The Wealth Effect of Swap Usage in the Food Processing Industry

Jian Yang, David J. Leatham, and Spencer A. CaseTexas A&M University

ABSTRACT

U.S. companies use interest rate swaps more than any other financial derivative. The effect of swapusage on the shareholders’ wealth is both controversial and unclear. Using a sample from the foodprocessing industry, we examined both short-run and long-run wealth effects associated with swapusage. A significant long-run wealth effect of swap usage on swap users was not found. However,there was a significant negative wealth effect during a short period before firms first disclosed swapusage to the SEC. This finding is consistent with the argument that derivative usage may not be in thebest interest of shareholders. © 2000 John Wiley & Sons, Inc.

1. INTRODUCTION

The increase in the volatility of interest rates in the 1980s propelled the development anduse of financial innovation to manage interest rate risks. Interest rate swaps are a major fi-nancial innovation that has experienced explosive growth both in domestic and internationalmarkets. The notional amount of unpaid interest rate swaps hit $3.85 trillion by the end of1992, dominating all other major derivative products in the marketplace (GAO, 1994,p.187). Several recent surveys, including Bodnar, Hayt, Marston, and Smithson (1995),CFO Forum (1992), and Phillips (1995), reveal that interest rate swaps are the most popu-lar derivative contracts used by U.S. firms.

Interest rate swap agreements are a type of derivative instrument used to manage inter-est rate risk. A “generic” or “plain vanilla” swap involves the exchange of one company’sfixed interest rate charges for another company’s floating interest rate charges. These swapsare based on a notational value over a set period of time and at a regular interval, typicallyevery 6 months or one year. The notional principal amount is never exchanged; it is onlythe base upon which interest charges are calculated. Thus, the result of the agreement is the effective transformation of a fixed rate obligation to a floating rate obligation or viceversa.

For example, one company (Company A) has a fixed rate debt of $1 billion and has a de-sire to obtain a floating rate debt to accompany its assets, which are also a floating rate. Asecond company (Company B) has $1 billion of floating rate debt but prefers to obtain afixed rate debt to match its fixed rate assets. Typically a swap dealer will bring these twocompanies together to negotiate the terms of the swap. Suppose that Company A will ex-

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change 10 percent fixed for LIBOR (London Inter Bank Offer Rate) plus 2 percent fromCompany B for 3 years with annual payments. If at the end of the first year LIBOR is 8 per-cent, Company A, who now has the floating rate obligation, needs to pay Company B $100million of interest charges. In addition, Company B would pay Company A $100 million tocover the fixed interest obligation it took on. The interest charges are netted out each peri-od and the companies settle the difference. Hence, there is no payment required by eitherparty in the first year. Suppose that LIBOR increases to 9 percent in the second year, butfalls to 6 percent in the third year. This results in Company A owing $110 million and Com-pany B owing $100 million in the second year. The net would be $10 million paid by Com-pany A to Company B in the second year. In the third year of this swap contract, CompanyA would owe $80 million and Company B would still owe $100 million. Therefore, Com-pany B would pay Company A $20 million in the third year of the swap contract. Consid-ering each company’s original obligations for interest payments along with the obligationsof the swap agreement, Company A has effectively obtained floating rate debt and Compa-ny B fixed rate debt.

In the academic literature on swaps, there are two dominant issues: the reasons for usingswaps, and swap pricing and hedging (Cooper & Mello, 1991). Few empirical studies, ex-cept for Brewer, Jackson, and Moser (1996), have examined how using swaps influencesthe performance of end-users. This is despite it being a pressing regulation policy topic re-cently complicated by some highly publicized derivative transaction losses (e.g., GAO,1994, p. 3–4; Crawford & Sen, 1996, p. 121–145). Interest rate swaps are widely arguedto provide benefits to their users, as indicated by a discussion of various economic ratio-nales of swap usage later in this article. However, much criticism over swap usage also ex-ists. Critics generally contend that swap usage may cause a firm to speculate too much andtherefore might be harmful to the firm’s interest. Brewer et al. examined the impact of us-ing interest rate futures and swaps on financial performance of depository institutions, find-ing that volatility of equity returns is negatively related to the involvement in swaps but notto interest rate futures. Their results suggest that the swap usage actually decreased the ex-posure for risk of depository institutions instead of increasing it. Clark and Perfect (1996)documented the negative impact on the performance of derivative dealers in response to aclient’s loss due to swap usage. However, because the depository institution may partici-pate in the swap market either as an end-user or as a dealer, the impact of swap usage is notyet clear for non-financial firms that are normally end-users.

