the main determinants of profitability and equity prices.
Exchange rates directly influence the international competitiveness of firms, given their impact
on input and output prices, (Joseph, 2002). Exchange rates are a major source of uncertainty.
This study was undertaken to examine the impact of exchange rates on different
American industries. The amount of ERR is determined by the proportion of sales abroad and
the exchange rate volatility for these countries. The amount of ERR may be different for
2
industries with limited international sales (e.g. retail) compared to industries with more
international sales (e.g. pharmaceutical or chemicals).
The primary goal of this research is to determine if certain industries are in fact more
affected by exchange rates than others. This information could be useful to investors and
managers alike.
LITERATURE REVIEW
A great deal has been written on exchange rate exposure. Adler and Dumas (1984) state
that firms without foreign operations, currency assets, liabilities, or transactions, are still
generally exposed to foreign currency movements. Consequently, firms without transaction or
translation exposure still may have economic exposure. Input and output prices are influenced by
exchange rate movements even for a purely domestic firm.
Jorion (1990) determines exchange-rate exposure to be positively correlated with the
degree of foreign involvement—the more foreign operations, the greater the risk. Choi and Kim
(2003) support this result by showing that higher export and Asian sales affect U.S. firms
adversely when the U.S. dollar appreciates. Having higher asset deployment in Asia, on the
other hand, has a positive effect on firm value with a strengthening dollar. Muller and Verschoor
(2005) also provide strong support for the hypothesis that the degree of international
involvement is a major determinant of a firm’s currency risk exposure.
Bodnar and Gentry (1993) find that exchange rate movements have a larger impact on
industry returns for Canada and Japan than for the United States. However, for all three
countries, between 20 and 35 percent of industries tested have statistically significant exchange
rate exposure. Although many industries do not display significant exposure, this doesn’t
necessarily mean that exchange rates are not an important factor in explaining industry returns.
Firms with inherently large exposures are the most likely to hedge their exposures through
financial hedging using forwards, futures, options and swaps.
Choi and Prasad (1995) observe that a firm's value is significantly affected by both real
and nominal exchange rates. They find that the effects vary in terms of degree and direction, but
that a higher percentage of firms with significant exchange rate exposure gain with a
4
depreciation of the dollar. Roughly 60 percent of the firms with significant exchange rate
exposure benefited from depreciation, while 40 percent lost. Since industry groups include firms
with positive and negative exchange rate exposure, this can result in a loss of variation, and
could explain why previous studies fail to document strong support for exchange risk sensitivity
associated with firm value.
Chow et al. (1997) shows that all assets are exposed to exchange-rate risk. However, the
effects of real exchange-rate changes are different for bonds than for stocks. Bonds are
responsive in both the short-run and long-run, whereas stock returns are responsive only to long-
run changes. The exchange-rate exposure for bonds is primarily related to changes in interest
rates associated with unexpected changes in real exchange rates. Exchange rate exposure for
stocks is related to both interest-rate and cash-flow effects. This result may also offer insight as
to why previous findings failed to find an association between stock returns and exchange rates.
Shin and Soenen (1999) show that U.S. multinationals have exposure to exchange rate
risk and find investors reflecting this exposure in the firms' stock prices. Of the industries tested,
two are found to have statistically significant exposure. Electrical equipment experienced
increased stock returns associated with a decline in the dollar, whereas the metal industry has
decreased stock returns. The metal industry uses the U.S. dollar as a reserve currency. The
electrical equipment industry is characterized by intense international competition and thus
dollar depreciation favors U.S. manufacturing.
Industry structure is an important determinant of economic exposure according to
Marston (2001). Competition between firms determines how exchange rates affect their cash
flows. A firm that has a monopoly in foreign markets will have very different economic
exposure than a firm that faces competition. Muller and Verschoor (2005) find firms with high
5
liquidity ratios and firms with high growth opportunities and leverage appear to be more
sensitive to currency fluctuations
Nguyen et al. (2007) find French firms diminish 1.44 percent in value when the local
currency appreciates by 1 percent against a trade weighted index. Results show that the
introduction of the Euro helped decrease internal foreign exchange risk for European firms, as
well as external foreign currency risk from the British pound and U.S. Dollar. Muller and
Verschoor (2005) also find European firms to be negatively affected by a depreciation of foreign
currencies.
Badhani et al. (2009) conclude that the exposure coefficient is larger for companies with
large market capitalization and lower for smaller sized companies. Sensitivity tests on six well
known Indian market indices all result in coefficients being positive and highly significant,
implying that when the rupee appreciates (depreciates) the stock prices increase (decrease).
