A research of debt maturity structure of manufacring companies listed on hose

A RESEARCH OF DEBT MATURITY STRUCTURE OF MANUFACTURING COMPANIES LISTED ON HOSE In Partial Fulfillment of the Requirements of the Degree of MASTER OF BUSINESS ADMINISTRATION In Finance By Mr: Tran Hoang Nhan ID: MBA05028 Advisor: Dr. Duong Nhu Hung International University - Vietnam National University HCMC September, 2014 A RESEARCH OF DEBT MATURITY STRUCTURE OF MANUFACTURING COMPANIES LISTED ON HOSE In Partial Fulfillment of the Requirements of the Degree of MASTER OF BUSINESS ADMINISTRATION In Finance by Mr: Tran Hoang Nhan ID: MBA05028 Advisor: Dr. Duong Nhu Hung International University - Vietnam National University HCMC September 2014 Under the guidance and approval of the committee, and approved by all its members, this thesis has been accepted in partial fulfillment of the requirements for the degree. Approved: ---------------------------------------------Chairperson ---------------------------------------Committee member ---------------------------------------------Committee member ---------------------------------------Committee member ---------------------------------------------Committee member ---------------------------------------Committee member 1 Acknowledge I would like to express my sincere gratitude to my advisor, Dr. Duong Nhu Hung, who recommended me the research idea and always instructed me dedicatedly and set my foot back on the right track during critical moment. Thanks for his recommendation and guidance, I can be able to complete my first research so far. Ho Chi Minh City, September 2014 TRAN HOANG NHAN 2 Plagiarism Statements I would like to declare that, apart from the acknowledged references, this thesis either does not use language, ideas, or other original material from anyone; or has not been previously submitted to any other educational and research programs or institutions. I fully understand that any writings in this thesis contradicted to the above statement will automatically lead to the rejection from the MBA program at the International University – Vietnam National University Hochiminh City. 3 Copyright Statement This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognize that its copyright rests with its author and that no quotation from the thesis and no information derived from it may be published without the author’s prior consent. © Tran Hoang Nhan / MBA05028 / 2014 4 Table of Contents CHAPTER 1: INTRODUCTION ................................ Error! Bookmark not defined. 1.1. Introductio ....................................................................................................... 1 1.2. Rationale.......................................................................................................... 2 1.3. Research questions ......................................................................................... 2 1.4. Objectives ........................................................................................................ 2 1.5. Research Scopes and limitations ..................................................................... 3 1.6. Research approach........................................................................................... 4 1.7. Research structure ........................................................................................... 5 CHAPTER 2: LITERATURE REVIEW ....................................................................... 6 2.1. Debt maturity structure.................................................................................... 6 2.2. Liquidity risk and signalling ........................................................................... 6 2.2.1. Leverage ................................................................................................... 7 2.2.2. Liquidity................................................................................................... 8 2.2.3. Firm Value Volatility ............................................................................... 8 2.2.4. Firm Quality ............................................................................................. 9 2.3 Agency costs ................................................................................................... 9 2.3.1. Maturity Matching ................................................................................... 9 2.3.2. Firm size................................................................................................. 10 2.3.3. Growth Opportunities ............................................................................ 11 2.4. Equity market conditions.................................................................................. 12 2.4.1 Equity Risk Premium ............................................................................. 12 2.4.2 Share Price performance ........................................................................ 12 2.5. Tax minimization ............................................................................................. 13 2.5.1 Effective tax rate ........................................................................................ 14 2.6. Gap filling......................................................................................................... 15 2.6.1 Gap fillingas ............................................................................................... 16 2.6.2 Time series variation in gap fillingas ......................................................... 16 2.7. Hypotheses and measurements ......................................................................... 17 CHAPTER 3: DATA COLLECTION AND RESEARCH METHODOLOGY ......... 21 3.1. Data collection............................................................................................... 21 3.2. Methodology ................................................................................................. 21 3.2.1 Regression Model .................................................................................. 22 5 3.2.3 Sample..................................................................................................... 23 3.2.3. Panel data ................................................................................................... 25 3.2.4. Method of Estimations...............................................................................25 CHAPTER 4: DATA ANALYSIS .............................................................................. 26 4.1. Descriptive findings ...................................................................................... 26 4.2. Results analysis ............................................................................................. 28 4.2.1. Stage 2 – Conduct testing and model application .................................. 28 4.2.1.1 Correlation coefficient..........................................................................28. 4.2.1.2 Regression model:.................................................................................29 a. Fixed-effect model............................................................................34 b. Ordinary Least Square model..........................................................36. 4.2.1.3 Time fixed variable result.....................................................................38 4.2.1.3 Dummy variable result..........................................................................44 4.2.1.3 Multicollinearity test.............................................................................45. 4.2.1.4 Heteroscedacsticity test........................................................................46. 4.2.1.5 Auto correlation....................................................................................47. 4.2.1.6 Normality statistics................................................................................48 4.2.2. Stage 3 – Data comparison ................................................................... 49 CHAPTER 5: CONCLUSIONS .................................................................................. 46 5.1. Discussion of the results ................................................................................ 52 5.3. Future research...............................................................................................53 6 List of Tables Table 1: The Variable definition and hypothesized relationship ................................. 20 Table 2: Descriptive statistics of firm specific and macroeconomic variable ............. 29 Table 3: Pearson Correlation ....................................................................................... 32 Table 4: Fixed-effect model result ............................................................................... 33 Table 5: Ordinary Least Square result ......................................................................... 37 Table 6:Collinearity matrix ......................................................................................... 47 Table 7: Normality statistics ........................................................................................ 50 7 List of Figure Figure 1 – The developement of companies debt maturity structure ....................30 Figure 2 – DMS Histogram.....................................................................................31 Figure 3 – DMS Scatterplot....................................................................................48 Figure 4 – DMS P-P plot........................................................................................49 8 List of Abbreviation Abbreviation Equivalence DMS Debt Maturity Structure LV Leverage Li Liquidity IRV Interest Rate Volatility FS Firm Size TS Term Structure ERP Equity-market Risk Premium TVV Time Variation Volatility GO Growth Opportunity FVV Firm Value Volatility MM Maturity Matching ETR Effective Tax Rate SPP Share Price Performance FQ Firm Quality GF Gap Filling 9 Abstract This study investigates the relationship between several macro economic as well as firm specific factors that affect the debt maturity structure of a company by applying Least Square Dummy Variable regression model . The study sample consists of 98 manufacturing companies listed on HOSE and the examined period is from the first quarter of 2008 to the fourth quarter of 2012. Liquidity risk and signaling, agency costs, equity market conditions, tax minimization and gap filling are adapted in our model in order to make hypotheses . Several examinations are made to see whether time or industries have any influence on company choice of debt maturity structure or if the decision is made independently. As for the liquidity risk theory, the main concern lies in postponing the refinancing risk, which is controlled by taking on debt of longer maturities. The gap filling hypothesis also has an impact on companies’ choice of debt maturity structure as we observe a positive relationship between the government’s and companies’ debt maturity structure. Finally, we find that companies’ choice of debt maturity structure is made on an individual basis, with no importance given to industry trends or structural breaks. 10 11 CHAPTER I – INTRODUCTION This chapter provides an overview of the study, the motivation to conduct this research in VietNam as well as other related issued such as research objectives, scope and limitation, and the general structure of the research. 