In this study, we examine both short-run and long-run wealth effects associated with thedisclosure of adoption of interest rate swaps by food processing companies, as well aschanges in the risk to equity holders of those firms. The food processing industry has beenamong the heaviest users of interest rate swaps. Unlike depository institutions which mayuse swaps in both assets and liabilities management, firms in the food processing industryprimarily use generic interest rate swaps in corporate liability management. This is preciselythe focus of the academic literature when exploring economic rationale of the use of inter-est rate swaps; the food processing industry is a very significant manufacturing industry.Thus, the food processing industry provides a relatively favorable environment for testingthe theories for using interest rate swaps. This study does not attempt to determine the ef-fect of any single-interest, rate swap agreement. Rather, it aims to gauge the stock market’soverall evaluation of information that a firm is using interest rate swaps.

Section 2 reviews the various arguments for the economic rationale for the use of inter-est rate swaps. Section 3 discusses the data, methodology, and empirical results. Section 4provides concluding remarks and directions for future research.

368 YANG ET AL

2. THEORIES FOR INTEREST RATE SWAPS

The theoretical motivations for managers using interest rate swaps tend to fall into one ofthe following categories. In the first category are the comparative advantage theories, whichargue that firms may have comparative advantage in borrowing in either the fixed rate orvariable rate market and use interest rate swaps to arbitrage this apparent mispricing. In thesecond category are the agency cost theories, which argue that interest rate swaps can mit-igate agency costs by allowing the firm to effectively borrow long term while being moni-tored by the short term debt market. The signaling theories generally argue that interest rateswaps may reduce the cost of debt by mitigating the effects of asymmetric information. Thewealth transfer theories generally argue that there is default risk in interest rate swaps andthis may transfer wealth to debt holders at the expense of shareholders. The remainder ofthis section discusses these theories in detail.

2.1. Comparative Advantage and Credit Arbitrage

Bicksler and Chen (1986) argued that borrowers with higher quality credits have a cost ad-vantage in both the fixed rate and floating rate markets, but have a comparative advantagein the fixed rate market. In contrast, borrowers with lower quality credit have a compara-tive advantage in borrowing at floating rates. The borrowers with higher quality credit mayborrow in the fixed rate market and swap the fixed interest rate stream for a floating ratestream with borrowers of lower quality credit who raise funds at a floating rate. The resultis that both parties in the swap are better off and divide between them unambiguous gainfrom credit arbitrage. However, there is much criticism over the comparative advantage ar-gument. As noted by Arak, Estrella, Goodman, and Silver (1988), the comparative advan-tage argument relies on the assumption that the relative credit risk premium of a floating-rate instrument for a high-rated versus low-rated borrower is less than the relative creditrisk premium for a fixed-rate instrument of the equal term.

Smith, Smithson, and Wakeman (1988) argued that apparent underpricing of floating ratecredit risk is essentially the result of overlooking a call option that is embedded in some float-ing rate agreements. They further claim that accounting for this call option would eliminatethe supposed pricing differential, and therefore the motivation of swaps as suggested by thecomparative advantage theory. Smith et al. argue that any additional apparent savings fromsynthetic long-term fixed borrowing via a swap can be attributed to losing the prepaymentoption because the firm typically has this option if it borrows directly in the long-term fixedrate market. They also note that if any interest rate differential is simply from market ineffi-ciencies, it certainly should not persist for a long time. In this case, the use of swaps to arbi-trage would reduce the differential over time, and implies a decrease in the use of swaps anda shrinking of swap markets. The evolvement of swap markets completely contradicts thisreasoning. In sum, the logic of this theory is somewhat flawed and tends to suggest short-run positive wealth effect of swap usage but does not suggest changes in risk.

2.2. Agency Cost

Wall (1989) and Wall and Pringle (1989) have argued that the use of interest rate swaps mayreduce agency cost. Interest rate volatility can increase the risk of financial distress. If fi-nancial distress is costly (a frequent argument) or if the management regards it as intolera-ble misbehavior, the management then has an incentive to mitigate such risks. The normal

