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The impact of industrial structure and the exchange-rate exposure of industry portfolio returns
Global Finance Journal 12 (2001) 285 – 297
Industrial structure and the exchange-rate exposure of industry portfolio returns Anand Krishnamoorthy* Troy State University – Atlantic, 2300 S. 24th Road #710, Arlington, VA 22206, USA Received 23 January 1998; received in revised form 19 January 1999; accepted 27 September 2000
Abstract The purpose of this paper is to demonstrate that industrial structure is an important determinant of the exchange-rate exposure of industry portfolio returns. A time series regression is conducted on the sample of industries by regressing the rate of change of a trade-weighted US dollar index on the industry portfolio return while controlling for the US market. The regression was conducted using monthly data over a 3-year period (1995 – 1997). The results indicate that industries that are classified as being globally competitive and those that primarily serve the consumer sector of the economy have significant levels of exposure. The paper also provides some evidence on market efficiency as it pertains to changes in the value of the dollar. D 2001 Elsevier Science Inc. All rights reserved.
1. Introduction Exchange rates are a major source of uncertainty for multinationals and to some degree also to companies that do not do business outside the United States. Since an appreciation in the US dollar makes foreign imports relatively inexpensive compared to US goods, fluctuations in the value of the dollar can impact a company even if it does not do business outside the home country. Exchange rates are typically four times as volatile as interest rates and ten times as volatile as inflation (Jorion, 1990). However, while the relationship between inflation rates or interest
* Tel.: +1-703-769-7810; fax: +1-703-525-5844. E-mail address: [email protected] (A. Krishnamoorthy). 1044-0283/01/$ – see front matter D 2001 Elsevier Science Inc. All rights reserved. PII: S 1 0 4 4 - 0 2 8 3 ( 0 1 ) 0 0 0 3 3 - 3
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rates and the value of the firm has been extensively analyzed, the relationship between exchange rates and the value of the firm has not been subject to much empirical research. This is one of the first studies to consider exchange rate exposure at the industry level. This study considers the fact that different industries may be differently exposed to exchange rate fluctuations. As a result, when a researcher attempts to measure exchange rate exposure for a broad cross-section of firms from various industries, canceling effects may obscure a significant relationship. This could be a potential explanation for the limited success of prior studies in documenting a causal link between stock returns and changes in the value of the dollar. This paper extends previous research on exchange rate exposure at the industry level by segregating a sample of industries based on industrial structure. Specifically, this paper will be looking at two forms of industrial structure: competitive vs. oligopolistic industries and consumer-oriented vs. institutionally oriented industries. One of the goals of this paper is to ascertain whether competitive and oligopolistic firms are affected in the same way by exposure to exchange rate fluctuations. The second goal is to ascertain whether firms whose customers are primarily individual consumers are affected in the same way by exposure to exchange rate fluctuations as firms whose customers are primarily institutional buyers. These goals will be achieved by forming industry portfolios based on these two classifications. Without presuming any causal link, exchange rate exposure represents the sensitivity of the value of the firm to exchange rate randomness and can be measured by the regression coefficient of the change in the value of the firm on the change in the exchange rate (Jorion, 1990). In industries that are highly competitive, changes in the value of the dollar can affect firms within the industry differently based on location of operations, operating structure (such as currency imbalances), hedging practices, etc. Therefore, fluctuations in the value of the dollar can affect firms differently or to different degrees. Furthermore, the change in the exchange rate will not affect US firms and foreign firms within that industry in the same manner. These differences may be reflected in the prices consumers pay for the company’s products. Therefore, changes in the value of the dollar may cause shifts in the market share among firms in that industry thereby making them more sensitive to exchange rate exposure. In oligopolistic industries, there is less competition and often times there is more cooperation among the firms in the industry. There could even be price adjustments by the firms to reflect changes in exchange rates. Consequently, from the consumer’s point of view, there may not be any benefits in changing from one company to another even if the companies themselves are differently exposed. Therefore, firms that operate in a oligopolistic environment should be less sensitive to exchange rate exposure. Individual consumers tend to price products more frequently than institutional buyers. Institutional consumers often times have long-term contracts to buy from certain suppliers. Furthermore, institutional consumers may not have the luxury of not buying a certain product if they wish to continue operations. Individual consumers, on the other hand, are not bound to consume a product unless it is an ‘‘absolute necessity.’’ If there are significant changes in prices due to exchange-rate changes, firms who rely more heavily on consumers for their revenues are more likely to be affected by such changes. Therefore, firms that are more oriented towards individual consumers should be more sensitive to exchange rate exposure than firms that are more oriented towards institutional consumers.
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The results of the study are consistent with the above predictions. They indicate that industries that are classified as being globally competitive are indeed more sensitive to exchange rate exposure than their oligopolistic counterparts. Likewise, industries that are more dependent on individual consumers for their revenues had significant exposure coefficients while those that rely more heavily on institutional consumers did not. These results have implications for financial managers of major US firms. Whether or not to hedge exchange rate risk is an ongoing debate in the business world. If certain firms are more exposed than others, the hedging decision for those firms is more critical. In addition to providing evidence on the impact of industrial structure on exchange rate exposure, a retest of the mispricing hypothesis was conducted by including a lagged dollar term in all regressions. The results of this paper indicate that prior dollar changes do not significantly explain stocks returns at the industry level. This study indicates that all the implications of foreign currency movements are impounded into the contemporaneous stock price. The remainder of this paper is organized as follows: Section 2 will provide a brief overview of the existing literature in the field. This section will also explain how this paper fills a gap in the existing knowledge. Section 3 will discuss the key hypotheses. This will be followed by a description of the sample selection process. Section 5 describes the methodology that is employed in the study. Section 6 presents the empirical results and Section 7 concludes.
