To What Extent is the Value of the Firm in a Non-Financial
Sector Reliant on the Proportion of Hedging Firms in the Given
Industry? US case
31/01/2018 Yelyzaveta Nazarenko 11024186 Dr. Liang Zou
Faculty of Economics and Business, Specialization in Finance and Organization
ABSTRACT
This paper presents theoretical arguments and empirical evidence of the relation among competitors’ hedging decisions in a specific industry and its significance for the firms that wish to increase their value. It explores to what extent the value of the firm in a non-financial sector is reliant on the proportion of hedging firms in the given industry based on the sample of cross industry data collected from the US companies for the financial year of 2016. Apart from the statistical data taken from WRDS, annual financial reports are reviewed to provide a unique insight into risk management strategies of 390 companies. The research finds that there is not enough statistical evidence to suggest that unhedged firms have a lower value than hedged firms if they belong to industries where hedging is popular and incur no value discount if hedging in the industry is rare or non-existent.
2 Statement of Originality
This document is written by Yelyzaveta Nazarenko, who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
3 Contents I. Introduction………4
II. Literature review………5
III. Methodology………..9
A. Data………...9
B. Model………...10
IV. Results and discussion………..12
V. Conclusion………16
References...……….18
4 I. Introduction
International companies are usually subject to various risks inherent in conducting business globally. Apart from the foreign laws, regulations and customs of local jurisdictions, companies face risks coming from foreign exchange rate exposure that encourage financial risk management. Under the assumptions of Modigliani - Miller theory corporate financial policy is irrelevant. Therefore, there is no need for financial risk management as each investor can adjust their personal portfolio to a certain risk level (Froot, Scharfstein, & Stein, 1993). Nevertheless, there has been a significant increase in risk management operations through derivatives usage starting from the end of twentieth century (Nance, Smith, & Smithson, 1993). Financial risk management is usually conducted through hedging: trading in particular futures, forwards, or option markets (Smith and Stultz, 1985). It is perceived as a tool to reduce financial distress costs, solve underinvestment problem and reduce expected taxes (Nain, 2004; Froot et al., 1993; Geczy, Minton, & Schrad, 1997). However, the true incentives and influencing factors for hedging still remain a controversial topic, even though most of the prior research concludes that efficient financial risk management may indeed lead to a higher firm value (Allayannis and Weston, 2001; Graham and Rogers, 2002; Allayannis, Lel, & Miller, 2003; Carter, Rogers, & Simkins, 2003). Froot et al. (1993) argue that risk management strategy depends on the hedging decisions of firms’ competitors, given that hedging has already been chosen as an optimal investment choice. Brown’s (2001) study has also revealed that a stated goal of the firm’s foreign currency hedging program is to reduce the adverse effects of currency movements on firm’s competitiveness.
This paper presents theoretical arguments and empirical evidence of the relation among competitors’ hedging decisions in a specific industry and its significance for firms that wish to increase their value. If the proportion of hedgers in the industry is high, the non-hedging firms would have a ‘discounted value’ or in contrast, hedging firms would have an increased value due to forward-looking assessment of a firm. It would imply that hedging creates value not only through decreased volatility of future earnings but is highly reliant on competitors’ decisions (Nain, 2004).
With the introduction of FASB NO.105 companies are required to disclose all information concerning financial instruments usage with off-balance-sheet risk of accounting loss in annual 10K and financial reports. Introduction of this law allows to explore the strategic motives for corporate risk management through verifying the hypothesis that hedged and unhedged firms
5
have the same value irrespective of the proportion of existent hedging firms in the industry. The paper conducts a cross-industrial research of US companies excluding financial sector firms. The reasoning behind it is that while financial firms use hedging for both speculative and hedging purposes, non-financial industry firms tend to adhere only to the latter one (Jorge and Augusto, 2016). This allows to explore purely hedging incentives and leads to the following research question: To what extent is the value of the firm in a non-financial sector reliant on the proportion of hedging firms in the given industry? US case.
The datasets in prior research were collected from surveys (before the law enforcement of mandatory disclosure of financial instruments usage) or consisted of either one specific firm or a sample based on very selective criteria. This research in its turn gives a broader overview and finds cross-industrial patterns mainly due to bigger sample size and elaborate data collection. The research finds that there is not enough statistical evidence to suggest that unhedged firms have a lower value than hedged ones if they belong to industries where hedging is popular and incur no value discount if hedging in the industry is rare or non-existent. By further examining the results, partial evidence to support the theory of hedging ‘at extremes’ is found, where either all firms use derivatives or none of the firms apply these financial risk management tools.