THE WEALTH EFFECT OF SWAP USAGE 369

way to minimize the risk of future financial distress is to fund long-term investment withlong-term, fixed-rate debt, and the short-term investment with short-term debt. Neverthe-less, long-term lending carries substantial risks from an outside investor’s viewpoint. Par-ticularly, after a low-rated borrower issues long-term debt, there is an incentive to make thefirm riskier at the expense of the bondholders (Wall, 1989). Bondholders would like to mon-itor the management’s discretion to make sure there will be no default in the debt. Agencycosts, however, are ultimately borne by the borrower, mostly through higher interest rates.Wall observed that synthetic fixed-rate financing should discourage management from pur-suing risky investment strategies. Because the firm issues short-term debt and swaps intofixed payments, the firm is monitored each period as it enters the short-term debt marketand consequently is not required to pay the long-term premium for agency cost. Thus, theuse of interest rate swaps should lower agency costs. Note that interest rate swaps do makeit possible for firms to reduce financing cost, according to Wall’s, or Wall and Pringle’s the-ory, but it results from lower agency cost and not from arbitrage. This theory tends to sug-gest both short-run and long-run positive wealth effects of swap usage. It also suggests riskreduction from using swaps.

2.3. Signaling

Arak et al. (1988) and Titman (1992) analyzed the function of interest rate swaps from theperspective of asymmetric information. They argued that the use of interest rate swaps shouldreduce the unfavorable consequences of asymmetric information. In the case of asymmetricinformation, borrowers have to signal information to lenders about their financial conditionsbecause outside investors cannot perfectly distinguish safe firms from risky ones. Risky firmsprefer long-term debt over short-term debt in order to lock in funding cost for a long time be-fore their financial condition deteriorates. If the safer firms also borrow long-term loans, theymay be pooled incorrectly with other risky firms and may be charged a higher default risk pre-mium and, thus a higher interest rate. Hence, safe firms may signal that they do not expecttheir condition to deteriorate in the future by employing a short-term funding strategy and, atthe same time, taking on liquidity risk that they may not be able to roll over the short-termborrowing. Swaps are then used to hedge firms’ cash flows from short-term borrowing andimmunize them from market (rate) risk. Titman hypothesized that lower-rated borrowers withan optimistic outlook can achieve the benefits of short-term borrowing, without the higher ex-pected costs of financial distress, by borrowing short-term and swapping a floating-rate oblig-ee for a fixed-rate obligation. On the other hand, borrowers with very high credit ratings mayissue a long-term debt and swap for a floating-rate debt to share the gains.

Kuprianov (1994) noted that the basic logic of the signaling argument runs closely par-allel to that of an agency cost argument. The borrowers in Titman’s (1992) model are urgedto choose short-term financing in order to signal management’s belief that the firm is ingood credit condition. However, the act of taking on short-term debt mitigates incentives totake on added risk once the firm receives loans, similar to the prediction of Wall’s (1989)model. Thus, the theory also predicts at least short-run positive wealth effect and risk re-duction for swap users.

2.4. Wealth Transfer

There are also a few arguments against the benefits of using swaps, particularly from theperspective of shareholders. Cooper and Mello (1991) paid special attention to the risk in-

370 YANG ET AL

volved with swap usage itself. They argued that in the common case of risky swaps, i.e.,swaps with default risk, any swap usage usually results in a wealth transfer to the debt hold-ers of the firm using swaps under the perfect market condition. This immediately suggestsa negative wealth effect for shareholders. Cooper and Mello further pointed out that thisconclusion holds when the firm uses synthetic financing to transform the floating rate debtto a fixed rate debt. If the swap is covenanted at the same time the floating rate is issued,there will be no wealth effect and the shareholders will be indifferent to whether the firmuses swaps or not. They also pointed out that the agency cost effect, as presented in Wall(1989) and Wall and Pringle (1989), may still exist and can be an extra benefit source toshareholders despite the possible negative wealth effect. They were also aware that if pric-ing inefficiency exists between the floating rate and fixed debt markets, the synthetic fi-nancing through swaps could have a positive wealth effect. However, pricing inefficiencymay prevail only in the early stages of swap markets, and arbitrageurs have gradually di-minished the window of the opportunity. Thus, according to Cooper and Mello, the posi-tive abnormal return from swap usage may not be expected during our sample period.

Cummins, Phillips, and Smith (1998) and Tufano (1996) propose the most challengingargument. They argued that managers engage in risk management in general, and in deriv-ative activity in particular, in order to manage risk for their own best interests, but not nec-essarily in the best interests of shareholders. Some risks reduced by derivatives can also bediversified by shareholders but not by managers. Job protection and stable incomes are ma-jor economic incentives for managers’ demand for risk management. Thus, such risk man-agement may be potentially value-reducing activities. In a recent major study, Tufanoclaims to find almost no evidence to support the various rationales that would make riskmanagement a value-maximizing decision and thus in the interests of shareholders. Rather,his findings from the gold mining industry suggest that mangers use derivatives to reducethe volatility of their own income stream. Note that the argument of Cummins et al. and Tu-fano does not contradict Cooper and Mello (1991), because one major incentive for man-agers is to protect the firm from bankruptcy and keep their jobs. To fulfil this goal, man-agers may satisfy the bondholders with the favorable wealth transfer. In sum, the theorypredicts negative or zero wealth effect of swap usage. It does not suggest any change in risk.

