Hedging and the Failures of Corporate Governance: Lessons from the Financial Crisis

Hedging and the Failures of
Corporate Governance: Lessons from
the Financial Crisis

Rodrigo Zeidan
2012

Hedging and the Failures of Corporate Governance: Lessons from the
Financial Crisis

Rodrigo Zeidan
Fundação Dom Cabral and
Nottingham University Business School China
rodrigo.zeidan@fdc.org.br

Abstract:

The paper identifies which failures of corporate governance allowed non-financial companies

around the world to develop hedging strategies that led to hefty losses in the aftermath of the

financial crisis. The sample is comprised of 346 companies from 10 international markets, of

which 49 companies (and a subsample of 13 distressed companies) lost a combined U$18.9

billion. An event study shows that most companies that presented losses in derivatives

experienced negative abnormal returns, including a number of companies in which the effect

was persistent after a year. The results of a probit model indicate that the lack of a formal

hedging policy, no monitoring to the CFOs, and considerations of hubris and remuneration

contributed to the mismanagement of hedging policies. For heavily distressed companies,

there is evidence that higher ownership concentration implies in a lower probability of

designing speculative hedging positions.

Keywords: Risk Management; Hedging; Derivatives; Monitoring; Corporate Governance
Structure

JEL Classification: G32; G34; G01

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Electronic copy available at: http://ssrn.com/abstract=2011297

Introduction

The purpose of this paper is to relate risk management and corporate governance by

analyzing the case of non-financial companies that posted hefty losses in derivatives trading

during the financial crisis that started in 2007. Dodd (2009) estimates that for 12 countries

that include Poland and economies of Asia and Latin America derivatives trading affected

possibly 50,000 firms, with losses totaling roughly $530 billion. Kamil, Sutton and Walker

(2009) present a small subsample of companies in Mexico (6 companies) with total losses of

U$4.7 billion (with an average loss of 23% of total assets) and 3 companies in Brazil with

total losses of U$5.5 billion - and an average loss of 46% of total assets.

There is one simple explanation for these losses: non-financial companies were hedging

financial positions – mainly currency exposures – and hence posted only book losses, with a

counterpart gain in revenue from the positions being hedged. However, this simple

explanation is insufficient to explain some relevant consequences from the disclosure of these

losses: many companies filed for bankruptcy; stocks plunged; some companies sued or were

sued by the banks that sold the derivatives contracts; accounting rules were changed (in

Brazil, India and China); and even the Chinese government's State-owned Assets Supervision

and Administration Commission (SASAC) got involved in trying to allow Chinese state-

owned companies involved in derivatives losses to walk away from contracts with

international banks (Reuters, 2009). The more plausible explanations are that companies

purposefully engaged in speculative positions and/or made mistakes in designing hedging

strategies involving derivatives to offset foreign-exchange exposure. Irrespective of which

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explanation addresses the question of why companies committed mistakes in derivatives

trading, there remains the question of which corporate governance mechanisms failed in

allowing shareholders to monitor the behavior of executives who were able to design

destroying value strategies involving derivatives.

In this paper I evaluate the failures of corporate governance in monitoring risk management

strategies in a sample of non-financial companies that posted derivatives losses resulting from

the financial crisis. First, I show that the disclosure of losses stemming from derivatives

contracts by non-financial companies resulted in negative abnormal results which are a clear

indicator that the companies were either speculating with derivatives or made mistakes in

their risk management strategies. Then I use a probit cross-sectional model to compare the

corporate governance structure of companies that posted derivatives losses with companies

that did not by establishing a dependent binary variable in which for the companies that

posted hefty losses it assumes value 1 and for the control group zero. Since we have a clearly

defined event – the losses – the probit model is suitable to analyze which corporate

governance mechanisms failed in preventing executives from implementing value-destroying

financial strategies involving derivatives.

There is precious little empirical work done in the relationship between corporate governance

and risk management for cross-country companies. Disclosure issues and the difficulty of

arriving at usable data are partially responsible, but the lack of a comprehensive theoretical

framework also hinders empirical analysis. The present paper contributes to the literature in

many ways. First, it presents a comprehensive theoretical framework that explains why

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international companies hedge. It then presents an analysis of a sample of companies that

posted heavy losses in derivatives trading on the wake of the recent financial crisis. Finally, it

relates these losses to failures in the corporate governance mechanisms of the selected

companies.

The structure of the paper is as follows: the first section describes in the research question

and main goal. The second section presents a theoretical discussion on the link between

corporate governance and risk management with the goal of developing a theoretical

framework that feed the next section, in which I develop the variables of the econometric

model and describe the data. The fourth section brings the results and analysis, while the last

section has some final comments.

I. RISK MANAGEMENT AND CORPORATE GOVERNANCE.

Tufano (1996) remarks (p.1097) that academics know remarkably little about corporate risk

management practices. Even though academia has catched up in the last 15 years [recent

models include Purnanandam (2008) and Fehle and Tsyplakov (2005)], we are still ignorant

on many risk management practices. Because risk strategies are not completely disclosed in

financial statements it is difficult to properly assess the extent of hedging policies and other

measures of risk management. In the present paper there is a clear event in which risk

management strategies are unveiled as a consequence of the financial crisis. The losses

posted by public companies and the effects on stock prices reveal a case of risk management

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gone wrong and I exploit it by relating it to the corporate governance structures of the

affected companies in different markets around the world.