The paper is built as follows: section II unveils findings of the existing literature, concerning financial risk management, followed up by the hypotheses formulation. Then, section III describes data collection and the OLS regressions which are used as a premise to answer the research question. Section IV critically evaluates the results. Section V concludes by making a decision regarding the research question, as well as stating limitations and suggestions for future research.
II. Literature review
This part explains how financial risk management can add value to the firm, which risks can be hedged, what type of hedging strategies exist and what they are dependent on.
Most of the theories explaining incentives of why firms would adhere to hedging rely on capital market imperfections (Nain, 2004). As it has already been mentioned in the previous section, Modigliani - Miller theory proves that corporate financing policy is irrelevant if a firm has a fixed investment strategy and there are no taxes and contracting costs. The distribution of future earnings remains unaffected as this theory assumes that each investor can maintain constant risk level. Even if the company changes its current hedging strategy an investor can
6
adjust holdings of risky assets in his or her own portfolio and change its risk level. From here it follows that if the value of the firm is affected by its hedging strategy, it must do so through market imperfections: taxes, contracting costs, or the dependency of the firm’s investment decisions on hedging policy (Smith et al., 1985). Froot et al. (1993) also argue that capital market imperfections generate a rationale for risk management. They have an adverse effect on the cost of externally generated funds and make them relatively more expensive to internal ones which encourages hedging. The work of DeMarzo and Duffie (1991) also adds that hedging helps to solve underinvestment problem through reducing such market imperfection as information asymmetry. Firm’s managers take into account information that is usually not available to outsiders when designing its hedging strategy and this helps investors to make better investment decisions through raised awareness of firm’s current state (Nain, 2004).
As Jorge et al. (2016) stated, derivatives can be used for hedging and speculative purposes. While financial entities are known for frequently engaging in speculation activities, non-financial firms usually adhere solely to hedging. There are 3 types of risks that companies tend to hedge: interest rate, foreign exchange rate and commodity price risks. Most of the attention has been devoted to currency derivatives in the prior research as they have been the most volatile ones. The research trend has changed now towards interest rate and commodity price derivatives as the market for them outgrew the foreign currency one. Jorge et al. (2016) confirm that interest rate and commodity price derivatives are now also perceived to lead to more risk exposure. However, the focus of non-financial international firms still remains mostly on currency derivatives due to firms’ major exposure to currency rate fluctuations through foreign sales and investment.
When firms face foreign exchange rate exposure, currency fluctuations can adversely affect sales through increased prices in foreign markets, especially in the short run (Brown, 2001). Foreign currency derivatives usage allows firms to endure exchange rate fluctuations. It allows companies to maintain competitiveness in the case of an exchange rate jump or drop in the short term and provides time to design a long-term solution (e.g. plant base reallocation).This is especially important for companies in exceptionally competitive industries where not losing customers requires consistent aggressive product pricing. Moreover, when the costs of price alteration in foreign markets is high, it may make it even more difficult to adjust to current state and significantly worsen firm’s reputation (Brown, 2001).
There is a wide variety of financial instruments that can be used to hedge foreign currency exchange rate risk: forward contracts, swaps, options and natural hedges. Some international
7
companies would use a combination of instruments in order to hedge a fixed quantity of investment in each country where they conduct operations, while others would adhere solely to one instrument or use derivatives for one of the countries only (Froot et al., 1993). Brown (2001) suggests that options are perceived to be one of the best foreign currency derivatives for the competitive reasons. Option premiums have only a limited impact on the hedge rate when the home currency devaluates, whereas when it appreciates the exchange rate fixed by a hedge can be distinctively better than the market spot rate. Companies can further benefit from a hedge by conducting aggressive pricing and taking a greater market share (Brown, 2001).
Hedging strategies of international companies are usually reliant on various factors. Froot et al. (1993) argue that the exchange rate exposure of both investment expenditures and revenues is widely considered when designing a hedging policy. An empirical study conducted by Brown (2001) confirms that exchange rate and exposure volatility are indeed the key factors determining the optimal hedging strategies. Moreover, the study finds managerial views and recent hedging history to be statistically significant. Nain (2004) contributes to the research by empirically confirming that the proportion of hedging firms in the industry is a strategy determinant as well.