3. DATA, METHODOLOGY, AND RESULTS

Based on the literature review, this study will examine the following hypotheses.

Hypothesis 1: H0: There is no significant long-run wealth effect before and after a SECdisclosure on swap usage occurred.

Hypothesis 2: H0: There is no significant risk change before and after a SEC disclosureon swap usage occurred.

Hypothesis 3: H0: There is no significant short-run wealth effect before and after a SECdisclosure on swap usage occurred.

The data was collected as follows. First, we identified swap users by searching the quar-terly SEC filing record from 1988 to 1996 among firms whose primary business is in thefood processing industry [primary two-digit Standard Industrial Classifications (SIC) code20]. (Before 1990, there was only one time reporting per year available.) From this users’list, we deleted the firms (1) whose stock return data were not available in the Center forResearch in Security Prices (CRSP) database; and (2) for which the relevant information

THE WEALTH EFFECT OF SWAP USAGE 371

about the size and book-to-market was missing in COMPUSTAT. This left 33 sample firmsduring 1988 to 1995 to examine long-run performance with monthly data and 10 samplefirms in 1996 to examine the short-run performance with daily data. Due to the unavail-ability of stock return data in CRSP tape covering at least one year after the event month(at the time of beginning this research), we did not include the 1996 sample firms in de-tecting the long-run performance. The event month for each swap user is set by the monththey first disclosed the use of interest rate swaps to the Securities and Exchange Commis-sion (SEC). The Financial Accounting Standards Board (FASB) issued the Statements ofFinancial Accounting Standards (SFAS) 119 to expand the required disclosure of deriva-tives significantly, which was effective for the 1994 annual reports. Before that time, therewas essentially little legal requirement for firms to report using swaps in time. However,this does not cause a serious problem with incomplete identification of swap user, becauseswap users were identified until 1995 and included in our event study with monthly data.

Appropriate control firms were selected to evaluate the long-run performance of swapusage with monthly data using the reported event months for 33 sample firms. Control firmswere matched as described by Barber and Lyon (1997), who indicated that matches basedon size and book to market ratio provide well specified test statistics in a long-run eventstudy model. The matching firms act as a benchmark against which to compare the samplefirms. This method adequately controls firm-specific factors that may influence stock per-formance particularly in the long-run (Barber and Lyon, 1997). Several requirements wereset a priori to identify those control firms for the long-run event study. First, potential match-es were listed for a period of one year before the event month and for at least one year af-ter the event month on COMPUSTAT. Second, firms were cross-listed on the CRSP tapesfor the same time span. A search for firms listed on the COMPUSTAT tapes was then con-ducted among firms from the same industry defined by the same two digit SIC classifica-tion. Specifically, we first identified all firms in the same industry with a market value ofequity between 70 percent and 130 percent of the market value of equity of the sample firm.Then, from this set of firms, we chose the firm with the book-to-market ratio closest to thatof the sample firm. The search for 10 control firms to examine the short-run performancewith daily data was based on similar guidelines. The only difference was that the matchfirms were required to have daily return data in CRSP and other financial data in COM-PUSTAT during three months before and three months after the event.

The information about swap usage may already reach the market before the disclosuremonth of swap usage. Before 1994, sample firms may have reported their swap usage sometime after they actually started using swaps. In fact, some sample firms indicated they usedswaps even more than one year earlier than the disclosure, while some firms did not giveany information about when they began using swaps. To address this concern, we exam-ined the stock performance for one year before the event month. It may be somewhat ar-bitrary to conduct a backward examination only up to one year before the disclosuremonth. However, we believe that backward examination up to one year is enough to ad-dress the concern. The predominant belief is that stock market is not strong form efficient,but rather is semi-strong or weak form efficient. Lack of strong market efficiency impliesthat privately known information of swap usage does not affect stock performance and thatonly publicly known information of swap usage may affect the stock performance. Thus,even if a firm started using swaps long before it became public knowledge, the stock pricemay not reflect swap usage until after the information is publicly announced. It is very un-likely that a firm would delay its report of swap usage to SEC longer than one year if hadalready become public knowledge. In sum, examination of stock performance one year be-

372 YANG ET AL

fore the SEC disclosure month at least can satisfy our major interest in the long-run per-formance.

