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Managerial biases and selective hedging∗ Tim R. Adam NUS Business School National University of Singapore 1 Business Link Singapore 117592 Tel.: +65 6516 4675 Fax: +65 6872 1438 E-mail: [email protected] Chitru S. Fernando Michael F. Price College of Business University of Oklahoma 307 West Brooks Norman, OK 73019 Tel.: (405) 325-2906 Fax: (405) 325-7688 E-mail: [email protected] Evgenia Golubeva Michael F. Price College of Business University of Oklahoma 307 West Brooks Norman, Oklahoma 73019 Phone (405) 325-7727 Fax (405) 325-7688 E-mail: [email protected]

November 2007

JEL Classification: G11; G14; G32; G39 Keywords: Corporate risk management; speculation; mental accounting; disposition effect; overconfidence.

∗

We thank Ted Reeve for providing us with his derivative surveys of gold mining firms and Leung Kam Ming for excellent research assistance. We also thank Jesus Salas for valuable assistance and seminar participants at the University of Oklahoma for their comments. We are responsible for any errors.


Managerial biases and selective hedging

Abstract

Using data on the selective hedging activity of a sample of 92 North American gold mining firms, we find that the degree of selective hedging is related to past performance of derivative positions after controlling for changes in the value of the underlying commodity (gold). We interpret this finding as consistent with the hypothesis that managers place derivative positions into a separate mental account and make hedging decisions based on the performance of derivatives positions alone. We also find that selective hedging responds positively to past derivative cash flows and negatively to past derivative profits suggesting that cash flows and profits are separate decision variables.

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1. Introduction While the extant theories of corporate risk management make no provision for firms to use derivatives for any purpose other than purely for hedging, there is now a considerable literature which documents derivatives usage that is inconsistent with a pure hedging motive. This evidence is surprisingly consistent with the argument advanced nearly 50 years ago by Working (1962), that the “traditional” risk avoidance notion of hedging – matching one risk with an opposing risk – is seriously deficient when it comes to explaining hedging behavior in practice. While reported incidences of pure speculation, i.e., the use of derivatives in the absence of an offsetting exposure or in general to increase a firm’s exposure to a specific risk factor, are relatively uncommon, there is considerable survey evidence that many managers systematically incorporate their market views into their risk management programs.1 Incorporating market expectations into hedging activity implies varying the extent to which a firm hedges its exposure based on the expected price behavior of the underlying risk factor, a practice that has been termed “selective hedging” (Working, 1962; Stulz, 1996). For example, a commodity producer that hedges selectively might increase the extent of its hedging if it expects the commodity price to decline and vice versa. The underlying premise of selective hedging, that managers are able to successfully time the market on a consistent basis, appears to stand in contrast to the concept of an efficient market. Nonetheless, Stulz (1996) has argued that some firms may acquire a comparative advantage in risk taking due to the access that their business activity provides them to specialized information that is not publicly available. He cites

1

See, for example, Dolde (1993), Bodnar, Hayt and Marston (1998), Glaum (2002), Adam and Fernando (2006) and Brown, Crabb and Haushalter (2006).

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the example of a large copper consumer that has access to specialized information about the copper market as a result of its copper purchasing activity. In such cases, he argues that it would be rational for firms to hedge selectively using their proprietary information. Stulz (1996) emphasizes the importance of firms truly understanding the source of their comparative advantage, if any, noting that being a large player in a particular market does not necessarily provide such a firm information that other firms in the market do not have. In addition, notwithstanding any private firm information, selective hedging exposes it to considerably more risk relative to the case in which it engages in pure hedging. Therefore, a firm that seeks to add shareholder value by selective hedging must not only have a comparative advantage in information but also have the balance sheet and capital structure to support the extra risk taking that selective hedging entails. The criteria established by Stulz (1996) for a successful selective hedging strategy seem stringent indeed. In contrast, the available evidence suggests that selective hedging activity is widespread, perhaps too widespread to meet the Stulz criteria for success. For example, Dolde (1993), based on a survey of 244 Fortune 500 firms, reports that almost 90% of the firms he surveyed engaged in selective hedging, at least occasionally. Similarly, Bodnar, Hayt and Marston (1998) reports that more than half of the firms in their survey sample of 399 U.S. non-financial firms engaged in selective hedging. Outside the U.S., Glaum (2002) surveys the risk management practices of the major nonfinancial firms in Germany and finds that the majority engaged in selective hedging. It is difficult to imagine that all these firms met the success criteria established by Stulz (1996), especially that they all had a comparative advantage in the access to information relative to firms that did not report engaging in selective hedging. In both Dolde’s (1993)