As for why companies use risk management strategies in the case of foreign-exchange

exposure, the main driver for a value-enhancing hedging policy is that in efficient markets

diversification transfer risks from the companies to the market. However, as Stulz (1996) and

Bartram (2000) show, to really generate value hedging policies have to deal with one of the

following possible gains: reductions in bankruptcy and distress costs, reductions in expected

tax payments, reductions in expected payments to stakeholders and/or reductions in costs of

raising funds. Moreover, when we add the perspective of management, canonical agency

theory models assume that egotistical agents maximize utility and thus corporate governance

should align this utility maximizing behavior with the interest of shareholders. Tufano (1996)

shows that two variables related to executives matter in determining if companies hedge or

not: the amount of shares owned by managers and the nature of the managerial compensation

contract. The author shows empirically that managers maximize their utilities through risk

management in two ways: if managerial wealth is affected by share prices companies hedge

substantially, with the converse being true – if management owns a small stake companies

hedge little; and if executive compensation involve options or similar features then managers

are more risk-prone and thus hedge less. More recent models, like Purnanandam (2008) and

Fehle and Tsyplakov (2005), expand the risk management theoretical literature by including

more sophisticated hypotheses, but the main results remain the same – risk management

should enhance value by dealing with one of the four characteristics present in Stulz (1996),

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but is constrained by managers’ incentives given by executives’ ownership of shares and the

structure of managerial compensation.

Even though the combination of corporate finance theory and agency theory explains most

patterns in risk management practices, some recent literature has also been investigating the

impact of the institutional context on the behavior of management in designing hedging

policies. The main idea from behavioral finance is that managers are also affected by the

institutional environment in which executives are embedded (Wiseman et al, 2012, is an

example of a theoretical contribution that highlights the institutional impact on risk

management theory). This means that legal issues, stakeholders’ proactivity, corporate

governance and even managers’ innate characteristics also influence risk management

practices. For instance, Zhang (2009) argues that new transparency regulation in the

American market (SFAS 133) may have discouraged firms' speculative use of derivative

instruments; Bremer et al (2009) show that the provision of job security as a proxy for

employee interests has a significant effect on the likelihood of CFO dismissal and affect the

risk-taking behavior of CFOs, thus affecting possible hedging strategies; Indjejikian and

Matějka (2008) argue that firms mitigate earnings management or other misreporting

practices in part by deemphasizing CFO incentive compensation; Magnan et al (2008) find

that hubris (characterized by exaggerated self-confidence, arrogance and oblivion to reality

by executives) may be a critical factor to understand corporate financial frauds; and Dionne

and Triki (2005) find that better educated directors relate positively to better risk

management strategies and enhance corporate value. Regarding overconfidence as a possible

explanation for the miscalculation of risks by executives, Ben-David et al (2006) show how

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CFOs build overconfidence by, among other factors, a focus on a recent series of past

successes. They also show that CFOs draw their “worst case scenario” from recent

realizations of the market and the firm, but upper confidence bounds are affected only by the

past 3-month stock market movement. The theoretical framework that I follow is the one

present in figure 1 below. On it, risk management and hedging policies are the outcome of a

combination of mechanisms based on finance theory, management incentives and the broader

institutional framework.

PLEASE INSERT FIGURE 1 HERE

In the present framework corporate governance is one of the main features of the institutional

framework that help shape companies’ hedging strategies. There are two ways in which the

corporate governance structure of a company reflects on hedging: monitoring and efficiency.

The monitoring aspect is clear because an effective corporate governance structure is

designed to prevent strategies that are against the interest of shareholders, usually preventing

speculative positions with financial instruments that are supposed to be used to hedge.

Specifically, non-executive directors are supposed to control risk-taking behavior or at least

align it to the interest of shareholders. This point relates to efficiency seeking by allocating

company resources to create value. However, in the case of risk management, speculative

positions create value by exploiting private information in the hands of the company, but

there are two conditions for this to be carried out: internal transparency and private

information.

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The empirical evidence on derivatives usage, however, shows unequivocally the relevance of

such instruments for non-financial companies. Earlier studies [e.g. Bodnar et al. (1995);

Phillips (1995); Berkman and Bradbury (1996); Berkman et al. (1997); Howton and Perfect

(1998); Bodnar et al. (1998); De Ceuster et al. (2000); Mallin et al. (2001)] analyze the

patterns of derivatives usage and its determinants, showing widespread usage of derivatives

by non-financial companies and its relevance to corporate risk management. More recently,

Bartram et al. (2009) show that, in their sample of 7,292 non-financial companies from 48

countries, 59.8% of the companies use derivatives in general, while of those mostly use

currency derivatives, followed by interest rate derivatives and commodity price derivatives.

Also, other than the size of the local derivatives market no other country-specific factor is

significant, while the companies hedge for risk management purposes and not speculation.

Regarding the risk management theory based on corporate finance, Bartram et al (2009) find

that companies are in line with the financial distress hypotheses, and tests indicate that

derivatives users have significantly higher leverage and income tax credits as well as lower

liquidity (as measured by quick ratios and coverage ratios). However, the authors also arrive

at some evidence that runs counter to corporate finance theory - specifically, more profitable

firms and firms with fewer growth opportunities (market-to-book ratios) tend to hedge more.

There is no explicit theory that relates corporate governance and risk management since the

theoretical background uses theories from corporate finance and agency theory, as previously

observed, but Dionne and Triki (2005) present some interesting empirical results regarding

CFOs personal characteristics. They establish a relationship between corporate hedging and

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the background and education of the board and the audit committee members. The authors

find that financially educated directors seem to encourage corporate hedging while

financially active directors and those with an accounting background play no active role in

hedging strategies. Dionne and Triki (2005) also find a positive relation between hedging and

firm performance, suggesting that shareholders are better off with financially educated

directors on their boards and audit committees.

II. HYPOTHESES, DATA AND METHODOLOGY.

II.A - Data Description.

Hundreds of companies posted losses with derivatives during the financial crises. However,

in most countries there is no duty to report risk management strategies, and most companies

with losses are not listed companies. Also, many losses were rolled over a number of periods,

were negotiated with banks or information was never made public. Because of such

constraints, data for the present paper were hand collected following three criteria: losses

should have been public, thus posted in the media through newspapers, websites, magazines

etc; financial statements should reveal those losses; finally, data on the corporate governance

structure of the companies (described in section 3.2) should have been able to be constructed.

The idea behind using news media as a source of information has a long tradition in empirical

research, leading to the development of the methodology of event studies, which is used to

analyze abnormal stock returns given some new publicly available information.