We first examined the Hypothesis 1, i.e., the long-run performance of using interest rateswaps, based on the 33 firms and their match firms. Following Barber and Lyon (1997), wecalculated the buy-and-hold abnormal return (BHAR) for each sample firm, i, and each pe-riod, T, 12 months before, 12 months after, 24 months after, and 36 months after the eventmonth as follows:

(1)

(2)

(3)

the BHAR is determined by calculating the difference between the compounded buy andhold returns for each sample firm and its match firm where Rit is the monthly return to thesample firm i in the tth month and Rmt is the monthly return to the match firm m. We thenaveraged the across the sample to estimate the overall effect of using interest rate swaps.s(BHARit)is the cross-sectional standard deviation of BHARs for the sample of n firms.Table 1 reports the results for Hypothesis 1. All but one of the BHARs was negative, whichindicates a possible negative wealth effect of swap usage in the long run. However, the ttest statistics suggest that they all are not significantly different from zero. Thus, there is nosignificant long-run wealth effect of swap usage at an annual return level during the oneyear before and three years after the disclosure month. Note that each firm individually mayhave certain accounting gains or losses resulting from its swap positions at a particular time,depending on the changing interest rate. However, counting or averaging these gains andlosses across firms would not be useful in terms of testing the underlying theories for usinginterest rate swaps. Measuring the stock market reaction to the new information of swap us-age by the sample firms, as done in this study, provides direct evidence as to whether usinginterest rate swaps is evaluated by the market as a good corporate policy.

The possible risk change associated with the use of swaps, as expressed in Hypothesis 2,is also of interest, because the interest rate swaps are for managing the financial risk. Therisk proxy used here was total risk, i.e., the variance or standard error of equity returns.There are two reasons for using the total risk as the risk proxy. First, the accurate beta esti-

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THE WEALTH EFFECT OF SWAP USAGE 373

TABLE 1. Buy and Hold Abnormal Return (BHAR) for Firms using Swaps

Period Relative Buy and Hold Abnormal Return (%)

to the Event Mean Standard Error t-statistics

1st year before 24.38 4.99 20.881st year after 3.20 8.47 0.382nd year after 22.35 13.89 20.173rd year after 227.72 36.06 20.77

Note. The applicable smallest critical value of one-tailed t-tests is 1.65 at 0.05 level.

mation needs a certain number of long time series of annual returns, and the four years ofbuy-and-hold returns in this study does not meet the requirement. The beta estimates usingdaily or monthly return data have little role in risk adjustment of stock returns. Second, thetotal risk measurement, variance of returns, is an acceptable risk proxy and an importantcomponent of performance evaluation. The performance evaluation is composed of meanand variance effects.

We examined the average annual total risk for four years, i.e., the year just before and thethree years immediately after the event table. For each year, we first calculated variancesof monthly returns during 12 months for each sample and match firm. Then we averagedthe variances across the sample firms and match firms, respectively. The square root of av-eraged variances measures the total risk. The results of Hypothesis 2 are reported in Table2. Firms had a slightly higher total risk in the first year after reporting the use of swaps, aslightly smaller total risk in the second year, and a slightly higher total risk in the secondyear. A similar risk pattern applies to the match firms. The match firms always had a slight-ly higher total risk during the four years than the sample firms. However, the F test statis-tics suggest that the total risk over these four years did not change. The difference in totalrisk between the swap users and the match firms also was negligible. The results suggestthat there is no effect of risk shift on the firms using swaps. This result is different fromwhat Brewer et al. (1996) reported. This may be because financial firms and non-financialfirms have a different sensitivity to the use of swaps. Particularly, most assets for finan-cial firms such as loans may be sensitive to the use of swap usage, but not for non-financialfirms. This likely brings an additional source of sensitivity for financial firm shareholders’equity to the use of swaps. The additional effects on the equity returns for financial firmsmay account for the difference between our finding and that of Brewer et al.