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and Glaum’s (2002) surveys, firms were hedging foreign exchange (FX) and interest rate risks, and while the Bodnar, Hayt and Marston (1998) survey covered FX, interest rate, commodity and equity exposures, the reported findings for selective hedging are only for FX and interest rate risks. Even if some commodity firms may have access to specialized information pertaining to their industries, as in the copper example provided by Stulz (1996), it is implausible that the same applies to predicting exchange rates and interest rates. Even in commodity industries, there is no evidence that firms are able to systematically beat the market by selective hedging. Adam and Fernando (2006) explicitly test the question of whether selective hedging creates shareholder value in the gold mining industry. While they find considerable evidence of selective hedging in their sample of gold mining firms, they find no economically significant cash flow gains from selective hedging. Brown, Crabb, and Haushalter (2006) also study selective hedging in the gold mining industry and arrive at a similar conclusion. Thus, it would appear that on average gold mining firms that engage in selective hedging do not have a comparative advantage in information gathering relative to the market. Given the widespread practice of selective hedging that we observe, the lack of evidence that it adds any value poses a puzzle. There are at least two other viable explanations for this practice. First, although selective hedging does not benefit shareholders, it may benefit managers due to incentive compensation. The potential link between selective hedging and managerial compensation is explored in several recent studies, with mixed results. Géczy, Minton and Schrand (2006), who base their study on the firms in the Bodnar, Hayt and Marston (1998) survey, find that CFO stock price

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sensitivity (delta) is positively associated with the probability of actively taking positions, while CEO stock price sensitivity is negatively related to speculation. For a sample of large U.S. non-financial firms with currency exposure, Beber and Fabbri (2006) find no statistically significant relation between CEO delta and selective hedging, although they find a decrease in hedging when the sensitivity of CEO compensation to stock price volatility (vega) increases. Brown, Crabb and Haushalter (2007) find no systematic relationship between selective hedging and several ownership and compensation measures in a sample of 38 gold producers. Adam, Fernando and Salas (2007) also examine the relationship between selective hedging and ownership/compensation for a sample of 92 gold producers over a 10-year period and find that selective hedging actually decreases with stock and option compensation (for both CEOs and CFOs) and insider ownership. They also rule out the possibility that firms may be speculating in this way to exploit information and/or financial advantages. The focus of our paper is on the alternative possible explanation for widespread selective hedging, that managers may be affected by behavioral biases such as mental accounting and/or overconfidence. The concept of mental accounting, first coined by Thaler (1980), is summarized by Grinblatt and Han (2007) as follows: “The main idea of mental accounting is that decision makers tend to segregate different types of gambles into separate accounts … by ignoring possible interactions.” Applied in the risk management context, mental accounting suggests that managers may maintain separate “mental accounts” for derivative positions and for the underlying commodity positions. Focusing excessively on profits and losses of derivative positions, managers may begin to hedge sub-optimally, essentially acting like derivative speculators. For example,

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managers may alter hedging positions following losses on derivatives even though the firm had been optimally hedged before because losses on hedges are tough to justify to the board of directors. In addition, the overconfidence hypothesis (e.g., Daniel, Hirshleifer and Subrahmanyam (1998); Gervais, Heaton and Odean (2003)) implies that managers may be overconfident in their ability to beat the market, engaging in excessive hedging under the mistaken belief that they have a comparative advantage in information gathering.2 To arrive at testable hypotheses, our paper borrows from the growing body of literature that applies the concept of managerial behavioral biases in the corporate context, modeling the manager as less-than-rational, and the market as rational. 3 Baker, Ruback, and Wurgler (2006) provide a thorough review of the literature on behavioral corporate finance. Although this area of research is new, some common themes appear to emerge. Managerial overconfidence is generally defined as either excessive optimism about the prospects of the firm due to overestimation of manager’s own ability to run the firm; or, more frequently, as the overestimation of the precision of private signal regarding the true value of the firm. The other common theme is related to the time-series dynamics of overconfidence: it is believed to increase immediately following successes; decrease by less (if at all) immediately following failures; but overall, decrease with the manager’s experience and tenure (e.g., Gervais, Heaton, and Odean (2003)). The asymmetric response to past successes and failures follows from self-attribution:

2

Stulz (1996) and Glaum (2002) also note that firms in financial distress will have an incentive to speculate, and Adam, Fernando and Salas (2007) find some evidence that firms closer to bankruptcy speculate more. However, this argument only provides a partial explanation at best and does not explain the widespread speculative activity that we observe in practice. 3 Studies include, e.g., Gervais and Odean (2001), Aktas et al (2007), Ben-David et al (2007), Malmendier et al (2007).