1. Introduction: In order to fund for current activities and prepare for new ones, a company has to raise financial. The most common way is either through borrowing debt or establishing new shares, thus leading to a mix of debt and equity within the companies’ capital structure. Due to debt’s advantage of tax deductible, many people consider it to be a cheaper source of financing than equity. However, debt disadvantages are still at large, one of them is that debt holders are claimants that can rightfully force a firm into liquidation. Thus, in order to maximize the value of a company, managers pay a lot of attention to this matter to find out the most sufficient use for debt. So as to control debt disadvantage and enhance its advantage, manager normally focus on balancing out short and long-term debt. The mix of short and long-term debt is referred to as the debt maturity structure. A well-balanced debt maturity structure is an opportunity first and foremost for borrowers to handle debt more efficiently, but also a chance for lenders to gain influence over the money invested into the company. From borrowers’ perspective, the adjustments regarding the debt maturity structure have potential to reduce refinancing risk, increase transparency and exploit tax related opportunities while on lenders’ perspective, the debt maturity structure can be used as a tool to increase monitoring and reduce the potential sub-optimal decisions made by the management. This paper is designed to offers an overall view of the Vietnam market as well as an integrated model that incorporates both firm specific and macroeconomic 1 determinants of debt maturity structure. This model enables us to analyze the company in a dynamic environment, rather than as an isolated individual not being affected by macroeconomic factors. Third, we use a cross-sectional model to capture individual company and industry differences and a time-effects model to capture time differences in our sample companies. Our findings show that both firm specific and macroeconomic variables are factors when it comes to variation in companies’ debt maturity structure. Also, our study reveals that it is mainly the liquidity risk and the gap filling theory that are taken into consideration by company manager when taking decisions regarding the debt maturity structure, with less emphasize put on signaling, agency costs, equity market conditions and tax minimization. 1.2 Rationale of the study: This research of debt maturity structure of manufacturing companies is conducted to identify key determinants that affect their debt maturity structure. Firstly, we want to contribute an overall investigation of a small fragment of Vietnam market. Secondly, we would like to provide an intergrated model that includes both firm specific determinants and macro economic factors of debt maturity structure, we expect this model to be able to analyze a company in a dynamic enviroment rather than just an isolated individual that not affected by any macroeconomic factors. Finally, we want to offer a contribution to the empirical studies relared to financial structure of Vietnam companies and help managers to have an overall view on debt structure of others related enterprises in the industry, formulate their own efficient financial plan, constrain negative factors and to improve the risk management. 1.3 Research questions This study addresses these following research questions: 2 1.4  What factors affect the capital structure of manufacturing companies ?  To what degree should those key factors be taken into consideration ? Objective General objective  General objective of this study is to investigate the impact of chosen determinants on firm capital structure. Specific objectives:  To review theories on determinants of capital structure and build up a conceptual summary of key determinants  To test the influence of these factors on listed manufacturing companies during 2008-2012 period  1.5 To compare result with previous study. Research scopes and limitations: This research focus on manufacturing companies with the time period using in this research are from the first quarter of 2008 to the last quarter of 2012. Samples are 98 companies in the stock exchange market of VietNam. The range of collection are: big orgnization with total asset 600 -1.000 billion VND, medium company with asset within 200 – 500 billions VND ; small firm with total asset equal or less than 100 billions VND. Datas are gathered within above mentioned period. 1.6 Research approach: Definitions of key factors are applied from past researches, articles and empirical studies, both international an domestic. 3 Literature review with relating theories and empirical researches from various sources are used to determined factors to investigate in the regression model. Quantitative methods with the support of analyzing tools including Eviews, SPSS, and Excel are applied for most of regression, statistical tests, and calculation in the research. Data is collected from various website: www.cophieu88.com, www.phuongnamsecurities.com. Eventhough data is compared with companies annual report and HOSE report afterward, it may not present the precise data. 1.7 Research structure The study consists of five chapters as follows: Chapter 1: Introduction This chapter briefly describes the overview of the study such as the motivation to choose this research, purpose of the research, scopes and limitations, etc. Chapter 2: Literature review The purposes of this chapter are to understand the theories relating to firm specific determinants as well as macro economy factors related to company financial structure, and to review some previous studies on debt maturity structure that were conducted domestically and abroad. Chapter 3: Data collection and research methodology This chapter shows how we collect data, the models applied and variables used for the analysis. The definitions of variables and introduction to stages to analyze data are also presented in this section. 4 Chapter 4: Data analysis & Tests This chapter presents the descriptive findings and empirical results of the study. The section applies quantitative method with the support of analyzing tools including Eviews 10.0, Stata and SPSS. Chapter 5: Conclusions and Recommendations This chapter summarizes the results of the research, provides conclusions to answer the research questions meeting the objectives of the study and finally gives suggestion for companies managers in planning for company financial structure 5 CHAPTER 2: LITERATURE REVIEW This section provides an overview of the theoretical and empirical research within the field of debt maturity structure. The main theories are classified as either firm specific, represented by the liquidity risk and signaling, agency costs, equity market conditions and tax minimization theories or macroeconomic, represented by the gap filling theory. This part provides an argumentation and a testable hypothesis for the relationships between each independent variable and the debt maturity structure. As the same independent variable can represent different theories, we proceed with the classification in the way we believe that is the most correct. 2.1 Debt maturity structure The debt maturity structure of a company is measured as the ratio of the company’s long-term debt to total debt. Following accounting conventions, the longterm debt is defined as debt maturing in more than one year, while short-term debt is defined as debt due within the next twelve months. Our definition follows that of Barclay and Smith Jr (1995) 2.2 Liquidity risk and signaling The liquidity risk and signaling theories refers to companies’ inability to efficiently communicate with investors, which leads to asymmetric information between the insiders (e.g. managers) and the outsiders (e.g. investors). The communication inefficiency leads to the risk that a solvent but illiquid borrower is unable to obtain financing (Diamond, 1991). This risk is called liquidity risk and is associated with companies holding a large amount of short-term debt and therefore being dependent on lenders to refinance their loans in a close and uncertain future. This theory investigates how liquidity risk impacts the debt maturity structure and how this risk is mitigated through the use of long-term debt. 6 The signaling hypothesis mainly focuses on the problem of communication inefficiency, where outsiders are unable to identify high quality companies from low ones and thus the true quality of the company remains private to insiders.The signaling theory argues that certain decisions made by companies, like the choice of debt maturity structure, reveal information about companies’ current and future status more accurately than public statements. The debt maturity structure represents a signal of quality and outsiders take this knowledge into consideration prior to an investment. This signal is then used as a tool to increase transparency, reduce the knowledge gap between outsiders and insiders and signal the inherent value of the company to investors (Berk and DeMarzo, 2007). Within the liquidity risk and signaling theory the variables leverage, liquidity and firm level volatility show how the management works with reducing liquidity risk and the variable firm quality shows how the management works with increasing transparency and reducing asymmetric information. 2.2.1 Leverage Management always have to face a risk of bankruptcy when using leverage, due to the fact that debt holders are the only claimants that can rightfully force a firm into liquidation. Thus, if a company decides to use leverage, it has to deal with the risk of bankruptcy and consequently trying to reduce such risk to the highest possible extent. Both Morris (1975) Leland and Toft (1996) argue that high leveraged companies are more inclined to take long-term debt, so as to offset the higher probability of liquidity risk and to delay exposure to bankruptcy risk. As the liquidity risk usually occurs when holding short- term debt, Morris (1975) argues that companies that want to decrease the burden of refinancing choose long-term debt. This creates an incentive for more risky companies to issue long-term debt and the expected relationship between leverage and 7 debt maturity structure, from the liquidity risk theory’s perspective, is positive, given the possibility for this type of companies to borrow long-term. However, these findings are contradicted by Myers (1977) and Dennis et al (2000), who argue for a negative relationship between leverage and debt maturity as a way to deal with agency costs. The authors state that reducing leverage, as well as shortening the debt maturity, are mechanisms for limiting perverse investment incentives such as the underinvestment. According to Myers (1977), the underinvestment problem occurs when shareholders have an incentive to reject projects with a positive net present value. This behavior is attributed to the fact that shareholders, in this situation, are not offered a normal return on their investment since the debt holders capture the bigger part of the benefits. Myers (1977) introduces a way to deal with this sub-optimal behavior through shortening the maturity of outstanding debt. Shortening the debt maturity offers debt holders a setting for continuous renegotiating and reduces the risk of sub-optimal investment decisions. 