While it is known from prior research that derivatives usage can enhance firm value through decreasing cash flow volatility, which in its turn creates bigger debt capacity, partially solves underinvestment problem and accommodates certain tax benefits (Graham et al., 2002), a new dependency has been found. The firm value of a derivatives (non)-user is actually reliant on a proportion of hedgers in the given industry and may not necessarily be enhanced simply by usage of financial instruments. There are two opposing views presented in the literature: ‘hedging at extremes’, either every firm hedges or no one does, and the median one, where approximately half hedges and the other one does not.
Nain (2004) explains the relation between the proportion of hedgers in a given industry and the effect of cost shocks. If the majority of firms in an industry faces a cost shock, the industry prices will adjust proportionally to costs as each firm changes output level accordingly. This implies that future earnings of an unhedged firm will be relatively unresponsive to cost shocks. However, when the number of derivatives users in a certain industry increases, prices become less dependent on costs and an unhedged firm incurs a loss if it wants to maintain competitive prices. Thus, the profit of an unhedged firm in this scenario is more volatile. In the case of all firms hedging a certain cost shock in a given industry, prices are predicted to be unaffected.
8
The firms are expected to engage in efficient risk management and reduce the impact of foreign exchange rate exposure on firm’s profit. If not hedging reduces the firm value, investors will punish the firm by valuing it at a discount. In contrast, investors add value through ‘forward-looking market assessment of a firm given the firm hedges and most of its competitors hedge as well. Consistent with the theory, Nain (2004) finds that unhedged firms have a lower value than hedged firms if they belong to industries where hedging is popular and incur no value discount if hedging in the industry is rare or non-existent.
Similarly to Nain (2004), Adam, Dasgupta, and Titman (2007) in their research present a function where the equilibrium output price is determined by an intersection of aggregate investment and hedging decisions function. This implies that a financial risk management of a firm is reliant on hedging and investing decisions of other firms in the industry. The model developed in the paper illustrates that the fraction of firms that hedge depends on various industry characteristics. The findings suggest that an equilibrium distribution of hedgers versus non-hedgers would be at the median, which has been also consistent with previous empirical studies that found that in a non-financial sector “hedging” and “not hedging” are equally common. Moreover, the exact fraction of derivatives users depends also on such industry characteristics as degree of competition (determined by the number of firms in the industry), the elasticity of demand, the convexity of production costs, and market size (Adam et al., 2007). The same logic also follows with investment. As the gains from additional investment would be higher when other firms in the industry invest less, companies would try to design their hedging strategies in a way that allows them to have more cash when other companies are cash constrained (Adam et al., 2007). This relates to hedging in the way that firms become more incentivized to hedge as more firms in the industry choose not to. Subsequently, despite the fact that all firms are ex-ante identical, an industry equilibrium arises at the point in which some firms hedge and others do not (Adam et al., 2007).
Adam et al. (2007) argue that probability of a certain company to engage in risk management is negatively correlated with the number of derivative users in a given industry. The model presented in the paper is based on certain assumptions. One of them being to base the decision of whether to hedge cost shocks or not is derived from the profit maximization function. Due to function convexity, uncertainty is perceived as a benefit because firms can choose output ex-post of observing market prices and production costs. As Nain (2004) mentions this ‘production flexibility’ causes firms to view uncertainty as an opportunity and leads to the results opposing Nain’s (2004) findings.
9
Following the discussion above, this paper examines if the value of the firm is dependent on the proportion of hedging firms in the given industry. This shows if there is any value difference due to this proportion and helps to answer the research question by testing empirically if indeed non-hedging firms are being punished through value discount if the proportion of hedgers in industry is high.
III. Methodology
The hypothesis of this paper states that the value of the firm is affected by the number of hedgers in the specific industry. This section describes the methodology used to examine how the value of the firm changes conditional on the extent of hedging in the industry.
A. Data
The sample used for this research is based on cross industry data collected from US companies for the financial year of 2016. The annual financials were taken from WRDS, namely from CRSP (share price and number of shares outstanding) and COMPUSTAT. For further clarifications annual financial reports of the given year were reviewed.
One of the main selection criteria for the following sample was for the firm to come from a non-financial sector, that is the SIC (Standard Industry Identification) code should not start with 61-67. As Geczy et al. (1997) have stated, companies from financial sector are known to be using derivatives for both: speculative and hedging purposes. In order to be able to track the consequences of using derivatives purely for hedging purposes, financial companies are excluded from the sample.