2. Review of related literature Levy (1987) feels that expanding international trade has raised the exposure of US businesses to exchange rate fluctuations. He developed a model that estimates the impact of changes in the US dollar on real before-tax corporate profits. He indicates that real profits are negatively and significantly related to the US dollar but the impact varies substantially by industrial sector. He found that US dollar changes have a large impact on profits of durable goods manufacturing industries but have a small or no effect on profits in certain serviceproducing industries. However, a recently emerging trend is the rising US dollar sensitivity of profits in several service sectors, reflecting the broadening of international competition. Luehrman (1990) examined the exposure of operating cash flows to exogenous changes in the exchange rate. He found exposure to be a function of two components whose magnitudes are significant particularly in light of asymmetries across markets and firms. Those two components are exchange rate induced demand shift and competitor’s reoptimization following an exchange rate shock. Jorion (1990) examined the exposure of US multinationals to foreign currency risk. He presented evidence demonstrating that the relationship between stock returns and exchange rates differs systematically across multinationals. He also found that the comovements between stock returns and the value of the dollar is positively related to the percentage of foreign operations of US multinational firms. In a subsequent paper, Jorion (1991) examined the pricing of exchange rate risk in the stock market using two-factor and multifactor arbitrage pricing models. Evidence is presented
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that the relation between stock returns and the value of the dollar differs systematically across industries. The empirical results did not suggest that exchange risk is priced in the stock market. The unconditional risk premium attached to foreign currency exposure was small and insignificant. He concluded that active hedging policies by financial managers cannot affect the cost of capital, and other reasons must explain why firms decide to hedge. Luehrman (1991) investigated the widely held hypothesis that an exogenous real home currency depreciation enhances the competitiveness of home country manufacturers vis a vis foreign rivals. In doing so, he considered the automobile and the steel industries during the period 1978–1987. He found that contrary to popular belief, for large fractions of both industries, a depreciation of the home currency is associated with a significant decline in their share of industry value. Bodnar and Gentry (1993) examined the industry level exposure to exchange rates for three countries: the United States, Canada, and Japan. They measured exposure by adding the change in the exchange rate to the domestic market model of industry portfolio returns. They found that some industries in all three countries display significant exposures. They also found that for each country, the exchange rate is important for explaining industry returns at the economy-wide level. In order to explore whether exchange rate exposures are systematically linked to the activities of the industries, they modeled exposure as a function of industry characteristics. They found that for all three countries, the relation between exposure and industry characteristics is consistent with economic theory which implies that the effect of exchange rate fluctuations on an industry depends critically on the industry’s relation with the world economy. Bartov and Bodnar (1994) failed to find a significant correlation between the abnormal returns of their sample firms that engage in international activities and changes in the dollar. They then investigated the possibility that this failure might be due to mispricing. They found lagged changes in the dollar to be a significant variable in explaining current abnormal returns of the sample firms, thereby suggesting that mispricing does indeed occur. They found that a trading strategy based upon these results generates significant abnormal returns. Choi and Prasad (1995) developed a model of firm valuation to examine the exchange risk sensitivity of 409 multinational firms during the period 1978–1989. They find that exchange rate fluctuations do affect firm value. Sixty percent of their firms with significant exposure gain from a depreciation of the dollar. They find that cross-sectional differences in exchange risk sensitivity are linked to key firm-specific operational variables. Donnelly and Sheehy (1996) found a contemporaneous relation between the foreign exchange rate and the market value of large exporters. They find a weak lagged relationship, which suggests that the stock market takes time to incorporate all of the implications of foreign currency movements into share prices. Fang and Loo (1996) empirically test the effect of foreign exchange risk on common stock returns using a three-factor arbitrage pricing theory (APT) model. The factors are world market return, national market return and foreign exchange rate movements. A Guass– Newton procedure is used to estimate the nonlinear seemingly unrelated regression equations of common stock returns and foreign exchange risk for 20 portfolios of the US, Canada, the
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UK, and Japan. It is found that international asset returns are significantly affected by foreign exchange risk cross sectionally. Chamberlain, Howe, and Popper (1997) examine the exchange rate sensitivities of US and Japanese banks using both daily and monthly data. Using daily data, they find the stock returns of approximately one-third of thirty large US banks appear to be sensitive to exchange rate changes. They attribute the relative strength of their results to the use of daily data. They find that relatively few Japanese banks appear to be sensitive to exchange rate changes. They claim that the difference may arise from a variety of differences in the operation and regulatory conditions of the firms in the two countries. Chow, Lee, and Solt (1997) claim that the failure of previous studies in finding significant exposure coefficients is due in part to the time horizon of the study. If exchange rate changes contain information about future interest rate and cash flows over more than one period, then using short horizons may not fully capture exchange exposure. This study finds that the changes in real exchange rates are important in explaining the temporal variation in expected returns on bonds and stocks and that all assets are exposed to exchange rate risk. Exchange exposure for stocks reflects both interest rate and cash flow effects. The authors find that the effect of unanticipated changes in the real exchange rate on earnings is negative over short horizons but positive over long horizons. They find that interest rate and cash flow effects are offsetting over short horizons but complementary over long horizons. This paper examines the level of exposure of industry portfolio returns to exchange rate variability by considering two different types of industrial structure. The idea is to examine the causal link between exchange rates and returns for these two different classifications. Hence, this is the major departure of this paper from that of previous related research.
3. Hypothesis development Theory dictates that industrial structure is one of the determinants of exchange-rate exposure. As explained earlier, firms that are classified as being globally competitive should be more sensitive to exchange-rate exposure than firms that are classified as being global oligopolies. The reason for this is that firms that operate in a more competitive environment are more vulnerable to exchange-rate induced demand shifts by consumers than firms that operate in a more oligopolistic environment. Likewise, firms that rely more heavily on individual consumers for their revenues are more likely to be affected by fluctuations in the value of the dollar than those firms that rely more heavily on institutional customers. The reason being that individual consumers tend to price products more frequently than institutional consumers who work more with long-term contracts. As a result, firms that are more oriented towards individual consumers should be more sensitive to exchange-rate exposure than those that are more oriented towards institutional consumers. Bartov and Bodnar (1994) claimed that a possible explanation for the limited success of research in documenting a relation between changes in the dollar and stock returns is the existence of mispricing arising from systematic errors by investors in the estimation of the
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relation between fluctuations in the dollar and firm value. The mispricing hypothesis claims that stock price adjustments due to movements in the US dollar take time. This hypothesis has never been tested at the industry level. The results of this paper contradict the mispricing hypothesis, at least at the industry level. They indicate that all the implications of foreign currency movements are impounded in the contemporaneous industry portfolio return. The ideas expressed above lead to the development of the following three hypotheses. Hypothesis 1 describes the exchange-rate exposure of globally competitive industries vs. that of globally oligopolistic industries. Hypothesis 2 describes the exchange-rate exposure of industries that are more oriented towards individual customers vs. that of industries that are more oriented towards institutional customers. Hypothesis 3 describes the test of the mispricing hypothesis at the industry level. Hypothesis 1: The portfolio returns of industries that are classified as being competitive are more sensitive to exchange rate changes than those that are classified as being oligopolistic. Hypothesis 2: The portfolio returns of industries that rely more heavily on individual customers are more sensitive to exchange rate changes than those that rely more heavily on institutional customers. Hypothesis 3: All the implications of foreign currency movements are impounded in contemporaneous portfolio returns. Consequently, lagged dollar changes do not significantly explain industry portfolio returns. The analysis of the paper will begin by providing some descriptive statistics on the exposure coefficients of industries that are used for the purposes of this study. Those industries will then be classified into the respective categories as described by hypotheses one and two for the remainder of the analyses.