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successes tend to be attributed to own skill while failures tend to be attributed to bad luck. The implication for financial decisions is that overconfident managers act more decisively and aggressively. Hence, managerial activity is hypothesized to intensify following successes. The main theme in the corporate studies on mental accounting is that managers maintain separate mental accounts for different decision variables and thus may weigh those variables sub-optimally when making a decision. Sautner and Weber (2006) report that managerial option exercise behavior is consistent with mental accounting: shares acquired on option exercise are more likely to be converted into cash than those acquired as required stock investment. Coleman (2007), in an experimental setting, studies managerial choices over risky alternatives and finds evidence that the surveyed managers maintain separate mental accounts for the consequences of decision outcomes and for the probabilities of those outcomes. Loughran and Ritter (2002) provide an explanation for IPO underpricing based on mental accounting: managers do not mind underpricing as long as it is not larger than the “gain” between the midpoint of the file price range and the first day closing price. Crane and Hartzell (2007) find evidence of disposition effect4 in the behavior of REIT managers: they are found to be more likely to sell properties that have earned high cumulative returns, i.e., “winners” while showing relative reluctance to selling “losers.” To the best of our knowledge, ours is the first paper to address managerial psychological biases in the context of corporate risk management decisions. The potential presence of psychological biases provides for several conjectures regarding the degree of

4

Shefrin and Statman (1985) derive the main theoretical framework for disposition effect. Also see, e.g., Odean (1998) and Grinblatt and Han (2007) for evidence.

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hedging activity. For one thing, mental accounting coupled with overconfidence implies that past successes resulting specifically from the derivative positions (as opposed to the underlying positions or the overall quality of the hedge) would make the manager more overconfident leading to more aggressive pursuing of selective hedging strategies; while past failures would affect the speculative behavior to a lesser degree. Hence, one would expect the relationship between past performance of the derivative positions and the degree of hedging activity in the current period to be positive on average, and to be stronger for past gains than for past losses. At the same time, mental accounting may lead to other patterns in hedging activity. As mentioned previously, past losses on derivative positions may lead to higher activity as the manager alters positions after being reprimanded by the board for having made some “wrong bets.” Alternatively, mental accounting may present itself in the form of disposition effect suggesting again that past gains may lead to excessive trading as managers attempt to realize profits by closing the gaining accounts (selling winners). Given these (somewhat conflicting) hypotheses, we are curious as to whether we can establish an empirical link between hedging activity and past performance of derivative positions after controlling for the change in the value of the underlying commodity. Finding such a relationship would be consistent with the potential presence of mental accounting biases in managerial risk management decisions, whether exhibited as overconfidence, as disposition effect, or in other ways. To address this question, we study the time-series relationship between past performance of derivative positions and future hedging activity using a panel of goldmining firms. Our methodology differs from the techniques employed by the other

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studies of corporate managerial biases. The existing studies fall under two categories: surveys, as in Ben-David, Graham, and Harvey (2007); and cross-sectional studies, as in Malmendier and Tate (2005). These studies look at various cross-sectional characteristics of corporate managers that are likely to affect the degree of biases such as overconfidence. Examples include personal characteristics (age, tenure, education, etc.) as well as personal wealth management practices (the tendency to hold disproportional amounts of own firm’s stock; the failure to exercise vested options). The question in these studies is whether managers labeled as biased engage in suboptimal corporate policies. In contrast, our study focuses on the time-series component. Another important methodological advantage of our paper comes from the nature of our database. It contains quarterly observations on all outstanding gold derivatives positions of a sample of 92 North American gold mining firms from 1989-1999.5 The key advantage of this data set is that we are able to precisely observe and measure actual derivatives transactions. We use several constructs to measure the past performance of the derivative positions for each firm. First, we compute quarterly cash flows from derivative positions per ounce of gold hedged. We look at the total derivative cash flow as well as the cash flow attributable to selective hedging. The latter is computed as in Adam and Fernando (2006) relative to a fixed hedge ratio benchmark, which is based on the average hedge ratio for the firm over the sample period. Selective hedging cash flow is an attractive measure because it reflects the part of the cash flow that results directly from the 5

Our dataset comes from Adam and Fernando (2006), which is based on the quarterly survey conducted by Ted Reeve, an analyst at Scotia McLeod, of outstanding gold derivatives positions at major North American gold mining firms. The data set contains information on all outstanding gold derivatives positions, their size and direction, the instrument types, maturities, and the respective delivery prices for each instrument.