2.2.2 Liquidity: The liquidity of an asset refers to the easiness that asset can be traded on the market. The more liquid assets a company has, the greater is their value when it comes to short notice sale or liquidation. Therefore, a company should find it easier to raise external financing against more liquid assets, since liquid assets give lenders greater value in the case of liquidation (Myers and Rajan, 1998). For that, we can see that a positive relationship between liquidity and debt maturity structure is projected by the liquidity risk theory. However, Myers and Rajan (1998) contradict this relationship by arguing that greater asset liquidity leads to a decrease in the company’s capacity to raise external financing. This relationship is attributed to the agency costs theory and occurs because 8 higher liquidity confers managers more freedom of choice; this freedom can result in managers acting in their own favor and at lenders’ expense. Thus, a higher liquidity leads to a greater potential for conflict between managers and lenders. Myers and Rajan (1998) argue that management’s ability to represent shareholders’ interests and commit credibly to an investment strategy can be questioned. 2.2.3 Firm value volatility: A risk that can lead to a refinancing issue in the future makes the company want to lengthen its debt maturity. The same pattern is identified in the case of firm value volatility, as investors might be reluctant to invest in a company that is experiencing instability. Wiggins (1990) confirms this positive relationship between firm value volatility and debt maturity structure since default risk premiums on debt are higher on long-term debt in companies with a higher volatility in their value. Thus, with this premium charged in high volatile companies on long-term debt, higher tax shields are gained since the interest payments on long-sterm debt are higher than those on shortterm debt. A negative relationship between firm value volatility and debt maturity structure was mentioned in Kane et Al (1985) As the market value of the company changes and debt remains constant, the equity acts as a cushion. Normally, companies with a high volatility in firm value will have a high volatility in the equity cushion and shareholders’ value. The volatility in equity leads to a more unstable capital structure where the leverage ratio is changing accordingly and this obliges the management to continuously rebalance the capital structure to avoid a too high leverage ratio. On contrary, low volatility in value companies have a stable amount of debt and equity, which allows them to build up a fixed capital structure. Without being forced to 9 continuously rebalance the capital structure and thus having a lower bankruptcy risk, companies with a low volatility in value have easier access to long-term debt. 2.2.4 Firm quality: The signaling theory points out some conflicting materials such as certain decisions made by companies reveal information on their current and future status more accurately than their public statements do. As Berk and DeMarzo (2007) claim, actions speak louder than words and the choice of the debt maturity structure is one of these actions. The debt maturity structure is often used as a health indicator by outsiders and such knowledge will be taken into account when investing in a particular company. Flannery (1986) is the first to examine to what extent the debt maturity structure can be used by insiders to signal the quality of a company when the outsiders’ information is less accurate than insiders’. He presents a model where the author distinguishes between good companies and bad ones, i.e. high quality companies and low quality ones. The author argues that in the case of information asymmetry, at its extreme level, the outsiders treat all companies equally and charge the same premium on issuing long-term debt. This behavior applies to long-term debt exclusively due to a higher risk of defaulting in this case. The default premiums paid by good companies on long-term debt are therefore too high, while the reverse applies for bad companies. Based on this, good companies will suffer when borrowing long-term and therefore prefer to borrow short-term instead. Bad companies, on contrary, borrow long-term to pay for a lower default premium than they would have otherwise, if no information asymmetry existed. Bad companies are also reluctant in borrowing short-term, as this brings along the refinancing risk. This refinancing would impose the management to reveal new information to the lenders, which is detrimental for bad companies. 10 2.3 Agency costs The agency costs theory refers to costs accrued due to conflicting interests between various stakeholders. Myers (1977) argues that the existence of debt may in some circumstances change the company’s actions. That is, when a company is leveraged, shareholders and debt holders with different investment decisions will most likely conflicts with each other. Such a conflict is more likely to occur when the financial distress is high (Berk and DeMarzo, 2007). We investigate how various types of agency conflicts impact the debt maturity structure and how these costs can be mitigated through the use of short-term debt. Short-term debt opens up monitoring opportunities for lenders as the managers need to approach lenders more frequently to renew it. Agency problems are inherently difficult to measure directly, and hence a more indirect approach is necessary. Past research used variables like maturity matching, firm size and growth opportunities to test for the determinants of debt maturity structure, when it comes to agency costs, and thus we proceed accordingly to these reseaches. 2.3.1 Maturity matching Maturity matching can be considered as a method to handle agency costs because by matching the maturity of debt to the maturity of assets one can control the risks and costs of financial distress. Morris (1975) is the first to bring up the idea of maturity matching, which rests on the immunization hypothesis. The immunization hypothesis argues that a company should match the maturity of its liabilities with that of its assets in order to mitigate interest rate risks and liquidation risks. Maturity matching is therefore a form of corporate hedging that reduces expected costs of financial distress. Thus, the debt maturity structure should be determined by its asset maturity structure (the average number of years of depreciation) because on the one 11 hand, if debt has a shorter maturity than that of the assets, the company may not have enough cash readily available to repay the principal at due date. On the other hand, if debt’s maturity is longer than that of the assets, the cash flows coming from assets finish, while the debt payments remain outstanding. Myers (1977) also argues that matching the maturities of assets and liabilities reduces the interest rate and liquidity risk and thereby provides a rationale for value maximization. Therefore, the longer the asset maturity is the longer debt maturities the company should issue. However, there is an observed divergence between theory and practice in which Stulz (1996) has pointed out. The author explains that in practice companies only partially hedge through maturity matching. Morris (1975) demonstrates that a perfect hedge does not exist, revealing in his study on industrial companies that 75 percent of the companies had an average debt maturity greater than average asset maturity. Also, Morris (1975) finds that companies matching the maturities of assets with those of debt had an overweight of long-term debt. In a comparison of the debt maturity choices of companies from UK and Italy, Schiantarelli and Sembenelli (1999) find that debt maturity structure is positively related to maturity matching, which is in line with the predictions of Morris (1975). Fan et al (2010) finds no evidence for any clear relationship between maturity matching and debt maturity structure. 2.3.2 Firm size Arguably, larger firms have lower asymmetric information and agency problems, higher tangible assets relative to future investment opportunities, and thus, easier access to long- term debt markets. The reasons why small firms are forced to use short-term debt include higher failure rates and the lack of economies of scale in raising long-term public debt. It is further argued that larger firms tend to use more long-term debt due to their remaining financial needs (Jalilvand and 12 Harris, 1984). Agency problems (risk shifting, claim dilution) between shareholders and lenders may be particularly severe for small firms. Then, bondholders attempt to control the risk of lending to small firms by restricting the length of debt maturity. Large (small) firms, thus, are expected to have more long (short)-term debt in their capital structure. Consequently, these arguments imply a positive relationship between firm size and debt maturity. 2.3.3 Growth opportunities Relationships between company’s growth opportunities and debt maturity structure have already been mentioned in some existing research in which they state that growth opportunities has the potential to affect the debt maturity structure since it is associated with numerous future investment decisions for the management of a company. And, an increasing number of investment decisions leads to an increasing potential for underinvestment problems. One way to deal with the potential sub-optimal behavior by of the management team is identified by Myers (1977), who suggests that companies should shorten the maturity of outstanding debt. By having debt that matures before the growth option is exercised, borrowers and lenders can monitor and renegotiate the terms of the contract and thereby reduce potential sub-optimal behavior. Also, Barclay and Smith Jr. (1995) argue for this inverse relation between growth opportunities and debt maturity structure, as a way to control the under investment problem arising from conflicting interest between the management and lenders. The liquidity risk argument (e.g. Diamond, 1991) predicts that the firms with long- term investment opportunities requiring ongoing managerial discretion prefer to hedge against liquidity risk by issuing long-term debt. Thus, a positive correlation between growth opportunities and debt maturity is predicted. However, 13 Ozkan (2000) finds a significant negative relationship between the growth opportunities and a non- debt maturity structure. Antoniou et al (2006) report significant relationship in Germany, France and the UK. Also, Fan et al (2010) does not find any significant relationship in their study of an international comparison of debt maturity choices. 2.4 Equity market conditions Few of current studies on debt maturity structure have explained how it is affected by the conditions on the equity market. We intend to capture the connection between the equity and the debt markets and incorporate in our study variables belonging to the equity market that might have an influence on the choice of debt maturity structure. This theory argues that variables like the past share price performance and the equity risk premium charged by investors could be used as predictors of the debt maturity structure. 