4. Sample selection The sample of industries used in the study was obtained from the Standard and Poor’s Industry survey. This survey lists various industries and major US and foreign firms within the industry. Additional firms within an industry group were obtained from the Companies International database by using the SIC code. The same data set was used for both industry classifications. Twenty industries were used for the purposes of the study. The industries were chosen in order to represent a large cross-section of different industries. In order to maximize the size of the list, two industries were not eliminated during the sample selection process simply because they were related to one another. An industry was classified as being globally oligopolistic if there were five or fewer major firms within that industry. Of the 20 industries, 11 were classified as being globally competitive and 9 were classified as being global oligopolies. The list of globally competitive and globally oligopolistic industries is given in Panel A of Table 1.
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Table 1 Panel A: The globally competitive and oligopolistic industries Competitive
Oligopolistic
Airline Automotive Banking Industrial chemicals Computer manufacturing Electronic components Household appliances Paper products Pharmaceuticals Retail stores Telecommunications equipment
Agricultural machinery Beverage Cement manufacturing Aerospace Construction manufacturing Shipbuilding Shoe producers Steel manufacturing Tobacco
Panel B: The consumer and institutional industries Consumer
Institutional
Airline Automotive Banking Beverage Computer manufacturing Electronic components Household appliances Paper products Pharmaceuticals Retail stores Shoe producers Tobacco
Aerospace Cement manufacturing Construction manufacturing Industrial chemicals Shipbuilding Steel manufacturing
The second classification was made based on personal knowledge since no official statistics are available on this type of classification. Not all 20 industries were used for the purposes of this classification since significant overlap exists in certain industries between the two types of consumers. Eighteen of the 20 industries for which data was collected were used in this classification scheme. Twelve of the 18 were classified as being more oriented towards individual customers and six were classified as being more oriented towards institutional customers. The list of consumer-oriented and institutionally oriented industries is given in Panel B of Table 1. From examining Panels A and B of Table 1, it appears that there is a degree of correlation between these two classification schemes. It seems that industries that are more competitive in nature seem to rely more heavily on individual customers and industries that are classified as global oligopolies seem to rely more heavily on institutional customers. However, that issue will not be explored in this paper. The period of examination for the study is January 1995 to December 1997. Firms were included within an industry group if data was available for the entire period. Stock and market
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return data were obtained from the Center for Research in Security Prices (CRSP) tapes. Exchange rate data was obtained from the Wall Street Journal. All data used in this study are monthly which is consistent with previous research in the field. The exchange rate index that is used in the study is composed of the European Currency Unit, the Japanese Yen and the Canadian dollar.
5. Empirical methodology Industry portfolio returns for each industry were computed by averaging the monthly stock returns for all firms in that industry. Monthly market returns were computed by cumulating the daily CRSP market returns for each month. The various exchange rates were trade-weighted using factors that are based on 1995 trade flows between the United States and foreign countries. These weights are incorporated into the Multilateral Exchange Rate Model (MERM) computed by the International Monetary Fund. The various exchange rates were collapsed into one multilateral trade-weighted exchange rate that is convenient to use. Furthermore, it avoids the problem of multicollinearity that arises because many cross-exchange rates are fixed relative to each other (Jorion, 1990). Estimates of the exposure coefficient were obtained from the time series regression presented below (Eq. (1)): Rit ¼ b0i þ bli Rst þ b2i Rmt þ eit ;
t ¼ 1; . . . ; T
ð1Þ
Rit = return of the ith industry in month t; Rst = trade weighted exchange-rate index in month t; Rmt = CRSP market index for month t; eit = error term. The exchange rate index was measured as the dollar price of the foreign currency. Therefore, a positive value for this index indicates a dollar depreciation. The specification of Model 1 is appropriate if changes in stock returns and exchange rates are essentially unanticipated. Another possibility would be to take the forward premium on the exchange rate as the expected rate of change in the exchange rate. However, there is some evidence that the forward rate is a biased predictor of the future spot rate and does not outperform the contemporaneous spot rate. Since the percentage of actual variation in the spot rate explained by the forward premium is relatively small, it can safely be concluded that most of the actual change in the spot rate is unanticipated (Jorion, 1990). Model 1 was run separately for each of the 20 industries. The results are presented in Table 2. The exposure coefficient varies across industries in terms of both sign and magnitude. In light of these large differences across industries, it is important to test the impact of industrial structure on the exposure coefficient. In order to perform such a test, Model 1 presented earlier was run jointly for all industries within each industry classification. The procedure is then repeated for the next classification scheme that is examined in this paper and so on. The model presented earlier is repeated here for convenience (Eq. (2)): Rit ¼ b0i þ bli Rst þ b2i Rmt þ eit ;
t ¼ 1; . . . ; T
ð2Þ
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Table 2 Exposure coefficient, b1i, from Model 1 Agricultural machinery Airline Automotive Banking Beverage Cement Commercial aviation Construction Industrial chemicals Computers Electronic components Household appliances Paper products Pharmaceuticals Retail stores Shipbuilding Shoe producers Steel manufacturers Telecommunications equipment Tobacco
0.367 (1.30) 0.45 (0.65) 0.138 ( 0.2) 0.179 (0.18) 0.378 ( 0.49) 0.823 (0.48) 0.565 ( 0.76) 0.102 (0.13) 0.18 (0.31) 0.519 ( 0.59) 0.228 (0.14) 0.307 ( 0.42) 0.205 ( 0.40) 0.212 (0.43) 0.491 (0.75) 0.505 ( 0.65) 3.275 (2.19) * 0.0514 (0.07) 0.335 (2.31) * 0.145 (0.24)
* Denotes significance at the 5% level. t statistics are in parenthesis.
Rit = return of the ith industry group in month t; Rst = trade weighted exchange-rate index in month t; Rmt = CRSP market index for month t; eit = error term. Model 2 was estimated four times, twice for each of the two classification schemes that are considered in the paper. The exposure coefficient obtained in this manner measures the overall sensitivity of the industrial structure in question to exchange rate variability. The results of this analysis are presented in Panels A and B of Table 3.
