Evidence From Trading Service And Plantation

Published Date: 02 Nov 2017

Chapter 1 Introduction

1.1 Background of Study

Risk management has been a matter of continuous concern to most corporations since the fall of Bretton Woods System in the middle of 1970s. Risk management involves identification, assessment and prioritization of risks. It focuses on minimizing, monitoring, and controlling the probability and/or effect of uncertain events by coordinating and economical application of resources. This also implies that it maximizes the realization of opportunities. In addition, by using an effective risk management helps firms to minimize risk exposure, meanwhile it also indicates to enhance or stabilize their future profits. Prof. Hawley, an American economist in 1907 remarks,

"The profit of an undertaking, or residue of the product after the claims of land, labour and capital are satisfied, is not the reward of management or coordination but of the risk and responsibilities that the undertaker subjects himself to".

In fact, all firms are seeking strategies to produce the maximum profits and to minimize the uncertainty from their investments portfolio. It helps to achieve primary goal of organization which is to maximize shareholder wealth. By understanding risk to both individual projects and portfolios, management will be able to make better strategic decisions. Cost commitments, revenue pipelines, and profit forecasts will be accurately stated for each level of risk. The sensitivity of the forecasts will be better understood. When informed by the risk assessment, the entire business will be more profitable. Therefore, it can consider as one of the strategies to satisfy desire from shareholders.

Risk management is often used to avoid the uncertainty arise from capital market. According the assumption from capital market theory, it is assuming complete market. A complete market is where under complete information or no asymmetric information and perfect rationality of agents. The equilibrium occurs where supply of funds equals to the demand, the interest rates clears the market. Since capital markets are perfect, firms can always obtain external funds at the same costs as internal funds to finance their investment opportunities. Thus, neither hedging nor financing decisions at the corporate level adding to performance of firm, it can be assumed near to impossible to justify.

In fact, assuming perfect capital market, factors such as asymmetric information, asymmetric taxes and transaction cost are not taken into consideration. So market imperfection came into the picture that demonstrates a firmâ€™s optimal capital structure measured in debt against equity may not be beneficial. It says when capital markets are less than perfect, circumstances do arise where corporate hedging can affect performance of firm and, thus, it is assumed that are able to justify. Corporate hedging can directly affect the cash flow of the firm. Exposures to volatile interest rate, exchange rate and commodity prices are costly for corporations. Therefore, risk management activities are able to help or lead to an increase in performance of firm during market imperfections.

In order to plan corporate hedging decision of whether and how to hedge, it depends on firm-level attributes that followed by determining the advantages derived from hedging. The derived benefits accrue to either shareholders or managers. In fact, the widespread use of derivatives for hedging by non-financial firms is well documented in the corporate hedging literature. Thus, it is important to understand and examine why firm hedge and whether corporate hedging decision creates value. So far, there are more empirical studies that have concentrated on European countries. For example, several studies of US firms report that more than 60 percent of firms use derivatives [1] . In the UK Grant and Marshall (1997) report that 90 percent of large firms use derivatives and Mallin, Ow-Yong and Reynolds (2001) find that 60 percent of firms use derivatives. Both studies in UK find that the overwhelming majority of UK firms use derivatives for the purposes of hedging. However, the limited empirical evidence on non-European firms suggests significant differences findings between firms that operate in different markets.

Despite the fact of modern portfolio and corporate finance theory, hedging does not alter firmâ€™s value, financial managers and treasurers are highly concerned about firmâ€™s exposures to corporate risks. Risks such as commodity risk, credit risk, currency risk, interest rate risk, and etc, are highly concerned and managed as it plays an important role in the success or failure of a business now. Over recent decades, numerous researches have been done in proving the performance of an effective risk management. There are more arguments in favor of the corporate risk management on performance of firm based on the Capital Asset Pricing Model and the Modigliani- Miller theorem. Financial risk is the possibility of a corporation defaulting on its bond or the risk exposures from price fluctuation, thus it can be directly or indirectly affected the corporation. It could be argued that financial risk management contributes to the firmâ€™s primary goal as one of the important corporate functions. Therefore, the particular research tries to argue what determine the use of derivatives in affecting the performance of firm.

In the nutshell, this paper presents the impact of the firm specific factors on the use of derivative instruments for Malaysia non-financial firms. This paper examines the literature that has focused on testing the various theories of hedging and the extent to which it supports or refutes them. The findings of the paper will help to determine how theories have been assessed in the empirical literature and whether the theories underpin what is observed in practice. The paper begins by looking at how the empirical literature has defined hedging and measured hedging. It then examines whether the definitions of hedging employed are appropriate indicators of hedging or are potential proxies for speculation. This is followed by a look at the choice of sample firms and then an examination of the econometric methodologies employed. The paper then evaluates the findings of the existing empirical literature examining separately the results of the various hypotheses tested. Finally, the paper concludes by assessing the impact a review of the empirical literature has for future empirical research in this area.