2.4.1 Equity Risk Premium This measures the cost of equity in relation to the return on risk-free investment. If equity premium is high, firms tend to prefer issuing debt rather than equity. Fama and French (1989) suggest that the premium of long-term share in total debt should have an impact on both equity and debt market. It is argued that expected bond returns are generally low when business conditions are good due to, e.g. the availability of profitable growth opportunities. Under such conditions, one may observe high equity returns. Baker and Wurgler (2000) find that firms tend to issue equity instead of debt when the future cost of equity is relatively low. Fama and French (1989) also report that expected returns on stocks and corporate bonds move together. Consequently, we expect equity risk premium to have different impact on debt maturity 14 2.4.2 Share price performance The signaling hypothesis argues that undervalued companies use the issuance of short-term debt as a way to signal their undervaluation. This choice of maturities, however, can also be a result of the past share price performance. Lucas and McDonald (1990) argue that a company that is about to reveal good news will wait to issue securities until the news reach the market and result in an increase in share price. Lucas and McDonald (1990) claim that long-term debt financing demands more information to be revealed by the borrowers so as to assure the lenders of the companies’ quality. An increase in share prices is perceived by the investors as a guarantee of that company’s financial health and thus, the companies that experience an increase in their share prices will have an advantage over other companies to issue longterm debt or equity. Empirically, the results have been contradictory. Guedes and Opler (1996) tested the idea described by Lucas and McDonald (1995)) but the results do not show any statistically significant association between the increase in past share price and the maturity of new debt issues. Deesomsak et al (2004) test the relationship on companies across countries in the Asian Pacific region, but find mixed evidence for it, with significant results in Australia and Singapore, while insignificant in Malaysia and Thailand. This discrepancy can be attributed to the idea that in countries with more developed financial markets, such as Australia and Singapore, information plays a fundamental role in share price performance than in countries with less developed, and thus less efficient, markets such as Malaysia and Thailand. Also, Antoniou et al (2006) find a mixed relationship between the share price performance and debt maturity structure. 15 2.5 Tax minimization Interest on debt is tax-deductible and therefore it is being widely and primely used in order to take advantage of tax shield. The tax shield creates an incentive for companies to use debt as opposed to equity when designing the capital structure. Thus, taxes and tax-related variables are affecting the choice of capital structure with the purpose of reaching an optimal balance between equity and debt (Berk and DeMarzo, 2007). A company’s optimal debt ratio is usually determined by a tradeoff of company’s costs and benefits of borrowing. The company takes on debt to take advantage of tax shields until that point where the extra gain from tax shields is equal to the extra loss from bankruptcy and agency costs (Myers, 1984). All in all, taxes affect the debt part of capital structure and tax-related variables interact to offer tax incentives in the debt maturity structure (Antoniou et al, 2006). The discussion on capital structure, in general, and on the advantage of tax shields, in particular, goes back to Franco Modigliani and Merton H. Miller’s irrelevance theory. The Modigliani and Miller (1958) irrelevance theory states that, in equilibrium, the market value of any company must be independent of its capital structure. The Modigliani and Miller theory argues that the main determinant of a company’s market value is either cash flow or market-share, not debt. In other words, how a company finances itself should have no relevance to its value, since it has no relevance to its cash flow. The theory holds under a set of conditions referred to as the perfect capital markets conditions, including no taxes, transaction costs or bankruptcy costs. However, these conditions do not portray the reality and the opponents of the irrelevance theory argue, among other things, that the value of the company can be increased by the use of debt as opposed to equity. The existence of taxes enables 16 interest payments to be deducted from the company’s taxable income, which results in a lower cost of capital (Berk and DeMarzo, 2007). The tax minimization theory is illustrated in our thesis through three variables that show how the management is working with market imperfections such as taxes to reach an optimal balance in the debt maturity structure. These variables are: effective tax rate, term structure of interest rates and interest rate volatility. 2.5.1 Effective tax rate The discussion of taxes as a market imperfection is continued by Kane et al (1985), who claim that the optimum debt maturity setting involves a tradeoff between the advantage of tax shield and the disadvantages of bankruptcy and flotation cost, the latter represented by the cost arising when issuing debt. On the one hand, given a constant tax shield, an increase in the floatation cost creates an incentive to lengthen the debt maturity so as the amortized floatation cost does not outweigh the benefits of tax shields. Another point is that given constant flotation cost, a decrease in tax shield would also create an incentive to lengthen the debt maturity so as the benefits of the tax advantages are bigger than its disadvantages. The relationship between flotation cost, tax shield and debt maturity leads to a negative relationship between the effective tax rate and debt maturity. Thus, a decrease in effective tax rate leads to a decrease in tax shield which would lead to an increase in debt maturity. 2.6 Gap filling The majority of theories explaining debt maturity structure focuses on firm- specific determinants and therefore misses out clear-cut implications for aggregate timeseries behavior. There is only scarce literature that tries to explain time variation in companies’ debt maturity structures by looking at market conditions, such as the general level of interest rates, the slope of the yield curve etc. Stein (1989) explains that 17 market conditions matter in analyzing the debt maturity structure because of the management’s value maximizing behavior. The management tries to maximize shortterm earnings at the expense of long-term value by borrowing at short-term maturities when the yield curve is upwards sloping, to keep their current interest expense low. Greenwood et al (2010) present an additional theory on the role of market condition as a determinant of the debt maturity structure. Greenwood et al’s (2010) theory is based on the timing hypothesis, where managers try to time the maturity of their debt issues to exploit the predictability of bond-market returns. Here, companies issue short-term debt when the expected return on short-term debt is below the expected return on long-term debt and vice versa. Greenwood et al (2010) argue that when the government funds itself with relatively more long-term debt, companies react by filling the resulting gap by issuing more short-term debt and vice versa. Greenwood et al (2010) base their theory on companies’ ability to absorb large supply shocks associated with changes in the maturity structure of government debt. When changes in the maturity structure of government debt occur and the supply of long- term Treasuries goes up relative to the supply of short-term Treasuries, longterm Treasuries offer a greater expected return and companies subsequently issue shortterm debt. This idea is based on Greenwood and Vayanos (2008) who investigate whether shifts in the relative supply of long-term bonds affect bond prices and excess returns. The authors predict that an increase in the relative supply of long-term bonds lowers their prices, thus raising their yields and risk premium, relative to short-term bonds. In this scenario, Greenwood et al (2010) argue that corporate issuers, who have to raise a certain amount of debt financing and choose between short or long maturities, have the capacity to absorb these supply shocks and thus issue short-term debt. 18 Companies’ ability in absorbing large supply shocks is derived by Greenwood et al (2010) from the logic of the Modigliani and Miller’s (1958) irrelevance theory. As mentioned before, they argue that companies are indifferent to the capital structure in a world without taxes or costs of financial distress. If then tiny differences in the expected returns are introduced, companies will respond very elastically. This behavior continues until the point where any expected return differentials are eliminated. In a more realistic setting, companies are likely to have well defined preferences over their maturity structures and will think is costly to deviate from this maturity target. However, to the extent that these costs are modest, patterns of corporate debt issuance still respond quite elastically to the differences in expected returns. The gap filling theory represented through the variables gap filling and time variation in gap filling shows how the corporate debt maturity structure is affected by changes occurring in the government debt maturity structure. 2.6.1 Gap filling Given the abovementioned reasons, Greenwood et al (2010) predict that companies fill in the supply gaps created by changes in the government financing patterns. When the government issues more long-term debt, companies respond by issuing more short-term debt and vice versa. Given this prediction, the relationship between the government’s debt maturity structure and companies’ one is negative. 2.6.2 Time series variation in gap filling By allowing for time-series variation in the size of the government and corporate debt markets, Greenwood et al (2010) make an additional prediction. When the government debt supply is increasing, the gap filling behavior by companies will be quantitatively stronger as the supply shocks give companies incentives for this behavior. 19 2.7 Hypotheses and measurements Based on previous research, we choose to test the relationships between the leverage, liquidity, firm value volatility, firm quality, maturity matching, firm size, growth opportunities, share price performance, equity risk premium, effective tax rate, term structure, interest rate volatility, gap filling, time variation in gap filling and the debt maturity structure, as reported in the table below. Table I Variable definition and hypothesized relationship Theory Variable Sign Firm Value Volatility Liquidity Risk & Signalling Agency Cost - Leverage Liquidity Firm Quality Maturity Matching + + + + + + + Firm size Growth opportunities Equity Market Condition Tax minimization Share price performance Equity risk premium Effective tax rate Term Structure Interest Rate Volatility Gap filling Formula Gap filling Time variation in gap filling Total Debt / Total Assets Current assets / Current liabilities (Net income + Depreciation) / Net debt Net PPE / Depreciation ln (Total Assets) MV Equity / BV Equity  Share price of two consecutive years ROE – Return T-bills Tax expense / Taxable income Yield LT T-bonds – Yield ST T-bills + ln (Stdev of the monthly gov. bond yield over the previous year) + LT gov. debt / Total gov. debt Gov. debt/GDP The debt maturity structure of a company is measured as the ratio of the company’s long-term debt to total debt . The long-term debt is defined as debt maturing in more than one year, while short-term debt is defined as debt due within the next twelve months. Our definition of the debt maturity structure follows that of Barclay and Smith Jr. (1995). Others calculation are represented in Titman and Wessels (1988) study in which those variables are measured as the ratio of short-term debt to 20 total assets and the ratio of long-term debt to total assets. However, their focus is more on companies’ leverage decision rather than on how companies’ debt maturity structure varies with companies’s characteristics. Leverage is measured as the ratio of total debt to total assets. By having a measure that is using book values of equity compared to market values, the ratio becomes more reliable as it is validated in the annual report. This is in line