Table 3 Exposure coefficient, b1i, from Model 2 Panel A: The competitive vs. oligopolistic classification Competitive industries Oligopolistic industries
0.423 ( 2.38)* 0.293 ( 0.21)
Panel B: The consumer vs. institutional classification Consumer-oriented industries Institutionally oriented industries * Denotes significance at the 5% level. t statistics are in parenthesis. ** Denotes significance at the 10% level. t statistics are in parenthesis.
0.819 ( 1.83)** 0.329 (0.83)
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Model 2 was also run using an interaction term to capture the effects of dollar appreciations and depreciations on the industry portfolio returns. That model is presented below (Eq. (3)): Rit ¼ b0i þ bli Rst þ b2i Rmt þ b3i D Rst þ eit
t ¼ 1; . . . ; T
ð3Þ
Rit = return of the ith industry group in month t; Rst = trade weighted exchange-rate index in month t; Rmt = CRSP market index for month t; D = 1 if the dollar depreciates in month t; eit = error term. The results of this analysis were not significant. Model 2 was also repeated for each industry group using each of the three currencies in question separately. This process would isolate the effects of exposure to certain currencies. This analysis also did not yield significant results. Due to the lack of significance, the results of these two sets of regressions are not presented in the paper, but are available from the author upon request. All regressions that were performed for the purposes of the paper were subject to the Durbin–Watson test for autocorrelation. Finally, the analysis was repeated for all the industry groups in question by using a lagged dollar term. Prior studies have incorporated contemporaneous and lagged dollar changes as independent variables in the same model. Since exchange-rate changes over time may not be independent of one another, this procedure may bias the regression coefficients. As a result, lagged and contemporaneous exchange-rate indices were not used in the same model. The regression model that captures the effects of lagged dollar changes is given below (Eq. (4)): Rit ¼ b0i þ bli Rst1 þ b2i Rmt þ eit ;
t ¼ 1; . . . ; T
ð4Þ
Rit = return of the ith industry group in month t; Rst 1 = trade weighted exchange-rate index in month t 1; Rmt = CRSP market index for month t; eit = error term. The results of this analysis are presented in Panels A and B of Table 4. The exposure coefficients for the lagged dollar changes are insignificant at conventional levels.
Table 4 Exposure coefficient, b1i, from Model 4 Panel A: Mispricing hypothesis: competitive vs. oligopolistic Competitive industries Oligopolistic industries
0.369 (0.91) 0.241 ( 0.81)
Panel B: Mispricing hypothesis: consumer vs. institutional Consumer-oriented industries Institutionally oriented industries t statistics are in parenthesis.
0.562 (1.13) 0.731 (0.19)
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6. Empirical results The results of the analyses discussed in Section 5 are presented and discussed in this section. Table 2 provides the results of running Model 1. As can be seen from examining Table 2, the exposure coefficients vary in sign and magnitude across the 20 industries. The largest exposure coefficient is 3.275 and the smallest is 0.565. The average exposure is 0.193. A majority of the industries do not exhibit significant levels of exposure at conventional levels. This is consistent with the findings of previous research. The results of running Model 2 are presented in Panels A and B of Table 3. Panel A shows that the exposure coefficient for the sample of industries that are classified as being globally competitive is significantly different from zero at the 5% level and that for the sample of industries that are classified as being global oligopolies is insignificant at conventional levels. Panel B shows that the exposure coefficient for the sample of industries that are classified as being more consumer-oriented is significant at the 10% level and that for the sample of industries that are classified as primarily serving institutional customers is insignificant. These results demonstrate that industrial structure is an important determinant of exchange rate exposure. Panels A and B of Table 4 present the results of running Model 4. This is the model that incorporates the lagged dollar term and so provides a test of the mispricing hypothesis at the industry level. All the exposure coefficients obtained from running Model 4 are insignificant at conventional levels. This implies that the market is efficient with respect to changes in the value of the dollar. Consequently, lagged dollar changes do not contain useful information for predicting industry portfolio returns.
7. Conclusions This paper has demonstrated that industrial structure is an important determinant of exchange-rate exposure. Firms that operate in a more competitive environment are more sensitive to exchange-rate exposure than those that operate in a more oligopolistic environment. Likewise, firms that primarily serve the consumer sector of the economy are more sensitive to exchange-rate exposure than those that serve the institutional sector. These findings have implications for the hedging decision by financial managers at multinational firms. The decision of whether or not to hedge exchange-rate risk for firms that operate in a more competitive environment or those that primarily serve individual consumers is more critical than that of firms that operate in a more oligopolistic environment or those that primarily serve institutional customers. This paper has also provided some evidence on market efficiency as it pertains to changes in the value of the dollar. Contrary to the results of prior studies, this paper has demonstrated that past dollar movements do not contain useful information for predicting industry portfolio returns. All the implications of foreign currency movements are impounded in the contemporaneous stock price.
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Most of the studies in this area have been at the firm level. This study is one of the few that has been conducted at the industry level. Furthermore, this study is the first one to investigate whether or not industrial structure can explain the differential exchangerate exposure across industry groups. Consequently, there are several avenues for future research. Perhaps segregating the competitive vs. oligopolistic classification into four categories (monopoly, oligopoly, monopolistic competition, and perfect competition) would provide additional insights into the effects of exchange rate variability on industry returns. One problem of trying to narrow the classification scheme is that it makes the analysis vulnerable to misclassification errors. However, if there was a way to circumvent this problem it would be interesting to see whether the theory developed in this paper still holds. One limitation of the second classification scheme, consumer vs. institutionally oriented firms, is that there is some overlap between the two categories. In other words, a particular firm’s products may have a high demand in the individual as well as the institutional sector of the economy. If there was a formal way to quantify and explicitly control for the overlap, the results may be different. If they are not then that would provide additional support for the results of this paper.
Acknowledgments I am grateful for the helpful comments of Jeff Madura and Marilyn Wiley.
References Bartov, E., & Bodnar, G. (1994). Firm valuation, earnings expectations and the exchange rate exposure effect. Journal of Finance, 49, 1755 – 1985 (December). Bodnar, G., & Gentry, W. (1993). Exchange rate exposure and industry characteristics: evidence from Canada, Japan and the USA. Journal of International Money and Finance, 12, 29 – 45. Chamberlain, S., Howe, J., & Popper, H. (1997). The exchange rate exposure of US and Japanese banking institutions. Journal of Banking and Finance, 21, 871 – 892. Choi, J. J., & Prasad, A. M. (1995). Exchange risk sensitivity and its determinants: a firm and industry analysis of US multinationals. Financial Management, 24, 77 – 88. Chow, E. H., Lee, W., & Solt, M. (1997). The exchange rate risk exposure of asset returns. Journal of Business, 70, 105 – 123. Donnelly, R., & Sheehy, E. (1996). The share price reaction of UK exporters to exchange rate movements: an empirical study. Journal of International Business Studies, 27, 157 – 165. Fang, H., & Loo, J. (1996). Foreign exchange risk and common stock returns: a note on international evidence. Journal of Business Finance and Accounting, 23, 473 – 480. Jorion, P. (1990). The exchange rate exposure of US multinationals. Journal of Business, 63, 331 – 345. Jorion, P. (1991). The pricing of exchange rate risk in the stock market. Journal of Financial and Quantitative Analysis, 26, 363 – 376.