When the BSE Sensex is used as a proxy for the market, the BSE IT and BSE TECK indices are
the only two industries having negative exposure. The negative exposure coefficients for these
two indices indicate that there is a negative relationship between the changes in the exchange
rate and stock prices resulting from the export-oriented companies in the IT and TECK
industries.
Agrawal et al. (2010) examine the returns from the Nifty index and the movement of the
Rupee-Dollar exchange rates. Unlike some of the results found by Badhani et al. (2009), there
was a slight negative correlation between the Nifty Index returns and the Rupee-Dollar exchange
rates. However, the Granger Causality test was applied to the two variables which indicate
unidirectional causality running from stock returns to exchange rates. An increase in Nifty
returns causes a decline in exchange rates, but the reverse was not found to be true.
DATA & METHODOLOGY
Sample Selection and Data
Closing prices for the nine sector ETFs, three currency pairs, and two indices were taken
from Bloomberg. Weekday data is used for all variables in this analysis. If a market is closed
during a weekday, the prior day’s closing price is subsequently used. The time period covers 14
years and 8 months from the inception of the Euro on January 1, 1999 until August 30, 2013
resulting in 3,825 observations.
Sector Exchange Traded Funds
Exchange traded funds (ETF) allow an investor to increase or decrease exposure among a
large variety of indexes. ETF shares represent fractional ownership of a trust. Investment
companies offer ETFs representing equities, currencies, fixed income, commodities, real-estate,
international markets, and other various investment styles and objectives.
The nine sector ETFs used in this analysis are shown in Table 1. A sponsor (e.g. State
Street) arranges to set aside shares representing the basket of securities that forms the index. It is
important to note that ETFs can be bought and sold during the day, sold short, and options are
available.
Indices – United States, World
The daily returns for two major stock market indexes are used in this analysis. For the
United States, the benchmark index used is the Standard and Poor’s 500 index. The S&P500 is a
capitalization-weighted index of 500 large companies having common stock listed on the New
York Stock Exchange and/or NASDAQ. Table 2 shows the industry weights of the S&P500.
7
The MSCI World Index is a free float-adjusted market capitalization weighted index
designed to measure the equity market performance of 24 developed nations. With 1,606
constituents, the index covers approximately 85% of the free float-adjusted market capitalization
in each country. Table 3 shows the industry weights of the MSCI World Index. Table 4 shows
the country weights of the MSCI World Index.
Exchange rates – U.S. Dollar, Euro, Japanese Yen, Chinese Yuan
Three separate currency pairs are used in the analysis. The first is the EUR/USD: the
most popular currency pair in the world representing the world’s two largest economies. The
second is the USD/JPY: with the Yen being one of the most traded currencies in the world. The
third and final currency pair is the USD/CNY: which represents the Chinese Renminbi often
referred to as the Yuan. China is the world’s largest exporter and second largest economy.
Table 1 – Variable names and definitions
Variable
XLB Materials Select Sector SPDR® Fund
XLE Energy Select Sector SPDR® Fund
XLF Financial Select Sector SPDR® Fund
XLI Industrial Select Sector SPDR® Fund
XLK Technology Select Sector SPDR® Fund
XLP Consumer Staples Select Sector SPDR® Fund
XLU Utilities Select Sector SPDR® Fund
XLV Health Care Select Sector SPDR® Fund
XLY Consumer Discretionary Select Sector SPDR® Fund
EUR-USD Euro / U.S. Dollar
USD-CNY U.S. Dollar / Chinese Yuan
USD-JPY U.S. Dollar / Japanese Yen
S&P500 Standard & Poor's 500 Stock Market Index
MXWO Morgan Stanley Capital International Stock Market Index (MSCI)
8
Table 2 - S&P500 components & weights
Sector Symbol
Index
Weight Companies
Materials XLB 3.52% 31
Energy XLE 10.48% 43
Financials XLF 16.37% 81
Industrials XLI 10.70% 63
Technology XLK 20.27% 73
Consumer Staples XLP 10.10% 40
Utilities XLU 3.18% 31
Health Care XLV 13.03% 55
Consumer Discretionary XLY 12.35% 83
100.00% 500
Table 3 – MSCI World Index components & weights
Sector
Index
Weight Companies
Materials 5.82% 93
Energy 9.62% 154
Financials 20.90% 336
Industrials 11.34% 182
Technology 15.49% 249
Consumer Staples 10.13% 163
Utilities 3.29% 53
Health Care 11.16% 179
Consumer Discretionary 12.25% 197
100.00% 1606
Table 4 – MSCI World Index country weights
Country
Index
Weight Companies
United States 53.45% 858
United Kingdom 9.20% 148
Japan 9.15% 147
Canada 4.23% 68
France 4.15% 67
Other 19.82% 318
100% 1606
9
Methodology
Regression analysis will be used to measure FX exposure following Adler and Dumas
(1984) and Jorion (1990). The former of these models can be expressed as:
Where
is the daily stock return of the firm(s)
is the daily return of the currency pair
is the error term
The latter model of FX exposure can be expressed as:
Where
is the daily stock return of the firm(s)
is the daily return of the market index
is the daily return of the currency pair
is the error term
The first model is a simple formulation for total exposure between industry returns and
the currency pair. A market index is incorporated as a control variable in the second stage of the
regression. When calculating returns, the first difference of the natural log of each variable for
each observation in the data set is used.