with Scherr and Hulburt study (2001). Liquidity is measured through the ratio of current assets to current liabilities . The reason behind it is because of its usefulness in showing the relation between liquid assets and current liabilities, specifically how much a company is holding in liquid assets to cover its current liabilities. This is in line with the definition presented by Antoniou et al (2006). Firm value volatility is measured through earnings volatility. Due to the absence of reliable cash-flow data, we choose to apply this general method as a substitution. Earnings volatility is measured as the absolute value of the change in EBITD between two consecutive years minus the average earnings change in EBITD between two consecutive years during the sample period. This is in line with the definition presented by Stohs and Mauer (1996). Firm quality is calculated as the ratio of net income (pretax income) minus income tax plus depreciation to net debt. This describes how much a company can cover of its debt through its earnings from that period. This is in line with the description presented by Antoniou et al (2006). Maturity matching represents the ratio of net property, plant and equipment to the annual depreciation expense. This measure shows the average of the company’s 21 assets remaining lifetime. This is in line with the Datastream Mnemonic code 21description presented by Antoniou et al (2006). Firm size is measured by looking at the total asset of a company. We compute the natural logarithm of total assets so as to control for a possible non-linearity in the data and to control for the consequent problem of heteroscedacsticity. This is in line with the description presented by Guedes and Opler (1996). Growth opportunities is computed as the ratio of market value of equity to book value of equity. We follow the definition given by Barclay and Smith Jr. (1995), Guedes & Opler (1996) , and Stohs & Mauer (1996). Share price performance is measured as the percentage change of share price, adjusted for dividend, between two consecutive years. The definition is according to Myers (1984) and Guedes and Opler (1996). Calculation for Equity risk premium is the difference between the actual return on equity and the actual return on T-bills. We lag both variables one year to allow for a time gap between the decision making process and the issuance of debt. This is in line with the definition presented by Antoniou et al (2006). The effective tax rate is represented by the ratio of tax expense to Pre-tax income. The definition follows that of Stohs and Mauer (1996) and Ozkan (2000). The term structure of interest rates is measured as the difference between the yield on a ten-year government bond and the yield on a thirty day T-bill. This variable is adjusted for the month of the company’s fiscal year-end. The definition follows that of Guedes and Opler (1996). The interest rate volatility is measured as the monthly standard deviation of the ten-year government bond yield over the previous year, adjusted for the company’s fiscal year-end. We compute the natural logarithm in order to control for a possible non22 linearity in the data and the consequent problem of heteroscedacsticity. Both the term structure and the interest rate volatility are lagged one year to allow for a time gap between the decision making process and the issuance of debt. This is according to the definition presented by Antoniou et al (2006). Gap filling is calculated by the ratio of long-term government debt to total government debt. Long-term government debt is defined as total payments due in more than one year and total government debt is defined as total payments in all future periods.By using the issue date, coupon rate, final maturity and the face value of each security, the payment streams are decomposed on a yearly basis for each outstanding issue into a series of principal and coupon payments. These payment streams are then adjusted for variation in the face value outstanding. Changes in the face value disclose repurchases and reopenings of an existing issue. The aggregate payments due in the following year are then divided by all issues outstanding. This is in line with the definition presented by Greenwood et al (2010). Time variation in gap filling is calculated as the ratio of government debt to GDP, since this ratio is a proxy for the size of the government bond market. This follows the definition given by Greenwood et al (2010). 23 CHAPTER 3: DATA COLLECTION AND RESEARCH METHODOLOGY This chapter shows how we collect data, model application and variables used for the analysis. Definition and calculation of variables are also mentioned in this section. 3.1 Data Collection: Data is drawn from these following sources: i) Data related to companies debt & equity structure and other internal information are obtained and calculated from their financial statement , as well as annual report(s) from each companies website. ii) Data related to macroeconomic factors and Vietnamese market including Tax rate, Goverment debt / T-bill / T-bond, Yield Long-term/Short-term bonds, Equity market conditions are collected from various sources, including: World Bank, State Bank of Vietnam, General Statistics Officer, many economicy Vietnamese & foreign news. 3.2 Data and Methodology: As the main objective of this research is to identify the relationship and understand the influence of liquidity risk & signalling, agency costs, equity market conditions, tax minimization, and gap filling theories on manufacturing companies choice of debt maturity structure, we divide our analysis into three primary stages:  Stage 1: Define the key determinants with a panel regression model.  Stage 2: Conduct testing, also employ chosen models to allows checking of unobservable differenes.  Stage 3: Result comparison. 24 3.2.1. Stage 1 – Define the determinants - Regression model DMit = β1 + β2LVit + β3Liit + β4FVit + β5FQit + β6FSit + β7FSit + β8GOit + β9 SPit + β10ERPit + β11ETRit + β12TSit + β13IRVit + β14GFit + β15TVVit + μiDummyindustry + μiDummycompany + λyear + uit where :  DMS = debt maturity structure, LV = leverage, Li = liquidity,  FVV = firm value volatility, FQ = firm quality,  MM = maturity matching, FS = firm size,  GO = growth opportunities, SPP = share price performance,  ERP = equity risk premium, ETR = effective tax rate,  TS = term structure, IVV = interest rate volatility,  GF = gap filling, TVV = time variation in gap filling. The coefficient β1; β2; ...; β15 are parameters which quantify the effect of independent variables on debt maturity. Each coefficient measures the average change in the dependent variable per unit change in a given independent variable, holding all other independent variables constant at the average values (Brooks, 2008) We adapt the fixed effects model in order to allow the intercept in the regression model to differ either on a cross sectional or on a time-series level. The fixed effects model is detected in the splitting up of the error term between μ, and uit , where μ does not vary over time, while uit varies over time and therefore we allow for both a cross sectional and a time-series variation. As to capture cross sectional variations in the panel data sample we use the cross sectional fixed effects model. By allowing the intercept to vary on a cross sectional level, we can discover differences on an industry and/or a company level. We employ the Least Square Dummy Variable (LSDV) regression model, where dummies account for individualities in the behavior of cross sectional 25 units; in our case for companies and industries (Gujarati, 2003). This model enables us to reject the hypothesis that all companies and/or industries have the same intercepts and thus observe if companies are acting in a similar way along with their industry group or rather on an individual level. To capture time-series variations in the panel data sample we use the time-fixed effects model. By allowing the intercept to vary on a time-series level, we can discover differences on a yearly basis. The dummy regression is used to capture time variation by including in the regression model a time varying intercept dummy that allows for time specific heterogeneity (Brooks, 2008). This model enables us to reject the hypothesis that all years are identical when it comes to companies’ debt maturity structure. We perform in our analysis a redundant fixed effects test, as described by Brooks (2008). This shows if the fixed effects model is important for our study. 3.2.2 Sample We test for a sample of 98 manufacturing companies listed on the Vietnam Stock Exchange for the period of 2008 to 2012. We exclude all financial and real estate companies due to specific characteristics of their capital structure that would potentially lead to a distorted result. We also exclude companies that do not report any leverage, as they are not testable. Companies that ceased to exist during the sample period, due to bankruptcy or other exit reasons, have also been removed from our study, as we aim to have a balanced panel data. This action leads to our study being exclusively done on companies that have survived during the sample period. In total, the sample includes 490 observations and the list of companies is found in Appendix. The financial data was obtained from www.cophieu68.com & www.phuongnamsecurities.com, and the scarce number of missing data was computed 26 manually with financial numbers taken from individual annual reports and public statements issued by HOSE. 3.2.3 Panel data Since we examine determinants of the debt maturity structure representative for both cross sectional and time series theories, we use a panel data technique. One advantage of using panel data is that we can address a broader range of issues and tackle more complex problems than with pure cross sectional or time series data only. Not only the number of observations is increasing, in a panel data technique, but we are also capable of measuring effects that occur jointly through the passing of time and through cross sectional variation (Baltagi, 2008). By combining cross-sectional data with time-series data, we increase the degrees of freedom, and thus the power of the test becomes higher. Finally, this way of combining data allows us to mitigate problems of multicollinearity that may arise if time series are modeled individually (Brooks, 2008). We use a balanced panel and thus the number of time series observations for each cross sectional unit and the number of cross sectional units at each point in time are the same. Also, By using a balanced panel data we exclude companies that due to bankruptcy or other reasons exit the market during the sample period and consequently miss data. 3.2.4 Methods of estimation We employ the fixed effects model that allows for individual unobservable differences through entity specific intercept terms, while still utilizing the ordinary least squares method (Brooks, 2008). The pooled ordinary least squares method is the dominant estimation method applied in empirical studies on debt maturity structure, where neither cross-sectional nor time specific differences are captured (Körner, 2007). 27 By using the pooled ordinary least squares method alone, we would make the strong assumption of cross-sectional and temporal differences having no statistically significant effect. Since we believe in the existence of these differences, we employ both the fixed effects model and the ordinary least squares method for them. However, several issues regarding the use of ordinary least squares method and fixed effects model exist. The main problems investigated and accounted for are: heteroscedasticity, autocorrelation and multicollinearity . 