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Levy, M. (1987). Corporate profits and the US dollar exchange rate. Business Economics, 31 – 36 (January). Luehrman, T. (1990). Exchange rate exposure of a global competitor. Journal of International Business Studies, 21, 225 – 242. Luehrman, T. (1991). Exchange rate changes and the distribution of industry value. Journal of International Business Studies, 22, 619 – 646.
Industrial structure and the exchange-rate exposure of industry portfolio returns Anand Krishnamoorthy* Troy State University – Atlantic, 2300 S. 24th Road #710, Arlington, VA 22206, USA Received 23 January 1998; received in revised form 19 January 1999; accepted 27 September 2000
Abstract The purpose of this paper is to demonstrate that industrial structure is an important determinant of the exchange-rate exposure of industry portfolio returns. A time series regression is conducted on the sample of industries by regressing the rate of change of a trade-weighted US dollar index on the industry portfolio return while controlling for the US market. The regression was conducted using monthly data over a 3-year period (1995 – 1997). The results indicate that industries that are classified as being globally competitive and those that primarily serve the consumer sector of the economy have significant levels of exposure. The paper also provides some evidence on market efficiency as it pertains to changes in the value of the dollar. D 2001 Elsevier Science Inc. All rights reserved.
1. Introduction Exchange rates are a major source of uncertainty for multinationals and to some degree also to companies that do not do business outside the United States. Since an appreciation in the US dollar makes foreign imports relatively inexpensive compared to US goods, fluctuations in the value of the dollar can impact a company even if it does not do business outside the home country. Exchange rates are typically four times as volatile as interest rates and ten times as volatile as inflation (Jorion, 1990). However, while the relationship between inflation rates or interest
* Tel.: +1-703-769-7810; fax: +1-703-525-5844. E-mail address: [email protected] (A. Krishnamoorthy). 1044-0283/01/$ – see front matter D 2001 Elsevier Science Inc. All rights reserved. PII: S 1 0 4 4 - 0 2 8 3 ( 0 1 ) 0 0 0 3 3 - 3
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rates and the value of the firm has been extensively analyzed, the relationship between exchange rates and the value of the firm has not been subject to much empirical research. This is one of the first studies to consider exchange rate exposure at the industry level. This study considers the fact that different industries may be differently exposed to exchange rate fluctuations. As a result, when a researcher attempts to measure exchange rate exposure for a broad cross-section of firms from various industries, canceling effects may obscure a significant relationship. This could be a potential explanation for the limited success of prior studies in documenting a causal link between stock returns and changes in the value of the dollar. This paper extends previous research on exchange rate exposure at the industry level by segregating a sample of industries based on industrial structure. Specifically, this paper will be looking at two forms of industrial structure: competitive vs. oligopolistic industries and consumer-oriented vs. institutionally oriented industries. One of the goals of this paper is to ascertain whether competitive and oligopolistic firms are affected in the same way by exposure to exchange rate fluctuations. The second goal is to ascertain whether firms whose customers are primarily individual consumers are affected in the same way by exposure to exchange rate fluctuations as firms whose customers are primarily institutional buyers. These goals will be achieved by forming industry portfolios based on these two classifications. Without presuming any causal link, exchange rate exposure represents the sensitivity of the value of the firm to exchange rate randomness and can be measured by the regression coefficient of the change in the value of the firm on the change in the exchange rate (Jorion, 1990). In industries that are highly competitive, changes in the value of the dollar can affect firms within the industry differently based on location of operations, operating structure (such as currency imbalances), hedging practices, etc. Therefore, fluctuations in the value of the dollar can affect firms differently or to different degrees. Furthermore, the change in the exchange rate will not affect US firms and foreign firms within that industry in the same manner. These differences may be reflected in the prices consumers pay for the company’s products. Therefore, changes in the value of the dollar may cause shifts in the market share among firms in that industry thereby making them more sensitive to exchange rate exposure. In oligopolistic industries, there is less competition and often times there is more cooperation among the firms in the industry. There could even be price adjustments by the firms to reflect changes in exchange rates. Consequently, from the consumer’s point of view, there may not be any benefits in changing from one company to another even if the companies themselves are differently exposed. Therefore, firms that operate in a oligopolistic environment should be less sensitive to exchange rate exposure. Individual consumers tend to price products more frequently than institutional buyers. Institutional consumers often times have long-term contracts to buy from certain suppliers. Furthermore, institutional consumers may not have the luxury of not buying a certain product if they wish to continue operations. Individual consumers, on the other hand, are not bound to consume a product unless it is an ‘‘absolute necessity.’’ If there are significant changes in prices due to exchange-rate changes, firms who rely more heavily on consumers for their revenues are more likely to be affected by such changes. Therefore, firms that are more oriented towards individual consumers should be more sensitive to exchange rate exposure than firms that are more oriented towards institutional consumers.
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The results of the study are consistent with the above predictions. They indicate that industries that are classified as being globally competitive are indeed more sensitive to exchange rate exposure than their oligopolistic counterparts. Likewise, industries that are more dependent on individual consumers for their revenues had significant exposure coefficients while those that rely more heavily on institutional consumers did not. These results have implications for financial managers of major US firms. Whether or not to hedge exchange rate risk is an ongoing debate in the business world. If certain firms are more exposed than others, the hedging decision for those firms is more critical. In addition to providing evidence on the impact of industrial structure on exchange rate exposure, a retest of the mispricing hypothesis was conducted by including a lagged dollar term in all regressions. The results of this paper indicate that prior dollar changes do not significantly explain stocks returns at the industry level. This study indicates that all the implications of foreign currency movements are impounded into the contemporaneous stock price. The remainder of this paper is organized as follows: Section 2 will provide a brief overview of the existing literature in the field. This section will also explain how this paper fills a gap in the existing knowledge. Section 3 will discuss the key hypotheses. This will be followed by a description of the sample selection process. Section 5 describes the methodology that is employed in the study. Section 6 presents the empirical results and Section 7 concludes.