1.2 Derivatives in Malaysia

Kuala Lumpur Commodities Exchange (KLCE) was set up as the first derivative exchange in Malaysia in 1980. The first derivative introduced by KLCE to the public and it was then actively traded was Crude Palm Oil (CPO) future contract. In fact, CPO futures still trade actively nowadays, it still the main derivative product from KLCE. Thereafter, KLCE has introduced and suggested to public on other commodity future contracts on rubber, tin, cocoa and etc. Indeed, these future contacts are not getting good responds from the public, and they are less actively traded compare to CPO future contracts. However, they would not be taken off from KLCE, this is due to these commodity future contracts are good substitute contracts traded in foreign exchange such as Tokyo, London and etc.

In the year of 1980, Malaysia set up the first derivative exchange which was known as Kuala Lumpur Commodities Exchange (KLCE). At the same year, KLCE introduced the first derivative product - Crude Palm Oil (CPO) future contract to the public and it was actively traded in KLCE. Up to present, CPO is still the main derivative product of KLCE, even though KLCE has introduced other commodity future contracts on rubber, tin, cocoa and etc. Although other commodity future contracts are less actively traded compared with CPO, but since they are good substitute contracts traded in foreign exchanges such as Tokyo, London and etc., therefore it would not be taken off from KLCE.

The other clearing house, the Kuala Lumpur Options and Futures Financial Exchange (KLOFFE) started trading in December 1995 after a long period of preparation. They offer a financial future contract on the Kuala Lumper Stock Exchange (KLSE) Composite Index. Six month later, Interest rate futures began to trade on the Malaysian Monetary Exchange (MME). In 1998, due to MME was unable to maintain the single contract, thus it was then merged to form the Commodity and Monetary Exchange of Malaysia (COMMEX), a multi-product futures exchange, with the KLSE acquiring KLOFFE a month later. The derivatives market in Malaysia finally uniting in one firm in the following year after KLOFFE merged with COMMEX. The firm is named Malaysian Derivatives Exchange (MDEX). As a result of merging, MDEX was a single exchange that for all derivatives trading needs to consolidate into this exchange. Due to MDEX was a subsidiary that wholly owned by KLSE, therefore KLSE was a single exchange in Malaysia that provided the trading in stocks as well as both commodity and financial derivatives. After that, KLSE has changed its name to Bursa Malaysia Berhad, therefore MDEX became Bursa Malaysia Derivatives Berhad (BMD).

There are nine derivatives contracts are being trade in today BMD, the nine derivatives contracts are stated in Table 1.1.

Commodity Derivatives

Equity Derivatives

Financial Derivatives

Crude Palm Oil Futures (FCPO)

FTSE Bursa Malaysia KLCI Futures (FKLI)

3 Month Kuala Lumpur Interbank Offered Rate Futures (FKB3)

USD Crude Palm Oil Futures (FUPO)

FTSE Bursa Malaysia KLCI Options (OKLI)

3- Year Malaysian Government Securities Futures (FMG3)

Crude Palm Kernel Oil Futures (FPKO)

Single Stock Futures (SSFs)

5- Year Malaysian Government Securities Future (FMG5)

Table 1.1 (Source: Bursa Malaysia, 2011 [2] )

Bursa Malaysia Derivatives Bhd. has become the market that is internationally competitive on futures trading as they enlarged the offering in commodity derivatives that denominated on USD to globalize Malaysia futures market. FUPO is a crude palm oil future contract that is denominated in USD, whereas FCPO is in Ringgit, both products are treated as the worldwide pricing benchmark for palm oil.

Statement of Problems

Over the recent decade, the usage of derivatives increased in firms to hedge their position. The derivative market has experienced a rapid growth over the recent years. The information on firm derivative usages is widely available, but however, the empirical finding of whether to use of derivative for hedging purposes has or has no impact on firm value is still inconclusive.

Modigliani and Miller (1958) suggested perfect capital markets existence without transaction costs, taxes, bankruptcy cost, agency costs and information asymmetry. The main reason is because rational investors are assumed to diversify their portfolio themselves and therefore there is no value to firms in hedging. However, does it possible without transaction costs, taxes, bankruptcy cost, agency costs and information asymmetry? It is still unknown and need further studies to explore more hidden potential indicator in explaining corporate hedging decision. Limited studies on corporate hedging decision are conducted in Malaysia [3] could not able to provide reliable information to firms in managing their exposure to financial risk by using derivatives.

As a whole, the findings of empirical studies remain controversial. It is not clear explained whether the decision to use derivatives will have any function or effect to manage the financial risks of firms. Therefore, this paper continues to explain the determinants of corporate hedging decision by testing the hypothesis of whether the use of derivatives is rewarded by a lower financial distress costs and lower exposure to the financial risks.

1.2 Objectives of Study

In previous decade, research studies have revealed unexpected outcome that the explored hedging rationales have little predictive power in explaining corporate risk management decisions [4] . Various empirical researches show their own interpretation. Therefore the main objective of this research study is to examine the influences of derivatives toward financial distress cost, tax, foreign exchange exposure, and firm size. The objectives are specifically stated in the forms show in the following:

To determine whether the derivative is positively related to financial distress cost.

