overconfidence ceos, hedging and...

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論文題目:

經理人過度自信、避險與公司績效

Overconfidence CEOs, Hedging and Performance

報名編號: F0083

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經理人過度自信、避險與公司績效

摘要

本論文探討過度自信經理人對公司避險決策與公司績效的影響。以 2005 年至 2010

S&P 500 公司為樣本，邏輯特迴歸分析結果顯示有過度自信 CEO 的公司比沒有過度自

信 CEO 的公司，有較高的機率進行衍生性金融商品避險。另在考慮樣本選擇偏誤後，

發現過度自信 CEO 的公司進行避險所涵蓋的資產標的，包括利率、貨幣與商品，比沒

有過度自信 CEO 公司的標的範圍為廣。但過度自信 CEO 對使用衍生性金融商品的工

具則無顯著影響。另發現公司績效與過度自信 CEO 進行避險有顯著正相關，且此正向

關係在金融風暴期間比非金融風暴期間更為顯著。過去的文獻認為公司財務特性與經

理人薪酬制度是影響公司避險策略的主因，本論文的結果顯示 CEO 過度自信的人格特

質也可能影響企業的避險決策與公司績效。

關鍵字: 避險、金融商品、過度自信、CEO、公司績效

Abstract

This study examines whether overconfidence CEOs affects firm derivatives hedging and firm value. Using a sample of S&P 500 over year 2005-2010, The thesis finds that overconfidence CEOs tend to hedge more than non-overconfidence CEOs with marginal probability about 19.41%. Our results show that overconfidence CEOs hedge on more types of underlying assets than non-overconfidence CEOs, while managers’ trait does not significantly relate to the type derivative instruments employed. I further examine the association between firm performance and derivative usage by overconfidence CEOs. I find positive associations between firm performance and derivative usage by overconfidence CEOs. The effect is particularly significant in crisis period (year 2008-2010) as compared to the one in non-crisis period (year 2005-2007). Prior studies propose that financial characteristics and compensation contracts relate to corporate risk management. Our findings suggest that manager’s optimism may play a role to explain company’s hedging activities.

Keywords: Hedging, Derivatives, Overconfidence, CEO, Performance

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1. Introduction What are the primarily determinants of firms’ hedging decisions? Does hedging

affect firm value? Along with the increased firm hedging activities, such issues have been frequently questioned by researchers and practitioners. Prior studies suggest that firm financial characteristics and compensation contracts play roles to explain company’s hedging activities. These explanations are mainly based on firm value maximization theory that builds on the perceptions of the costs and benefits to the firm (Smith and Stulz, 1985; Tufano, 1996; Hentschel and Kothari, 2001; Jin and Jorion, 2006; Campello et al., 2011) and managerial incentives theory that focuses on the managerial utility function maximization (Smith and Stulz, 1985; Froot et al., 1993; Graham and Rogers, 2002; Lin and Smith, 2007; Fauver and Naranjo, 2010). Most of the analysis and empirical findings stand on the assumption that managers are rational. Yet the influence of managers’ traits such as optimism on managerial hedging choices is not fully explored.

The hedging activities of firms have vastly increased with the development of financial derivatives since the early 1990s (Lin et al., 2008). The financial instruments’ innovation further increases the accessibility and diversity of firm’s hedging in recent years. Although active corporate risk management would be irrelevant in a Modiglianni-Miller world of perfect capital market, managers’ hedging decisions have become one of the important corporate financial decisions in practice.

Heaton (2002) and Malmendier and Tate (2005) propose that managers’ investment decisions subject to managerial optimistic and overconfident biases. This study extends their conception and examine whether managers’ personality traits affect firm hedging activities in three aspects:

(1) First, by using logistic regression, this study analyzes whether overconfidence CEOs affect firm hedging activities.

(2) Second, to understanding the intensity and complexity of derivative usage, this study investigates whether overconfidence CEOs affect the types of underlying assets covered and the instruments employed in hedging activities. Considering CEOs may select to using derivatives in hedging, Heckman two stage analysis is performed.

(3) Finally, this study examines the association between firm performance and hedging activities by overconfidence CEOs by conducting fixed effect regression analysis.

Traditional financial theory assumes that managers are fully rational (Kamoto, 2010) and proposes that financial characteristics and compensation contracts relate to corporate risk management. Furthermore, while prior studies suggest a relation between a relationship between derivative usage and firm value, they do not indicate how overconfidence CEOs affect firm performance through hedging activities. The findings of this study are expected to provide evidence for the impact of managers’ traits (overconfidence) on firm hedging.