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managerial market timing, i.e., speculative, actions. Theoretically, suppose a manager believes that the gold price is going to rise and therefore reduces the hedge ratio relative to the benchmark. If she is correct in her forecast, then the total derivative cash flow will be negative (since she is short overall) but the selective component will be positive: the firm does not lose as much on the hedge as it could have. Therefore, it is important to look at selective hedging cash flow. The disadvantage of selective hedging cash flow (relative to total cash flow) is that it is harder to observe and to communicate to the decision makers. Our second measure of past performance is derivative profit, which is computed as the change in the value of derivative positions over a given period. Just like cash flows, profits are affected by the change in the price of gold. Specifically, derivative profit per ounce hedged will normally have the opposite sign of the change in the price of gold. A less-than-perfect (selective) hedge will cause a larger deviation of derivative profit from this benchmark. Hence, we adjust derivative profits for the corresponding changes in the price of gold and look at residual profits as well as total profits. The clear advantage of the derivative profit measure is that it is directly observable and is used for reporting purposes at known points in time, as opposed to cash flows that may or may not be realized in a given period. To measure the amount of selective hedging, we use two alternative constructs: (i) the volatility of quarterly hedge ratios and (ii) the absolute value of residual hedge ratio from the Cragg model estimated as in Adam and Fernando (2006). We focus on the shortterm hedging activity (trading contracts maturing within one year). The Wharton survey (Bodnar, Hayt, and Marston (1998)) suggests that leaving aside speculation, even

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hedging is a relatively short-term phenomenon with the bulk of the action confined to the 0-12 month window. In our sample of hedgers, firms that use derivatives over short time horizons are much more prevalent than those using derivatives over longer horizons. Like our explanatory variables (cash flows and profits), our left-hand-side variables may also respond to changes in the price of gold. Given that gold mining firms are less than perfectly hedged on average (many gold producers do not hedge their production at all) a drop in the price of gold may increase the probability of financial distress as the industry falls on hard times. As a result, firms may begin to hedge more. In this case one would expect a negative relationship between the change in gold price and the change in the value of hedge ratio. An important distinction between this case and the research question in our paper is that we do not address the sign of the change in the hedge ratio of gold firms. Instead, we focus on the second moment, the volatility. Nevertheless, we control for the direction of the change in the hedge ratio in our empirical tests. Using panel data and industry aggregate data, our initial evidence indicates that the level of selective hedging activity is positively correlated with past derivative cash flows (as measured by the hedge ratio volatility and especially by the absolute Cragg residuals). We find evidence that this positive relationship is stronger for past gains than for past losses, consistent with the predictions of the overconfidence hypothesis. At the same time, and interestingly, we report a significantly negative relationship between past profits on derivative positions and the degree of hedging activity. The positive sign for cash flows and the negative sign for profits both persist, and the magnitudes of the regression coefficients are barely altered when profits and cash flows are put jointly on

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the right-hand side of the regression. Together, our evidence indicates that there may indeed be conflicting messages coming from different performance indicators and that managers may respond to different performance indicators differently. The remainder of the paper is organized as follows. The following section describes our sample. Section 3 addresses the methodology and findings. Section 4 summarizes and discusses the directions for future research.

2. Data Our selective hedging data is from Adam and Fernando (2006). The sample consists of 92 gold mining firms in North America, encompassing the majority of firms in the gold mining industry. These firms are included in the Gold and Silver Hedge Outlook, a quarterly survey conducted by Ted Reeve, an analyst at Scotia McLeod, from 1989 to 1999. Firms not included in the survey tend to be small or privately held corporations. The survey contains information on all outstanding gold derivatives positions, their size and direction, maturities, and the respective delivery prices for each instrument. The derivatives portfolios consist of forward instruments (forwards, spot-deferred contracts, and gold loans) and options (put and call). A total of 1,295 firm-quarters represent nonzero hedging portfolios of one-year maturity. Each of the sample firms has at least one non-zero observation for a one-year maturity hedge ratio with an average of 13 observations per firm. Out of 92 sample firms, 46 firms have more than ten quarterly observations with non-zero hedge ratio of one-year maturity. For comparison, 28 firms have more than ten nonzero observations for three year maturity and 12 firms have more than ten nonzero observations for five year maturity. Hence, our data is consistent with