2. Review of related literature Levy (1987) feels that expanding international trade has raised the exposure of US businesses to exchange rate fluctuations. He developed a model that estimates the impact of changes in the US dollar on real before-tax corporate profits. He indicates that real profits are negatively and significantly related to the US dollar but the impact varies substantially by industrial sector. He found that US dollar changes have a large impact on profits of durable goods manufacturing industries but have a small or no effect on profits in certain serviceproducing industries. However, a recently emerging trend is the rising US dollar sensitivity of profits in several service sectors, reflecting the broadening of international competition. Luehrman (1990) examined the exposure of operating cash flows to exogenous changes in the exchange rate. He found exposure to be a function of two components whose magnitudes are significant particularly in light of asymmetries across markets and firms. Those two components are exchange rate induced demand shift and competitor’s reoptimization following an exchange rate shock. Jorion (1990) examined the exposure of US multinationals to foreign currency risk. He presented evidence demonstrating that the relationship between stock returns and exchange rates differs systematically across multinationals. He also found that the comovements between stock returns and the value of the dollar is positively related to the percentage of foreign operations of US multinational firms. In a subsequent paper, Jorion (1991) examined the pricing of exchange rate risk in the stock market using two-factor and multifactor arbitrage pricing models. Evidence is presented
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that the relation between stock returns and the value of the dollar differs systematically across industries. The empirical results did not suggest that exchange risk is priced in the stock market. The unconditional risk premium attached to foreign currency exposure was small and insignificant. He concluded that active hedging policies by financial managers cannot affect the cost of capital, and other reasons must explain why firms decide to hedge. Luehrman (1991) investigated the widely held hypothesis that an exogenous real home currency depreciation enhances the competitiveness of home country manufacturers vis a vis foreign rivals. In doing so, he considered the automobile and the steel industries during the period 1978–1987. He found that contrary to popular belief, for large fractions of both industries, a depreciation of the home currency is associated with a significant decline in their share of industry value. Bodnar and Gentry (1993) examined the industry level exposure to exchange rates for three countries: the United States, Canada, and Japan. They measured exposure by adding the change in the exchange rate to the domestic market model of industry portfolio returns. They found that some industries in all three countries display significant exposures. They also found that for each country, the exchange rate is important for explaining industry returns at the economy-wide level. In order to explore whether exchange rate exposures are systematically linked to the activities of the industries, they modeled exposure as a function of industry characteristics. They found that for all three countries, the relation between exposure and industry characteristics is consistent with economic theory which implies that the effect of exchange rate fluctuations on an industry depends critically on the industry’s relation with the world economy. Bartov and Bodnar (1994) failed to find a significant correlation between the abnormal returns of their sample firms that engage in international activities and changes in the dollar. They then investigated the possibility that this failure might be due to mispricing. They found lagged changes in the dollar to be a significant variable in explaining current abnormal returns of the sample firms, thereby suggesting that mispricing does indeed occur. They found that a trading strategy based upon these results generates significant abnormal returns. Choi and Prasad (1995) developed a model of firm valuation to examine the exchange risk sensitivity of 409 multinational firms during the period 1978–1989. They find that exchange rate fluctuations do affect firm value. Sixty percent of their firms with significant exposure gain from a depreciation of the dollar. They find that cross-sectional differences in exchange risk sensitivity are linked to key firm-specific operational variables. Donnelly and Sheehy (1996) found a contemporaneous relation between the foreign exchange rate and the market value of large exporters. They find a weak lagged relationship, which suggests that the stock market takes time to incorporate all of the implications of foreign currency movements into share prices. Fang and Loo (1996) empirically test the effect of foreign exchange risk on common stock returns using a three-factor arbitrage pricing theory (APT) model. The factors are world market return, national market return and foreign exchange rate movements. A Guass– Newton procedure is used to estimate the nonlinear seemingly unrelated regression equations of common stock returns and foreign exchange risk for 20 portfolios of the US, Canada, the
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UK, and Japan. It is found that international asset returns are significantly affected by foreign exchange risk cross sectionally. Chamberlain, Howe, and Popper (1997) examine the exchange rate sensitivities of US and Japanese banks using both daily and monthly data. Using daily data, they find the stock returns of approximately one-third of thirty large US banks appear to be sensitive to exchange rate changes. They attribute the relative strength of their results to the use of daily data. They find that relatively few Japanese banks appear to be sensitive to exchange rate changes. They claim that the difference may arise from a variety of differences in the operation and regulatory conditions of the firms in the two countries. Chow, Lee, and Solt (1997) claim that the failure of previous studies in finding significant exposure coefficients is due in part to the time horizon of the study. If exchange rate changes contain information about future interest rate and cash flows over more than one period, then using short horizons may not fully capture exchange exposure. This study finds that the changes in real exchange rates are important in explaining the temporal variation in expected returns on bonds and stocks and that all assets are exposed to exchange rate risk. Exchange exposure for stocks reflects both interest rate and cash flow effects. The authors find that the effect of unanticipated changes in the real exchange rate on earnings is negative over short horizons but positive over long horizons. They find that interest rate and cash flow effects are offsetting over short horizons but complementary over long horizons. This paper examines the level of exposure of industry portfolio returns to exchange rate variability by considering two different types of industrial structure. The idea is to examine the causal link between exchange rates and returns for these two different classifications. Hence, this is the major departure of this paper from that of previous related research.
3. Hypothesis development Theory dictates that industrial structure is one of the determinants of exchange-rate exposure. As explained earlier, firms that are classified as being globally competitive should be more sensitive to exchange-rate exposure than firms that are classified as being global oligopolies. The reason for this is that firms that operate in a more competitive environment are more vulnerable to exchange-rate induced demand shifts by consumers than firms that operate in a more oligopolistic environment. Likewise, firms that rely more heavily on individual consumers for their revenues are more likely to be affected by fluctuations in the value of the dollar than those firms that rely more heavily on institutional customers. The reason being that individual consumers tend to price products more frequently than institutional consumers who work more with long-term contracts. As a result, firms that are more oriented towards individual consumers should be more sensitive to exchange-rate exposure than those that are more oriented towards institutional consumers. Bartov and Bodnar (1994) claimed that a possible explanation for the limited success of research in documenting a relation between changes in the dollar and stock returns is the existence of mispricing arising from systematic errors by investors in the estimation of the
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relation between fluctuations in the dollar and firm value. The mispricing hypothesis claims that stock price adjustments due to movements in the US dollar take time. This hypothesis has never been tested at the industry level. The results of this paper contradict the mispricing hypothesis, at least at the industry level. They indicate that all the implications of foreign currency movements are impounded in the contemporaneous industry portfolio return. The ideas expressed above lead to the development of the following three hypotheses. Hypothesis 1 describes the exchange-rate exposure of globally competitive industries vs. that of globally oligopolistic industries. Hypothesis 2 describes the exchange-rate exposure of industries that are more oriented towards individual customers vs. that of industries that are more oriented towards institutional customers. Hypothesis 3 describes the test of the mispricing hypothesis at the industry level. Hypothesis 1: The portfolio returns of industries that are classified as being competitive are more sensitive to exchange rate changes than those that are classified as being oligopolistic. Hypothesis 2: The portfolio returns of industries that rely more heavily on individual customers are more sensitive to exchange rate changes than those that rely more heavily on institutional customers. Hypothesis 3: All the implications of foreign currency movements are impounded in contemporaneous portfolio returns. Consequently, lagged dollar changes do not significantly explain industry portfolio returns. The analysis of the paper will begin by providing some descriptive statistics on the exposure coefficients of industries that are used for the purposes of this study. Those industries will then be classified into the respective categories as described by hypotheses one and two for the remainder of the analyses.