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2. Literature Review 2. 1 Conventional Theories-Incentives to hedge

The financial literature has explored a number of reasons that managers might want to hedge. These explanations can be divided into firm value maximization or managerial personal incentives.

For firm value maximization, conventional financial theory suggests that hedging could add firm value firm value through tax reduction, lowering the cost of financial distress and avoidance of underinvestment problem. Smith and Stulz (1985) argue that hedging reduces the variability of pre-tax income and expected tax liability. Therefore hedging arises firm value if the cost is low enough. Since the provisions of corporate tax code in most districts display a convex function, a firm with excess pre-tax profit (profit) has stronger incentives to hedge in order to obtain tax credits.

Cost of bankruptcy can induce corporation to hedge. Smith and Stulz (1985) propose that hedge reduce the probability of financial distress imposed by bond covenants. Hedging is beneficial to shareholders due to the reduction of the likelihood that a covenant becomes binding. Therefore firms with higher probability of financial distress have higher incentives to hedge (Guay, 1999; Guay, 1999; Haushalter, 2000).

Froot et al., (1993) show that hedging can reduce cash flow volatility. Thus a firm with more investment opportunities has higher need to hedge than one with low growth opportunities. Campello et al. (2011) find that hedgers pay lower interest spreads and are less likely to have capital expenditure restrictions. Geczy et al. (1997) also propose that growing firms are expected to engage more hedging activities due to investment opportunities and higher demand in funding.

Another strand of theory claims that hedging stems from managerial incentives to maximize their personal utility functions (Managerial Utility Maximization Hypothesis). Risk-averse managers have incentives to hedge due to undiversified human capital and financial claims on firms (Jin and Jorion, 2006). By observing 48 North American gold mines, Tufano (1996) find firms whose managers hold more options manage less gold price risk, and firms whose managers hold more stock manage more gold price risk.

However, the results regarding the relation of managerial incentives to hedging activities are inconsistent. Tufano (1996), Guay (1999), Geczy (1997), Gay and Nam (1998), Allayannis and Ofek (2001), and Haushalter (2002) find a relative small portion and insignificant evidence of managerial stock and option holding in determining risk management. On the other hand, Schrand and Unal (1998) find that managers’ security holdings are related to risk management of thrift institutions at the time of conversion from mutual to stock firms. Rogers (2002), by modeling managerial risk-taking incentives as endogenous variables and measuring this variable based on Black-Scholes equation, he find a strong negative relation between CEO risk-taking incentives and the amount of derivative holdings. That is, CEOs with more options are less likely to hedge.

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2.2 Overconfidence CEOs and Hedge Recent behavior finance literature suggests that senior managers’ optimism plays an

important role in corporate operating decisions. A large number of studies show that managerial “hubris” explain a significant portion of the observed variation in acquisitions (Malmendier and Tate, 2005; Lin et al., 2005; Doukas and Petmezas, 2007; Huang et al., 2011), investment (Lin et al., 2005; Malmendier and Tate, 2005; Malmendier and Tate, 2008; Huang et al., 2011) and capital budgeting (Hackbarth, 2008; Lin et al., 2008). Yet the effect of managerial overconfidence on firm hedging is unresolved.

According to Malmendier and Tate (2005), optimistic managers are more likely to undertake new projects or to make operation change due to illusion of control and a high degree of commitment to good outcomes. Top executives are particularly likely to possess such personality (Goel and Thakor, 2008). Thus an overconfident manager has a higher likelihood than a rational manager to create firm value through high-risky project taking.

Hackbarth (2008) proposes that managers with growth perception bias overestimate the growth of future earnings and view companies’ securities to be undervalued by the market. Thus the managers perceive external financing being unduly costly than debt and are therefore reluctant to seek external financing. On the other hand, managers with risk perception bias underestimate volatility of future earnings and perceive that the value of debt finance is undervalued. Therefore, managers with risk perception bias prefer issuing equity than debt and display a reverse pecking order preference. In addition, Delong et al. (1991) found that optimistic managers usually overestimate the growth rate of cash flows and underestimate the riskiness of cash flow. Alsubaie (2009) predict that overconfident managers undervalue risk, and consequently have less derivative hedging than non-overconfident managers. However, by using a sample of S&P 500 for the year 2001, he finds insignificant relationship for interest rate hedging and managers’ overconfidence.

These analysis and findings suggest that managers with perception bias believe the companies have higher value and lower risk than other companies. Therefore, overconfidence managers may have lower incentives to conduct risk management than rational managers.