RESULTS
Simple Regression – Euro / U.S. Dollar
The following table shows the Euro / Dollar exchange rate results for the nine industries
in the S&P500.
Table 5 1999-2013
R2 Coefficient Standard Error t-value p-value
Materials 0.0462 0.0001 0.0003 0.56 0.577
FX Rate 0.5411 0.0398 13.61 <.0001***
Energy 0.0489 0.0003 0.0003 1.09 0.277
FX Rate 0.6138 0.0438 14.02 <.0001***
Financials 0.0098 -0.0001 0.0003 -0.18 0.8608
FX Rate 0.3149 0.0512 6.15 <.0001***
Industrials 0.0141 0.0001 0.0002 0.64 0.5223
FX Rate 0.2586 0.0349 7.41 <.0001***
Technology 0.0003 0.0000 0.0003 -0.04 0.9678
FX Rate 0.0471 0.0436 1.08 0.2805
Consumer Staples 0.0045 0.0001 0.0002 0.59 0.5554
FX Rate 0.1027 0.0248 4.14 <.0001***
Utilities 0.0124 0.0000 0.0002 0.24 0.8073
FX Rate 0.2130 0.0307 6.94 <.0001***
Health Care 0.0039 0.0002 0.0002 0.86 0.3877
FX Rate 0.1134 0.0292 3.89 0.0001***
Consumer Discretionary 0.0058 0.0002 0.0002 0.83 0.4049
FX Rate 0.1760 0.0373 4.71 <.0001***
***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
All nine sectors have positive coefficients on the FX rate which implies that when the
dollar weakens, industry returns also decrease. The materials and energy sectors have the largest
t-values suggesting that exchange rates have the most significant effect in these sectors. Eight of
the nine industries show foreign exchange (FX) to be statistically significant at the 1% level. It
is possible that the dot-com bubble influenced the returns of the technology sector resulting in no
statistical significance.
11
Simple Regression – U.S. Dollar / Chinese Renminbi
The following table shows the Dollar / Renminbi exchange rate results for the nine
industries in the S&P500.
Table 6 1999-2013
R2 Coefficient Standard Error t-value p-value
Materials 0.0008 0.0001 0.0003 0.42 0.6722
FX Rate -0.6041 0.3363 -1.8 0.0725*
Energy 0.0016 0.0003 0.0003 0.87 0.3856
FX Rate -0.9278 0.3706 -2.5 0.0123**
Financials 0.0001 -0.0001 0.0003 -0.21 0.8335
FX Rate -0.2741 0.4251 -0.64 0.5191
Industrials 0.0007 0.0001 0.0002 0.50 0.6173
FX Rate -0.4823 0.2905 -1.66 0.0969*
Technology 0.0002 0.0000 0.0003 -0.11 0.9098
FX Rate -0.2811 0.3603 -0.78 0.4353
Consumer Staples 0.0005 0.0001 0.0002 0.47 0.6368
FX Rate -0.2716 0.2055 -1.32 0.1863
Utilities 0.0005 0.0000 0.0002 0.14 0.8912
FX Rate -0.3465 0.2551 -1.36 0.1744
Health Care 0.0004 0.0001 0.0002 0.75 0.4511
FX Rate -0.2944 0.2414 -1.22 0.2229
Consumer Discretionary 0.0001 0.0002 0.0002 0.79 0.4311
FX Rate -0.1870 0.3094 -0.60 0.5456
***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
None of the nine industries exhibited statistical significance at the 1% level. Exchange
rates are a significant determinant of returns at the 5% level in the energy sector and are
significant at the 10% level in the material and industrial sectors. All FX rate coefficients are
negative. This implies that when the dollar strengthens, industry returns increase.
12
Simple Regression – U.S. Dollar / Japanese Yen
The following table shows the Dollar / Yen exchange rate results for the nine industries
in the S&P500.