The sample has been further restricted to companies who face exchange rate risk as this justifies the absence of derivatives usage to be seen not as a lack of foreign currency risk exposure but a choice to not use this type of financial instrument (see e.g. Nain, 2004; Graham et al., 2002). The company was assumed to have this type of risk if it has reported a non-null value of ‘exchange rate effect’ in its annual financials. This has also been done in previous research, for example in the work of Geczy et al. Sample has also been restricted to companies that have positive value of total assets and net income, and different from zero R&D and capital expenditures. It has been further restricted to companies that have disclosed needed data to calculate market capitalization (price and number of shares outstanding found through CRSP).
10
The sample initially contained 1139 observations after complying with the aforementioned criteria. Further, in order to determine what type of derivatives has been used by a firm and more specifically if exchange rate hedging was applied, elaborate hand collected data is used. 10k reports and annual financials of year 2016 are being searched for the following key words: ‘swap’, ‘forward’, ‘hedge’ ‘designated as’, ‘contract’, and further verified. As it is required by SFAS 119 to explicitly state whether they speculate with derivatives and all types of derivatives used, it allowed to classify any firm using foreign currency derivatives as a ‘hedger’. This was done to 390 companies that have reported gain/loss on financial derivatives. This narrowed down the initial sample of foreign exchange derivatives users to the final one of 237 observations.
B. Model
The dependent variable is a firm value which is calculated as the natural log of Tobin’s Q1. Variables Size, R&D, CapEx, LTDebt represent the factors that exogenously influence the value of the firm. Size is calculated as the natural log of total assets and represents firm’s magnitude. R&D and CapEx represent growth opportunities and are calculated as R&D expense and capital expenditure in the given year scaled by total assets. LTDebt represents leverage usage and equals total long-term debt over total assets. NetIncome exhibits firms’ profitability and is calculated as a ratio of net income over total assets. FCD is a dummy variable that equals 1 if a company is a non-user of foreign currency derivatives. A company is considered a non-user if it did not disclose use of foreign currency derivatives for accounting purposes in its annual financials. DFraction represents the proportion of firm’s competitors who are engaged in foreign currency hedging activities. Companies are treated as ‘competitors’ if they have the same first 3 digits of SIC (Standard Industry Classification) code.
Subsequently, the following hypothesis are being checked with the help of four Ordinary Least Squares (OLS) regressions in order to examine accuracy of the following assumptions.
1# #$ %&'()* +,-*-'./0.1∗%&'() 3(04)56#-'7 8**)-*9:#;;#. <=,0->9?)$$)(()/ 6'@)*
6#-'7 8**)-*
11
Hypothesis 0: Value of the firm does not depend on proportion of hedging
companies in the given industry.
Hypothesis 1: Value of the firm depends on proportion of hedging companies
in the given industry.
Economic theory is used in order to design the regression which would predict results with high accuracy. However, the empirical tests are also needed to verify if the regression has indeed high explanatory power and does not incur serious endogeneity problems.
The following regression examines what is the effect of FCD dummy, which is originally the primary explanatory variable of interest. It allows to check if being a derivatives user or nor has a significant effect on firm value. It is worth mentioning that from economic theory perspective, there are more variables that can potentially influence firm value and should be taken into account.
FirmValue = β0 + β1 FCD (1)
The statistical hypotheses are as follows: H0: β1 = 0,
H1: β1 ≠ 0.
The following regression takes into account not only the fact of being a (non)-hedger itself but it also adds an interaction variable of it with the proportion of hedgers in a given industry. The variable of interest in this regression is FCD * DFraction and its effect on the value of a firm.
FirmValue = β0 + β1 FCD + β2 FCD * DFraction (2)
The statistical hypotheses are as follows: H0: β2 = 0,
H1: β2 ≠ 0.
The regression (3) controls for various factors that are known to influence firm value in order to see what is the effect of using foreign currency derivatives and its combination with proportion of hedgers on the dependent variable.
FirmValue = β0 + β1 Size + β2 R&D + β3 CapEx + β4 LTDebt + β5 NetIncome + β6 FCD
+ β7 FCD * DFraction (3)
The statistical hypotheses are as follows: H0: β7 = 0,
12
As R&D and CapEx are known to be highly correlated, the regression (4) excludes CapEx to avoid the statistical problem of imperfect multicollinearity and to examine any changes occurred due to its exclusion.