On the other hand, overconfident managers tend to be more optimistic toward their decisions and expected outcomes. That is, overconfident individuals tend to overstate their knowledge, underestimate risks, and exaggerate their ability to control events (Doukas and Petmezas, 2007). They predict that favorable future events are more likely than actually, and they believe that they have more precise knowledge about future events than they actually have (Hackbarth, 2008). The “better than average” effects also affect the attribution of causality that overconfident individuals are more likely to attribute good outcomes to their actions, but bad outcome to bad luck (Miller and Ross, 1975; Malmendier and Tate, 2005).

For example, overconfident managers are more likely to invest on non-profitable projects (Malmendier and Tate, 2005; Huang et al., 2011) or conduct more acquisitions than

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non-overconfident managers (Malmendier and Tate, 2008). The optimistic managers tend to underestimate project risk and make value-destroying investment than rational managers (Goel and Thakor, 2008).

2.3 Overconfidence CEOs, hedge and performance Extant literature shows inconsistent findings regarding the performance by hedging.

Allayannis and Weston (2001) find a positive relation between foreign currency derivatives and firm value. Adam and Fernando (2006) find hedging firms generate more cash flow gains. Carter et al. (2006), using a sample of 26 U.S. airlines, find that firms with fuel price hedging have premium about 14% through derivatives usage. Nelson et al. (2005) examine annual stock performance for derivative usage of 1308 U.S. firms over 1995 to 2000. They find the hedgers outperform other securities by 4.3% per year. Mackay and Moeller (2007), by using a sample of 34 U.S. oil refiners, show that derivatives usage could increase firm value about 3% when revenues and cost are nonlinearly related to prices.

In contrast, several studies show that either no valuation effects or negative effects on derivative usage. Guay and Kothari (2003) propose that the equity prices and cash flow changes are minimal through derivative usage. Lookman (2009) finds no relation between firm value and derivative usage for 125 exploration and production firms. Jin and Jorion (2006) examine hedging activities of 119 U.S. oil and gas producers. They find that hedging reduces stock price sensitivity to oil and gas prices, but does not affect firm value (Tobin’s Q). Hentschel and Kothari (2001) then measure the relation between derivative usage and firm risk. They find that the effects of derivatives on return volatility are simply close to zero in both economic and statistical terms. That is, derivative usage does not measurably increase or decrease firm value.

Some studies show that the value effect of derivative usage is associated with agency and monitoring problem. Leland (1998) find that derivatives usage may be beneficial to shareholders when agency costs are small. John et al. (2008), using a cross-country panel and a U.S. sample, find that better investor protection lead corporations to undertake riskier but value-enhancing investment. Fauver and Naranjo (2010), by examining a sample of 1746 U.S. firms, conclude that derivative usage has a negative impact on firm value in firm with greater agency and monitoring problems.

For the value created by overconfidence managers, the literature show negative findings. Overconfidence managers believe they have above-average abilities than others (better-than-average). They perceive that the projects they participate have future performance with mean value above average (dispositional optimism) and narrow confidence intervals of variance (miscalibration) (Libby and Rennkkamp, 2011). Thus overconfident managers are more likely to undertake value-destroying projects that rational managers may forgo (Malmendier and Tate, 2008).

On the other hand, optimistic managers may overestimate their ability in hedging skill and underestimate the risk and complexity of derivative usage. Therefore, they may buy

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excess derivative contracts and pay extra cost on hedging activities than rational managers. That is, no matter taking some value-destroying projects or overpaying on hedging activities, misperception of overconfidence managers seems more likely to result in less efficient hedging decisions and less synergy gains.

3. Sample and variables 3.1 Sample

In order to examine the relation of overconfidence CEO and firm hedging activity, I obtain data from various databases by following steps. The initial sample consists of S&P 500 firms over year 2005 to 2010. I exclude regulated utility firms (SIC=4900-4999) and financial service firms (SIC=6000-6999) because their special financial structure and regulations.

I then search derivative usage information of remained firms on their annual reports (10 K) and delete firms without related information (use or not use derivative). This thesis collects CEO share and compensation information on proxy statement (DEF 14A) from EDGAR system (Electronic Data Gathering, Analysis, and Retrieval System). To identify overconfidence CEO, following Malmendier and Tate (2008), firms whose CEO not working consecutively during sample period and CEOs for whom data on stock purchases not present is excluded. Finally, companies without complete financial data in COMPUSTAT are also deleted. This screening procedure leaves an unbalanced panel of 125 firms with 621 firm-year observations.