To find out whether derivative is negatively related to foreign exchange exposure

To determine whether derivative is positively related to tax.

To find out whether derivative is positively related to firm size.

The minor objectives of this research study are:

To examine the hedgers in Malaysia are giving proper attention and view derivatives as valuable financial tools.

To study the effect of financial crisis in 2009 [5] on non-financial firms in Malaysia.

To display an overview on the development of derivatives among the hedgers in Malaysia.

To find out whether the number of trading volume of derivatives among the hedgers in Malaysia has increased.

.

1.4 Significance of the Study

This research and study enhance the existing literature review on the determinants of corporate hedging decisions in Malaysia. On the others hand, this research also provides a recent data set of Malaysia non-financial firms on the usage of derivatives toward determinants of corporate decisions.

1.3 Scope of Study

The scope of study of this paper research is to study the relationship between determinants of corporate hedging decision and the total notional amount of derivatives. In this study, it consists of five chapters. In chapter one, background or overview of the research paper is provided and it is clearly mentioned about the problem statements, the objectives of study and the significance of the study.

It is then proceed to chapter two which covers the empirical review by researchers. This includes the background of study and the past researches on this issue. The discussion of the determinants of corporate hedging decision, hedging and also risk management are included in this part.

Chapter three explains the methodology conducted to test the relationship between dependent variable and independent variables. Analysis and discussion of the results is covered under chapter four. Finally, the conclusion and summary of the main findings will be highlighted in this chapter. Limitations and recommendations are also considered under this part.

Chapter 2 Literation review

2.1 Definition of Derivatives

Derivatives are defined as the financial instruments whose returns are derived from those of other financial instruments (Chance and Brooks, 2009). That is, their performances are relying on how effectively other financial instruments perform. Derivatives are designed as a valuable purpose in managing the financial risk. They protect the firm from unanticipated events, where adverse foreign-exchange or interest-rate movements and unexpected increases in input costs. There are different types of derivatives such as forward contract, future contract, swap, option, equity derivative, foreign exchange derivative, interest rate derivative and commodity derivative.

2.2 The Use of Derivatives to Hedge Risk

A survey from the International Swaps and Derivatives Association [6] reported that 94% of the worldâ€™s 500 largest companies in 2009 used the derivatives to manage and hedge their business and financial risks. Another interesting founding from the survey was that it discovered the usage of the derivatives in the particular year. The foreign exchange derivatives were reported the most widely used instruments which were 88 percent, followed by interest rate derivatives (83 percent) and commodity derivatives.

David Harper (2010) [7] mentioned the uses and the functions of derivatives. The study also described that the firms can use derivatives to manage the following risks:

Foreign Exchange Risk: It is also known as currency risk. The risk that arises from the changes in the currency exchange rate will cause an adverse effect on the firmâ€™s revenue.

Interest Rate Risk: The risk that an investment's value will changeÂ due to a change in the absolute level of interest rates, in the spread between two rates, in the shape of the yield curve or in any other interest rate relationship.

Commodity or Product Input Hedge: Firms that are heavily exposed to the price change of commodities or raw-material inputs normally faced the risk. The given example in airline industry, a lot of jet fuels is needed. In the past, a great deal of consideration has given by most airlines to hedge against crude-oil price.

2.3 Research Theory

2.3.1 Derivatives and Financial Distress or Bankruptcy Cost

Firms with greater variability of cash flows are more likely to find themselves in financial distress. Higher leverage or more volatile cash flows lead to higher risk in levered firms [8] . That is, their cash flows are not sufficient to meet all fixed payment obligations in full and timely. Firms are forced into bankruptcy when they are unable to fulfill its fixed payment obligations, at which point shareholder and creditor try to recover their investments in the firms.

Empirical evidence are found by Campbell and Kracaw (1987), Bessembinder (1991), Nance, Smith and Smithson (1993), Dolde (1995), Mian (1996), as well as Getzy, Minton and Schrand (1997) and Haushalter (2000) that firms with highly leveraged capital structures are tend to hedge by using derivatives. Hedging can reduce the probability of financial distress by shielding future stream of cash flows from the changes in the exchange rates (Myer, 1977; Bessembinder, 1991). Given that corporate risk management can turn lowers the expected costs of financial distress or bankruptcy cost, firms with high probability of default and/or high bankruptcy cost should be more likely to engage in corporate hedging. This decrease in expected costs increases the firmâ€™s expected cash flows and therefore benefits shareholders. [9] Using the long-term debt ratio and interest coverage ratio as proxies for the financial distress, this hypothesis can be examined. Both of these indicators give an indication of the probability of financial distress. For instance, a firm with low leverage has lower payment obligations, and ought to thus be less likely to experience difficulties in honoring these commitments. So, it might have stronger incentives to hedge.

However, GÃ©czy et al. (1997) then argue about the implication of long-term debt ratio. If other industry-specific, exogenous factors (such as rivalry, level of competition, etc.) can influence the financial distress costs, which indicates that the financial distress costs do not only depend on a firmâ€™s debt ratio, a firm with high distress costs might have a low debt ratio, and still face high incentives to hedge. A modified financial distress proxy was suggested by subtracting the industryâ€™s median leverage ratio from the firmâ€™s leverage ratio. A debt ratio above-industry level would indicate a high probability of distress.