28 CHAPTER 4: DATA ANALYSIS This chapter presents the descriptive findings and empirical results of the study. The study applies quantitative method with the support of analyzing tools including Excel 2007, Eviews 8.0 and SPSS 10.0. 4.1. Descriptive findings This section provides the relationship between the independent variables and the debt maturity structure. Table II describes our sample, Figure I shows the development of the debt maturity structure over our sample period, Table III shows the correlation between the variables and Table IV shows the results from regression model. Table 2 - Descriptive statistics of firm specific and macroeconomic variables DMS = debt maturity structure, LV = leverage, Li = liquidity, FVV = firm value volatility, FQ = firm quality, MM = maturity matching, FS = firm size, GO = growth opportunities, SPP = share price performance, ERP = equity risk premium, ETX = Effective tax rate, TS = term structure, IRV = interest rate volatility, GF = gap filling, TGF = time variation in gap filling. Mean Median Stdev. Variance Kurtosis Skew. Min. Max. Obs. DMS LV 0.17713 0.5425 0.0847 3.0747 0.2165 0.2825 0.47 0.08 4.2251 25.6641 1.425 2.8452 0 0 0.9272 3.0747 490 490 Li 1.4661 1.1437 2.18 118.066 8.7489 0 23.847 490 FV (0.1724) 0 23.847 62.4119 5.82 68.6674 (3.168) (30.99) 18.175 490 0.0749 4.1334 17.09 266.682 13.105 (29.5) 78.363 490 FQ 0.34221 GO 1.046 0.772 1.745 3.045 65.1072 4.258 (9.125) 23.547 490 FS 12.668 12.728 1.386 1.921 16.162 (1.606) 0 15.708 490 MM 0.8637 0.152 10.53 110.9 58.739 2.0737 (88.453) 124.64 490 SPP 0.0274 0 0.5207 0.271 13.627 2.5445 (0.8357) 3.6628 490 ERP (3.6338) (3.521) 0.505 0.255 2.152 (0.289) (4.458) (2.913) 490 ETR 0.188 0.1595 0.284 0.081 52.798 5.452 (1.517) (3.12) 490 TS 1.256 1.288 0.097 0.009 1.3682 (0.226) 1.1245 1.373 490 IRV 0.008 0.007 0.004 0.322 1.294 0.1517 0.003 0.013 490 GF 0.59 0.565 0.1035 0.009 2.305 0.701 0.481 0.753 490 TVV 0.496 0.549 0.0894 0.008 1.458 (0.502) 0.36 0.578 490 29 As seen in Table 2, the regression is based on 98 companies during the period 2008-2012, ending up in 490 observations. The debt maturity ranges from 0 to 0.9272 and has a mean of 0.17 throughout the sample. Leverage ranges from 0 to 3.0747 and has a mean of 0.54. Liquidity has a mean of 1.46, implying that on average, our sample companies hold a liquidity cushion. Maturity matching shows that our sample companies’ assets have an average remaining lifetime of 0.86 years. The effective tax rate is, on average, 18.8 percent. The yield curve is downward. The gap filling shows that the government is half Long-term debt. Figure I - The development of the debt maturity structure for the sample period Debt Maturity Structure .20 .18 .16 .14 .12 .10 .08 .06 .04 2008 2009 2010 ME AN 2011 2012 M E DIA N Years Variable Measure 2008 2009 2010 2011 2012 LT D/Total D Mean 0.1673 0.1875 0.196 0.176 0.158 Median 0.068 0.105 0.121 0.087 0.053 Figure I shows that the range of the average debt maturity structure for the sample 30 period is between 0.158 and 0.96, implying that during the sample period the shortterm debt has been dominant over long-term debt. As seen in Table II, the average debt maturity throughout the sample is 0.1771. During the years 2008 to 2012 the average debt maturity structure goes up, reach the highest point in 2010 and noticeably goes down afterward. The peak is reached in 20010, in the debt maturity structure, with companies holding 19.6% long-term debt. Figure 2 – DMS Histogram According to the chart, 7,4% have their debt mature in 2years, 53% of sample companies have their debt due in 1 year, 20% have DMS of 1 year and19,6% with DMB above 2years. 4.2. Results analysis 4.2.1. Stage 2 – Model application and conduct testing This section examines the relationship between variables and debt maturity structure. Firstly, we apply Pearson’s correlation coefficient to test how well the variables are related. Then we run regression model (1) using Least Square Dummy Variable model 31 4.2.1.1. Correlation coefficient Table 3 presents a Pearson’s correlation analysis for pair of variables. In this table, we can see that at 1 percent significance level, the debt maturity structure is significantly correlated with Firm Size, Leverage. Independent variables that are correlated 0.3 and on a 1 percent significance level are gap filling, time variation volatility, term structure, term structure and liquidity, interest rate volatility, growth opportunity , firsm size and firm quality. Table 3 - Pearson Correlation ** significance at 1 percent level, * significance at 5 percent level DM = debt maturity structure, LV = leverage, LQ = liquidity, FV = firm value volatility, FQ = firm quality, MM = maturity matching, FS = firm size, GO = growth opportunities, SP = share price performance, ER = equity risk premium, TX = effective tax rate, TS = term structure, IV = interest rate volatility, GF = gap filling, TGF = time variation in gap filling. Correlations dms dms 1 fvv -.006 1 spp -.045 .050 1 go -.064 .023 .294** 1 fs .283 ** -.028 -.041 -.029 1 etr -.030 .036 -.043 -.003 -.058 1 .051 * .046 .087 .058 1 -.017 .012 .051 1 .170 ** .082 .036 -.082 .197 ** .038 ** .886** 1 ** -.483** -.813** ** -.835 ** ** * .007 .041 mm irv erp tvv ts gf -.073 -.024 .033 .004 .027 -.003 fq -.037 lv ** li .164 .033 fvv -.148 spp ** -.069 -.168 ** .206 ** .142 ** .038 -.013 -.001 -.092 go fs -.437 ** -.179 ** -.246 ** -.067 ** ** .193 .085 .244 ** -.083 .018 -.177 ** .024 .128 .031 .000 -.038 -.016 etr -.185 ** -.202 ** .039 .124 ** -.083 .076 -.041 -.066 mm -.024 -.016 irv .143 -.190 .165 erp -.031 .006 -.108 .035 -.059 -.014 -.068 .022 .036 tvv gf fq lv li 1 -.105 * **. Correlation is significant at the 0.01 level *. Correlation is significant at the 0.05 level 32 ts 1 .810** 1 -.032 .042 .006 1 .051 -.053 -.056 -.060 1 * ** .013 -.393** -.940 -.114 * .103 .124 1 4.2.1.2 Panel Regression model: a) Fixed – effect model : Our results are based on regression: DMit = β1 + β2LVit + β3LQit + β4FVit + β5FQit + β6FSit + β7FSit + β8GOit + β9 SPit + β10ERit + β11TXit + β12TSit + β13IVit + β14GFit + β15TGFit + μiDummyindustry + μiDummycompany + λyear + νit Where: DM = debt maturity structure, LV = leverage, LQ = liquidity, FV = firm value volatility, FQ = firm quality, MM = maturity matching, FS = firm size, GO = growth opportunities, SP = share price performance, ERP = equity risk premium, ETR = effective tax rate, TS = term structure, IV = interest rate volatility, GF = gap filling, TGF = time variation in gap filling. The regression is based on 98 companies during the period 2008-2012, ending up in 490 observations Table 4 presents the results of equation (1) when we run regression with Least Square Dummy Variable model Table 4 – Fixed – effect model result Adjusted R2 value is reported. Our results are based on regression (1): Independent Variable Intercept Effective tax rate Term structure Interest rate volatility Leverage Liquidity Firm value volatility Firm quality Maturity matching Equation P>|t| (0.996) 0.02 0.09 (0.017) 0.153 0.015 (0.002) (0.002) (0.001) 0.002 0.523 0.792 0.527 0.00 0.029 0.634 0.338 0.205 33 Firm size Growth opportunities Share price performance Equity risk premium Gap filling Time variation in gap filling Icompany_2 Icompany_3 Iindustry_2 Adjust R2 R Square N F (16 , 468) Prob > F 0.07 (0.007) (0.002) 0.04 0.323 (dropped) (0.094) 0.068 0.054 16,05 % 18,83% 485 6.79 0.00 0.00 0.206 0.906 0.492 0.578 0.005 0.004 0.004 The effective tax rate has a positive and significant finfluence on the debt maturity structure. This is in line with theory about the hypothesized relationship between flotation cost, tax shield and debt maturity. The term structure has a positive and significant influence on the debt maturity structure. The sign indicates that VietNam companies tend to lengthen their debt maturity a little as the slope of the term structure increases to take advantage of the tax shield. Interest rate volatility has a negative and insignificant influence on debt maturity structure. This is contradictory to theory and against the argumentation that companies lengthen their debt maturity as the interest rate volatility increases so as to increase the value of the tax-timing option. Leverage has a positive and significant influence on the debt maturity structure. This is consistent with Morris (1975)’s suggestion that companies that hold more debt lengthen its maturity in an attempt to control for the refinancing risk and the cost of financial distress. 34 Liquidity has a positive and insignificant influence on the debt maturity structure. This relation indicates that companies with high liquidity are able to raise long-term debt as the liquid assets give creditors greater value in a potential liquidation. Firm value volatility has a negative and insignificant influence on the debt maturity structure. This result against the liquidity risk theory and reveals that companies’ attempts to avoid possible liquidation when having a high volatility in value. The relationship is also in line with Wiggins’ (1990) theory, that companies with high volatility in firm value are targeting the higher tax shield from having more long-term debt. Firm quality has a negative and insignificant influence on the debt maturity structure. The negative sign is an indication that sample companies are following the signaling theory and that debt maturity structure is used as a tool by insiders to signal the quality of a company to outsiders. A reason for the insignificant coefficient could be attributed to the measurement of firm quality. Another measure of firm quality is through credit ratings, which is used by Diamond (1991). Maturity matching has a negative and insignificant influence on the debt maturity structure. The sign is an indication that Vietnamese companies match the maturities of assets and liabilities Firm size has a positive and insignificant influence on the debt maturity structure. The sign shows that Vietnam companies attemp to take advantage of all the benefits associated with larger firm sizes, i.e. lower transaction costs, easier access to capital markets and lower information asymmetries, all of which being relevant when issuing long-term debt. A reason for the insignificant result could be attributed to the measurement of firm size. Another measure of firm size is companies’ total sales, which is used by Scherr and Hulburt (2001) and Fan et al (2010). 