3.2 Hedging activities Firms’ derivative activities are collected from notes on financial statements. Following

Jin and Jorion (2006), I search annual reports (10 K) for some key words related to hedging. The keywords include: risk management, derivative, hedge, swap, option, futures, forwards, interest, currency and commodity. If the notes in financial statement show relevant activities, the companies are considered having hedging activities.

To see the extent and instrument of risk management by each firm in detail, I count the types of underlying assets covered by firm derivative activities. The types of underlying assets include interest, currency and commodity. Firm’s derivative usage cross any kind of asset obtains one point of score. Therefore the maximum obtainable score for hedging firms is three. I also collect information regarding the instrument utilized by each firm. I separate derivative instruments into futures, options, swaps and futures. Each type of instrument utilized obtains one point of score. Therefore, the instrument score ranges from one to four.

Table 1 summaries the derivative held by sample firms. Panel A shows the distribution of hedging activities by year. Among 621 observations, 532 (86%) firm-years have hedging activities. The overall ratio of derivative usage is quite stable with range from 85% to 87%. Year 2009 have highest hedging ratio about 87%, probably due to financial crisis starting in year 2008 that companies may use more derivatives for risk management.

Panel B in Table 1 shows the derivative usage by underlying asset. The statistics show

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that over 50% of firms buy derivatives cross two or more assets. About one third (33%) of sample firms used derivative for one type of asset, in which currency is the major covered item. Panel C shows derivative usage by instrument. Among 532 derivative usage sample, 369 (69%) observations use two or more types of instruments. This statistics show that firms are used to using different type of instruments at same period of time. For hedging instruments, forwards is the top one and swaps is the second one applied in hedging.

< Table 1 is about here >

3.3 Overconfidence CEO Following Malmendier and Tate (2005), I measure CEO overconfidence by observing the tendency of CEOs to purchase additional shares of his/her company stock (Net buyer). A CEO is considered bearing high idiosyncratic risk since his/her human capital contribution and shares ownership go to the same company. A risk-averse CEO would seek to minimize his/her risk through selling company shares. Instead, a CEO habitually increases his/her equity position by acquiring new shares (Net buyer) are considered as overconfidence due to his/her optimism in company’s future. The CEO is classified as net buyer if he/she bought stocks at least one more year than he/she sold stock during sample period.

Table 2 presents CEO and derivative usage condition. Panel A presents derivative usage by overconfidence CEOs and non-overconfidence CEOs. The statistics show that about two thirds of CEOs are net buyers over year 2005-2010. Panel B shows that overconfidence CEOs have higher ratio in derivative usage on each asset (interest, currency and commodity) than non-overconfidence CEOs. Panel C in Table 2 shows that overconfidence CEOs use more types of derivative instruments (futures, options, swaps, forwards) than non-overconfidence CEOs. In general, the statistics in Table 2 show that overconfidence CEOs use more types of derivatives instruments cross more kinds of underlying assets than non-overconfident CEOs.


3.4 Control variables

Prior literature suggests several firm characteristics may affect managers’ hedging decision. These factors are listed as control variables as the followings: (1) Compensation: According to managerial utility maximization hypothesis (Smith and Stulz, 1985), managers’ risk-aversion can lead them to hedge. A CEO with higher payment from companies may have higher incentives to hedge. I use CEO salary plus bonus as the proxy for CEO’s incentive to hedge. (2) Geographic: Based on Fauver and Naranjo (2010), firms operating in multiple countries are more likely to hedge due to higher risk from assets exposure. I use the number of countries where the firm operates as the proxy of risk exposure on foreign geographic segments. (3) FCF/assets: Jensen (1986) proposes that higher free cash flow may result in potential conflicts between managers and shareholders. Excess cash flow may also cause monitoring

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problem that affect managers’ derivative usage (Fauver and Naranjo, 2010). I use free cash flow divided by assets as proxy of this potential agency problem. (4) Dividend: Based on Graham and Rogers (2002), companies pay dividends have higher incentives to hedge to smooth cash flows. I use dividend dummy variable equal to one if a company pays dividend and otherwise zero. (5) Operating income/sales: Based on Fauver and Naranjo (2010), profitable firms have stronger incentives to hedge. I use operating income to sales as the proxy of profitability. (6) Capital expenditure/ sales: A growing firm is expected to have higher chance to hedge (Geczy et al., 1997). Following Fauver and Naranjo (2010), I use capital expenditure to sales as the proxy of growth. (7) Tax loss carry forward: Stulz (1996) and Leland (1998) suggest that tax shields provide an incentive for firm risk management. Following Lin and Smith (2007), I divide loss carry forward by total asset. (8) Log assets: Large firms are expected to have greater latitude in their choice of risk management strategies (Tufano, 1996). Nelson et al. (2005) find that large firms are more likely to use derivatives and hedge a larger proportion of their sales or assets, as compared to small firms. The study takes the log value of assets as proxy of firm size. (9) Closely shares: Bartram et al. (2009) propose that firms with poor monitoring mechanisms may be associated with greater selective use of derivatives by managers for self-interests. This study uses the ratio of insider’s shareholdings as the proxy of control on agency problems. (10) Debt ratio: Value-maximization theory points that lowering the cost of financial distress is a major concern to hedge. This study uses debt ratio (Smith and Stulz, 1985) as proxies of firm financial risk.