Table 7 1999-2013
R2 Coefficient Standard Error t-value p-value
Materials 0.03 0.0002 0.0003 0.68 0.4982
FX Rate 0.4248 0.0391 10.87 <.0001***
Energy 0.0366 0.0003 0.0003 1.21 0.2251
FX Rate 0.5178 0.0430 12.06 <.0001***
Financials 0.0674 0.0000 0.0003 -0.06 0.955
FX Rate 0.8047 0.0484 16.62 <.0001***
Industrials 0.0574 0.0002 0.0002 0.78 0.4374
FX Rate 0.5078 0.0333 15.26 <.0001***
Technology 0.036 0.0000 0.0003 0.03 0.9741
FX Rate 0.4987 0.0417 11.95 <.0001***
Consumer Staples 0.0528 0.0001 0.0002 0.71 0.4791
FX Rate 0.3445 0.0236 14.6 <.0001***
Utilities 0.0313 0.0001 0.0002 0.34 0.7322
FX Rate 0.3291 0.0296 11.11 <.0001***
Health Care 0.0385 0.0002 0.0002 0.97 0.3329
FX Rate 0.3457 0.0279 12.38 <.0001***
Consumer Discretionary 0.0608 0.0002 0.0002 0.97 0.3323
FX Rate 0.5567 0.0354 15.74 <.0001***
***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
In all nine industries the FX rate was statistically significant at the 1% level. Financials,
consumer discretionary, and industrials had the three largest t-values suggesting that exchange
rates have the most significant effect in these sectors. All nine sectors have positive coefficients
on the FX rate which implies that when the dollar weakens, industry returns increase. In such a
scenario, the weaker dollar has helped exports by making products cheaper for the rest of the
world.
13
Multiple Regression – Euro / U.S. Dollar and S&P500
The following table shows a multiple regression model comparing the Euro / Dollar
exchange rate to nine different industries while using the S&P500 Index as a control variable.
Table 8 1999-2013
R2 Coefficient Standard Error t-value p-value
Materials 0.6030 0.0001 0.0002 0.48 0.6329
FX Rate
0.3359 0.0258 13.01 <.0001***
S&P500 0.9495 0.0130 73.22 <.0001***
Energy 0.5033 0.0002 0.0002 1.19 0.2334
FX Rate
0.4094 0.0318 12.86 <.0001***
S&P500 0.9457 0.0160 59.14 <.0001***
Financials 0.7226 -0.0002 0.0002 -0.85 0.3926
FX Rate
0.0215 0.0273 0.79 0.4306
S&P500 1.3574 0.0137 99.11 <.0001***
Industrials 0.7918 0.0001 0.0001 0.76 0.4467
FX Rate
0.0492 0.0161 3.05 0.0023***
S&P500 0.9691 0.0081 119.50 <.0001***
Technology 0.7257 -0.0001 0.0001 -0.61 0.5432
FX Rate
-0.2038 0.0230 -8.87 <.0001***
S&P500 1.1607 0.0116 100.53 <.0001***
Consumer Staples 0.4551 0.0001 0.0001 0.50 0.6171
FX Rate
-0.0101 0.0185 -0.55 0.5832
S&P500 0.5219 0.0093 56.23 <.0001***
Utilities 0.4225 0.0000 0.0002 0.04 0.9651
FX Rate
0.0794 0.0236 3.36 0.0008***
S&P500 0.6179 0.0119 52.09 <.0001***
Health Care 0.6119 0.0001 0.0001 0.97 0.3296
FX Rate
-0.0405 0.0183 -2.21 0.027**
S&P500 0.7122 0.0092 77.38 <.0001***
Consumer Discretionary 0.729 0.0001 0.0001 1.06 0.2885
FX Rate
-0.0391 0.0196 -1.99 0.0464**
S&P500 0.9953 0.0099 100.98 <.0001***
***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Negative coefficients for the FX rate imply that when the dollar weakens, industry returns
increase. Positive coefficients for the FX rate imply that when the dollar weakens, industry
returns decrease. When controlling for the S&P500 market index, statistical significance at the
14
1% level for the FX rate is reduced from nine industries to five. The FX rate is significant at the
5% level for two industries. The three sectors showing the strongest FX rate coefficient, in order,
are materials, energy and technology.
The technology industry displays the highest r-squared with 72.57% of variation being
explained by the Euro / Dollar rate and the S&P500 market index. However, it’s important to
note, the technology sector represents the largest portion of the S&P500 market index at 20.27%.
Materials and energy actually display the most significant coefficients for the FX rate. The
S&P500 Index is the strongest control variable for the industrials sector with a t-value of 119.50.
The S&P500 Index is significant at the 1% level for all nine industries.