However, the above BHAR result does not exclude the possibility that the stock marketresponded to the new information in a much shorter time than one year. We addressed thepossible short-run wealth effects, as expressed in Hypothesis 3, first by examining themonth-by-month stock performance around the SEC disclosure. We use the following for-mula to calculate the monthly abnormal return (AR) for swap users during 12 months be-fore and 12 months after the disclosure month:

ARit 5 Rit 2 Rmt (t 5 212, 211, ...0, ..., 11). (4)

The benchmark still is the same match firm for the firm i. The ARs are averaged over thesample firms (AR¯̄ it) and the cross-sectional standard deviation of the ARs (s( ARit)) is usedto calculate the t-statistic (tAR):

374 YANG ET AL

TABLE 2. Total Risk of Sample Firms and Match Firms over Time

Period RelativeSquare Root of the Mean of Variances (%)

to the Event Swap Users Match Firms F-test

1st year before 7.40 7.57 1.051st year after 7.84 8.14 1.082nd year after 6.78 7.09 1.093rd year after 7.75 8.31 1.07

Note. The critical value of F test is 3.58, F (11, 11) at 0.05 level.

(5)

(6)

These statistics are used to estimate the overall effect of using interest rate swaps and itssignificance.

The short-run performance using monthly data will detect possible market reactions toswap usage for one or several months. Table 3 reports the results of Hypothesis 3 usingmonthly data. The abnormal return of 22.29 percent in month 9 was significantly differentfrom zero at a 5 percent level. It suggests that swap users in the 1988–1995 sample on av-erage had a wealth loss of 2.29 percent in the month that is 9 months before the SEC disclosure month. The wealth loss of 2.29 percent in one month is statistically as well aseconomically significant. The significant wealth effect in month 9 verifies our previous ar-gument of one year backward examination, and roughly measured the average delay for1988–1995 sample firms in reporting swap usage.

To check robustness of the above results on Hypothesis 3, we further examined short-runwealth effects of swap usage using daily data. We focused on 1996 firms. This sample de-sign may give us the most accurate information available about the impact of swap usagebecause the accuracy of when firms started using swaps is more certain in 1996 than for the1988–1995 sample firms. As indicated before, the first SEC disclosure time for these firmsmay be after they begin using swaps, the only feasible event in this study. For 1995 samplefirms, the time difference between disclosure time and actual use of swaps may be quitelong. For example, some firms which did not disclose swap usage until 1994 were forcedby law to report using swaps in 1995, and these may actually have used swaps more thanone year earlier. Through reading the relevant information in the financial statements of thesample firms, we chose roughly one year as a reasonable period to account for the full im-plementation of the 1994 disclosure regulation. Thus, we expect the first SEC disclosure

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THE WEALTH EFFECT OF SWAP USAGE 375

TABLE 3. Monthly Abnormal Returns Around the Disclosure Time of Swap Usage (n 5 33)

One Year Before the Event One Year After the Event

Abnormal Returns (%) Abnormal Returns (%)

Month Mean Standard Error t-statistic Month Mean Standard Error t-statistic

212 0.62 1.91 0.32 0 1.66 1.84 0.90211 21.91 1.24 21.54 1 0.68 1.21 0.56210 22.33 1.70 21.37 2 0.28 1.47 0.1929 22.29 1.35 21.70* 3 20.57 1.78 20.3228 1.55 1.46 1.06 4 20.66 1.69 20.3927 20.86 2.40 20.36 5 1.43 1.72 0.8326 0.53 1.71 0.31 6 2.23 2.05 1.0925 20.74 1.64 20.45 7 0.33 1.72 0.1924 0.65 2.12 0.31 8 0.22 1.61 0.1423 20.60 1.22 20.50 9 22.04 2.62 20.7822 2.51 1.61 1.56 10 20.54 1.63 20.3321 20.81 1.41 20.58 11 21.68 2.41 20.70

Note. The * indicates significance at the 5% level where the applicable critical value of one-tailed t-test is 1.65.

for 1996 firms to be close enough to the actual time of swap usage, allowing for a shortertime lag in reporting corporate activity to the SEC. This more accurate timing plus higherfrequency data may provide us another useful check on the effect of swap usage.

The examination of daily data still employs the same specification as that for a long-runevent study. We examined the daily abnormal returns during three months before and dur-ing the event month. The selection of three months before (day 263 to day 21) the eventmonth (day 0 to day 21) may well capture an information leakage of swap usage before theSEC disclosure. The fact is that nearly 90 percent of public firms did report their activity tothe SEC within three months after occurrence. In addition, selecting three months tends tobias against finding significant wealth effect, and does not invalidate our results if we findsignificantly positive or negative abnormal returns.