4. Main Results 4.1 Descriptive statistics

Table 3 provides descriptive statistics of variables and compares subgroup of overconfidence and non-confidence CEOs. Hedging firms (Hedge) presents about 86% of our sample. Overconfidence CEO group has more hedging activities than non-overconfidence group (0.88 vs. 0.81). About 67% of sample has CEOs with net share increase over four years during 2005-2010. In general, firms with overconfidence CEOs have higher compensation, tax loss carry forward amount and higher leverage. These firms also pay more dividends than non-overconfidence CEOs group firms. Instead, firms with non- overconfidence CEOs have higher quick ratio and inside shareholdings than those with overconfidence CEOs.


Table 4 presents pairwise correlations among overconfidence measures, hedging and firm characteristics. Overconfidence CEOs are found positively related to hedging activities, dividend payout, tax loss carry forward amount and CEO compensation.

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4.2 Overconfidence CEOs and Hedging

Based on literature mentioned above, I examine whether overconfidence CEOs affect hedging activities by using equation (1) as the followings:

it tiitit

ititit

ititit

ititititit

IndustryDebt ratio aresClosely sh Log assets forwardLoss carryTax alesenditure/sCapital esincome/salOperating oQuick rati ummyDividend d

FCF/assets Geographic onCompensati OCHedge

Year exp

211716131211

1098

765

43210

(1)

, where 4 SIC dummies and 5 year dummies are utilized to control for industry and year fixed effects.

Table 5 reports the results of logistic regression on relation between derivative usage and overconfidence CEOs. The coefficients of overconfidence CEOs (OC) is positive and significant at 0.10 level. The results show that overconfidence CEOs tend to hedge more than non-overconfidence CEOs with probability of 19.41%. Malmendier and Tate (2005) find overconfidence managers are more likely to investment than non-overconfidence managers. They interpret that that overconfident managers tend to take new project as challenge due to their personal traits. My findings suggest that overconfidence CEOs are more likely to take the challenge on derivative usage. In addition, I test for collinearity by computing the variance of the inflation factors (VIF) for the independent variables. The variance of the inflation factors is below 10, indicating no serious problem of multicollinearity in our data. VIF is not reported for brevity.

I also examine the results on each type of underlying assets. For interest related assets, the coefficient of OC is positive and significant at 0.05 level, indicating an increase of 15% probability than non-overconfidence CEOs in derivative usage.

For control variables, Table 5 finds that companies operating in more foreign countries (Geographic) are more likely to hedge on currency assets (coefficient=0.1632, p-value=0.06). This result is consistent to Pantzalis et al. (2001). The coefficients of tax loss carry forward are positive at 0.05 significance level. This finding agrees to Myers (1997) and Smith and Stulz (1985). Larger firms (Log assets) tend to hedge more on interest and commodity assets. That is consistent to Lamont et al. (2001). The coefficients of debt ratio are both negative and significant for interest and currency assets, indicating the association of derivative usage with high risky firms (Graham and Rogers, 2002). Finally, the coefficients of closely shares are all negative and significant on currency and commodity assets. The findings are similar to Bartram et al. (2009) and Fauver and Naranjo (2010), indicating less derivative usage of firms with concentrated ownership due to better monitoring.


4.3 Derivative instruments and underlying assets by overconfidence CEOs Since the CEOs may select the types of derivative instruments and decide the types of

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underlying assets for hedging, I use Heckman two-stage method to test the number of derivative instruments employed and the types of underlying assets covered. The more kinds of instruments employed or more types of underlying assets covered may indicate aggressive attitude toward derivative usage by overconfidence CEOs.