4. Sample selection The sample of industries used in the study was obtained from the Standard and Poor’s Industry survey. This survey lists various industries and major US and foreign firms within the industry. Additional firms within an industry group were obtained from the Companies International database by using the SIC code. The same data set was used for both industry classifications. Twenty industries were used for the purposes of the study. The industries were chosen in order to represent a large cross-section of different industries. In order to maximize the size of the list, two industries were not eliminated during the sample selection process simply because they were related to one another. An industry was classified as being globally oligopolistic if there were five or fewer major firms within that industry. Of the 20 industries, 11 were classified as being globally competitive and 9 were classified as being global oligopolies. The list of globally competitive and globally oligopolistic industries is given in Panel A of Table 1.
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Table 1 Panel A: The globally competitive and oligopolistic industries Competitive
Oligopolistic
Airline Automotive Banking Industrial chemicals Computer manufacturing Electronic components Household appliances Paper products Pharmaceuticals Retail stores Telecommunications equipment
Agricultural machinery Beverage Cement manufacturing Aerospace Construction manufacturing Shipbuilding Shoe producers Steel manufacturing Tobacco
Panel B: The consumer and institutional industries Consumer
Institutional
Airline Automotive Banking Beverage Computer manufacturing Electronic components Household appliances Paper products Pharmaceuticals Retail stores Shoe producers Tobacco
Aerospace Cement manufacturing Construction manufacturing Industrial chemicals Shipbuilding Steel manufacturing
The second classification was made based on personal knowledge since no official statistics are available on this type of classification. Not all 20 industries were used for the purposes of this classification since significant overlap exists in certain industries between the two types of consumers. Eighteen of the 20 industries for which data was collected were used in this classification scheme. Twelve of the 18 were classified as being more oriented towards individual customers and six were classified as being more oriented towards institutional customers. The list of consumer-oriented and institutionally oriented industries is given in Panel B of Table 1. From examining Panels A and B of Table 1, it appears that there is a degree of correlation between these two classification schemes. It seems that industries that are more competitive in nature seem to rely more heavily on individual customers and industries that are classified as global oligopolies seem to rely more heavily on institutional customers. However, that issue will not be explored in this paper. The period of examination for the study is January 1995 to December 1997. Firms were included within an industry group if data was available for the entire period. Stock and market
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return data were obtained from the Center for Research in Security Prices (CRSP) tapes. Exchange rate data was obtained from the Wall Street Journal. All data used in this study are monthly which is consistent with previous research in the field. The exchange rate index that is used in the study is composed of the European Currency Unit, the Japanese Yen and the Canadian dollar.
5. Empirical methodology Industry portfolio returns for each industry were computed by averaging the monthly stock returns for all firms in that industry. Monthly market returns were computed by cumulating the daily CRSP market returns for each month. The various exchange rates were trade-weighted using factors that are based on 1995 trade flows between the United States and foreign countries. These weights are incorporated into the Multilateral Exchange Rate Model (MERM) computed by the International Monetary Fund. The various exchange rates were collapsed into one multilateral trade-weighted exchange rate that is convenient to use. Furthermore, it avoids the problem of multicollinearity that arises because many cross-exchange rates are fixed relative to each other (Jorion, 1990). Estimates of the exposure coefficient were obtained from the time series regression presented below (Eq. (1)): Rit ¼ b0i þ bli Rst þ b2i Rmt þ eit ;
t ¼ 1; . . . ; T
ð1Þ
Rit = return of the ith industry in month t; Rst = trade weighted exchange-rate index in month t; Rmt = CRSP market index for month t; eit = error term. The exchange rate index was measured as the dollar price of the foreign currency. Therefore, a positive value for this index indicates a dollar depreciation. The specification of Model 1 is appropriate if changes in stock returns and exchange rates are essentially unanticipated. Another possibility would be to take the forward premium on the exchange rate as the expected rate of change in the exchange rate. However, there is some evidence that the forward rate is a biased predictor of the future spot rate and does not outperform the contemporaneous spot rate. Since the percentage of actual variation in the spot rate explained by the forward premium is relatively small, it can safely be concluded that most of the actual change in the spot rate is unanticipated (Jorion, 1990). Model 1 was run separately for each of the 20 industries. The results are presented in Table 2. The exposure coefficient varies across industries in terms of both sign and magnitude. In light of these large differences across industries, it is important to test the impact of industrial structure on the exposure coefficient. In order to perform such a test, Model 1 presented earlier was run jointly for all industries within each industry classification. The procedure is then repeated for the next classification scheme that is examined in this paper and so on. The model presented earlier is repeated here for convenience (Eq. (2)): Rit ¼ b0i þ bli Rst þ b2i Rmt þ eit ;
t ¼ 1; . . . ; T
ð2Þ
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Table 2 Exposure coefficient, b1i, from Model 1 Agricultural machinery Airline Automotive Banking Beverage Cement Commercial aviation Construction Industrial chemicals Computers Electronic components Household appliances Paper products Pharmaceuticals Retail stores Shipbuilding Shoe producers Steel manufacturers Telecommunications equipment Tobacco
0.367 (1.30) 0.45 (0.65) 0.138 ( 0.2) 0.179 (0.18) 0.378 ( 0.49) 0.823 (0.48) 0.565 ( 0.76) 0.102 (0.13) 0.18 (0.31) 0.519 ( 0.59) 0.228 (0.14) 0.307 ( 0.42) 0.205 ( 0.40) 0.212 (0.43) 0.491 (0.75) 0.505 ( 0.65) 3.275 (2.19) * 0.0514 (0.07) 0.335 (2.31) * 0.145 (0.24)
* Denotes significance at the 5% level. t statistics are in parenthesis.
