Partial adjustment behavior Towards target leverage ratio: evidence from China. Zhou QIN MSc Business Economics Finance track

Transcription

1 Partial adjustment behavior Towards target leverage ratio: evidence from China Zhou QIN MSc Business Economics Finance track August 2012

2 Abstract In this research, I investigate the financial behavior towards target capital structure of China s listed companies using an unbalanced panel data covering 2139 listed firms during I employ a dynamic framework to estimate the speed of adjustment towards optimal leverage, which is unobserved but proved to be determined by a range of firm-specific characteristics. The findings are consistent with both the theoretical predictions on capital structure and empirical evidence from previous literature. My analysis supports the hypothesis that firms in China would take steps to gap the distance from the observed leverage to the target level, though at a slower pace, compared with the adjustment speed in other countries. Additionally, leverage in China s listed firms increases with size, growth and tangibility, yet is inversely associated with non-debt tax shield and profitability. In other words, most of the explanatory variables of target leverage also work out in China. Key words: capital structure; target leverage; partial adjustment

3 Table of content 1. Introduction Literature review Capital structure theories Trade-off theory VS. Pecking order theory Empirical studies on capital structure Target leverage determination Determinants of target leverage Effects of determinants on target leverage Partial adjustment towards target leverage Methodology specification and data Database and Variable Definition Data Source Variable Definition Descriptive Statistics and Correlation between Variables Model Design Estimation Strategy Empirical results Robustness test Stability over time Stability over firm size Conclusion Reference... 35

4 1. Introduction Capital structure is at the core of modern corporate finance and undoubtedly has become a key issue for financial decision makers. Ever since Modigliani and Miller (1958) derived their famous theorem of irrelevance between capital structure and firm value from a series of strict assumptions, numerous literature attempt to address the puzzle on capital structure and firm value in face of market imperfection (tax, financial distress cost, agency costs as well as information asymmetry). Aiming to figure out the driving force and mechanism behind the change of capital structure, or debt-equity choice, researchers have developed a number of capital structure theories. Among them, there are two dominant theories, the trade-off theory and pecking order theory. According to trade-off theory (TOT), by making financing choices and obtaining a balance between benefits and costs of debt, a firm can improve its value. The main benefits of debt originate from tax deduction of interest expense. While the costs of debt consist of two parts: bankruptcy costs and agency costs. Bankruptcy costs arise if a firm takes too much debt but fail to pay back. In line with conflicting interests between different groups, typical agency story displays in two ways: one is between debt holders and shareholders and the other is between managers and shareholders. On one hand, to maximize their own benefit, shareholders are tempted to invest in high-risk projects and expect higher return relative to that of low-risk projects; such investing behavior causes excessive risk for debt holders without corresponding compensation when there is a debt overhang. In turn, such risk-pursuing behavior leads to potential value loss to firm. On the other hand, managers tend to use excess free cash flow to build empire so as to achieve personal reputation, which may be not optimal for firm value. The equilibrium where the benefit of the last unit of debt is offset by its cost is called optimal debt ratio. As a result, a firm is deemed as identifying a target debt-equity ratio and gradually moving towards it as trade-off theory predicts. Developed by Myers and Majluf (1984), the pecking order theory (POT) suggests that managers usually have better knowledge on investment prospects than external investors. Such asymmetric information leads investors to discount the newly issued risky securities in turn. Therefore, to avoid adverse selection, firms prefer retained earnings which involve no asymmetric information problem, then to debt financing (also with low risk and negligible information asymmetry) and to 1