I first use logistic regression to evaluate a firm’s decision to hedge or not, as specified in the following equation:

it iititit

ititit

ititit

ititititit

IndustryφDebt ratioφDebt ratioφ aresClosely shφ Log assetsφ forwardLoss carry φ alesenditure/sCapital φ

esincome/salOperating φoQuick rati φummyDividend d φ FCF/assets φGeographic φonCompensati φOCφφHedge

1714131211

1098

765

43210

exp (2)

, where Hedge is a binary variable equal to 1 when firm i has an unwritten condition of derivatives usage regarding the types of instruments and underlying assets at time t, and 0 otherwise. I then incorporate the inverse Mill’s ratio from Eq. (3) into the Tobit regression to analyze the decisions of using financial instruments and covering financial assets. The inverse Mill’s ratio reflects the conditional probability of the observation being in the observed sample into outcome model. This methodology helps obtain full information for the absent and observed observations (Long, 1997).

For the types of financial instruments utilized (futures, options, swap and forwards), the second stage Tobit regression on derivative instruments is

it itititit

ititit

ititit

ititititit

InvMillφDebt ratioφDebt ratioφ aresClosely shφ Log assetsφ forwardLoss carry φ alesenditure/sCapital φ

esincome/salOperating φoQuick rati φummyDividend d φ FCF/assets φGeographic φonCompensati φOCφφInstrument

14131211

1098

765

43210

exp

(3)

, where Instrument is the type of instruments employed and InvMill is the inverse Mill’s ratio obtained from eq. (3). The second stage Tobit regression on underlying assets (interest, currency and commodity) is

it itititit

ititit

ititit

ititititit

InvMillDebt ratioDebt ratio aresClosely sh Log assets forwardLoss carry alesenditure/sCapital

esincome/salOperating oQuick rati ummyDividend d FCF/assetsGeographic onCompensati OCφAssets

14131211

1098

765

43210

exp

(4)

, where Assets is the types of underlying assets covered. InvMill is the inverse Mill’s ratio obtained from eq. (2).

Table 6 presents the results of second stage Tobit regression. The coefficients of the inverse Mill’s ratio are statistically significant in both equations, indicating a selective derivative usage in deciding the instruments and underlying assets. As shown in first column, the coefficient of OC on Instruments is positive but insignificant. In contrast, the coefficient of OC on Assets in second column is statistically positive. The results show that the decision on selecting derivative instruments does not significantly relate to CEO’s individual trait. In contrast, optimism CEOs hedge on more than one types of assets (coefficient=0.1087, p

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value=0.08). As shown in Panel B in Table 1, about 67% observations use derivatives to cover two or more kinds of assets, while 33% observation cover one type of underlying asset only. The results of control variables are similar to Table 4 and Table 5.

4.4 Performance and derivative usage by overconfidence CEOs Furthermore, while prior studies suggest a relation between a relationship between

derivative usage and firm value, they do not indicate how an overconfidence affect firm performance through hedging activities. Based on Fauver and Naranjo (2010), I examine the influence of derivative usage on firm performance by overconfidence CEOs. The dependent variable is Tobin’s Q and the model is listed as the followings:

it tiitit

ititit

ititit

ititititititit

IndustryDebt ratio aresClosely sh Log assets forwardLoss carryTax alesenditure/sCapital

esincome/salOperating oQuick rati ummyDividend d FCF/assets GeographicHedgeOC Hedge OCQsTobin

Year exp

* '

221817141312

11109

876

543210

(5)

Table 7 reports the association of firm performance and derivative usage by overconfidence CEOs controlling the difference in firm characteristics. The first column shows the regression results of all observations. The second column and the third column show the regression results for observations in recent crisis period (year 2008-2010) and non-crisis period (year 2005-2007). Since companies bear higher risk during crisis period than non-crisis period, I expect the influence of hedging on firm performance might be different in crisis or non-crisis period.

The coefficient of Hedge in first column is negative and significant at 0.01 level. This finding indicates the derivative usage has a negative effect on firm value and is consistent to the findings of Fauver and Naranjo (2010). The coefficient of interaction term of Hedge and OC is 0.8904 and statistically positive. This finding suggests that hedging activities by overconfidence CEOs are associated with higher firm value. For the influence of hedging in crisis and non-crisis period, I find similar results. However, the coefficient of joint item of Hedge and OC*Hedge of crisis period is 0.3535. It is higher than that of non-crisis period 0.0769, indicating the value of hedging during crisis period.

For control variables, companies operating in foreign countries, paying dividend, with higher quick ratio, free cash flow and operating income relate to better performance. Instead, firms with more capital expenditure, larger size and debt ratio relate to lower performance. The findings on control variables are consistent with Fauver and Naranjo (2010).