Despite the potential endogeneity problems in the long-term debt ratio, it is still commonly used to test the financial distress hypothesis. Most studies obtained a significant positive relation between the long-term debt ratio and corporate hedging (Bartram, Brown and Fehle, 2009; Dionne and Triki, 2005; Graham and Rogers, 2002; Haushalter, 2000; Guay, 1999; Gay and Nam, 1998; Howton and Perfect, 1998; Fok et al., 1997; Berkman and Bradbury, 1996; Mian, 1996).

By hedging financial risks such as currency, interest rate and commodity risk, firms can decrease cash flow volatility. By reducing the cash flow volatility, firms can decrease the expected financial distress, thereby enhancing the present value of expected future cash flows. In addition, reducing cash flow volatility can improve the probability of having sufficient internal funds for planned investments, (e.g. see: Stulz, 1984; Smith and Stulz, 1985; Froot, Scharfstein and Stein, 1993; 1994) eliminating the need to either cut profitable projects or bear the transaction costs of external funding. The main hypothesis is that, if access to external financing (debt and/or equity) is costly, firms with investment projects requiring funding will hedge their cash flows to avoid a shortfall in own funds, which could precipitate a costly visit to the capital markets. An interesting empirical insight based on this rationale is that firms with substantial investment opportunities that are faced with high costs of raising funds under financial distress will be more motivated to hedge against risk exposure than average firms. This rationale has been explored by numerous scholars, among others by Hoshi, Kashyap and Scharfstein (1991), Bessembinder (1991), Dobson and Soenen (1993), Froot, Scharfstein and Stein (1993), Getzy, Minton and Schrand (1997), Gay and Nam (1998), Minton and Schrand (1999), Haushalter (2000), Mello and Parsons (2000), Allayannis and Ofek (2001) and Haushalter, Randall and Lie (2002). The results of the studies mentioned above confirm that companies using derivative instruments to manage financial risks are more likely to have larger investment opportunities. Evidence also suggests that users of derivatives exhibit lower short-term liquidity than those without. That is, users of derivatives are more likely to meet their short-term obligations than those without derivatives (Lin and Smith, 2007; Allayannis et al., 2003; Dionne and Garand, 2003; Fok et al., 1997; GÃ©czy et al., 1997; Tufano, 1996).

2.3.2 Derivatives and Foreign Exchange Exposures

Collier and Davis (1985) argue about the organization and practice of currency risk management by U.K. multi-national firms (Belk and Glaum 1990). In their study, they revealed that there is a degree of centralized control of group currency risk management and also existence of formal exposure management policies. Besides, they also revealed that these multi-national firms are performing active management of currency transaction risk. The findings continued and focused on foreign exchange risk management practice and product usage of large Australia-based firms (Batten, Metlor, and Wan, 1992). The study revealed that 70% of the firms act as foreign exchange risk bearers by trading their foreign exchange exposure in attempt to optimize firm return. Somewhat it indicates that transaction exposure emerged as the most relevant exposure.

Ahmed and El-Masry (2006) revealed that foreign exchange risk is the risk most commonly managed with derivatives and interest rate risk is the next most commonly managed risk. They concluded the usage of derivatives among all size of firms and figure the highest usage of derivatives among the type of firms. Besides that, they revealed half of firms do not use derivative instruments because their exposures are not significant, concerns about disclosures of derivatives activity under FASB rules, cost of establishing and maintaining derivatives programmes exceed the expected benefits. This implies that for those firms that their exposures are significant, most of them would prefer to use derivatives instrument in order to manage the volatility in cash flows.

2.3.3 Derivatives and Taxes

It has been argued that if the tax schedule is convex, where a firm faces a convex tax function, then hedging reduces the volatility of taxable income and the firmâ€™s expected tax liability. For instance, volatile taxable income would result in a higher tax burden than stable pre-tax income if taxes increase more than proportionally with taxable income. Corporate hedging could stabilize the taxable income, where it creates value since saving from higher income states exceed additional taxes from lower income states, thus lowering the average corporate tax burden (Stulz, 2001; Bartram, 2000; Graham and Smith, 2000; 1999; Santomero, 1995; Smith, 1995; Kale and Noe, 1990; Mayers and Smith, 1990; Rawls and Smithson, 1990; Smith et al., 1990; Smith and Stulz, 1985).

Convexity in tax schedule can influence to statutory progressivity. However, according to Mayer and Smith (1990), they explained that the statutory progressivity is relatively limited in most tax systems. In addition, indirect effects can be attributed to the convex tax function. Special tax preference items, explicitly tax-loss carry forwards and/or investment tax credits, which are subject to restrictive rules and regulation, most often, subject to indirect effects. Consequently, firms with low income or losses firms are not able to fully utilize the benefits of these effects (MacKie-Mason, 1990). Through corporate risk management, there is potential that firm could reduce the tax burden. Given the potential to reduce tax burden, firms should have stronger incentives to use derivatives to hedge, especially the firms with higher income in the convex region of tax code or with more special tax items.