Multiple Regression – Euro / U.S. Dollar and MSCI World Index
The following table is a multiple regression model comparing the Euro / Dollar exchange
rate to nine different industries while using the MSCI World Index as a control variable.
Table 9 1999-2013
R2 Coefficient Standard Error t-value p-value
Materials 0.5539 0.0001 0.0002 0.48 0.629
FX Rate
0.0684 0.0281 2.43 0.0151**
MXWO 1.1245 0.0171 65.95 <.0001***
Energy 0.4511 0.0003 0.0002 1.16 0.2444
FX Rate
0.1498 0.0344 4.36 <.0001***
MXWO 1.1035 0.0209 52.92 <.0001***
Financials 0.5476 -0.0001 0.0002 -0.6 0.5491
FX Rate
-0.2999 0.0358 -8.38 <.0001***
MXWO 1.4622 0.0217 67.4 <.0001***
Industrials 0.6854 0.0001 0.0001 0.68 0.4983
FX Rate
-0.2108 0.0204 -10.33 <.0001***
MXWO 1.1166 0.0124 90.30 <.0001***
Technology 0.5882 -0.0001 0.0002 -0.44 0.6635
FX Rate
-0.4978 0.0289 -17.2 <.0001***
MXWO 1.2959 0.0175 73.87 <.0001***
15
Table 9 cont’d
Consumer Staples 0.3383 0.0001 0.0001 0.50 0.6158
FX Rate
-0.1315 0.0209 -6.28 <.0001***
MXWO 0.5571 0.0127 43.91 <.0001***
Utilities 0.3426 0.0000 0.0002 0.08 0.9377
FX Rate
-0.0761 0.0259 -2.94 0.0033***
MXWO 0.6876 0.0157 43.81 <.0001***
Health Care 0.5081 0.0001 0.0001 0.91 0.361
FX Rate
-0.2247 0.0212 -10.6 <.0001***
MXWO 0.8043 0.0129 62.59 <.0001***
Consumer Discretionary 0.5902 0.0001 0.0002 0.93 0.3549
FX Rate
-0.2905 0.0248 -11.72 <.0001***
MXWO 1.1097 0.0150 73.84 <.0001***
***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Negative coefficients for the FX rate imply that when the dollar weakens, industry returns
increase. Positive coefficients for the FX rate imply that when the dollar weakens, industry
returns decrease. The three sectors showing the most significant FX rate coefficients, in order,
are technology, consumer discretionary, and health care.
When controlling for the MSCI World Index instead of the S&P500, eight of nine
industries display statistical significance at the 1% level for the FX rate. The FX rate is
significant at the 5% level for the materials industry. The MSCI World Index is significant at the
1% level for all nine industries. The MSCI World Index is most powerful in explaining industrial
returns with a t-value of 90.30. Industrials also display the highest r-squared with 68.54% of
variation being explained by the Euro / Dollar exchange rate and the MSCI World Index.
In Figure 1, the technology FX rate coefficient is negative from 1999 until 2013.
However, the coefficient drops from a large negative value to a small negative value beginning
in 2008 with the financial crisis. The relationship has lost strength and may no longer be
significant or may be in the process of completely changing directions. In Figure 2, the health
care FX rate coefficient displays statistical significance from 1998 until 2010. Similar to the
16
technology sector FX rate coefficient, the financial crisis in 2008 caused the negative coefficient
of the health care FX rate to be reduced even more dramatically. Figure 3 is included to show
how dramatic the financial crisis caused the industrial FX rate coefficient to break its trend.
Multiple Regression – U.S. Dollar / Chinese Renminbi and S&P500
The following table shows a multiple regression model comparing the Dollar / Renminbi
exchange rate to nine different industries while using the S&P500 Index as a control variable.