5. Conclusion

Recent studies have pointed that overconfident managers may influence corporate investment decisions, financing policies, merge and acquisitions, and earnings forecasting. Yet the influence of managers’ traits such as optimism on managerial hedging decisions is not fully explored. This thesis examines whether overconfidence CEOs affect firm derivatives hedging and firm value.Following Malmendier and Tate (2005), I identifiy a CEO as overconfident if he /she is a net buyer of company share and examine how

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overconfidence CEOs affect firm derivative usage and performanc.

The results show that overconfidence CEOs tend to hedge more than non-overconfidence CEOs with marginal probability about 19.41%, and the influence is particular significant on hedging on interest related assets. Heckman two-stage analysis shows that overconfidence CEOs hedge on more types of assets than non-overconfidence CEOs, while managers’ trait does not significantly relate to the use of derivative instruments. Finally, I find positive association between firm performance and derivative usage by overconfidence CEOs. This effect on performance is particularly significant in crisis period (year 2008-2010) as compared to non-crisis period (year 2005-2007).

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Table 1 Descriptive Statistics for Derivative Hedging This table presents derivative activities for S&P 500 firms whose CEOs have at least 6-year tenure in the data over year 2005-2010.

Panel A: Distribution of Derivative Hedging Year Hedge Non-Hedge Total 2005 89 (86%) 15 (14%) 104(100%) 2006 89 (85%) 16 (15%) 105(100%) 2007 89 (86%) 15 (14%) 104(100%) 2008 87 (85%) 15 (15%) 102(100%) 2009 90 (87%) 14 (13%) 104(100%) 2010 88 (86%) 14 (14%) 102(100%) Total 532 (86%) 89 (14%) 621(100%)

Panel B: Derivative usage by underlying asset Three assets 75 (14%)

Two assets 283 (53%) One asset 174 (33%)

Interest 48 Currency 121 Commodity 5

Panel C: Derivative usage by instrument Four types 28 ( 5% ) Three types 116 (22%) Two types 225 (42%) Pure one 163 (31%)

Futures 5 Options 2 Swaps 63 Forwards 93

Table 2 CEO and Derivative Hedging

The data contains CEOs with consecutive tenure during 2005 to 2010 and exclude CEOs for whom data on stock purchases is not present. CEOs are classified as overconfident if they were net buyers, that is, if CEOs bought stock on net more than they sold on net.

Panel A: CEO type Overconfidence CEO 413 (67%) Non-Overconfidence CEO 208 (33%)

Total 621 (100%)Panel B: CEO and derivative usage by underlying asset

CEO Interest Currency Commodity Total Overconfidence 283(41%) 315(46%) 85(13%) 683(100%)Non-Overconfidence 109(39%) 149(53%) 24 ( 8%) 282(100%)

Total 392(41%) 464(48%) 109 (11%) 965(100%)Panel C: CEO and derivative usage by instrument

CEO Futures Options Swaps Forwards Total Overconfidence 45(6%) 130(17%) 267(35%) 313(42%) 755(100%Non-Overconfidence 12(4%) 45(14%) 122(38%) 139(44%) 318(100%)

Total 57(5%) 175(16%) 389(36%) 452(43%) 1073(100%)

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Table 3 Summary Statistics Hedge is derivative dummy variable, which is equal to one if a firm reports the use of any type of interest, currency or commodity derivatives and zero otherwise. OC is a dummy variable equal to one if CEOs bought stock on net more than they sold on net. Compensation is CEO’s compensation. FCF is operation income before depreciation, tax less dividend. Tobin’s Q is year-end market value of common stock plus the book value of preferred stock and debt over the year-end book value of total assets. Dividend dummy equals one if a company pays dividend and zero otherwise. Quick ratio is current asset minus inventory and prepaid expenses divided by current liability. Operating income/sales is the ratio of capital expenditures to sales. Capital expenditures/sales is the ratio of capital expenditures to sales. Log asset is the natural log of book value of total assets. Tax loss carry forward is net operating loss carry forwards divided by total assets. Closely shares are shares held by insiders divided by total shares issued. Debt ratio is total debt to total assets. Geographic is the total number of countries where it operates. Five SIC dummies and Year dummies are included in all regressions but not reported for brevity. The p-values are in parentheses. *, ** and *** denote significance at 0.1, 0.05 and 0.01 level or better, respectively.

All Sample (N=621) OC (N=413) Non-OC (N=208) Wilcoxon test (p-value) Mean Median Std. Mean Median Std. Mean Median Std.