The cumulative abnormal returns (CAR) over the period starting at time t and ending attime T for firm i are calculated as:

(7)

The CARs are averaged over the sample firms (CAR¯¯¯i,t,T¯¯¯) and the cross-sectional standard

deviation of the CARs (s(CARi,t,T) is used to calculate the t-statistic (tCAR):

(8)

(9)

The cross-sectional procedure of variance estimator used here is better than the time seriesprocedure of variance estimator because it allows for changing variance over time and theproblem of autocorrelation of daily data (Brown & Warner, 1985; MacKinlay, 1997). Wealso conducted a time series procedure to test the significance of abnormal and cumulativeabnormal returns. We found that this procedure yields more observations with negative ab-normal returns. This result may be indicative of the autocorrelation problem. However, inthe context of this study, both procedures reached qualitatively the same conclusion. Table4 reports the results of Hypothesis 3 using daily data. We found that significant negative cu-mulative abnormal returns exist during day 263 to day 249 (trading days) and that thereare no significant abnormal returns afterward. The negative cumulative abnormal returnsrange from 21.72% to 25.79%, within which the previous estimate from monthly data22.29% falls. Also note that twelve 1996 firms provide a sufficient sample size and thatthe event dates for these twelve firms are dispersed across four quarters. We did not findany other compounding event during 1996 with a significant negative impact lasting for thewhole year on stock performance of the food processing companies. Thus, these results con-firm our previous findings that there is a negative wealth effect of swap usage and it oc-curred before SEC disclosure.

4. CONCLUSIONS

Using monthly data, we failed to find any significant long-run wealth effect (Hypothesis 1),measured by buy-and-hold abnormal returns, associated with swap usage. This may be an

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376 YANG ET AL

THE WEALTH EFFECT OF SWAP USAGE 377

TABLE 4. Cumulative Abnormal Daily Returns ThreeMonths Before and After the Event Month (n 5 11)

Trading DayRelative to

Cumulative Abnormal Daily Returns (%)

the Event Mean Standard Error t-statistic

263 21.72 0.75 22.28*262 22.99 1.75 21.71*261 23.36 2.07 21.63260 23.22 1.83 21.76*259 22.00 2.10 20.95258 22.87 2.44 21.18257 22.49 2.00 21.24256 22.54 2.11 21.21255 22.40 2.19 21.09254 23.55 2.10 21.70*253 23.61 2.51 21.44252 25.31 2.44 22.18*251 25.79 2.80 22.07*250 24.84 2.68 21.81*249 24.37 2.48 21.76*248 24.49 2.93 21.53247 23.86 4.13 20.93246 24.85 4.74 21.02245 26.49 5.34 21.22244 27.85 6.37 21.23243 26.17 6.93 20.89242 27.47 8.16 20.92241 27.17 9.32 20.77240 28.80 10.49 20.84239 27.58 8.94 20.85238 25.85 7.13 20.82237 27.15 8.10 20.88236 29.36 9.52 20.98235 211.64 11.45 21.02234 210.64 10.57 21.01233 210.58 11.31 20.93232 214.41 11.34 21.27231 215.32 11.70 21.31230 215.15 11.86 21.28229 214.94 11.46 21.30228 215.24 11.30 21.35227 214.57 11.57 21.26226 212.21 10.32 21.18225 212.69 10.88 21.17224 212.31 11.19 21.10223 212.48 11.24 21.11222 212.70 11.74 21.08221 213.04 12.82 21.02220 214.01 12.35 21.13219 213.07 10.56 21.24218 213.12 10.27 21.28217 210.02 9.13 21.10216 210.12 8.09 21.25215 211.72 8.38 21.40214 210.48 8.74 21.20

(continued)

indication that the use of interest rate swaps is endogenous to the firm and does not affectshareholders’ wealth in the long run. It may also explain why shareholders can still tolerateswap usage despite the short-run negative wealth effect. We did find a negative wealth ef-fect during a short period (about one month) before firms first disclosed swap usage to theSEC, using both monthly data and daily data (Hypothesis 3). We did not find evidence fora risk shift in equity return after the SEC disclosure date (Hypothesis 2). Our finding forrisk shifts differ from the finding of risk reduction for financial firms in Brewer et al. (1996).In sum, we found that negative wealth effect and no risk shift are most consistent with thewealth transfer theory. The adoption of interest rate swaps causes unfavorable wealth trans-fer for the shareholders in the short run and contradicts predictions from comparative ad-vantage, agency cost, and signaling arguments. Further research may seek to distinguishhow wealth transfers from shareholders ultimately are divided among bondholder and man-agers.