35 Growth opportunities has a negative and insignificant influence on the debt maturity structure. This result is the same as the theory that predicts an inverse relationship and ontrail with the argumentation which states that companies experiencing high growth opportunities deal with the underinvestment problem by issuing short-term debt. A reason for the insignificant coefficient could be attributed to the measurement of growth opportunities. The market to book value is a result of many components such as accounting principles, and therefore difficult to isolate to a companies’ growth opportunities solely. Share price performance has a negative and insignificant influence on the debt maturity structure. This is contradictory to theory and against the argumentation that companies issue long-term debt after an increase in share price. Equity risk premium has a positive and insignificant influence on the debt maturity structure. This is contradictory to theory that value enhancing managers raise long-term debt, instead of equity, when the equity premium is high. The gap filling has a positive and significant influence on the debt maturity structure. This is a contradictory to theory and against the argumentation that companies issue cheaper short-term debt when the government issues long-term debt. The time variation in gap filling has no influence on the debt maturity structure. This is a contradictory to theory and to the prediction which state that when government debt supply is large, gap filling by companies will be quantitatively stronger. Thus, we exclude it from regression model. The insignificant variables in Fixed-Effect modelare : firm quality, maturity matching, firm size, growth opportunities, share price performance, Liquidity, Firm value volatility, Equity risk premium and interest rate volatility. However, the discussion regarding these variables should be taken more as an indication of their 36 relationships with debt maturity, but since it is insignificant it should be treated with caution. b) Ordinary Least Square model: Table 5 – Ordinary Least Square result Independent Variable Intercept Effective tax rate Term structure Interest rate volatility Leverage Liquidity Firm value volatility Firm quality Maturity matching Firm size Growth opportunities Share price performance Equity risk premium Gap filling Time variation in gap filling Adjust R2 R Square N F (13, 471) Prob > F Equation (0.5387) (0.004) 0.034 (0.021) 0.126 0.017 (0.0009) (0.002) (0.001) 0.045 (0.007) (0.009) 0.048 0.325 (dropped) 10.86 % 13,2% 485 5.54 0.00 P>|t| 0.888 0.926 0.453 0.001 0.01 0.809 0.34 0.043 0.00 0.218 0.684 0.427 0.587 The effective tax rate has a negative and insignificant influence on the debt maturity structure. This is contradictory to theory and against the hypothesized relationship between flotation cost, tax shield and debt maturity. The term structure has a positive and insignificant influence on the debt maturity structure. The sign indicates that VietNam companies tend to lengthen their debt maturity a little as the slope of the term structure increases to take advantage of the tax shield. Interest rate volatility has a negative and insignificant influence on debt maturity structure. This is contradictory to theory and against the argumentation that companies 37 lengthen their debt maturity as the interest rate volatility increases so as to increase the value of the tax-timing option. Leverage has a positive and significant influence on the debt maturity structure. This is consistent with Morris (1975)’s suggestion that companies that hold more debt lengthen its maturity in an attempt to control for the refinancing risk and the cost of financial distress. Liquidity has a positive and insignificant influence on the debt maturity structure. This relation indicates that companies with high liquidity are able to raise long-term debt as the liquid assets give creditors greater value in a potential liquidation. Firm value volatility has a negative and insignificant influence on the debt maturity structure. This result contradicts with the liquidity risk theory and reveals that companies in Vietnam dont attempt to avoid possible liquidation when having a high volatility in value. The relationship neglect Wiggins’ (1990) theory, that companies with high volatility in firm value are targeting the higher tax shield from having more longterm debt. Firm quality has a negative and insignificant influence on the debt maturity structure. The negative sign is an indication that sample companies are following the signaling theory and that debt maturity structure is used as a tool by insiders to signal the quality of a company to outsiders. A reason for the insignificant coefficient could be attributed to the measurement of firm quality. Another measure of firm quality is through credit ratings, which is used by Diamond (1991). Maturity matching has a negative and insignificant influence on the debt maturity structure. The sign is an indication that Vietnamese companies match the maturities of assets and liabilities 38 Firm size has a positive and significant influence on the debt maturity structure. The sign indicates that Vietnam companies are experiencing the benefits associated with larger firm sizes, i.e. lower transaction costs, easier access to capital markets and lower information asymmetries, all of which being relevant when issuing long-term debt. A reason for the insignificant result could be attributed to the measurement of firm size. Another measure of firm size is companies’ total sales, which is used by Scherr and Hulburt (2001) and Fan et al (2010). Growth opportunities has a negative and insignificant influence on the debt maturity structure. This result contradicts with the theory that predicts an inverse relationship and ontrail with the argumentation which states that companies experiencing high growth opportunities deal with the underinvestment problem by issuing short-term debt. A reason for the insignificant coefficient could be attributed to the measurement of growth opportunities. The market to book value is a result of several things, like accounting principles, and therefore difficult to isolate to a companies’ growth opportunities solely. Share price performance has a negative and insignificant influence on the debt maturity structure. This is contradictory to theory and against the argumentation that companies issue long-term debt after an increase in share price. Equity risk premium has a positive and significant influence on the debt maturity structure. This is contradictory to theory that value enhancing managers raise long-term debt, instead of equity, when the equity premium is high. The gap filling has a positive and significant influence on the debt maturity structure. This is a contradictory to theory and against the argumentation that companies issue cheaper short-term debt when the government issues long-term debt. 39 The time variation in gap filling has no influence on the debt maturity structure. This is a contradictory to theory and to the prediction which state that when government debt supply is large, gap filling by companies will be quantitatively stronger. Thus, we exclude it from regression model. The insignificant variables in OLS result are : firm quality, maturity matching, firm size, growth opportunities, term structure, Firm value volatility, Equity risk premium, share price performance, effective tax rate, and interest rate volatility. However, the discussion regarding these variables should be taken more as an indication of their relationships with debt maturity, but since it is insignificant it should be treated with caution. 4.2.1.3 Time fixed-effect result: Time Variable Iyear_2009 Iyear_2010 Iyear_2011 Iyear_2012 Coef t P>[t] (Dropped) (Dropped) (0.005) (0.03) (0.33) 1.59 0.739 0.039 ( 1) _Iyear_2009 = 0 ( 2) _Iyear_2010 = 0 ( 3) _Iyear_2011 = 0 ( 4) _Iyear_2012 = 0 Constraint 1 dropped Constraint 2 dropped F( 2, 374) = 2.21 Prob > F = 0.1113  We see that the Prob > F is >0.05, so we failed to reject the null that the coefficient for all years are jointly equal to zero, therefore no time-fixed effect are needed in this case. 40 4.2.1.4 Dummy variable result: Dummy Variable Effective tax rate Term structure Interest rate volatility Leverage Liquidity Firm value volatility Firm quality Maturity matching Firm size Growth opportunities Share price performance Equity risk premium Gap filling Time variation in gap filling I_industry_2 I_Company_2 I_Company_3 Intercept OLS (0.004) 0.034 (0.021) 0.126*** 0.017* (0.0009) (0.002) (0.001) 0.045*** (0.007) (0.009) 0.05 0.324 0 (0.54) OLS_Dummy 0.02 0.09 (0.017) 0.153*** 0.015* (0.002) (0.002) (0.001) 0.07*** (0.007) (0.002) 0.04 0.323 0 0.054** (0.095)*** 0.07** (0.996) * p 4.82. B is calculated as: ( B=n[ ) ] 1 n 3 n Σ i=1(ui – ū) Skewness = 1 n 2 3/2 n Σ i=1[(ui – ū) ] 1 n 4 n Σ i=1(ui – ū) Kurtosis = 1 n 2 2 n Σ i=1[(ui – ū) ] Table 7 - Normality statistics Variable Effective tax rate Term structure Interest rate volatility Leverage Liquidity Firm value volatility Firm quality Maturity matching Firm size Growth opportunities Share price performance Equity risk premium Gap filling Time variation in gap filling Skewness 5.4522 (0.2264) 0.1517 2.8452 8.7489 (3.1583) 13.1056 2.0738 (1.606) 4.258 2.5445 (0.2888) 0.7013 (0.503) 44 Kurtosis 52.7976 1.3683 1.2945 25.6641 118.0661 68.6674 266.6819 58.73947 16.1623 65.1072 13.627 2.152 2.305 1.4584 B 10611.39 11.70885 12.25319 2229.672 55314.39 17771.1 286711.9 12756.71 749.5493 16046.79 566.8935 4.29863 10.00544 13.83665 According to Table VII all variables B > 4.82 except Equity Risk Premium. Thus we exclude it from the equation. Natural logarithm Normality test: Table 8 – Log Normality statistics Variable Effective tax rate Debt maturity structure Term structure Interest rate volatility Leverage Liquidity Firm value volatility Firm quality Maturity matching Firm size Growth opportunities Share price performance Equity risk premium Gap filling Time variation in gap filling Skewness (2.794) (1.211) (0.263) (0.223) (1.168) (0.149) (1.164) (0.181) (0.729) (0.386) 0.569 (0.591) (0.2888) 0.505 (0.57) Kurtosis 19.411 1.281 (0.164) (0.161) 2.304 4.032 4.391 0.78 1.584 (0.13) 0.576 0.177 (0.73) (0.83) (1.433) B 6010.933 176.42 205.75 203.82 118.82 23.07 147.09 101.19 82.62 207.86 143.4 187.33 284.93 313.78 419.02 According to Table VII all variables B > 4.82 except Equity Risk Premium. Thus we exclude it from the equation. 4.2.2 Stage 3 - Data comparison: Model result comparison Variables that have noticeably difference in result betweem Ordinary Least Square model and Fixed-Effect model : ETR, TS, FS. Compared with OLS result: + ETR variable in FE model has a positive and significant influence on firm DMS. FE result contradicts with the theory which mentions that the relationship between flotation cost, tax shield and debt maturity leads to a negative relationship between the effective tax rate and debt maturity while OLS model result strongly support this theory. 45 + OLS result of Term structure variable is insignificant while FE models shows a significant influence on firm DMS which indicates that when interest rate goes up, sample companies positively tends to lengthen their long-term debt more to take advantage of tax shield. + FE model result for FS varibale is slightly more significant than OLS result. We can says that in practical, medium & large in Vietnam tends to have more longterm debt as opposed to smaller companies. CHAPTER 5: CONCLUSIONS 5.1. Discussion: The debt maturity structure of a company is described in previous research as an efficient tool to minimize the risks associated with debt but also increase the level of benefits arising from it. A balanced debt maturity structure is, from the borrowers’ perspective, a way to reduce refinancing risk, increase transparency and exploit tax related opportunities. From the lenders’ perspective, the debt maturity structure is used as a tool to increase monitoring and reduce managements’ potential sub-optimal decisions. General objective of this study is to understand the relationship between key determinants and their influence on companies debt structure. By following ideas from international and dosmestic studies and research, we select: Leverage, Liquidity, Firm Value Volatility, Firm Quality, Maturity Matching, Firm Size, Growth Opportunities, Share Price Performance, Equity Risk Premium, Effective Tax Rate, Term Structre, Interest Rate Volatility, Gap filling and Time variation volatility in gap filling as our 46 variables. We use Pool ordinary Least Square method with Fixed-effect and Time fixed-effect model, to identify determinants as well as capturing individual difference in companies, industries and time. Research model can be described in