Annals of the University of North Carolina Wilmington … · 2020. 4. 23. · EXCHANGE RATE...

Annals of the

University of North Carolina Wilmington

International Masters of Business Administration

http://csb.uncw.edu/imba/

http://www.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


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


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


FX Rate -0.1870 0.3094 -0.60 0.5456


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


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***


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***


FX Rate 0.5567 0.0354 15.74 <.0001***


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


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***


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***


FX Rate

-0.0391 0.0196 -1.99 0.0464**

S&P500 0.9953 0.0099 100.98 <.0001***


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


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


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***


FX Rate

-0.2905 0.0248 -11.72 <.0001***

MXWO 1.1097 0.0150 73.84 <.0001***


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


Table 10 1999-2013


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***


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***


FX Rate

0.1646 0.1612 1.02 0.3075

S&P500 0.9934 0.0098 101.32 <.0001***


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


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


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.


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***


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


Table 12 1999-2013


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***


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***


FX Rate

0.0423 0.0197 2.14 0.032**

S&P500 0.9873 0.0102 97.07 <.0001***


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


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***


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***


FX Rate

0.2574 0.0238 10.81 <.0001***

MXWO 1.0361 0.0148 69.95 <.0001***


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.

REFERENCES

Adler, Michael, and Bernard Dumas. "Exposure to Currency Risk: Definition and

Measurement." Financial management (1984): 41-50.

Agrawal, Gaurav, Aniruddh Kumar Srivastav, and Ankita Srivastava. "A study of exchange rates

movement and stock market volatility." International Journal of Business and Management 5.12

(2010): p62.

Badhani, K. N., Rajani Chhimwal, and Janki Suyal. "Exchange Rate Volatility: Impact on

Industry Portfolios in Indian Stock Market." ICFAI Journal of Applied Finance 15.6 (2009): 33-

48.

Bodnar, Gordon M., and William M. Gentry. "Exchange rate exposure and industry

characteristics: evidence from Canada, Japan, and the USA." Journal of international Money and

Finance 12.1 (1993): 29-45.

Choi, Jongmoo Jay, and Anita Mehra Prasad. "Exchange risk sensitivity and its determinants: A

firm and industry analysis of US multinationals." Financial Management (1995): 77-88.

Choi, Jongmoo Jay, and Yong-Cheol Kim. "The Asian exposure of US firms: Operational and

risk management strategies." Pacific-Basin Finance Journal 11.2 (2003): 121-138.

Chow, Edward H., Wayne Y. Lee, and Michael E. Solt. "The exchange-rate risk exposure of

asset returns." Journal of Business (1997): 105-123.

Jorion, Philippe. "The exchange-rate exposure of US multinationals." Journal of Business

(1990): 331-345.

Joseph, N. (2002). Modeling the impacts of interest rate and exchange rate changes on UK Stock

Returns. Derivatives Use, Trading & Regulation, 7(4), 306-323

28

Marston, Richard C. "The effects of industry structure on economic exposure." Journal of

International Money and Finance 20.2 (2001): 149-164.

Muller, Aline, and Willem Verschoor. "The Impact of Corporate Derivative Usage on Foreign

Exchange Risk Exposure." Available at SSRN 676012 (2005).

Nguyen, Hoa, Robert Faff, and Andrew Marshall. "Exchange rate exposure, foreign currency

derivatives and the introduction of the euro: French evidence." International review of economics

& finance 16.4 (2007): 563-577.

Shin, Hyun-Han, and Luc Soenen. "Exposure to currency risk by US multinational corporations."

Journal of Multinational Financial Management 9.2 (1999): 195-207.

Annals of the University of North Carolina Wilmington … · 2020. 4. 23. · EXCHANGE RATE...

Documents

Transcript of Annals of the University of North Carolina Wilmington … · 2020. 4. 23. · EXCHANGE RATE...