FirmValue = β0 + β1 Size + β2 R&D + β3 LTDebt + β4 NetIncome + β5 FCD
+ β6 FCD * DFraction (4)
The statistical hypotheses are as follows: H0: β6 = 0,
H1: β6 ≠ 0.
When running the regressions robust standard errors have been applied. As there is no economic intuition to suggest that the residuals of the regression have the same variance, it is better to use the statistical rule for heteroscedastic errors, which assumes not constant variance of the residuals, as it also works for homoscedastic errors but not vice versa.
IV. Results and Discussion
The null hypotheses concerning regressions (1) and (2) are rejected. In the regressions (1) and (2) the foreign currency derivatives non-user dummy is significant and has a plus sign that implies positive effect on firm value when the company does not hedge. In regression (2) the interaction variable is significant and has a minus sign that implies that if the firm is a non-hedger and the proportion of non-hedgers in the given is high, the firm suffers a value discount. This would support Nain’s (2004) theory. However, when robustness check is done to verify the significance of the interaction variable, results change.
The multivariate setting was applied in regressions (3) and (4) to control for other variables that can have an effect on firm value. Interestingly enough, the coefficients of both foreign currency derivatives users dummy and the interaction variable of proportion of hedgers and the dummy become insignificant at 5% significance level. This implies that the null hypothesis concerning regression (3) is not rejected. However, the interaction variable still remains significant at 10% and maintains a negative sign, implying occurrence of value discount. It is also important to note that the coefficient value decreased almost two-fold. Regression (3) finds all control variable significant at 5% level except for the capital expenditure over total assets ratio (CapEx). This means that all chosen control variables with the exclusion of CapEx indeed have an effect on
13
Table 1: Foreign Currency Derivatives (FCD) Use and Firm Value
This table displays the effect of FCD use on firm value for a sample of firms that face foreign exchange exposure. A firm is defined as having ex-ante exchange rate exposure if it discloses exchange rate effect in the annual COMPUSTAT. The dependent variable is the natural log of Tobin’s Q, which is calculated as market value of equity (calculated as shares outstanding times share price taken from CRSP) plus total assets less common equity and deferred taxes, all scaled by total assets. FCD non-user dummy equals 1 if the firm does not disclose the use of foreign currency derivatives in its 2016 10K reports for accounting hedging purposes and 0 otherwise. Fraction of competitors who use FCD is calculated as the number of firms with the same 3-digit SIC code who face exchange rate exposure and disclose the use currency derivatives divided by the total number of firms with the same 3-digit SIC who face foreign exchange exposure. Size is the log of total assets. R&D Expense/Total Assets is research and development expense divided by total assets. CapEx/Total Assets is capital expenditures divided by total assets. Long-Term Debt Ratio is long-term debt divided by total assets. Return on Assets is net income over total assets. Panel 1-4 exhibits results of OLS regressions that contain various explanatory variables. Standard errors are provided in parenthesis. * p < 0.05, ** p < 0.01, *** p < 0.001 (1) (2) (3) (4) Firm Value Firm Value
Firm Value Firm Value
FCD non-user dummy 0.0883* 0.162** 0.0774 0.0775
(0.0422) (0.0525) (0.0499) (0.0498) FCD non-user dummy * Fraction of
competitors who use FCD
-0.424** -0.254 -0.250 (0.160) (0.146) (0.146)
Size -0.0452*** -0.0443***
(0.0110) (0.0110)
R&D Expense/Total Assets 2.081*** 2.081***
(0.309) (0.310)
CapEx/Total Assets 0.386
(0.564)
Long-Term Debt Ratio 0.663*** 0.660***
(0.118) (0.118) Return on Assets 0.914*** 0.917*** (0.181) (0.181) _cons 28.08*** 28.08*** 28.80*** 28.79*** (0.0362) (0.0362) (0.237) (0.237) N 1139 1139 1139 1139
14
firm value in this setting. This could be due to a statistical problem of multicollinearity (is examined further in this section) that occurs when independent variables are highly correlated and can be solved by exclusion of the variable. The null hypothesis of regression (4), which does not have CapEx variable, is not rejected as the coefficient of interaction variable remained insignificant at 5% level.