Hedge 0.86 1 0.35 0.88 1 0.32 0.81 1 0.40 -2.47 (0.0068)

OC 0.67 1 0.47 1 1 0 0 0 0 -24.90 (

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Table 4 Correlations The table represents pairwise correlations between each variable. The variables are defined in Table 3. *, ** and *** denote significance at 0.1, 0.05 and 0.01 level or better, respectively.

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 Tobin's Q 1.00

2 Hedge -0.21*** 1.00

3 Compensation -0.15*** 0.17*** 1.00

4 Geographic 0.16*** 0.13*** 0.09** 1.00

5 OC 0.02 0.10** 0.26*** 0.05 1.00

6 FCF/assets -0.04 0.10*** 0.17*** -0.07* -0.09** 1.00

7 Dividend dummy -0.11*** 0.06 0.29*** -0.01 0.10** -0.02 1.00

8 Quick ratio 0.38*** -0.13*** -0.34*** 0.19*** -0.15*** 0.15*** -0.32*** 1.00

9 Operating income/sales 0.36*** 0.05 0.13*** 0.27*** -0.02 0.18*** -0.06 0.31*** 1.00

10 Capitial expenditures/sales -0.09** 0.08** 0.08** 0.08** 0.00 -0.02 0.02 0.01 0.28*** 1.00

11 Log assets -0.14*** 0.09** 0.46*** -0.01 0.02 0.74*** 0.05 -0.13*** 0.11*** 0.05 1.00

12 Tax loss carry forward 0.01 0.10** 0.16*** -0.05 0.08* 0.08** 0.24*** -0.21*** 0.11*** -0.03 0.08* 1.00

13 Closely shares 0.00 -0.05 -0.24*** -0.13*** -0.09** 0.02 -0.02 -0.01 -0.16*** -0.09** -0.05 -0.10** 1.00

14 Debt ratio -0.41*** 0.35*** 0.36*** -0.08** 0.09** 0.05 0.31*** -0.62*** -0.32*** -0.09** 0.18*** 0.14*** 0.07* 1.00

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Table 5 Overconfidence CEOs and Derivative usage This table reports the coefficients and marginal effects of logit regressions on a sample of S&P 500 over year 2000-2006. The dependent variable is hedge that equals one if companies use any type of financial assets: interest, currency and commodity. OC is a binary variable equal to one if CEOs bought on net more than they sold on net over sample period. Other variable definitions please see Table 3. The p-value is in parentheses. *, ** and *** denote significance at 0.01, 0.05 and 0.10 level.

Variables Hedge=1 Assets type

Interest Currency Commodity Coef.

(p-value) Marginal effect %

Coef. (p-value)

Marginal effect %

Coef. (p-value)

Marginal effect %

Coef. (p-value)

Marginal effect %

Intercept 8.6548 (0.9584)

-7.0723***(

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Table 6 Derivative instruments and underlying assets by overconfidence CEOs

This table presents the estimating coefficients and t-statistics for Tobit regressions (the second-stage of Heckman two-stage regressions) of hedging on the number of derivative instruments employed (Instruments) and the types of underlying assets covered (Assets) by Eq. (3)-(4). In the first stage, the dependent variable is Hedge, which is defined as 1 when the firms decide to use derivative; otherwise defined as 0. In the second stage, the dependent variables are the types of derivative instruments employed and the types of underlying assets covered by derivative usage. Other variable definitions please see Table 3. The p-value is in parentheses. *, ** and *** denote significance at 0.01, 0.05 and 0.10 level.

Dependent variable Instruments Assets

Intercept 2.0521*** (0.0000)

1.2866** (0.0035)

OC 0.1060 (0.1945)

0.1087* (0.0802)

Compensation 0.0004** (0.0254)

0.0002** (0.0231)

Geographic 0.0975*** (0.0000)

0.0528* (0.0064)

FCF/assets 0.0001** (0.0147)

-0.0001 (0.394)

Dividend dummy 0.3466*** (

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Table 7 Performance and derivative usage by overconfidence CEOs This table presents the association between firm performance and derivative usage by overconfidence CEOs of S&P 500 firms over year 2005-2010. The dependent variable is Tobin’s Q that is defined as the year-end market value of common stock plus the book value of preferred stock and debt over the year-end book value of a firm’s total assets. Crisis period is year 2005 to 2007 and non-crisis period is 2008 to 2010. Other variable definitions please see Table 3. The p-value is in parentheses. *, ** and *** denote significance at 0.01, 0.05 and 0.10 level.

All sample Crisis period Non-crisis period Intercept 4.8436***

(

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