Overall, the empirical evidence based on a tax code progressivity dummy indicating income in the convex tax region provides support for the tax hypothesis (Haushalter, 2000; Howton and Perfect, 1998; Nance et al., 1993). In contrast, marginal (or average) tax rate proxies can lead to significant results, but in the wrong direction (Haushalter, 2000). There is, however, some evidence to support the tax hypothesis when the tax savings from volatility reductions are considered (Dionne and Triki, 2005; Dionne and Garand, 2003). This might be explained by the fact that the tax incentive to hedge in order to increase leverage is larger than the tax incentive of progressivity (Graham and Rogers, 2002; Graham and Smith, 1999). Scaled values and dummies for tax preference items generally lead to qualitatively similar findings. In the majority of cases, tax-loss carry-forwards do not significantly associate with corporate hedging. In contrast, tax credits provide (stronger) incentives to engage in financial risk management (Bartram, Brown and Fehle, 2009; Dionne and Garand, 2003).

In sum, there is some, albeit weak empirical support for the tax hypothesis. The difficulties of finding empirical support for this hypothesis might be due to the fact that the tax incentives to hedge are relatively small compared to other incentives and might thus be hard to identify in statistical tests. It is also possible that firms with the strongest convexity in their tax code are small firms with zero expected income. Last but not least, income volatility can be reduced by other means than corporate risk management (Graham and Rogers, 2002).

2.3.4 Derivatives and Firm Size

Bodnar et al. (1995) found that the usage of derivatives among large firms was higher than smaller firms. The study also revealed that derivatives are use most commonly to reduce the volatility of firmâ€™s cash flows. Philips (1995) figured 67% of firms surveyed use derivatives in conjunction with obtaining funding and 21% for investment purpose. Bartram et al (2003) had conducted a comparison in the usage of derivative instruments in 48 countries, mainly US firms, he found that 59.8% use derivatives with 43.6% using foreign exchange derivatives and 32.5% using interest rate derivatives.

Mallin, Ow-Yong and Reynolds (2001) carried out an empirical study of derivative usage by some 231 UK non-financial firms. They compared between US and UK findings in the past recent years. Their findings revealed that broadly similar trends in the usage of derivatives and derivatives usage to hedge financial price risk is well established amongst larger UK firms (Philips, 1995; and Jesswein et al., 1995). Ahmed and El-Masry (2006) conducted an empirical study of using or not using using derivatives for 401 UK non-financial firms. The study revealed that larger firms are more likely to use derivatives than medium and smaller firms.

Several ways are used to measure the size of a firm. Batten, Mellor and Wan (1993) suggest that use foreign exchange turnover to measure the firm size as it has the most important effect. The size of a firm apparently measured on the usage of computer technology, especially on the numbers of financial derivative instruments that apply on the firm. Besides the consideration of the capacity of computer technology, Allayannis and Ofek (2001) suggest that larger firms are more toward to use foreign debt. Aobo (2005) suggest that use the log of consolidated total assets to measure the size of a firm had a positive relationship to the relative importance of foreign debts.

Chapter 3 Methodology

3.1 Sample

This research study was conducted on the non-financial firms in Malaysia and the criterion for selecting firms is they are one of the firms under trading or services sector and plantation sector on main board in Bursa Malaysia. Bursa Malaysia is an exchange holding firm approved under Section 15 of Capital markets and Services Act 2007. Besides, data needed to explore hedging rationales in analyzed firms were collected from the annual reports and notes to the financial statements. 222 non-financial firms under trading or services sector and 44 non-financial firms under plantation sector in Malaysia have met the required criteria of being one of the firms trading or services sector and plantation sector on the main board in Bursa Malaysia.

This research paper included in our investigation the financial year-end total notional amount of derivatives (in Ringgit Malaysian) such as Forward and Future contracts, whereas, for interest rate derivatives, Forward and Swaps contracts. Othman and Ameer (2009) reported that Forward contracts are used in high proportion to hedge market risks by Malaysian firms followed by Future and Swap contracts. Out of 222 firms in trading or services sector, 23 firms are categorized as hedger. While in plantation sector, 12 firms are categorized as hedger. However, 19 hedgers in trading or services sector and 9 hedgers in plantation sector met our criteria of having complete data on derivatives and other variables for 2010 and 2011. It was designed to explore the numbers of firms which are able to manage financial risks and also the existence of corporate hedging decision among them.

3.2 Conceptual Framework

Dependent variable

Independent variables

3.3 Data Description

Description of all the variables

The data used in this study is collected on yearly basis. The data range is from 2010 to 2011, where the data conducted is 2 years data.