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The final sample has 49 companies in 10 financial markets (Australia, Brazil, China, Hong

Kong, India, Indonesia, Japan, South Korea, Mexico and Poland). Companies did not post

losses in the same period due to different accounting systems and the timing of the financial

crisis, hence I use for each company and the other companies in the same market the financial

year in which losses are revealed. The earliest period in the sample is June of 2008 for Indian

companies and the latest March of 2009 for some Chinese companies. I treat this window as a

single event for the purpose of relating it to the corporate governance structure of the

companies at this and earlier periods and for the event study. The 49 companies in the sample

lost a combined U$18.9 billion. Table I below presents figures for the absolute losses and the

ratio between the losses and revenues, as well as market capitalization. We can see that losses

affected small and large companies and it did not discriminate by developing or developed

markets or by regions – the sample include companies from five continents and the range of

revenue is U$26 million (C-Motech from Korea) to U$33.7 billion (China Railway Group).

Even though the sample is heavily skewed towards industrial and exporting companies that

present foreign exchange exposure, it also presents companies that were hedging other risks,

such as oil prices.

PLEASE INSERT TABLE I HERE

Losses as a percentage of revenue range from 0.6% to 88.1%, while as a share of market

capitalization from 0.9% to 651%, which shows that companies are impacted in different

ways. Because of this I create a subsample composed of companies that experienced major

distress. Major distress is defined as bankruptcy, acquisition by another company, major asset

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sales (Win4Net had to sell its headquarters building) or a restructuring of derivative contracts

with banks to avoid a default on the contracts. This subsample includes 14 companies (APN

Property, Aracruz, Sadia, Citic Pacific, Win4Net, C-Motech, Taesan, Baiksan, Kalbe Farma,

Controladora Mexicana, Gruma, Vitro, Ropczyce and Odlewnie Polskie).

II.B - Event Study.

The idea behind the event study is to verify if the losses by the sample companies can be

regarded as value destroying. If the risk management strategies were sound companies should

only suffer accounting losses and there should be no effect on stock prices. Here I follow the

standard methodology summarized in MacKinlay (1997). The main caveat is that there is no

precise way to define the exact period for the analysis since it is based on the date in which

the media reported the event. For each company the event starts at time 0. As usual I present

results for 7, 60 and 360-day windows. The abnormal rate of return (ARit) is the difference

^
between the actual return (ri,t+e) and the forecast rate of return ( r i,t+e):

^
ARite  ri ,t e  r i ,t e (1)

The average and variation equations are based on Corhay and Tournai (1996), a GARCH

model that accounts for time-varying volatility effects. Variables  t and hit1 are

respectively the information set at time t for firm i and the conditional variance. Equations

are:

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rit  Ci  rmt   h ri ,t h   it (2)

  it 1 ~ N (0, hit .d ), hit  i   ik  2 i ,t k   ij hi ,t  j (3)

Results are in table II. The patterns we can see are: for distressed companies 11 of 14

companies show significant negative abnormal returns in the first day, an effect that is

persistent for 10 companies one year later. For the non-distressed companies 21 of 35

companies present abnormal negative returns in the first day, but only 7 companies still

present negative returns after one year. The sample companies use value-destroying hedging

strategies, a result that was persistent over a period of time.

PLEASE INSERT TABLE II HERE

II.C - Hypotheses.

The hypotheses for the econometric testing follow the theoretical framework presented in

figure 1. Since the main objective is to relate corporate governance and risk management I

build hypotheses regarding how the governance structure of a company can impact the design

of hedging strategies. Relating the theoretical framework to workable hypotheses is

conditional on building variables that are constructible from financial statements and public

information on the corporate governance structure. I use previous research and focus on the

role of the CFO and the underlying structure that allow the design of misplaced hedging

strategies. Unfortunately, unlike Dionne and Triki (2005), I have no data on the background

of executives, but a comprehensive study of the companies’ governance structure yields other

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qualitative indicators of the relationship between governance, agency theory and risk

management. The following hypotheses are tested in the econometric model:

H1: No monitoring (MON): Lack of proper CFO monitoring increases the probability of

leveraged positions in derivatives.

In many companies the CFO is directly responsible for designing and monitoring the risk

management strategy, presenting the results to the board. No good monitoring of the CFO’s

strategies can have a real impact, as exemplified by the Brazilian company Sadia. The

company redesigned its governance structure right after disclosing its losses in 2008. Figure 2

shows the old and the redesigned governance structure regarding its risk management

strategies. As we can see, the old structure gave too much discretionary power to the CFO,

because he was responsible to monitor the strategy and present the no compliance report to

the rest of the Board. Data for MON is binary and come from the financial statements of the

companies. It assumes value 1 for companies in which the CFO is responsible for managing

and answering for the risk management strategy and 0 otherwise.

PLEASE INSERT FIGURE 2 HERE.

H2: Disclosure (DIS): Disclosure leads to better monitoring by shareholders and less

probability of leveraged positions in derivatives.

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Financial disclosure rules are different worldwide. In the United States the standard for

financial reporting of derivatives, SFAS 133 Accounting for Derivative Instruments and

Hedging Activities, went into effect in 2001 and presents a series of compulsory financial

disclosures such as differentiating between hedging and speculation and requiring derivative

contracts to be marked-to-market and recorded as assets or liabilities on the balance sheet.

Derivatives used in speculation are marked-to-market with gains or losses realized in the

current period's income. For many countries, such as Brazil, India, and China, disclosure on

derivatives dealings was optional before the crisis, with most companies choosing to share

minimal or no information. In Brazil, the regulatory agency requested, in October 17th 2008

and weeks after the first news regarding the hefty losses of companies such as Sadia and

Aracruz, that public companies disclose more information on derivatives. Since then further

regulations have been enacted to tighten the disclosure of such information, with CVM

(Comissão de Valores Mobiliários) requesting account restatements of some companies

regarding derivatives trading in 2010. I build a qualitative binary variable regarding the

quality of disclosure of derivatives information. Data come from the notes of the financial

statements of the companies.