5 issue equity as a last resort only. Myers (1984) argues that in a pecking order world, there is no concept of leverage targets. Changes in capital structure are mainly associated with investment opportunities and profitability (Fama and French, 2002). Increasing investment opportunities induce needs for funds, if internal financing is not sufficient, firms will turn to external financing, thus reaching a higher leverage level and vice versa. Alternatively, pecking order theory attributes the current leverage ratio as the cumulative historical financing behaviors rather than a deliberate optimum leverage. Both the trade-off theory and pecking order theory have validity in explaining firm financial behavior to some extent (Fama and French (2002) and Huang and Ritter (2009)). With regard to the disagreement between them, mainly on the contention of target leverage, researchers raise questions like whether companies have an optimal capital structure and if so, will companies choose to gap the difference between the target and actual ratio or just stay in disequilibrium. It should be interesting to examine what the case is like in China, the largest emerging economy around the world. Still transforming from a planned economy to market-oriented economy, China exhibits a distinct feature, that is, the state still plays an important role in the governance of most listed companies, not only on financing choice, but also on investment and profit distribution. As described by Qian, Tian and Wirjanto (2009), China is also characterized with less-than-ideal legal institution and underdeveloped financial system. All of the above-mentioned reasons inspire me to study borrowing behavior of China s listed firms. Therefore, I investigate in panel data consisting of 2139 listed firms in China with a time span of 12 years from 2000 to A dynamic adjustment model is employed so as to control for the unobserved time-invariant factors and obtain more accurate estimates. The following questions are studied in this research: 1. Do China s companies have leverage targets? If so, what are the determinants of the target leverage? 2. Will companies adjust their leverage ratio at a relatively lower speed towards target leverage in China than that in developed countries? 2

6 This thesis will originally contribute in three ways to the existing literature: Initially, existing systematic empirical studies are based on developed countries (Rajan and Zingales(1995) and Wald (1999))and there are limited studies focusing on the financing behavior of capital structure from an emerging-markets perspective, this gap is to which my research would try to address; Secondly, with regard to empirical analysis, I adopt the dynamic panel data methodology with a set of firm-level characteristics instead of the static approach, hence complementing the existing studies in China (Chen, 2004). Last but not least, aiming to give a full picture of corporate finance behaviors of China s listed companies during a transitional period, I use a larger sample compared with those in previous literature (Qian, Tian and Wirjanto, 2009), thus give more comprehensive reference to both authorities and management. The rest of my thesis proceeds as follows: Section 2 starts by a literature review, consisting of three subsections: capital structure theories with regard to target leverage, determinants of target debt ratio and partial adjustment; section 3 describes the dataset and outlines the empirical model; section 4 reports and discusses the main empirical results; section 5 presents the robustness test and section 6 provides concluding remarks. 3

7 2. Literature review 2.1 Capital structure theories Trade-off theory VS. Pecking order theory Modigliani and Miller (1958) outline a perfect world where there is no tax, no bankruptcy cost, no information asymmetry and efficient financial markets. They show that firm value in such a world is independent of its capital structure (Modigliani and Miller, 1958). The perfect world assumptions have led to a wide range of studies questioning what if these conditions are not satisfied, namely when there are tax, bankruptcy costs and asymmetric information, will capital structure affect firm value. As two of the most predominant theories on capital structure, trade-off theory (TOT) and pecking-order theory (POT) both attempt to provide an explanation for the puzzle that whether capital structure will exerts influence on firm value when taking market imperfection(tax, asymmetric information and agency cost) into account. The trade-off theory is based on the balance of benefits and costs of debt, while the pecking order accounts for the effects of asymmetric information on financing decisions. The trade-off theory (TOT) supposes that firm value is maximized when the benefits from debt equal the marginal costs of debt financing. Debt benefits mainly originate from the tax-deduction of interest payment and reduction of excessive free cash flow. Income tax of firms is based on earnings after interest of debt is paid, thus taking more debt reduces tax expense, so as to offset capital cost(deangelo and Masulis, 1980); moreover, less free cash flow leads management to focus on investment with better prospect rather than bad investment or managers personal benefits.(jensen and Meckling, 1976) The costs of debt imply potential bankruptcy cost and agency cost relative to debt financing. Generally speaking, bankruptcy costs simply result from over-borrowing, yet it is more complex with respect to agency cost. According to Jensen and Meckling (1976), there are two types of agency cost; one is between the debt holders and shareholders, while the other occurs due to the conflicting interests of managers and shareholders. The former usually results from shareholders concerning gains from a successful project will be firstly distributed to debt holders who enjoy prior claim than shareholders. Usually, high-risk investment 4