Rit = return of the ith industry group in month t; Rst = trade weighted exchange-rate index in month t; Rmt = CRSP market index for month t; eit = error term. Model 2 was estimated four times, twice for each of the two classification schemes that are considered in the paper. The exposure coefficient obtained in this manner measures the overall sensitivity of the industrial structure in question to exchange rate variability. The results of this analysis are presented in Panels A and B of Table 3.
Table 3 Exposure coefficient, b1i, from Model 2 Panel A: The competitive vs. oligopolistic classification Competitive industries Oligopolistic industries
0.423 ( 2.38)* 0.293 ( 0.21)
Panel B: The consumer vs. institutional classification Consumer-oriented industries Institutionally oriented industries * Denotes significance at the 5% level. t statistics are in parenthesis. ** Denotes significance at the 10% level. t statistics are in parenthesis.
0.819 ( 1.83)** 0.329 (0.83)
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Model 2 was also run using an interaction term to capture the effects of dollar appreciations and depreciations on the industry portfolio returns. That model is presented below (Eq. (3)): Rit ¼ b0i þ bli Rst þ b2i Rmt þ b3i D Rst þ eit
t ¼ 1; . . . ; T
ð3Þ
Rit = return of the ith industry group in month t; Rst = trade weighted exchange-rate index in month t; Rmt = CRSP market index for month t; D = 1 if the dollar depreciates in month t; eit = error term. The results of this analysis were not significant. Model 2 was also repeated for each industry group using each of the three currencies in question separately. This process would isolate the effects of exposure to certain currencies. This analysis also did not yield significant results. Due to the lack of significance, the results of these two sets of regressions are not presented in the paper, but are available from the author upon request. All regressions that were performed for the purposes of the paper were subject to the Durbin–Watson test for autocorrelation. Finally, the analysis was repeated for all the industry groups in question by using a lagged dollar term. Prior studies have incorporated contemporaneous and lagged dollar changes as independent variables in the same model. Since exchange-rate changes over time may not be independent of one another, this procedure may bias the regression coefficients. As a result, lagged and contemporaneous exchange-rate indices were not used in the same model. The regression model that captures the effects of lagged dollar changes is given below (Eq. (4)): Rit ¼ b0i þ bli Rst1 þ b2i Rmt þ eit ;
t ¼ 1; . . . ; T
ð4Þ
Rit = return of the ith industry group in month t; Rst 1 = trade weighted exchange-rate index in month t 1; Rmt = CRSP market index for month t; eit = error term. The results of this analysis are presented in Panels A and B of Table 4. The exposure coefficients for the lagged dollar changes are insignificant at conventional levels.
Table 4 Exposure coefficient, b1i, from Model 4 Panel A: Mispricing hypothesis: competitive vs. oligopolistic Competitive industries Oligopolistic industries
0.369 (0.91) 0.241 ( 0.81)
Panel B: Mispricing hypothesis: consumer vs. institutional Consumer-oriented industries Institutionally oriented industries t statistics are in parenthesis.
0.562 (1.13) 0.731 (0.19)
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6. Empirical results The results of the analyses discussed in Section 5 are presented and discussed in this section. Table 2 provides the results of running Model 1. As can be seen from examining Table 2, the exposure coefficients vary in sign and magnitude across the 20 industries. The largest exposure coefficient is 3.275 and the smallest is 0.565. The average exposure is 0.193. A majority of the industries do not exhibit significant levels of exposure at conventional levels. This is consistent with the findings of previous research. The results of running Model 2 are presented in Panels A and B of Table 3. Panel A shows that the exposure coefficient for the sample of industries that are classified as being globally competitive is significantly different from zero at the 5% level and that for the sample of industries that are classified as being global oligopolies is insignificant at conventional levels. Panel B shows that the exposure coefficient for the sample of industries that are classified as being more consumer-oriented is significant at the 10% level and that for the sample of industries that are classified as primarily serving institutional customers is insignificant. These results demonstrate that industrial structure is an important determinant of exchange rate exposure. Panels A and B of Table 4 present the results of running Model 4. This is the model that incorporates the lagged dollar term and so provides a test of the mispricing hypothesis at the industry level. All the exposure coefficients obtained from running Model 4 are insignificant at conventional levels. This implies that the market is efficient with respect to changes in the value of the dollar. Consequently, lagged dollar changes do not contain useful information for predicting industry portfolio returns.
7. Conclusions This paper has demonstrated that industrial structure is an important determinant of exchange-rate exposure. Firms that operate in a more competitive environment are more sensitive to exchange-rate exposure than those that operate in a more oligopolistic environment. Likewise, firms that primarily serve the consumer sector of the economy are more sensitive to exchange-rate exposure than those that serve the institutional sector. These findings have implications for the hedging decision by financial managers at multinational firms. The decision of whether or not to hedge exchange-rate risk for firms that operate in a more competitive environment or those that primarily serve individual consumers is more critical than that of firms that operate in a more oligopolistic environment or those that primarily serve institutional customers. This paper has also provided some evidence on market efficiency as it pertains to changes in the value of the dollar. Contrary to the results of prior studies, this paper has demonstrated that past dollar movements do not contain useful information for predicting industry portfolio returns. All the implications of foreign currency movements are impounded in the contemporaneous stock price.
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Most of the studies in this area have been at the firm level. This study is one of the few that has been conducted at the industry level. Furthermore, this study is the first one to investigate whether or not industrial structure can explain the differential exchangerate exposure across industry groups. Consequently, there are several avenues for future research. Perhaps segregating the competitive vs. oligopolistic classification into four categories (monopoly, oligopoly, monopolistic competition, and perfect competition) would provide additional insights into the effects of exchange rate variability on industry returns. One problem of trying to narrow the classification scheme is that it makes the analysis vulnerable to misclassification errors. However, if there was a way to circumvent this problem it would be interesting to see whether the theory developed in this paper still holds. One limitation of the second classification scheme, consumer vs. institutionally oriented firms, is that there is some overlap between the two categories. In other words, a particular firm’s products may have a high demand in the individual as well as the institutional sector of the economy. If there was a formal way to quantify and explicitly control for the overlap, the results may be different. If they are not then that would provide additional support for the results of this paper.
Acknowledgments I am grateful for the helpful comments of Jeff Madura and Marilyn Wiley.
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