Table 10 1999-2013
R2 Coefficient Standard Error t-value p-value
Materials 0.5856 0.0001 0.0002 0.39 0.6929
FX Rate
-0.2617 0.2167 -1.21 0.2272
S&P500 0.9675 0.0132 73.44 <.0001***
Energy 0.4825 0.0002 0.0002 0.99 0.321
FX Rate
-0.5854 0.2669 -2.19 0.0284**
S&P500 0.9673 0.0162 59.59 <.0001***
Financials 0.7226 -0.0001 0.0002 -0.76 0.4502
FX Rate
0.2069 0.2240 0.92 0.3556
S&P500 1.3588 0.0136 99.78 <.0001***
Industrials 0.7914 0.0001 0.0001 0.66 0.5066
FX Rate
-0.1384 0.1328 -1.04 0.2972
S&P500 0.9716 0.0081 120.36 <.0001***
Technology 0.7201 -0.0001 0.0002 -0.57 0.5702
FX Rate
0.1258 0.1907 0.66 0.5094
S&P500 1.1497 0.0116 99.14 <.0001***
Consumer Staples 0.4552 0.0001 0.0001 0.44 0.6615
FX Rate
-0.0871 0.1518 -0.57 0.566
S&P500 0.5212 0.0092 56.48 <.0001***
Utilities 0.4209 0.0000 0.0002 -0.01 0.9935
FX Rate
-0.1263 0.1942 -0.65 0.5156
S&P500 0.6221 0.0118 52.67 <.0001***
Health Care 0.6115 0.0001 0.0001 0.93 0.3513
FX Rate
-0.0431 0.1506 -0.29 0.7748
S&P500 0.7099 0.0092 77.53 <.0001***
Consumer Discretionary 0.7287 0.0001 0.0001 1.15 0.2502
FX Rate
0.1646 0.1612 1.02 0.3075
S&P500 0.9934 0.0098 101.32 <.0001***
***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
17
When controlling for the S&P500 index, none of the industry FX rate coefficients exhibit
statistical significance at the 1% level. The FX rate is significant at the 5% level for the energy
sector. The S&P500 Index is most powerful in explaining industrial returns with a t-value of
120.36. This is the largest t-value of all the regressions in this study. The S&P500 Index is
significant at the 1% level for all nine industries.
The energy sector has a negative coefficient which implies that when the dollar weakens,
industry returns decrease. However, when monitoring how the FX coefficient changed over
time, the result shows the FX rate displayed statistical significance for only about two of the
entire 15 years.
Multiple Regression – U.S. Dollar / Chinese Renminbi and MSCI World Index
The following table shows a multiple regression model comparing the Dollar / Renminbi
exchange rate to nine different industries while using the MSCI World Index as a control
variable.
Table 11 1999-2013
R2 Coefficient Standard Error t-value p-value
Materials 0.5537 0.0001 0.0002 0.69 0.4928
FX Rate
0.4461 0.2253 1.98 0.0478**
MXWO 1.1372 0.0165 68.8 <.0001***
Energy 0.4484 0.0003 0.0002 1.21 0.2263
FX Rate
0.1132 0.2762 0.41 0.682
MXWO 1.1272 0.0203 55.64 <.0001***
Financials 0.5408 -0.0001 0.0002 -0.26 0.7959
FX Rate
1.0383 0.2888 3.6 0.0003***
MXWO 1.4210 0.0212 67.09 <.0001***
Industrials 0.6774 0.0001 0.0001 0.95 0.343
FX Rate
0.5212 0.1654 3.15 0.0016***
MXWO 1.0867 0.0121 89.54 <.0001***
Technology 0.5578 0.0000 0.0002 -0.12 0.9069
FX Rate
0.8486 0.2402 3.53 0.0004***
MXWO 1.2233 0.0176 69.43 <.0001***
18
Table 11 cont’d
***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
All sectors have positive coefficients on the FX rate which implies that when the dollar
weakens, industry returns increase. When controlling for the MSCI World Index instead of the
S&P500, four of nine industries display statistical significance at the 1% level for the FX rate.
The four industries showing the most significant FX coefficients, in order, are consumer
discretionary, financials, technology and industrials. The FX rate is significant at the 5% level
for the health care and materials sector. The MSCI World Index is most powerful in explaining
industrial returns with a t-value 89.54. The MSCI World Index is significant at the 1% level for
all nine industries.
It is interesting to note, that when the MSCI World Index replaces the S&P500 as the
control variable, the FX rate coefficient flips from negative to positive. The MSCI World Index
causes the energy sector FX coefficient to no longer display statistical significance. The
consumer discretionary FX coefficient displays statistical significance during the entire 14 years
and 8 months of this study. This result shows that the MSCI World Index caused the consumer
discretionary FX coefficient to display much more statistical significance than the S&P500.
Consumer Staples 0.3317 0.0001 0.0001 0.61 0.5413
FX Rate
0.2251 0.1684 1.34 0.1815
MXWO 0.5379 0.0124 43.53 <.0001***
Utilities 0.3414 0.0000 0.0002 0.2 0.8393
FX Rate
0.2789 0.2076 1.34 0.1791
MXWO 0.6772 0.0152 44.48 <.0001***
Health Care 0.4944 0.0001 0.0001 1.11 0.2685
FX Rate
0.4183 0.1721 2.43 0.0151**
MXWO 0.7717 0.0126 61.12 <.0001***
Consumer Discretionary 0.5773 0.0002 0.0002 1.27 0.2054
FX Rate
0.8000 0.2017 3.97 <.0001***
MXWO 1.0688 0.0148 72.24 <.0001***
19
Multiple Regression – U.S. Dollar / Japanese Yen and S&P500
The following table shows a multiple regression model comparing the Dollar / Yen
exchange rate to nine different industries while using the S&P500 Index as a control variable.