378 YANG ET AL

TABLE 4. (Continued)

Trading DayRelative to

Cumulative Abnormal Daily Returns (%)

the Event Mean Standard Error t-statistic

213 211.70 9.27 21.26212 211.94 8.68 21.38211 212.69 7.85 21.62210 211.26 7.33 21.5429 211.34 7.63 21.4928 211.06 7.94 21.3927 211.71 8.23 21.4226 211.83 7.84 21.5125 212.17 7.90 21.5424 212.11 7.76 21.5623 211.99 8.15 21.4722 212.15 7.58 21.6021 214.21 9.71 21.460 213.22 10.71 21.231 212.01 9.84 21.222 211.43 8.65 21.323 212.02 8.98 21.344 211.97 9.19 21.305 212.37 9.62 21.296 214.25 9.36 21.527 214.31 9.57 21.508 214.23 9.44 21.519 213.74 9.94 21.3810 213.60 10.03 21.3611 212.73 10.60 21.2012 213.70 10.40 21.3213 211.71 10.22 21.1514 211.38 9.79 21.1615 210.06 10.04 21.0016 29.72 9.88 20.9817 213.02 11.46 21.1418 212.07 10.32 21.1719 210.82 10.97 20.9920 210.97 10.49 21.0521 211.08 11.10 21.00

Note. The * indicates significance at the 5% level.

REFERENCES

Arak, M., Estrella, A., Goodman, L.S., & Silver, A. (1988). Interest rate swaps: An alternative expla-nation. Financial Management, 17, 12–18.

Barber, B.M., & Lyon, J.D. (1997). Detecting long-run abnormal stock returns, The empirical powerand specification of test statistics. Journal of Financial Economics, 43, 341–372.

Bicksler, J., & Chen, A.H. (1986). An economic analysis of interest rate swaps. Journal of Finance,41, 645–655.

Bodnar, G.M., Hayt, G.S., Marston, R.C., & Smithson, C.W. (1995). Wharton survey of derivativesusage by U.S. non-financial firms. Financial Management, 24, 104–114.

Brewer, E., Jackson, W.E., & Moser, J.T. (1996). Alligators in the swamp: The impact of derivativeson the financial performance of depository institutions. Journal of Money, Credit, and Banking,28, 482–497.

Brown, S.J., & Warner, J.B. (1985). Using daily stock returns: The case of event studies, Journal ofFinancial Economics, 14, 3–31.

CFO Forum. (1992, April). Which derivatives do CFOs really use? Institutional Investor, 26, 141.Clark, J.A., & Perfect, S.B. (1996). The economic effects of client losses on OTC bank derivative

dealers: Evidence from the capital market. Journal of Money, Credit, and Banking, 28, 527–545.Cooper, L.A., & Mello, A.S. (1991). The default risk of swaps. Journal of Finance, 46, 597–620.Crawford, G., & Sen, B. (1996). Derivatives for decision makers: Strategic management issues. New

York: John Wiley & Son, Inc.Cummins, D.J., Phillips, R.D., & Smith, S.D. (1998, First Quarter). The rise of risk management. Fed-

eral Reserve Bank of Atlanta Economic Review, 30–39.GAO. (1994, May). Financial derivatives: Actions needed to protect the financial system. United

States General Accounting Office, GAO/GGD-94-133, Washington, D.C.Kuprianov, A. (1994). The role of interest rate swaps in corporate finance. Federal Reserve Bank of

Richmond Economic Quarterly, 80, 49–68.MacKinlay, A.C. (1997). Event studies in economics and finance. Journal of Economic Literature, 35,

13–39.Phillips, A.L. (1995). 1995 Derivatives practices and instruments survey. Financial Management, 24,

115–125.Smith, C.W., Smithson, C.W., & Wakeman, L.M. (1988). The market for interest rate swaps. Finan-

cial Management, 17, 67–73.Titman, S. (1992). Interest rate swaps and corporate financing choices. Journal of Finance, 47, 1503–

1516.Tufano, P. (1996). Who manages risk? An empirical examination of risk management practices in the

gold mining industry. Journal of Finance, 51, 1097–1137.Wall, L.D. (1989). Interest rate swaps in an agency theoretic model with uncertain interest rates. Jour-

nal of Banking and Finance, 13, 261–270.Wall, L.D., & Pringle, J.J. (1989). Alternative explanations of interest rate swaps. Financial Manage-

ment, 13, 59–73.

Jian Yang is a graduate student in the Department of Agricultural Economics at Texas A&M Uni-versity.

David J. Leatham is a professor in the Department of Agricultural Economic at Texas A&M Uni-versity.

Spencer A. Case is a graduate student in the Department of Finance at Texas A&M University. Thisstudy reports research conducted through the Texas Agricultural Experiment Station of the TexasA&M University System.

THE WEALTH EFFECT OF SWAP USAGE 379