Total notional amount of derivatives (DERV) â€“ Dependent Variable

The data on the total notional amount of derivative instruments were collected from the firmsâ€™ financial reports. These financial reports of each sector were downloaded from the firm announcements under Bursa Malaysia Company Announcement Webpage in PDF format. Thereafter, the "find" function in PDF was used to locate a firmâ€™s disclosure on the derivative instruments. Key terms such as "derivative", "notional" and "contract value" are used to detect the particular section related to the derivatives in financial instruments. The total notional amount of derivatives was converted from USD to MYR if the financial report disclosed the amount in USD. Data are extracted from the balance sheet and income statements from the firmsâ€™ annual report.

Long-term debt ratio (DEBT) â€“ Independent Variable

Long-term debt ratio is used in this research study to measure the impact of the derivatives on the cost of financial distress. It is calculated by using the formula of total of non-current liabilities [10] divide by the total assets. This formula is used to measure the value of long-term debt allocated under each of the underlying assets. The data of the total of non-current liabilities and the total assets are extracted from the balance sheet from the firmsâ€™ annual report in MYR. The formula was suggested by numerous authors of research studies [11] to study the effect on the cost of financial distress.

Long-term debt ratio (DEBT) =

H0: There is a positive relationship between long-term debt ratio (DEBT) and derivatives.

H1: There is a no relationship between long-term debt ratio (DEBT) and derivatives.

Earnings volatility before interest and tax (RISK) â€“ Independent Variable

Earning volatility before tax and interest is used in this research study to detect any impact of the derivatives on the cost of financial distress as well. It is calculated by using formula of standard deviation of the earnings before interest and tax (EBIT) divide by the total assets. This formula is used to measure the risk of the earnings before interest and tax allocated under each of the underlying asset. Earnings volatility before interest and tax also defined as the coefficient of variation of various earnings before interest and tax. The data of the EBIT and the total assets are extracted from the balance sheet and income statements from the firmsâ€™ annual report in MYR. The formula was included in Altman Z-score model [12] to detect the financial distress. Also, it was suggested by Allayannis and Ofek (1998) to measure the cost of financial distress on currency notional values of derivative instruments.

Earnings volatility before interest and tax (RISK) =

H0: There is a positive relationship between earnings volatility before interest and tax (RISK) and derivatives.

H1: There is a no relationship between earnings volatility before interest and tax (RISK) and derivatives.

Liquidity of underlying assets (LIQUIDITY) â€“ Independent Variable

Nonetheless, liquidity of underlying asset is also used in this research study to detect any impact of the derivatives on the cost of financial distress. It is calculated by using the formula of total current assets divide by the total current liabilities. The total current assets that used in this research study have excluded the cost of inventory as inventory can be less liquid than other current assets. The formula is also named quick ratio or acid-test ratio. It is a measure of a firmâ€™s ability to meet its short-term obligation using its most liquid assets. A higher ratio indicates greater short-term financial health. The data of the total current assets, the cost of inventory and the total current liabilities are extracted from the balance sheet from the firmsâ€™ annual report in MYR. The formula was suggested by few research studies such as Bartram et al. (2007), Madsen and Prevost (2005), and Allayannis et al. (2001).

Liquidity of underlying assets (LIQUIDITY) =

H0: There is a positive relationship between liquidity of underlying assets (LIQUIDITY) and derivatives.

H1: There is a no relationship between liquidity of underlying assets (LIQUIDITY) and derivatives.

Market to book value of equity (GROWTH) â€“ Independent Variable

Market to book value of equity is used in this research study to measure the impact of derivatives on the performance of firms. It is calculated by market value of equity divide by book value of equity. Market value of equity for each year is obtained from the Bloomberg stocks and indexes [13] . The market value of equity is refers to the year-end stock closing price quote. Book value of equity is calculated by number of share capital divide by the total equity. Market to book value of equity is a useful way of measuring the performance of firm, where it measures the relative value of a firm compared to its stock price or market value. The data are extracted from the balance sheet from the firmsâ€™ annual report in MYR. This formula was suggested by many research studies, one of them is the research study conducted in Malaysia by Dr. Rashid Ameer in 2010.

Book value of equity (GROWTH) =

Market to book value of equity (GROWTH) =

H0: There is a positive relationship between market to book value of equity (GROWTH) and derivatives.

H1: There is a no relationship between market to book value of equity (GROWTH) and derivatives.

Tax-loss carryforward (TAX) â€“ Independent Variable

Tax-loss carryforward is used in this research study to measure the impact of derivatives on tax. It is calculated by using dummy variable approach which indicates the availability of tax-loss carryforward. The firms are denoted by 1 if there is any tax-loss carryforward and by 0 if there is no any tax-loss carryforward. This research study refers the tax-loss carryforward to the deferred tax in assets and liabilities, whereby it can be found in balance sheet from the firmsâ€™ annual reports. If the deferred tax in liabilities exceeds the deferred tax in assets, the firm is considered consists of tax-loss carryforward, which will be denoted by 1. The formula was suggested by few research studies, such as Mardsen and Prevast (2005), Allayannis and Ofek (2001), Howton and Perfect (1998), Mian (1996), and Berkman and Bradbury (1996).

H0: There is a positive relationship between tax-loss carryforward (TAX) and derivatives.