Table 12 1999-2013
R2 Coefficient Standard Error t-value p-value
Materials 0.5866 0.0001 0.0002 0.49 0.621
FX Rate
-0.0857 0.0265 -3.23 0.0012***
S&P500 0.9797 0.0137 71.73 <.0001***
Energy 0.4819 0.0003 0.0002 1.22 0.2224
FX Rate
0.0145 0.0327 0.44 0.6576
S&P500 0.9661 0.0169 57.31 <.0001***
Financials 0.7236 -0.0001 0.0002 -0.82 0.4097
FX Rate
0.1044 0.0274 3.81 0.0001***
S&P500 1.3441 0.0141 95.27 <.0001***
Industrials 0.7913 0.0001 0.0001 0.77 0.4396
FX Rate
0.0016 0.0163 0.10 0.9226
S&P500 0.9716 0.0084 115.94 <.0001***
Technology 0.7216 -0.0001 0.0001 -0.67 0.5005
FX Rate
-0.1081 0.0233 -4.64 <.0001***
S&P500 1.1645 0.0120 97.02 <.0001***
Consumer Staples 0.4577 0.0001 0.0001 0.53 0.5955
FX Rate
0.0786 0.0185 4.24 <.0001***
S&P500 0.5105 0.0096 53.41 <.0001***
Utilities 0.4208 0.0000 0.0002 0.06 0.9532
FX Rate
0.0053 0.0238 0.22 0.8251
S&P500 0.6215 0.0123 50.7 <.0001***
Health Care 0.6117 0.0001 0.0001 0.96 0.3396
FX Rate
-0.0261 0.0184 -1.42 0.1571
S&P500 0.7136 0.0095 75.1 <.0001***
Consumer Discretionary 0.729 0.0001 0.0001 1.07 0.2847
FX Rate
0.0423 0.0197 2.14 0.032**
S&P500 0.9873 0.0102 97.07 <.0001***
***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
When controlling for the S&P500 index, four of the industry FX rate coefficients exhibit
statistical significance at the 1% level. The FX rate is significant at the 5% level for the
consumer discretionary industry. The four sectors showing the most significant FX coefficients,
20
in order, are technology, consumer staples, financials, and materials. Materials and technology
display a negative coefficient for the FX rate implying that when the dollar weakens, industry
returns decrease. Financials, consumer staples, and consumer discretionary display a positive
coefficient for the FX rate implying that when the dollar weakens, industry returns increase. The
S&P500 Index is significant at the 1% level for all nine industries.
Multiple Regression – U.S. Dollar / Japanese Yen and MSCI World Index
The following table shows a multiple regression model comparing the Dollar / Yen
exchange rate to nine different industries while using the MSCI World Index as a control
variable.
Table 13 1999-2013
R2 Coefficient Standard Error t-value p-value
Materials 0.5548 0.0001 0.0002 0.52 0.6052
FX Rate
0.1000 0.0269 3.72 0.0002***
MXWO 1.1238 0.0167 67.13 <.0001***
Energy 0.4536 0.0003 0.0002 1.22 0.2216
FX Rate
0.1987 0.0329 6.04 <.0001***
MXWO 1.1044 0.0205 54.01 <.0001***
Financials 0.5561 -0.0001 0.0002 -0.55 0.5836
FX Rate
0.4088 0.0340 12.04 <.0001***
MXWO 1.3702 0.0211 64.87 <.0001***
Industrials 0.6853 0.0001 0.0001 0.72 0.4741
FX Rate
0.2011 0.0196 10.29 <.0001***
MXWO 1.0616 0.0122 87.32 <.0001***
Technology 0.5596 -0.0001 0.0002 -0.44 0.6624
FX Rate
0.1513 0.0287 5.28 <.0001***
MXWO 1.2022 0.0178 67.41 <.0001***
Consumer Staples 0.3479 0.0001 0.0001 0.55 0.5797
FX Rate
0.1958 0.0199 9.84 <.0001***
MXWO 0.5149 0.0124 41.59 <.0001***
Utilities 0.3464 0.0000 0.0002 0.11 0.9141
FX Rate
0.1383 0.0247 5.59 <.0001***
MXWO 0.6604 0.0154 42.93 <.0001***
21
Table 13 cont’d
Health Care 0.4987 0.0001 0.0001 0.92 0.3602
FX Rate
0.1275 0.0205 6.22 <.0001***
MXWO 0.7553 0.0128 59.24 <.0001***
Consumer Discretionary 0.5881 0.0001 0.0002 0.96 0.3367
FX Rate
0.2574 0.0238 10.81 <.0001***
MXWO 1.0361 0.0148 69.95 <.0001***
***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
When controlling for the MSCI World Index instead of the S&P500 index, all nine of the
industry FX rate coefficients exhibited statistical significance at the 1% level. The three sectors
showing the most significant FX coefficients, in order, are financials, consumer discretionary,
and industrials. The industrials sector displays the highest r-squared with 68.53% of variation
being explained by the Dollar / Yen exchange rate and the MSCI World Index. All nine
industries have a positive coefficient for the FX rate implying that when the dollar decreases,
industry returns increase. The MSCI World Index is significant at the 1% level for all nine
industries.