Nain (2004) suggested that due to the effect of the proportion of hedgers, firm value can be affected and hedging will occur ‘at extremes’. As the empirical results of the OLS regression (3) and (4) did not find the interaction variable significant, the further research is needed in order to see if indeed Nain’s theory of hedging does not hold in this case.
Exhibit 1 shows that the majority of firms in the given sample are in the industries with low level of hedging. This only partially supports Nain’s (2004) predictions as there is no peak at (0,85 - 1) proportion of hedging companies. Only 10% are in the industries where approximately half of the firms adhere to hedging while the other half does not. This does not support the findings of Adam et al. (2007).
Exhibit 1: Proportion of Hedgers that Firms Face in the Given Industry
This chart exhibits the cross-industrial trends concerning proportion of hedgers that companies face in their industries. The y-axis corresponds with the proportion of hedgers while the x-y-axis shows the total number of companies that incur a given proportion of foreign currency derivatives users in their industries.
Number of companies
Source: WRDS database and annual financials
The discrepancy with the empirical results found by Nain (2004) on data of 1990s could be due to a couple of factors. Implementing an effective hedging strategy is difficult as it relies on a variety of variables, such as supply and demand, relative prices, income (Clark and Mefteh, 2010). Smith et al. (1985) in their research argue that managerial share ownership can have a
15
significant impact on a design of hedging strategy and subsequently firm value. When managers’ compensation largely depends on it, they may engage in risk management activities to protect their earnings and not necessarily benefit firm’s shareholders (Allayannis et al., 1998). This may be one of the reasons why hedging at extremes is not the only one that is being observed. The paper also examines the potential problem of multicollinearity among the independent variables of the regression by looking at their correlation coefficients.
Following the rule of thumb, which states that correlation between two variables can be considered high if it exceeds 70%, the results show only one such case (Hinkle, Wiersma, & Jurs, 1998). Return on assets, which is calculated as net income over total assets, is highly correlated with R&D expense over total assets ratio. It can be due to the fact that both of these ratios have the same denominator and it could have also potentially affected the final results. However, the found correlation between R&D expense over total assets and capital expenditure over total assets turned out to be surprisingly low, given the fact that both of them are used in the model as an estimator of growth opportunities. This concludes that imperfect multicollinearity could have caused the discrepancy between Nain’s (2004) empirical findings and the ones of this research.
16
Table 2: Correlation Matrix of Independent Variables
This table displays the correlation coefficients among independent variables of the OLS regressions researched in this paper. Size is the log of total assets. R&D Expense/Total Assets is research and development expense divided by total assets. CapEx/Total Assets is capital expenditures divided by total assets. Long-Term Debt Ratio is long-term debt divided by total assets. Return on Assets is net income over total assets. FCD non-user dummy equals 1 if the firm does not disclose the use of foreign currency derivatives in its 2016 10K reports for accounting hedging purposes and 0 otherwise. Fraction of competitors who use FCD is calculated as the number of firms with the same 3-digit SIC code who face exchange rate exposure and disclose the use currency derivatives divided by the total number of firms with the same 3-digit SIC who face foreign exchange exposure.
Size R&D Expense/ Total Assets CapEx/Total Assets Long-Term Debt Ratio Return on Assets FCD non-user dummy FCD non-user dummy * Fraction of competitors who use FCD
Size 1
R&D Expense/Total Assets -0.4405 1
CapEx/Total Assets 0.1505 -0.0861 1
Long-Term Debt Ratio 0.4649 -0.2878 0.0331 1
Return on Assets 0.4714 -0.7225 0.1097 0.1819 1
FCD non-user dummy -0.3802 0.1743 -0.0317 -0.1955 -0.1769 1
FCD non-user dummy * Fraction
17 V. Conclusion
This paper examines incentives of a firm to use financial risk management tools and unveils implications of the extent of hedging in the given industry on a firm value. It tests the hypotheses that the value of the firm does not depend on proportion of hedging companies in the industry. The suggestions are being tested using comprehensive hand collected data on foreign currency derivatives usage by public firms in the United States for the year of 2016. It evaluates the theory that unhedged firms have a lower value than hedged ones if they belong to industries where hedging is popular and incur no value discount if risk management in the industry is rare or non-existent.