H1: There is a no relationship between tax-loss carryforward (TAX) and derivatives.

Firm size (SIZE) â€“ Independent Variable

Firm size is used in this research study to measure the relationship between firm size and derivatives. It is calculated by using natural log of the total assets. Many economic phenomena are heteroscedastic. That is, the estimation error is not random, and it increases predictably with some variable- such as time in asset pricing. The log transform is a common method of fixing this econometric issue [14] . An intuitive explanation is saying that the natural log gives you the time needed to reach a certain level of growth. The data are extracted from the balance sheet from the firmsâ€™ annual reports in MYR. The natural log of the total assets was suggested by many studies, one of the studies is Amrit Judge (2002).

Firm size (SIZE) = LN (Total assets)

H0: There is a positive relationship between firm size (SIZE) and derivatives.

H1: There is a no relationship between firm size (SIZE) and derivatives.

3.3 Methods

This study employed few tests in analyzing the impact of explanatory variables on the corporate hedging decision. First of all, we carry out descriptive analysis to examine the descriptive statistics of each variable. Next, multiple regression analysis is used in estimating the coefficient of each explanatory variable for model specification, as well as to carry out further investigation of the model through hypothesis testing (t-test) and F-test based on the significance criteria. Then, diagnosis checking will be run to test if there are any violations of the classical econometric assumptions. These include normality test, multicollinearity test, autocorrelation test and heteroscedasticity test.

3.3.1 Descriptive analysis

Descriptive analysis presents the basic statistics such as mean, median, maximum, minimum, standard deviation, sum, sum square deviation and observations to describe each of the variables.

3.3.2 Multi Regression

The regression analysis is used to determine the relationship between dependent variable and independent variables.

General Form of Multiple Regression Model

Independent Variables

Dependent Variable

Error term

Yi = B0 + B1X1i + B2 X2i + B3X3i + B4 X4i + â‚¬I

Coefficient

Constant or Y-intercept

Linear regression is a form of regression analysis in which relationship between one or more independent variables and dependent variables. It is modeled by a least square function. Significant value is set to 10% significance level in this research paper. The regression is based on the model framework which has been set earlier in this research. Hypothesis null suggests that there is no relationship between the independent variable and the other independent variable. And hypothesis alternative suggests that there is a relationship between the independent variable and dependent variable.

3.3.3 F test

This test is to test whether the changes of independent variables would affect the dependent variable. F statistics are the ratios that explain the variability and the unexplained variability

Hypothesis null, in this case, suggests that the coefficient of independent variables is equal to zero. Hypothesis alternative suggests that at least one of the coefficients of independent variable is not equal to zero. In another word, at least on variable explains the model framework. A critical value is determined and same goes to the relevant F statistic. Next, reject null hypothesis if F statistic is greater than the critical value. Alternatively, accept null hypotheses if critical value is greater than F statistic.

3.3.4 Correlation coefficient and Multicollinearity test

Correlation coefficient indicates that the relationship between all the variables which involved in the model framework. It shows a negative or positive relationship between two variables which involved. Garson (1998) suggest that correlation coefficient varies from -1 to +1. The description of each of indicator stated as below:

Values

Descriptions

-1

Perfect negative linear relationship

Random

Perfect positive linear relationship

Multicollinearity test is sort of has the same function as the correlation coefficient test. However, for multicollinearity test, auxiliary regressions are generated. In this test, average value of all the variables in 3 years period would only be considered. This test suggests that the higher the Variation Inflation Factor, it shows that there is a high multicollinearity between the variables tested.

3.3.5 Normality test

Normality test determine whether the error term in the sample data is normally distributed. It is conducted through the histogram representation of the residuals in the sample data. Null hypothesis suggests that the error term is normally distributed while the alternative hypothesis suggests that the error term is not normally distributed. To obtain the normality result, Jarque Bera statistic is compared to the chi-squared critical value at the respective degree of freedom. Reject null hypothesis if the JB statistic is greater than the critical value. In this paper, normality test is not a major concern as the t-test is believed to be robust to non-normality on large sample size.

3.3.6 Autocorrelation test

Autocorrelation test is conducted to ensure that there is no serial correlation between the error terms of the sample data. For this test, the null hypothesis suggests that there is no autocorrelation between the error terms while the alternative hypothesis suggests that there is autocorrelation between the error terms. To test the autocorrelation problem, we compare Durbin Watson statistic to its optimal value, which is 2. There is no serial correlation between the error terms when the Durbin Watson statistic is close to 2. Autocorrelation problem can be solved by using the Generalized Least Square method (GLS).

3.3.7 Heteroscedasticity test

The error term has no constant variance when there is a heteroscedasticity problem. The heteroscedasticity problem is detected using the Heteroscedasticity White Test. Null hypothesis represents that there is no heteroscedasticity problem while alternative hypothesis represents that there is heteroscedasticity problem. In order to determine whether the error term has a constant variance, we compare the value obtained from the White Test (Observations*R-squared) with the chi-square critical value at the respective degree of freedom. Reject null hypothesis if the estimated value is greater than the critical value and vice versa. Heteroscedasticity problem can be solved using the Weighted Least Square method (WLS).