Figure 4 shows the industrial FX rate coefficient is statistically significant from 1999-
2001 and again from 2002 until 2011. Figure 5 shows the consumer staples FX rate coefficient
is significant from 1999 until 2011. The financial crisis of 2009 caused the consumer staples FX
rate coefficient to lose statistical significance. Figure 6 shows the consumer discretionary FX rate
coefficient and how robust the statistical significance is. The statistical significance existed
during the entire time period beginning in 1999 and lasting until the study was completed in
August of 2013.
Simple Regression Summary
The simple regression results show that the nine American industries representing the
S&P500 display more FX exposure with the Euro / Dollar and the Dollar / Yen trade than with
22
the Dollar / Renminbi. The results suggest U.S. industries have more FX rate exposure with
Europe and Japan than with China. However, China's currency was fixed until 2005 when it
began free floating. From 1999 until 2005 exchange rate volatility in the Dollar / Yuan pair was
low. The energy, materials, and industrials sectors were the three industries to display statistical
significance across all three currency pairs. These three industries are expected to be
significantly affected by commodity prices.
Multiple Regression Summary
Financials, industrials, materials, technology, health care and consumer discretionary
display FX exposure with all three currency pairs using the MSCI World Index as a control
variable. The consumer discretionary sector has the highest FX rate exposure for China and
second highest for Europe and Japan. Industrials FX rate exposure is third highest for Japan and
fourth highest for Europe and China. The largest exposure of any industry and currency
combination is the technology sector and the Euro. It is interesting to note that the technology
FX rate displayed such high t-values for the MSCI World Index and Yen combination, but not
for the S&P500 and Yen combination.
23
Figure 1 - Euro / U.S. Dollar and MSCI World Index - Technology
Figure 2 - Euro / U.S. Dollar and MSCI World Index – Health care
24
Figure 3 - Euro / U.S. Dollar and MSCI World Index – Industrials
Figure 4 - U.S. Dollar / Yen & MSCI World Index - Industrials
25
Figure 5 - U.S. Dollar / Yen & MSCI World Index – Consumer staples
Figure 6 - U.S. Dollar / Yen & MSCI World Index – Consumer discretionary
CONCLUSION
In this paper, industry valuation and exchange rate sensitivity are evaluated. An asset’s
exposure to exchange rate fluctuations is measured as the correlation between the value of the
asset and the exchange rate. The research examines the volatility of industry returns and
movements of the Euro / Dollar, Dollar / Yen, and Dollar / Yuan exchange rates. The motivation
for investigating foreign exchange rates is to better understand its impact on the profitability of
different industries. Industries may be influenced differently.
Smoothed estimates of the time-varying regression coefficients allow for the visual
documentation of individual changing patterns and major events (e.g. recessions, terrorist
attacks, intro of the Euro, free float of Yuan, etc.) The two recessions during this sample period
had a significant effect on the FX rate coefficient and especially for the energy sector. The price
of oil went from a high of $145 in July 2008 to less than $40 by December 2008. The volatility
of energy prices during the recession influenced the FX coefficient significantly.
The correlation between the S&P500 and MSCI World Index is 92.86% and significant at
the 1% level. However, there is a significant change in the results when the MSCI World Index
replaces the S&P500 as a control variable. When the global index replaces the American index,
more industries show FX rate exposure. For Europe the number of industries showing FX rate
exposure jumps from five to eight, China FX rate exposure increases from zero to four, and
lastly, Japan increases from four to nine. The S&P500 findings support Bodnar and Gentry’s
(1993) conclusion that 20 and 35 percent of industries tested have statistically significant
exchange rate exposure. The MSCI World Index draws a different conclusion with higher levels
of FX rate exposure.
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