H3: Shares in the American Market (USA): Compliance with SEC rules leads to better

monitoring by shareholders and less probability of leveraged positions in derivatives.

If a company has shares in the American market it has to disclose its derivatives dealings,

which provides even more opportunities for shareholders’ monitoring. USA is a binary

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variable, assuming value 1 when the company does not have shares in the American market.

Data come from the companies’ websites.

H4: Concentration (CON): Higher ownership concentration enhances monitoring and

decreases the probability of leveraged positions in derivatives.

Ownership concentration is a standard variable in empirical corporate governance studies.

Even though dispersed capital enhances value, it creates monitoring problems in relation to

risk management strategies. CON is here defined as the proportion of shares in the hands of

the three major shareholders, hence it is a continuous variable in the (0,1) interval. Data come

from Datastream and Compustat Global.

H5: Institutional Investors (IIN): Institutional investors in the Board enhance monitoring

and decrease the probability of leveraged positions in derivatives.

Participation of institutional investors on the board should help monitoring of financial

management. Data for the binary variable come from the financial statements or companies’

websites.

H6: Formal Hedging Policy (FHP): lack of a formal hedging policy increase the probability

of leveraged positions in derivatives.

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Many companies specify a formal hedging policy – Air China, for instance, hedge at most

50% of its oil costs. Such policies should be a determent for speculative positions in

derivatives. Data for the binary variable come from the financial statements or companies’

websites. Variable assumes value 1 when the company has a policy in place and 0 otherwise.

H7: Trend of Major Source of Risk (TRE): a clear trend on the risk being hedged increase

the probability of a leveraged position in derivatives.

Hubris is the most difficult proposition to test for regarding agency theory, since it is almost

impossible to prove intent. I provide two indirect measures that account for the possibility of

hubris through the creation of an environment conductive to overconfidence, following Ben-

David et al (2006). The first is a medium-term trend in the risk being hedged. Years of

currency appreciation, for instance, may build confidence in leveraged positions in

derivatives to exploit the continuation of the trend. The variable is designed as the linear

trend of the underlying risk in the last 3 years, which means that for Brazilian companies it is

the linear trend of the Real, for Korean companies the Won, and for companies that were

hedging oil prices it is the trend of oil (Brent) prices.

H8: Recent Financial Results (FIN): recent financial gains increase the probability of a

leveraged position in derivatives.

The second variable pertaining to an indirect indication of hubris is a qualitative binary

variable that represents the last three quarter of financial results. It indicates if the company

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posted better than market-average financial results, and assumes value 1 only if the result of

the company beat the market in all three previous quarters. The idea is that, following Ben-

David et al (2006), positive reinforcement breeds overconfidence. Data come from financial

statements.

H9: Remuneration (REM): a remuneration package for the CFO that is based on short-

term incentives increase the probability of a leveraged position in derivatives.

The variable relate to the design of the remuneration of the CFO. If remuneration is tied to

medium-term performance, it assumes value 0, otherwise it assumes value 1. The rationale is

that short-term incentives are tied to excessive risk-taking. Data come from companies’

websites and corporate governance reports.

H10: Management Stake (MAN): Higher management stake increase the probability of a

leveraged position in derivatives.

Following Tufano (1996), MAN is a continuous variable that is the average of the

management stake in the previous three years.

Variables relating to independent directors and CEO duality, which are usually a mainstay of

corporate governance studies, were dropped because of poor predictability in the model. It is

safe to argue that those variable show a poor relation to risk management strategies, since

neither independent directors nor CEOs as Chairman of the Board would, in principle, be able

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to curtail or give incentives of excesses in risk management strategies just by being

independent or by acquiring a dual role in a company. Even though independent directors and

CEOs who are not Chairmen of the Board are usually figures that result in better governance

in the literature, in the present case we cannot see how they impact in a better risk

management monitoring role. The failures in risk management seem to be institutional in

nature in the present case. If incompetence plays a role, it is not one borne out of the role of

independent directors and CEOs as Chairmen, but it is the result of Directors, including the

CFOs, who weren’t able to curtail excesses in risk management strategies regardless of their

inherent role in the companies.

II.D - Probit cross-sectional model.

The dependent variable of the model that tests the relationship between corporate governance

and risk management is a binary variable which assumes value 1 for the companies that

posted derivatives losses and value 0 for companies that did not. The selection process of

companies for the test is very simple: public companies from the same market and industry as

the companies that posted losses, restricted by data availability. As an example, for the

Brazilian market the selection comprises 30 companies from petrochemical, steel, food, pulp,

and textile industries. For the 10 international markets the total of selected companies,

including the sample, is 346. Since the dependent variable is binary, the resulting model is a

probit cross-sectional model. The general model is given by equation 1, in which C is the

vector of controls and D the vector of sector dummies:

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1 MON   2 DIS   3USA   4 CON   5 IIN   6 FHP   7TRE  
Yi  1 0 (1)
 8 FIN   9 REM  10 MAN  ' C  ' D   i 

The resulting control vector (after iterations of the model with other financial variables) is

composed of:

Family-Owned: binary variable, resulted from hand-collected data;

Age: continuous variable with data coming from the companies’ websites;

Leverage: continuous variable based on the average financial debt ratio for the last three

years. Data came from financial statements.

As for dummies, I used market dummies in the first round of estimation, one for each

international market, but none improved the models. I dropped it in presenting the final

results.

The error term should capture all variation that is not explained by the selected variables. It is

impossible to perfectly model risk management decisions. In the present context we cannot

expect that the constructed variables can capture all the decision making process regarding

the losses in derivatives. The cognitive process that leads to decision making is truly multi-

dimensional and the present variables can at most capture the incentives that may lead to the

decision to overhedge or speculate with derivatives. Ideally one should have very descriptive

data on the risk management department and the CFO characteristics, plus its relationship

with the Board. Since such data do not exist or is unavailable, we should not expect an

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overtly fit model to the available data, even though the theoretical framework presently

developed should be appealing.