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the findings reported in Bodnar et al (1998) that the bulk of hedging activity is concentrated in short term maturity contracts. Adam and Fernando (2006) provide a detailed description of how the quarterly net cash flows pertaining to (a) the use of derivatives and (b) selective hedging are calculated using this data together with market data on gold spot and futures prices, interest rates and gold lease rates. Operational data, e.g., gold production figures, production costs per ounce of gold, etc., we collect by hand from firms’ financial statements.

3. Empirical tests As a motivating exercise, and before turning to the detailed panel framework that accounts for both time series and cross-sectional effects, we focus purely on the timeseries relationship between performance of derivative positions and subsequent hedging activity. Figure 1 plots two aggregate time series: (i) the difference (from the same quarter of last year) in the industry cross-sectional mean of the absolute quarterly change in the one-year-maturity hedge ratio; and (ii) the lagged difference (from the same quarter of last year) in the cross-sectional mean of total derivative cash flow. The crosssectional means are estimated each quarter over all gold-mining firms who report a nonzero value of hedge ratio during that quarter, i.e. over all hedging firms. The two series on the plot are visibly correlated suggesting the existence of a general relationship between derivative cash flows and hedging activity in the gold-mining industry. We next analyze this possibility in detail using panel framework and implementing controls.

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Our first measure of speculative activity is the annual volatility of hedge ratios. We say that a given firm is a hedger with respect to maturity j in a given quarter t if this firm has a non-zero, non-missing observation for the hedge ratio of that maturity in that quarter. The raw values of the hedge ratio exhibit strong seasonal patterns. To remove the seasonal component from the hedge ratio we run firm-by-firm regressions over the nonmissing non-zero observations on the seasonal dummies. Next, for each firm and for each maturity, we compute hedge ratio volatility over four quarters ending in quarter t:

VH

j

t

= STD ( HRt j−3 , HRt j− 2 , HRt j−1 , HRt j ) .

(1)

In (1), HR represents the seasonality-adjusted value of the hedge ratio, which is the residual from a regression of the observed hedge ratio on the seasonal dummy variables. Our second measure of speculative activity is the absolute value of the residual hedge ratio from Cragg model, estimated as in Adam and Fernando (2006). Hereafter, we refer to it as the Cragg residual. For the year ending in quarter t, we compute the annual mean of the quarterly Cragg residuals:

RES j t = MEAN (QRES t j−3 , QRES t j− 2 , QRES t j−1 , QRES t j ) .

(2)

Our independent variables are as follows. The first variable is the mean of the quarterly derivative cash flows CFt:

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CF j t = MEAN (QCFt −j 3 , QCFt −j 2 , QCFt −j1 , QCFt j ) .

(3)

Similarly, we compute the mean annual selective hedging cash flows SCFt. The selective hedging cash flow in a given quarter t is the difference between total and benchmark cash flows BCFt, the latter estimated as in Adam and Fernando (2006) based on a fixed hedge ratio benchmark equal to the average hedge ratio for the firm over the sample period. In addition, we compute the annual derivative profit PROFt as the change in the value of derivative positions per ounce of gold hedged over the calendar year. We adjust the raw profit for the change in the price of underlying commodity (gold) by adding the change in the price of gold to obtain net profit NETPROFt. If gold price drops over a given period, the change in the value of short derivative positions is likely to be positive. Hence by adding the (negative) change in the price of gold we make the net profit less positive than the raw profit measure. Similarly, if gold price rises over a given period, the change in the value of short derivative positions would be negative. Hence by adding back the increase in the price of gold we make the net profit less negative than the raw measure. Table 1 presents the descriptive statistics and correlations between the variables of interest reported on an annual basis. Several observations emerge from the tables. First, not surprisingly, changes in gold price are negatively correlated with the total derivative cash flows and especially with the unadjusted derivative profits. Second, consistently with Adam and Fernando (2006), selective hedging cash flows are zero on average suggesting that selective hedging does not add value to the firm. The correlation between the total and the benchmark cash flow (unreported) is high at around 0.6. We observe a positive correlation between our two major measures of hedging activity: the

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Cragg residual and the hedge ratio volatility. Also, there is a positive correlation between net (selective hedging) cash flows and net derivative profits. Total derivative cash flows and total unadjusted profits, interestingly, are virtually uncorrelated.