following equation: DMit = -0.996 + 0.153LVit + 0.015Liit – 0.002FQit + 0.07FSit – 0.007GOit – 0.002SPPit + 0.04ERPt + 0.02ETRit + 0.09TSit - 0.017IVVit + 0.323GFit + 0.054Dummyindustry + (0.068 - 0.094) Dummycompany + (-0.0047)λyear Our findings in Table 4 of Fixed-effect model reaveals some variables that has a significant influence and can explain listed firm choice of debt maturity structure at the rate of 16.05%. They are: Leverage (0.153), Firm size (0.07), Term structure (0.09), equity risk premium (0.04) and Gap filling (0.323). According to past reseach of Ramussen (Stockholm) on Swedish market and Antonious on European market, their significant variable that has a strong influence on firm choice of DMS are: Leverage, Liquidity, Firm Value Volatility. Gap filling and Time variation in gap filling.These past factors explain 55 % of sample companies debt maturity structure. All detected variables belong to Liquidity risk theory, partial of gap filling theory, Equity market conditions and Agency cost theory. Therefore, we can see that both firm specific and macroeconomic variables are influencers when it comes to variation in company’s debt maturity structure. The insignificant theories in our research are: Signaling and Tax minimization, Gap filling and Leverage variable are the two most important that portrays the determinants of the debt maturity structure of sample companies in this research. Our results show a strongly positive relationship between Goverment’s issuance of longterm debt and companies’s debt maturity structure. This behavior contradicts the gap filling theory and indicate that companies tend to issue debt that has the maturity equal to Goverment’s. One possible explanation is that when the economy is going well, 47 Goverment will issue long-term debt, thus allowing companies to access long-term debt easier. Another significant variable is Leverage, our results indicate that companies with high leverage structure tend to lengthen their debt maturity structure in order to reduce liquidity risk and to postpone risk of bankruptcy. Term Structure result indicates that VietNam companies tend to lengthen their debt maturity a little as the slope of the term structure increases to take advantage of the tax shield. Firm size also plays an important part in explaning companies’s debt maturity structure. Our result shows that Vietnam companies are taking advantage of benefits associated with large firm size such as lower transaction costs, easier access to capital markets and lower information asymmetries. Maturity matching, in theory, acts as a hedge function against interest rate fluctuation and liquidation risk. We expect it to be significant but our findings show a contradicted result. One possible explanation is that this variable might be more importance for Industrial / Raw material / Insurance / Banks industry (sectors not included much in our sample companies). Companies in these indistries tend to issue debt with maturities that match their assets. Out of the three dummy variables: Industry - Company –Year, Company and Industry variable show significant influence on companies’s DMS. We can conclude that sample companies choose their debt maturiy structure on an individual basis. In other words, companies choose their structure of debt based on their own expertise , knowledge and past experience. A possible explanation for the irrelevance of the signaling theory could be attributed to the fact that a large proportion of our sample companies are mature and 48 stable companies with an already established and good reputation and therefore the need to signal their quality is smaller As for Tax minimization theory, our observations of sample companies show that they are not affected by Interest rate and its volatility when deciding their debt maturity structure. A possible explanation is that comapnies only take tax shield benefical to a certain level when deciding between short and long-term debt. This section requires further investigation. Variables related to Agency cost theory have no influence on companies debt maturity structure decision. This lack of interest can be due to Vietnam market limitation: Transparency and resitrction in gathering data, distance relationship between stakeholders that may lead to conflicts between them. 5.2 Limitation and Future reseach: As mentioned in Limitation article, the descriptive result may not be accurate due to transparency and restriction in gathering data and information. There are also some other limitation in our study. First of all, our study may suffer from survivor bias as we are doing a regression on a balanced panel data and thus excluding companies that do not have complete data for the sample period that we are investigating. Another limitation is the fact that variables used in this study have alternative measurements that can influence the outcome. Finally, the existence of heteroscedacsticity can have an impact on our results. We suggest for future studies to expand the macroeconomic aspect, by including more variables in the analysis. 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Titman, Sheridan and Wessels, Roberto, "The determinants of capital structure choice", Journal of Finance 43(1988): 1–19 APPENDIX Company Industry Company Industry Company Industry AAA Plastic&Enviroment BTT Trading CTD Construction AAM ABT ACC ACL AGD AGF AGM ALP ALT Fisheries Aqua Product Asphal & Concrete Fisheries Seafood Fisheries Im & Export Agri Generator&Mechanic Printing & Packaging Equipment Seafood Agri & Seafood Oil & Chemical Aerial & Transport Construction Petrolium &Chemical Seafood Agri &Sea food Construct / Decorate Confectionery Construction&Engi Equipment & Services Book & Equipment Book & Equipment Sugar Ceramic Equipment & Services Mineral Mineral Plastic Packaging Rubber BVG BXH C21 C32 C47 C92 CAN CAP CCI Steel Cement Packing Transport Construction Construction Construction Can food Agri Food Industrial Developing CTI CVN CYC DAC DAD DAE DAG DBC DBT Construction Construction Ceramic Ceramic Book&Equipment Book&Equipment Plastic Pet food Pharma CCM CDC CIC CID CIG CII CJC CLC CLG CLW CMC Cement Constructing supply Constructing supply Constructing supply Mechanic Infrastructure Electrical Mechanical Packaging Land-house Water Supply Construct&Mechanical DC2 DC4 DCL DCS DCT DHA DHC DHG DHI DHT DIC Construction Construction Material Pharma Trading Roofsheet&Construct Mining Industrial Packaging Packaging&Printing Pharma Construct CMG CMT Information&Network DID Information&Network DIH CMV Gas CMX CNG CNT COM CPC CSC CTA CTB Frozen Seafood Gas Construct&Material Petroleum Pesticides Construct&Material Construct Pump Manufacturing ANV APC APP ARM ASM ASP ATA AVF B82 BBC BCE BDB BED BHS BHV BKC BMC BMP BPC BRC BT6 BTH BTP BTS CTC Electric material Building&Construct Cement Book & Equipment 53 DL1 DLG EID HHS HJS HLC HMC Construct Construct Transport Wood&Rubber tree Book-Printing&Equipm Car parts Hydropower Coal Metal [...]... gap filling theory that are taken into consideration by company manager when taking decisions regarding the debt maturity structure, with less emphasize put on signaling, agency costs, equity market conditions and tax minimization 1.2 Rationale of the study: This research of debt maturity structure of manufacturing companies is conducted to identify key determinants that affect their debt maturity structure. .. only partially hedge through maturity matching Morris (1975) demonstrates that a perfect hedge does not exist, revealing in his study on industrial companies that 75 percent of the companies had an average debt maturity greater than average asset maturity Also, Morris (1975) finds that companies matching the maturities of assets with those of debt had an overweight of long-term debt In a comparison of. .. average number of years of depreciation) because on the one 11 hand, if debt has a shorter maturity than that of the assets, the company may not have enough cash readily available to repay the principal at due date On the other hand, if debt s maturity is longer than that of the assets, the cash flows coming from assets finish, while the debt payments remain outstanding Myers (1977) also argues that... bankruptcy and agency costs (Myers, 1984) All in all, taxes affect the debt part of capital structure and tax-related variables interact to offer tax incentives in the debt maturity structure (Antoniou et al, 2006) The discussion on capital structure, in general, and on the advantage of tax shields, in particular, goes back to Franco Modigliani and Merton H Miller’s irrelevance theory The Modigliani... Others calculation are represented in Titman and Wessels (1988) study in which those variables are measured as the ratio of short-term debt to 20 total assets and the ratio of long-term debt to total assets However, their focus is more on companies leverage decision rather than on how companies debt maturity structure varies with companies s characteristics Leverage is measured as the ratio of total debt. .. the value of a company, managers pay a lot of attention to this matter to find out the most sufficient use for debt So as to control debt disadvantage and enhance its advantage, manager normally focus on balancing out short and long-term debt The mix of short and long-term debt is referred to as the debt maturity structure A well-balanced debt maturity structure is an opportunity first and foremost... shortterm debt A negative relationship between firm value volatility and debt maturity structure was mentioned in Kane et Al (1985) As the market value of the company changes and debt remains constant, the equity acts as a cushion Normally, companies with a high volatility in firm value will have a high volatility in the equity cushion and shareholders’ value The volatility in equity leads to a more unstable... significant results in Australia and Singapore, while insignificant in Malaysia and Thailand This discrepancy can be attributed to the idea that in countries with more developed financial markets, such as Australia and Singapore, information plays a fundamental role in share price performance than in countries with less developed, and thus less efficient, markets such as Malaysia and Thailand Also, Antoniou... a constant tax shield, an increase in the floatation cost creates an incentive to lengthen the debt maturity so as the amortized floatation cost does not outweigh the benefits of tax shields Another point is that given constant flotation cost, a decrease in tax shield would also create an incentive to lengthen the debt maturity so as the benefits of the tax advantages are bigger than its disadvantages... establishing new shares, thus leading to a mix of debt and equity within the companies capital structure Due to debt s advantage of tax deductible, many people consider it to be a cheaper source of financing than equity However, debt disadvantages are still at large, one of them is that debt holders are claimants that can rightfully force a firm into liquidation Thus, in order to maximize the value of ... on industrial companies that 75 percent of the companies had an average debt maturity greater than average asset maturity Also, Morris (1975) finds that companies matching the maturities of assets... its asset maturity structure (the average number of years of depreciation) because on the one 11 hand, if debt has a shorter maturity than that of the assets, the company may not have enough cash... volatility and debt maturity structure was mentioned in Kane et Al (1985) As the market value of the company changes and debt remains constant, the equity acts as a cushion Normally, companies