III – RESULTS AND ANALYSIS.

I divide the results in two, one for the whole sample of 49 companies and one for the

distressed sample composed of 13 companies. For both samples the dependent variable

assumes value 1 for affected companies that lost in derivatives and value 0 for the other

companies in the sample. The main diagnostic test is the cross-dependence (CD) test based

on Pesaran (2004) and Hsiao et al (2007) that indicates independence across the sample (it is

based on the Lagrange Multiplier, and the null hypothesis is for cross-section independence -

H0 : R = IN).

The probit econometric model is run using STATA 10.0 and is based on equation 1. Some

sensitivity analyses are also performed. In particular, many other controls are used in first

trials of the model, such as liquidity, price/earnings ratio and other financial variables. All

financial variables other than leverage result in poorer modeling performance and are

dropped. Market dummies are also dropped due to poor performance. Table II presents the

econometric results and table III the marginal effects of the probit model. Controls are

omitted for brevity, but no control other than financial leverage (and only for distressed

companies and even so, marginally) is significant.

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PLEASE INSERT TABLE III HERE.

PLEASE INSERT TABLE IV HERE.

Results for the whole sample show that corporate governance plays an important role in the

design of risk management strategies (in the present case, the dependent variable when it

assumes values 1 represents mismanagement of derivatives), as does the trend of the source

of risk and the remuneration incentives of the CFO. For the whole sample three hypotheses

relating to corporate governance are statiscally significant: lack of formal monitoring

structures (MON); shares in the American market (USA); and formal hedging policy (FHP).

All post the expected sign, with lax monitoring resulting in higher probability of leverage

with derivatives, and shares in the American market and formal hedging policy acting as

deterrents to mismanagement of such instruments. The marginal probabilities are not

particularly high, but are significant nevertheless. No monitoring structure enhances the

probability of mismanagement of derivatives, in the present model, in 2.3%, while shares in

the American market and a formal hedging policy decrease the probability by 1.9% and

1.2%, respectively. Hypothesis 2 - disclosure does not present a significant impact, and its

explanation is that most companies in a single market usually follow the same disclosure

rules and thus we can conclude that voluntary disclosure is lacking in the markets analyzed.

Hypothesis 7, relating to the trend of source of risk variable posts an interest result. It is an

indicative of an incentive to overconfidence by building strategies based on previous results.

It was certainly used as an explanation in the media by executives – in an interview the CFO

of Companhia Siderúrgica Nacional of Brazil dismissed the hefty losses by arguing that the

derivatives strategies have netted sufficient gains in the past to make it worthwhile, even if

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shares dropped heavily after the company’s losses were announced. As for hypothesis 9,

remuneration, it clearly shows that there is an incentive for CFOs to hedge more if their

compensation is tied to short-term performance, as the probability of hedging increases with

this incentive.

Results change somewhat when we consider the sample with distressed companies.

Monitoring and shares in the American market are not significant anymore while

concentration and management stake play a role in the case of companies who suffered the

most with derivatives losses. Hypothesis 4, concentration, does not present the expected sign.

It is expected that market monitoring through dispersed shares enhances the probability of

giving the correct hedging incentives, but the results show that higher concentration

decreases the probability of major distress in derivatives dealings by 1.5%. In the markets

that comprise the sample there is a culture of ultimate owners with high levels of control,

which results in more incentives to monitor high leverage. In fact, the same reason that makes

academics argue that dispersed companies are in general more efficient may have resulted, in

the specific case of speculation with derivatives, in a more risk-taking position by managers,

in contrast with the usually more cautious and centralized approach when a company has a

single or a small group of owners. Also, the result corroborate Tufano (1996), since both

reasons raised by the author, represented in the present sample by remuneration and

management stake, are statistically significant.

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III.A - Implications for the Regulation of Financial Markets.

The results are especially significant if we think in terms of regulation of stock markets.

Ultimately shareholders and stakeholders shared the burden in the losses by the companies.

The results of the two models show that skewed incentives for the managers coupled with

some lax governance structure (especially a formal hedging policy and no monitoring)

contributed to the companies’ mismanagement of the hedging policies. There are two

important implications: we can argue that it is the responsibility of shareholders to effectively

monitor the companies’ strategies and hence the issue was not one of a market failure or lack

of regulation; or we can argue that for incipient and developing markets the evolution of

regulation should embody rules and codes that prevent the possibility of the design of

possible deleterious strategies. Since we find no evidence of mismanagement, in the context

of the financial crisis, in the American or other developed markets, it may be assumed that

market failures in developing countries contributed somewhat to the effects experienced by

these companies. If we take the example of Brazil, in which the regulatory agency (CVM –

Comissão de Valores Mobiliários) requested ex-post that companies disclose their derivatives

position, we can see a reaction to an acknowledgement that transparency played a role. Even

though I found no evidence that transparency was statiscally an issue – after all, companies

that did not lose with derivatives were not compelled to disclose their position – we can go

beyond the simple transparency prescription to address issues of the design of corporate

governance structures. As figure 2 shows, the checks and balances of risk management

strategies were more important than simple disclosure issues. Not only investors can learn

from what happened with these companies, but the regulatory agencies now have subsidies to

23

request formal structures that comply with a situation in which companies should not be

allowed to unwittingly speculate with derivatives. In developing markets corporate

governance structures matter more than in mature markets – hence the development of

features like the New Market (Novo Mercado) in Brazil. It should be the purpose of the

regulator to help shareholders prevent hubris and incompetence from hurting not only

companies, but as we can see from the hefty losses, whole markets.