{Table 1 about here}

Table 2 presents the result of the annual panel regressions estimated on these variables. This is the main table in our paper. In all of the regressions, we control for the lagged dependent variable and for the beginning-of-year level of hedge ratio: high levels of one-year-maturity hedge ratio at the beginning of the year may indicate a large transfer of two-year positions at the end of the fiscal year, which may affect hedge ratio volatility for non-speculative reasons. In unreported work, we also control for the direction of the change in the hedge ratio. (For the Cragg residual regressions, we control for the signed value of the Cragg residual.) Doing so does not noticeably affect the inference regarding the variables of interest. The general pattern that emerges from the results is that past derivative cash flows are positively related to current year’s hedging activity, while past profits are negatively affecting hedging activity. These results are robust in terms of both sign and magnitude to the inclusion of both cash flows and profits into the regression. Importantly, the results are strong when Cragg residuals are used as a dependent variable. For the annual volatility of the hedge ratio, the general message of the results is the same as for Cragg residuals; however the results lack statistical significance for essentially any variable of interest.

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Additionally, we ask whether we can detect any asymmetries in hedging activity following past successes and failures. Given our previous evidence that Cragg residuals respond positively on average to past derivative cash flows, we estimate the following panel regressions separately for the positive and for the negative values of the lagged total derivative cash flow (the time subscripts indicate quarters).

VH 1t = a + b1VH t1−4 + b2 CFt + b3 CFt − 4 + b4 HRt1−4

(4)

RES 1t = a + b1 RES t1−4 + b2 CFt + b3CFt − 4 + b4 HRt1− 4

(5)

Table 3 presents the results for the years ending in December, and Table 4 for the overlapping years. Panel A in each table presents the results for the annual hedge ratio volatility as the dependent variable (4); Panel B, with the average Cragg residual as the dependent variable (5). The coefficient of interest is b3. We control for the lagged dependent variable, for the contemporaneous correlation (if any) between derivative cash flows and hedge ratio volatility, and for the beginning-of-year level of hedge ratio, for the reason outlined above. For the overlapping years, we adjust the beginning-of-year level of hedge ratio for seasonality. (Doing so or not does not affect our results.) The results in Tables 3 and 4 reveal that the positive relationship with past derivative cash flows is stronger in the sub-sample of positive cash flows, both for the

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December-ending years and the overlapping years. The relationship, again, is generally stronger when the Cragg residual is used as the dependent variable.



To summarize, our evidence suggests that current levels of speculative activity by hedging firms are related to past levels of derivative cash flows and to past profits from derivative positions. The conclusion is especially strong when speculative activity is measured by the Cragg residuals. Specifically, our panel analysis reveals that speculative activity of a given firm in a give year is positively and significantly related to the previous year’s derivative cash flows (either total or selective). At the same time, the relationship with the past derivative profits is significantly negative.

4. Conclusions In this paper, we examine one of the possible explanations for the empirically established widespread practice of selective hedging, that it may be partly due to managerial psychological biases such as mental accounting and overconfidence. We analyze the hedging practices of the North American gold mining firms over 1990 – 1999 using a unique dataset and study the relationship between a firm’s hedging activity and the past performance of its derivative positions alone while controlling for the changes in the price of the underlying commodity (gold). Our empirical investigation reveals the

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existence of a relationship between the past performance of derivative positions and the subsequent amount of selective hedging. This observation is in line with the conjecture that managers maintain separate mental accounts for the value of derivative positions and for the value of the underlying commodity. Importantly, we report mixed results for the sign of this relationship: we find it to be positive when we use derivative cash flows as the performance measure and negative when we use derivative profits. At this point, we speculate that profits and cash flows may be viewed by managers as two separate indicators of performance, hence affecting managerial decisions differently. For example, derivative profits are reported periodically and are easily observable by the board. Hence, a period of negative derivative profits, which are difficult to justify to the board, may force the manager to alter hedging positions thus increasing her hedging activity. At the same time, cash flows from derivative positions may be viewed by the manager as a measure of success that is not directly observable by the board but that may affect the manager’s degree of overconfidence. We plan to continue to investigate the potential explanations for this intriguing finding in our further research.