Chapter 4 Data Analysis

4.1 Descriptive Statistic on Variables

The descriptive analysis for both dependent and independent variables are presented in Table 4.1.0. Summary statistics of the mean, median, maximum, minimum, standard deviation, skewness and kurtosis are shown in the table.

LNDERV

LIQUIDITY

SIZE

DEBT

GROWTH

RISK

TAX

Mean

19.674840

2.088402

22.324550

0.209561

1.159130

0.033066

0.785714

Median

20.458550

1.296838

22.669130

0.190561

0.817674

0.024016

1.000000

Maximum

22.943980

15.980030

25.052430

0.585768

6.802902

0.158701

1.000000

Minimum

14.666520

0.295643

18.607720

0.001745

0.050813

0.001077

0.000000

Std. Dev.

2.417675

2.432238

1.716070

0.164233

1.209701

0.032681

0.414039

Skewness

-0.564200

3.729181

-0.339002

0.602270

2.331829

2.140715

-1.392621

Kurtosis

2.084393

20.437660

2.187130

2.385772

10.248130

8.515514

2.939394

Table 4.1.0 Descriptive Statistic

Table 4.1.1 look into how many hedgers in plantation sector and trading or services sector. The results shows there are 12 hedgers out of 44 firms in plantation sector. On the other hand, there are 23 hedgers out of 221 firms in trading or services sector. However, only 28 hedgers are with complete data from 2010 to 2011.

Table 4.1.1 Type of firms

Table 4.1.2 illustrates the usage of derivatives from 2010 to 2011 for plantation and trading or services sector. The results show forward foreign exchange contracts and commodity derivatives are highly traded in plantation sector. This is due to firms in plantation sector are exposed to commodities risk [15] and currency or exchange rate risk, so these firms tend to use commodity derivatives and forward foreign exchange contracts to offset these financial risks. In addition, there is an increase in the total usage of derivatives from 2010 to 2011, which are 13 to 21 derivatives traded.

Table 4.1.2.1 Usage of derivatives in Plantation Sector

In trading or services sector, interest rate swaps and foreign exchange contracts are highly traded in the sector. Most firms in trading or services sector indicate that their groupâ€™s activities are exposed it to market risk, credit risk and liquidity risk. They are using risk management programme seeks to minimize adverse effect from the unpredictability of financial markets on the Groupâ€™s financial performance. Thus, financial instruments such as interest rate swaps and caps, and foreign currency forwards to manage these financial risks. In addition, there is an increase in the total usage of derivatives from 2010 to 2011, which are 38 to 45 derivatives traded.

Table 4.1.2.2 Usage of derivatives in Trading/Services Sector

Referring to recent research study [16] in Malaysia from 2003-2007, which is conducted by Dr. Rasheed Ameer, it has reported that the total usage of derivatives are 15 derivatives traded in plantation sector and 7 derivatives traded in trading or services sector. Based on the results obtained, the total usage of derivatives from 2009 to 2010 are 33 derivatives traded in plantation sector and 83 derivatives traded in trading or services sector. By comparing between both results, apparently there is an increase in total usage of derivatives from 2007 to 2010. This implies that increase in awareness among the firms in Malaysia of the importance of financial instruments to get rid with the price risks, commodity risks, interest rate risks, etc.

4.2 Regression Result on the relationship between derivatives and determinants of corporate hedging decision

4.2 Regression Statistic

Variable

Expected Sign

Coefficient

Std. Error

t-Statistic

Prob.

LIQUIDITY

(-)

-0.00296

0.101126

-0.02928

0.9768

SIZE

(+)

0.625599

0.18244

3.429068

0.0012***

DEBT

(+)

4.94118

1.870097

2.642206

0.011**

GROWTH

(+)

0.098103

0.250915

0.390982

0.6975

RISK

(+)

0.741309

9.561087

0.077534

0.9385

TAX

(+)

-1.2527

0.596489

-2.10012

0.0409*

5.525353

3.838768

1.439356

0.1564

R-squared

0.54792

Adjusted R-squared

0.492563

F-statistic

9.89798

Prob(F-statistic)

Durbin-Watson stat

1.50456

4.2 Regression Test (Please refer to appendix for complete table)

The below equation is the equation of the regression model by using Least Square Method (refer to Table 4.2)

LN (DERVt) = 5.52535 - 0.00296*(LIQUIDITYt-1) + 0.625600*(SIZEt-1) + 4.94118*(DEBTt-1) +

(1.439356) (-0.02928) (3.429068***) (2.642206**)

0.098103*(GROWTHt-1) + 0.741309 *(RISKt-1) â€“ 1.2527*(TAXt-1) + 0.596489 Îµit

(0.390982) (0.077534) (-2.10012*)

Two-sided T-test (SIZE)

H0: There is no relationship between total notional amount of derivatives (DERV) and firm size (SIZE).

HA: There is a relationship between total notional amount of derivatives (DERV) and firm size (SIZE).