Can markets alone prevent developments like the positions in derivatives that bankrupted

some companies and yielded major losses to others? If markets are truly efficient the

leveraged position of companies is already part of the shareholders’ portfolio. However, we

know that earlier derivatives lessons, like the one from Metallgesellschaft (MG) - which

posted losses of over U$1 billion in the mid-90s, have not prevented companies around the

world to leverage their positions, intentionally or not, in these instruments. In developing

markets regulation is supposed to foster a better business environment, especially because

information asymmetry and lack of liquidity prevent full market efficiency. In the case of

non-financial companies, regulations like the one in Brazil which now requires better

disclosure of derivatives positions, is a step forward in that direction. However, regulators

should also recognize that disclosure alone is insufficient. As we can see from the results,

hubris may have played a role, as did skewed incentives to management through

remuneration and management stakes. Moreover, the simple lack of a formal hedging policy

(Air China had one in place which allowed management to hedge at most 50% of oil costs)

also contributed to these losses. In fact, not all the losses were derived from speculative

positions, but in all cases it caught management and investors unaware, and such failures

24

should be avoided in the future. Better governance regulation imply, for instance, changes in

the relationship between monitoring of risk management strategies, while leaving for

shareholders to devise correct incentives for CEOs in relation to risk-taking positions.

IV. FINAL COMMENTS.

The main goal of the paper was to establish a relationship between corporate governance and

risk management by focusing on the case of companies that posted heavy losses in

derivatives during the financial crisis. I built a sample of 49 companies from 10 international

markets, from Latin America, Europe, Asia and Oceania. The combined losses of these

companies were a combined U$18.9 billion. Moreover, 13 of these companies went into

bankruptcy or suffered heavy restructuring, being acquired by other companies and such.

For the purpose of relating corporate governance and risk management first I built a

theoretical framework that encompasses regular finance models with agency theory and

behavioral finance. To test hypotheses concerning this theoretical framework I hand-collected

data on many qualitative indicators of corporate governance, proxies for hubris and other

management characteristics such as the remuneration scheme and the stake of management in

the companies. The empirical testing through a probit cross-sectional model reveals that some

corporate governance characteristics, such as the inexistence of formal hedging policy, lax

monitoring of the risk management department and dispersed ownership concentration are

relevant to the mismanagement of derivatives instruments. Results also show that variables

25

relating to overconfidence and incentives to executives are also relevant. The main impact of

this study is related to the regulation of stock markets – it clearly shows that the failures in

risk management are due to reasons not only related to transparency. In fact, the first

response by market regulators to the distress of many companies was change disclosure

policies regarding derivatives instruments. While better disclosure policies is probably

effective in allowing shareholders better monitoring, we can clearly see from the results that

the losses experienced by the selected companies resulted from more complex issues than

that. Since the analyzed markets are mostly in developing countries, with relevant issues like

imperfect and asymmetric information being part of the environment, regulators should look

into designing policies that correct incentives for proper risk management by non-financial

companies.

Several avenues of research remain. This study only analyzed a sample of 49 companies in a

cross-sectional study. Future research should complement the present one by focusing on the

evolution of risk management strategies. Are companies devising better governance

structures to curb future losses? The finance literature is full of examples of companies that

posted hefty derivative losses, and still companies around the world lost an estimated U$500

billion during the financial crisis. As other markets develop, new companies enter and history

is forgotten there is no guarantee that a new cycle of derivatives losses will not happen.

Social welfare should not be affected by events like this, but in many developing markets

widespread losses lead to less liquidity and hamper the development of capital markets.

26

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29

Figure 1 – Theoretical Framework that impact the design of Hedging Strategies.

Hedging
Strategies

30

Figure 2 - Old and New Risk Management Structure of Sadia.
Figure 2 shows the change in structure of Sadia after it lost U$1.29 in currency derivatives in 2008. The old
structure allowed too much discretion to the CFO, while the new structure reveals better governance for risk
management. Sadia was acquired by its main competitor, Perdigão, in May of 2009. The resulting company is
called Brasil Foods. In 2006 Sadia attempted a hostile takeover of Perdigão but was not successful.

Source: adapted from Sadia (2008).

31

Table I – Companies and Losses with Derivatives in the Financial Crisis
Table I reports data on the 49 companies in the sample with losses in derivatives in the year 2008. Columns give
the companies’ names, country of origin, the book value of losses, and the ratios of those losses, annual revenue
and market capitalization. Market capitalization is at the immediate date before the reported losses.
Losses Losses/ Losses/
Company Country
(U$ million) Revenue Market Cap
APN EUROPEAN RETAIL PROPERTY Australia 86.14 88.1% 651.0%
WESTFIELD TRUST Australia 504.79 35.6% 2.3%
BRASKEM Brazil 737.75 7.5% 10.9%
SADIA Brazil 1,290.52 20.8% 24.3%
COMPANHIA SIDERÚRGICA NACIONAL Brazil 863.15 11.3% 10.8%
EMBRAER SA Brazil 103.02 1.6% 2.4%
ARACRUZ Brazil 1,145.91 32.2% 34.5%
VICUNHA TEXTIL SA Brazil 81.20 13.3% 21.8%
AIR CHINA China 281.13 3.7% 1.8%
CHINA COSCO HOLDINGS China 338.36 1.8% 3.4%
CHINA EASTERN AIRLINES China 857.84 14.2% 11.5%
CHINA RAILWAY CONSTRUCTION China 180.92 0.6% 2.1%
CHINA RAILWAY GROUP China 595.74 1.8% 6.1%
SHENZHEN NANSHAN POWER CO China 30.56 6.7% 6.8%
CHINA HAISHENG JUICE HLDG Hong Kong 24.54 11.3% 14.7%
CITIC PACIFIC Hong Kong 2,084.07 35.0% 27.5%
AUROBINDO PHARMA India 66.68 9.4% 6.1%
HCL TECHNOLOGIES India 68.41 2.9% 0.9%
KPIT CUMMINS INFOSYSTEMS India 20.58 11.3% 5.4%
RAJSHREE SUGARS & CHEMICALS India 3.27 3.7% 12.9%
SABERO ORGANICS GUJARAT India 3.19 3.7% 3.1%
SUNDARAM MULTI PAP India 5.65 19.0% 8.2%
ZEE ENTERTAINMENT ENTERPRISE India 20.34 4.1% 2.5%
ANEKA TAMBANG Indonesia 27.74 3.0% 1.4%
ELNUSA Indonesia 3.22 1.2% 1.5%
KALBE FARMA Indonesia 22.56 2.4% 1.1%
TIMAH Indonesia 12.57 1.6% 1.0%
AJINOMOTO Japan 372.58 3.2% 5.8%
ARIAKE JAPAN Japan 8.01 3.8% 1.6%
SAIZERIYA Japan 160.45 18.8% 20.5%
BAIKSAN Korea 25.45 15.6% 22.3%
C-MOTECH Korea 5.37 20.0% 26.8%
DAEWOO SHIPBUILDING & MARINE Korea 1,773.05 15.9% 28.2%
HAN KWANG Korea 3.68 10.0% 19.2%
MONAMI Korea 18.51 7.5% 10.2%
TAESAN LCD Korea 685.67 81.3% 94.2%
UJU ELECTRONICS Korea 10.90 13.3% 5.4%
WIN4NET Korea 39.28 56.0% 71.9%
ALFA Mexico 537.29 5.2% 6.8%
CEMEX Mexico 1,480.93 7.3% 21.9%
CONTROLADORA COML MEXIC Mexico 2,187.34 45.7% 50.8%
GRUMA Mexico 1,321.16 32.9% 10.8%
GRUPO INDUSTRIAL SALTILLO Mexico 215.65 24.4% 45.3%
GRUPO POSADAS Mexico 128.58 20.8% 18.9%
VITRO Mexico 368.03 14.1% 20.5%
APATOR Poland 11.96 7.7% 4.2%
ODLEWNIE POLSKIE Poland 47.39 77.3% 46.6%
ZAKLADY MAGNEZYTOWE ROPCZYCE Poland 23.91 12.7% 9.0%
ZELMER Poland 10.91 5.4% 4.5%