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Figure 1 - Hedge ratio volatility and cash flows: the industry aggregate time series MVH1: four-lag difference in the cross-sectional mean of the quarterly volatility of one-year-maturity hedge ratio (left scale). Quarterly volatility is computed as the absolute value of the change in the one year maturity hedge ratio over the quarter. The cross-sectional mean is estimated over all sample firms that report a nonzero value for the one year maturity hedge ratio in that quarter. MCF: four-lag difference in the cross-sectional mean of total derivative cash flows (right scale). MCF is plotted with one quarterly lag. 15

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Table 1 – Descriptive statistics and correlations The table reports the descriptive statistics (Panel A) and correlations (Panel B) for the variables of interest on an annual basis. For the purpose of calculating these variables, we keep only those quarterly observations in which firms report a non-zero value for the one-year-maturity hedge ratio. VH is the annual standard deviation of the seasonality-adjusted one-year-maturity hedge ratio. RES is the annual average of the absolute quarterly Cragg residual. CF is the annual average of the total quarterly cash flow from derivative positions per ounce hedged. SCF is the annual average of the quarterly selective hedging cash flow per once hedged. The selective hedging cash flow is equal to the total derivative cash flow minus the benchmark cash flow. The benchmark cash flow is based on a fixed hedge ratio benchmark equal to the firm average hedge ratio over the sample period. PROF is the change in the value of derivative positions over the calendar year per once hedged. GOLD is the change in the price of gold over the calendar year. NETPROF if the change in the value of derivative positions net of the change in gold price, computed as NETPROF = PROF + GOLD. The descriptive statistics and correlations of these variables are estimated over ten sample years for each firm and then averaged across the firms. In parentheses, we report the tstatistics for the cross-sectional means. A. Descriptive statistics Variable Number of Firms VH

82

RES

44

CF

82

SCF

82

PROF

66

NETPROF

66

GOLD

66

Mean

St.Dev

Min

Max

0.13 (10.46) 0.25 (3.59) 7.88 (8.2) 0.38 (0.55) 2.59 (1.17) -8.04 (-3.71) -10.66 (-4.34)

0.08

0.00

1.38

0.11

0.00

3.12

8.04

-16.97

87.13

4.04

-17.59

49.17

29.14

-127.77

98.31

31.52

-129.17

57.70

38.04

-80.50

57.70

CF

SCF

PROF

B. Correlations VH

RES

NETPROF

VH

1.00

RES

0.39

1.00

CF

0.08

0.05

1.00

SCF

-0.04

0.06

0.11

1.00

PROF

-0.10

-0.01

-0.08

0.31

1.00

NETPROF

-0.04

-0.05

-0.58

0.16

0.17

1.00

GOLD

-0.01

-0.08

-0.36

-0.07

-0.57

0.50

22

GOLD

1.00


Table 2 – Panel OLS regressions for years ending in December The table presents the results of the panel OLS regressions. In Panel A, the dependent variable is the annual average of the absolute quarterly Cragg residual, RESt. In Panel B, the dependent variable is the annual standard deviation of the quarterly seasonality-adjusted one year maturity hedge ratio, VHt. The independent regressors are as follows. CFt is the annual average of the total quarterly derivative cash flow per once hedged. HRt is the level of the seasonality-adjusted one year maturity hedge ratio at the end of year t. SCFt is the annual average of the quarterly selective hedging cash flow per once hedged. BCFt is the annual average of the quarterly benchmark cash flow per once hedged. The benchmark cash flow is estimated as in Adam and Fernando (2006) relative to a fixed hedge ratio benchmark equal to the firm average hedge ratio over the sample period. PROFt is the change in the value of derivative positions over the calendar year per once hedged. GOLDt is the change in the price of gold over the same year. NETPROFt if the change in the value of derivative positions net of the change in gold price, computed as NETPROFt = PROFt + GOLDt. The regressions are estimated for the years ending in December. The overall adjusted R2 and the number of panel observations are reported in the bottom two rows. The tstatistics are reported in parentheses, with the number of starts indicating the level of significance (* at ten percent, ** at five percent and *** at one percent). We correct the t-statistics using the Newey-West algorithm. A. Dependent variable is RESt Model 9 Parameter Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 CONST 0.2016 0.2026 0.2215 0.2127 0.2163 0.2024 0.2049 0.2059 0.2096 (9.83***) (9.85***) (11.91***) (11.48***) (11.40***) (9.68***) (9.87***) (10.77***) (10.79***) RESt-1 -0.2787 -0.2725 -0.2842 -0.2682 -0.2722 -0.2823 -0.2694 -0.2580 -0.2612 (-2.85***) (-2.77***) (-2.86***) (-2.13**) (-2.94***) (-2.86***) (-2.67***) (-2.59***) (-2.62***) CFt-1 0.0040 0.0056 0.0040 0.0077 (2.51***) (2.24**) (1.65*) (2.37**) BCFt-1 -0.0021 -0.0059 (-0.83) (-1.70*) SCFt-1 0.0040 0.0066 0.0069 (1.66*) (2.18**) (2.29**) NETPROFt-1 -0.0007 -0.0009 (-2.13**) (-2.58***) PROFt-1 -0.0009 -0.0003 -0.0008 -0.0010 (-2.31**) (-0.75) (-1.51) (-2.79***) GOLDt-1 -0.0006 -0.0001 -0.0005 -0.0007 (-1.60*) (-0.22) (-0.97) (-2.03**) HRt-1 0.0889 0.0956 0.1003 0.0813 0.0867 0.0958 0.1156 0.1112 0.1182 (1.65*) (1.76*) (1.82*) (1.56) (1.65*) (1.74*) (2.07**) (2.01**) (2.13**) Adj. R2 Observations