The research finds that in the conducted setting there is not enough statistical evidence to confirm this theory and contradicts recent empirical findings of Nain’s (2004) research that are based on a dataset of 1990s. This paper also evaluates whether hedging is more widespread ‘at extremes’, where all firms in the industry use derivatives or no one does, or at the median, where approximately half of the firms hedge, while the other one does not. It finds evidence to partially confirm ‘hedging at extremes’ as the majority of the companies experienced extremely low proportion of hedging firms in their industries. However, as only 10% of the firms are in the industries where approximately half of their competitors hedge, the theory described in research of Adam et al. (2007) is not supported in this sample.
The limitation of this work consists of complexity and individuality of each company’s hedging strategy. As database containing elaborate information on derivatives usage of each company does not exist, it complicates the data evaluation process. Therefore, a suggestion for future research could be to explore the hedging strategies of companies not only in terms of foreign currency derivatives but all types of financial instruments. Conducting this type of research for a big sample allows to find cross-industrial hedging patterns.
18 References
Adam, T., Dasgupta, S., & Titman, S. (2007). Financial Constraints, Competition, and Hedging in Industry Equilibrium. The Journal of Finance, 62(5), 2445-2473. http://dx.doi.org/10.1111/j.1540-6261.2007.01280.x
Allayannis, G., & Weston, J. (1998). The Use of Foreign Currency Derivatives and Firm Market Value. SSRN Electronic Journal. http://dx.doi.org/10.2139/ssrn.138498
Allayannis, G., Lel, U., & Miller, D. (2003). Corporate Governance and the Hedging Premium around the World. SSRN Electronic Journal. http://dx.doi.org/10.2139/ssrn.460987 Brown, G. (2001). Managing foreign exchange risk with derivatives. Journal of Financial
Economics, 60(2-3), 401-448. http://dx.doi.org/10.1016/s0304-405x(01)00049-6
Carter, D., Rogers, D., & Simkins, B. (2003). Does Fuel Hedging Make Economic Sense? The Case of the US Airline Industry. SSRN Electronic Journal, 1-49. http://dx.doi.org/10.2139/ssrn.325402
Clark, E. and Mefteh, S. (2010). Asymmetric Foreign Currency Exposures and Derivatives Use: Evidence from France. Journal of International Financial Management &
Accounting, 22(1), 27-45.
DeMarzo, P., & Duffie, D. (1991). Corporate financial hedging with proprietary
information. Journal of Economic Theory, 53(2), 261-286.
http://dx.doi.org/10.1016/0022-0531(91)90156-x
Financial Accounting Standards Board. (1990). Statement of Financial Accounting Standards
No. 105, 1-49. Connecticut.
Froot, K., Scharfstein, D., & Stein, J. (1993). Risk Management: Coordinating Corporate Investment and Financing Policies. The Journal of Finance, 48(5), 1629-1657. http://dx.doi.org/10.2307/2329062
Geczy, C., Minton, B., & Schrand, C. (1997). Why Firms Use Currency Derivatives. The
Journal of Finance, 52(4), 1323-1354. http://dx.doi.org/10.2307/2329438
Graham, J., & Rogers, D. (2002). Do Firms Hedge in Response to Tax Incentives?. The Journal
of Finance, 57(2), 815-839. http://dx.doi.org/10.1111/1540-6261.00443
Hinkle, D., Wiersma, W. and Jurs, S. (1998). Applied statistics for the behavioural sciences. Boston: Houghton Mifflin.
Jorge, M., & Augusto, M. (2016). Is hedging successful at reducing financial risk exposure?.
19
Nain, A. (2004). The Strategic Motives for Corporate Risk Management. SSRN Electronic
Journal. http://dx.doi.org/10.2139/ssrn.558587
Nance, D., Smith, C., & Smithson, C. (1993). On the Determinants of Corporate Hedging. The
Journal of Finance, 48(1), 267-284. http://dx.doi.org/10.2307/2328889
Smith, C., & Stulz, R. (1985). The Determinants of Firms' Hedging Policies. The Journal of
20 Appendix
Chart 1: Industry Representation
This chart displays the industries of the firms examined in this paper. The total number of firms is 1139 and the percentage of industry representation is calculated as the number of firms representing that industry scaled by the total number of firms. The firm is considered to be a representative of a certain sector based on its SIC (Standard Industry Classification) code.
Source: WRDS database Agriculture, Foresty, Fishing 0.35% Mining 1.32% Construction 0.18% Manufacturing 75.07% Transportation & Public Utilities 1.84% Wholesale Trade 0.61% Retail Trade 0.44% Services 19.84% Public Administration 0.35% Other 20.63% INDUSTRY REPRESENTATION