32

Table II -Abnormal Daily Stock Market Performance after Announcement of Derivative Losses.
Table II reports cumulative abnormal return (CARt) up to the specified day t in event time. Column 1 has each
company, followed by the market, the date that the media announced the derivative loss of each company. Column 4
is the stock return on the day of announcement. Event time is days relative to the media announcement of derivative
losses. Abnormal returns are computed given the market model parameters which are estimated with the GARCH
model rit  Ci  rmt   h ri ,t h   it through the period [-190; -10] in event time, where rit is the continuously
compounded local return on date t in country i and rmt is the continuously compounded daily market return on index
on day t. The sample period runs from 90 trading days before the date in the third column to the dates in columns 5 to
8. Values in bold are statistically significant at 5%. Table is divided in two categories: major distressed companies and
non-distressed companies. Major distress is defined as bankruptcy, acquisition by another company, major asset sales
or a restructuring of derivative contracts with banks to avoid a default on the contracts.
Company Market Date Day 1 t=0 t=7 t=60 t=360
Major Distress
APN EUROPEAN RETAIL PROPERTY Australia 8/26/2008 -5.35% -1.948 -4.030 -2.307 -16.999
SADIA Brazil 9/25/2008 -35.50% -32.439 -41.945 -38.778 -60.477
ARACRUZ Brazil 9/29/2008 -19.60% -17.009 -42.741 -55.820 -74.124
CITIC PACIFIC Hong Kong 10/21/2008 -55.10% -54.972 -45.952 -35.381 -19.103
C-MOTECH Korea 6/2/2008 -9.50% -5.599 -1.966 -2.297 -14.492
BAIKSAN Korea 7/7/2008 -8.28% -7.414 -2.675 -7.244 5.901
TAESAN LCD Korea 8/18/2008 -14.88% -14.337 -9.992 -11.930 -16.558
WIN4NET Korea 8/14/2008 -14.85% -12.894 -8.550 3.522 4.424
KALBE FARMA Indonesia 10/10/2008 -5.54% -1.796 2.890 7.267 13.921
CONTROLADORA COML MEXIC Mexico 10/8/2008 -75.39% -74.325 -72.157 -56.709 -31.301
GRUMA* Mexico 10/13/2008 -55.09% -53.260 -51.672 -48.449 -24.593
VITRO Mexico 10/10/2008 -20.13% -16.988 -15.483 -1.932 -2.075
ODLEWNIE POLSKIE Poland 10/15/2008 -5.03% -1.472 -12.025 -36.975 -35.190
ZAKLADY MAGNEZYT. ROPCZYCE Poland 11/26/2008 -8.57% -8.565 -22.680 -34.576 -19.400
Non-Distress
WESTFIELD TRUST Australia 8/26/2008 -3.36% -1.371 -2.298 -5.031 -2.030
BRASKEM Brazil 10/3/2008 -6.30% -3.302 -0.874 -1.134 -1.811
COMPANHIA SIDERÚRGICA NAC. Brazil 10/1/2008 -5.50% -1.894 2.229 1.344 0.277
EMBRAER SA Brazil 11/4/2008 -3.40% -1.382 0.380 1.097 5.235
VICUNHA TEXTIL SA Brazil 11/11/2008 -11.11% -10.574 -6.992 -9.859 -12.954
AIR CHINA China 10/16/2008 -10.01% -8.704 -1.620 -1.204 -2.059
CHINA COSCO HOLDINGS China 12/12/2008 -5.20% -5.178 -0.719 0.524 -1.287
CHINA EASTERN AIRLINES China 10/24/2008 -5.27% -2.151 2.185 1.353 0.323
CHINA RAILWAY CONSTRUCTION China 10/22/2008 -9.97% -6.169 -1.262 -0.276 0.155
CHINA RAILWAY GROUP China 10/10/2008 -3.23% 0.101 1.724 3.175 8.523
SHENZHEN NANSHAN POWER CO China 10/24/2008 -3.16% 0.944 3.235 -0.622 0.861
CHINA HAISHENG JUICE HLDG Hong Kong 10/22/2008 -14.26% -13.266 -8.402 -12.109 -22.797
AUROBINDO PHARMA India 3/4/2008 -3.73% -0.806 0.964 0.006 -0.006
HCL TECHNOLOGIES India 7/11/2008 -5.62% -1.719 1.050 0.163 -0.113
KPIT CUMMINS INFOSYSTEMS India 4/28/2008 -12.41% -9.111 -5.782 -1.232 -7.201
RAJSHREE SUGARS & CHEMICALS India 3/17/2008 -8.74% -6.872 -4.159 -1.825 0.224
SABERO ORGANICS GUJARAT India 1/7/2008 -5.00% -0.044 0.775 2.117 9.442
SUNDARAM MULTI PAP India 1/16/2008 -4.51% 0.299 1.358 1.762 2.812
ZEE ENTERTAINMENT ENTERPRISE India 6/10/2008 -3.53% -1.808 0.490 0.338 0.129
ANEKA TAMBANG Indonesia 10/17/2008 -7.85% -4.250 -1.705 -3.523 -11.033
ELNUSA Indonesia 10/8/2008 -13.03% -9.257 -5.957 -7.796 -12.609
TIMAH Indonesia 10/6/2008 -31.28% -29.182 -25.012 -13.640 -5.518
AJINOMOTO Japan 10/16/2008 -10.88% -10.801 -4.835 -1.573 -0.595
ARIAKE JAPAN Japan 10/22/2008 -9.95% -6.825 -2.696 -1.365 2.386
SAIZERIYA Japan 11/27/2008 -15.56% -14.192 -10.135 -2.068 -1.388
DAEWOO SHIPBUILDING & MARINE Korea 10/16/2008 -12.64% -8.421 -4.570 1.703 -2.973
HAN KWANG Korea 7/2/2008 -21.84% -20.952 -9.745 2.335 7.747
MONAMI Korea 7/9/2008 -19.70% -16.210 -12.135 -3.711 -1.399
UJU ELECTRONICS Korea 7/7/2008 -6.70% -2.664 1.429 -1.142 2.970
ALFA Mexico 10/6/2008 -9.59% -5.765 -3.643 2.777 5.445
CEMEX Mexico 10/1/2008 -7.10% -6.925 -6.070 -2.678 -5.042
GRUPO INDUSTRIAL SALTILLO Mexico 10/8/2008 -13.03% -9.903 -7.503 -21.964 -6.631
GRUPO POSADAS Mexico 10/14/2008 -13.33% -10.616 -10.207 -12.298 -1.338
APATOR Poland 10/16/2008 -8.79% -4.853 -1.223 -0.275 0.151
ZELMER Poland 10/14/2008 -5.53% -5.565 2.680 -0.770 0.557
*The Mexican Stock Exchange froze trading on Gruma until October 30.