0.0039

0.0032

0.0251

0.0003

0.0001

0.0002

0.0029

0.0021

0.0035

154

154

154

142

142

140

140

140

140

23


Table 2 – Panel OLS regressions for years ending in December (continued) B. Dependent variable is VHt Parameter Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 CONST 0.1850 0.1850 0.1952 0.1955 0.1955 0.1880 0.1879 0.1951 0.1954 (12.67***) (12.63***) (14.01***) (13.04***) (12.77***) (11.53***) (11.24***) (12.96***) (12.74***) VHt-1 -0.2486 -0.2510 -0.2408 -0.2494 -0.2493 -0.2542 -0.2542 -0.2493 -0.2498 (-3.18***) (-3.18***) (-3.03***) (-2.95***) (-2.93***) (-2.99***) (-2.98***) (-2.94***) (-2.93***) CFt-1 0.0020 0.0017 0.0021 0.0020 (2.10**) (1.08) (1.31) (1.01) BCFt-1 0.0004 0.0001 (0.24) (0.03) SCFt-1 0.0006 0.0012 0.0012 (0.41) (0.63) (0.64) NETPROFt-1 -0.0001 -0.0002 (-0.52) (-0.67) PROFt-1 -0.0001 0.0001 0.0001 -0.0002 (-0.47) (0.39) (0.34) (-0.66) GOLDt-1 -0.0001 0.0001 0.0001 -0.0002 (-0.48) (0.39) (0.36) (-0.58) HRt-1 -0.0056 -0.0071 -0.0110 -0.0024 -0.0024 0.0045 0.0042 0.0043 0.0044 (-0.18) (-0.22) (-0.34) (-0.07) (-0.07) (0.13) (0.11) (0.11) (0.12) Adj. R2 Observations

0.0391

0.0391

0.0815

0.0863

0.0863

0.0615

0.0615

0.0788

0.0786

227

227

227

200

200

200

200

200

200

24


Table 3 – Panel OLS regressions estimated on the sub-samples: years ending in December The table presents the results of estimating the OLS panel regressions for the entire sample and separately for the two sub-samples: the one with positive cash flows from derivative positions, and the other with negative cash flows from derivative positions. The three columns marked A present the results for the annual standard deviation of the quarterly seasonality-adjusted one year maturity hedge ratio, VHt. The three columns marked B present the results for the annual average of the quarterly absolute Cragg model residuals, RESt. In each case, the following regressions are estimated:

A: VH 1t = a + b1VH t1−4 + b2 CFt + b3 CFt − 4 + b4 HRt1−4 B: RES 1t = a + b1 RES t1−4 + b2 CFt + b3 CFt − 4 + b4 HRt1− 4 The independent regressors are as follows. CFt is the annual average of the total quarterly derivative cash flow per once hedged. HRt is the level of the seasonality-adjusted one year maturity hedge ratio at the end of year t. The regressions are estimated for the years ending in December. The overall adjusted R2 and the number of panel observations are reported in the bottom two rows. The t-statistics are reported in parentheses, with the number of starts indicating the level of significance (* at ten percent, ** at five percent and *** at one percent). Parameter

A: Dependent variable is VHt

B: Dependent variable is RESt

All

CFt-4>0

CFt-40

CFt-40

CFt-40

CFt-4