33

Table III – Results from the Probit Model
Table III relates corporate governance and other variables to risk management through a probit panel data
model. Dependent variable is 1 for companies that posted losses in derivatives for the year 2008, and 0 for other
companies. MON represents lack of monitoring; DIS relates to disclosure; USA shares in the American market;
CON ownership concentration; IIN participation of institutional investors; FHP formal hedging policy; TRE
trend of major source of risk; FIN recent financial results; REM remuneration incentives for the CFO; and MAN
management stake. Last two variables follow Tufano (1996). Total number of companies is 346, and the last
column represent only distressed companies, i.e., companies in which derivative losses resulted in bankruptcy,
acquisition by another company of major restructuration. Ommited controls are: age of each company, average
financial debt ratio and a binary variable for family-owned company.
Whole Sample Distressed Companies
β σ Β σ
H1 No monitoring 0.429* 0.08 0.232 0.07
H2 Disclosure 0.218 0.44 0.561 0.91
H3 Shares in the US market -0.812* 0.35 -0.270 0.67
H4 Concentration -0.054 0.24 -0.343* 0.15
H5 Institutional Investors 0.044 0.42 0.157 0.87
H6 Formal hedging policy -0.558* 0.22 -0.780* 0.30
H7 Trend of source of risk 0.754* 0.26 0.785* 0.32
H8 Recent financial results 0.234 0.62 0.059 0.64
H9 Remuneration 0.861* 0.22 0.338* 0.14
H10 Management stake -0.261 0.60 0.042* 0.09
N obs 346 310
LR X2 97.56 89.35
Prob > X2 0.000 0.001
Log likelih. -239 -208
Pseudo-R2 0.34 0.32
Cross-dep (H0 : R = IN) 0.015 0.018
* variable significant at 5%.

34

Table IV – Marginal Effect After the Probit Model
Table IV is the marginal effect of the probit mode that relates corporate governance and other variables to risk
management. Dependent variable is 1 for companies that posted losses in derivatives for the year 2008, and 0
for other companies. MON represents lack of monitoring; DIS relates to disclosure; USA shares in the American
market; CON ownership concentration; IIN participation of institutional investors; FHP formal hedging policy;
TRE trend of major source of risk; FIN recent financial results; REM remuneration incentives for the CFO; and
MAN management stake. Last two variables follow Tufano (1996). Total number of companies is 346, and the
last column represent only distressed companies, i.e., companies in which derivative losses resulted in
bankruptcy, acquisition by another company of major restructuration. Ommited controls are: age of each
company, average financial debt ratio and a binary variable for family-owned company.
Whole Sample Distressed Companies
Β σ Β σ
H1 No monitoring 0.023* 0.010 0.002 0.010
H2 Disclosure 0.002 0.019 0.006 0.009
H3 Shares in the US market -0.019* 0.008 -0.002 0.007
H4 Concentration -0.004 0.004 -0.015* 0.005
H5 Institutional Investors 0.003 0.002 0.001 0.019
H6 Formal hedging policy -0.012* 0.005 -0.020* 0.007
H7 Trend of source of risk 0.036* 0.012 0.021* 0.003
H8 Recent financial results 0.002 0.003 0.009 0.021
H9 Remuneration 0.037* 0.012 0.016* 0.005
H10 Management stake -0.001 0.001 0.014* 0.007
Mg effect 0.1987 0.2140
N obs 346 310
* variable significant at 5%.

35

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