8 yields higher returns, thus shareholders are tempted to substitute low-risk investment with high-risk ones to benefit from the excessive profit, yet leaving added risk to debt holders without compensation, this is called asset substitution. What s more, shareholders even forgo low-risk though profitable projects to maximize their own wealth at the cost of firm value, as well as the benefit of creditors in the meantime, which is called underinvestment (Myers, 1976). The second type of agency cost occurs when managers have excessive free cash flow in control. As a result, managers tend to build empire to achieve professional reputation or waste free cash flow on bad investments or perquisites (Jensen, 1986). In a word, debt financing will not only induce high-risk projects implementation on the one hand (agency cost of debt), but also cut free cash flow to avoid manager pursuing personal benefits (agency benefit of debt) on the other hand. To sum up, the benefits of debt include the tax deductibility of interest and the reduction of free cash flow problems. The costs of debt include potential bankruptcy costs and agency conflicts between stockholders and debt holders. Consequently, firms achieve the maximal value at the point where all the benefits and costs are offset. The pecking order theory (POT), developed by Myers and Majluf (1984), focus on the asymmetric information between firm insiders and investors. Managers may use private information to issue risky securities when they are over-priced. Being aware of this, investors would discount the newly issued risky securities. To avoid such distortion of investment and other associated costs, managers have a hierarchy of preference in terms of investment financing. In line with the degree of information asymmetry, firms choose to finance new investment first with internal financing, such as retained earnings, then with safe debt, followed with risky debt, and finally with equity. In spite of the fact that both the trade-off theory (TOT) and pecking order theory (POT) are in force to account for capital structure decisions, they have some disagreements on whether firms have target leverage, which management attempt to approach. The trade-off theory (TOT) demonstrates that managers would deliberately trade-off the benefits of debt (mainly tax-deduction and free cash flow reduce) and costs of debt (financial distress cost and agency costs) to obtain the optimum leverage where firm value is maximized (Titman and Wessel, 1988). 5

9 Unlike trade-off theory, there is no concept of target leverage ratio that firms seek to achieve in the world of pecking order theory; instead, the debt ratio varies from time to time depending on firms profitability and how many investment opportunities they are faced with. All things being equal, more profitable firms tend to have lower leverage, because they usually have more retained earnings available to fund new projects. Yet, as investment opportunities grow, the deficit of internal financing may generate need for external funds, namely debt and equity. In a word, debt or equity issue depends on the internal financing deficit/surplus (Byoun, 2008). Also, a great number of empirical studies are conducted to test whether TOT or POT account for firm behavior better. First of all, the field study by Graham and Harvey (2001) conclude that the contention of debt ratio target is soft, about 71% of CFOs in their survey have target leverage ratio. Moreover, a faction of 10% of CFOs surveyed even follow very strict leverage target. Surprisingly, managers claim that achieving the leverage targets is not of prime importance, but to maintain financial flexibility, which is consistent with pecking order hypothesis to some degree. Moreover, the three most crucial factors CFOs bear in mind in making financing choice is financial flexibility, credit rating and earning volatility, revealing that CFOs care more of keeping firms away from financial distress, and this notion confirms the trade-off hypothesis. Furthermore, Frank and Goyal (2003) report that firms exhibit some behavior in line with the pecking order theory, that is, firms prefer safer financing device than riskier ones. However this is not robust over time, firms in the largest quartile follow the pecking order in earlier years, yet show increasing preference for equity in 1990s. Fama and French (2002) conduct a research to test the validity of trade-off theory and pecking order theory by investigating the dividend payout and leverage behavior of firms. They conclude it s difficult to tell which theory wins the horse race since neither of them is able to predict the results of empirical results alone. Specifically, TOT is weak in explaining why profitability is negatively related to leverage; meanwhile their finding of the positive relationship between firm size and leverage contradicts the prediction of POT. Whereas, they claim the regression provides reliable evidence that leverage is mean reverting, namely, their study empirically supports the existence of target leverage. All these tests on theory validity lead to a consideration that it is no longer necessary to give interpretation or prediction to financial behavior with either TOT or POT alone, instead, regard the two models as stable mates, as Fama and 6

10 French(2005) put it. Besides, Byoun (2008) also suggests the two competing theories (trade-off theory and pecking order theory), largely been evaluated in isolation, can and should be viewed as complements Empirical studies on capital structure With regard to studies on the target leverage hypothesis, Marsh (1982) provides evidence that the UK companies conduct financing behaviors as if they have target levels of debt in mind. Moreover, they posit the target level as a function of several firm features such as company size, bankruptcy risk and asset composition. A study by Leary and Roberts (2005) also reveals that the driving force behind the rebalancing of leverage ratio is the optimal range of debt-to-asset ratio, which brings about active adjustment of leverage in spite of adjustment frictions. Rajan and Zingales (1995) investigate the capital structure in major industrialized nations (the G-7 countries), and they find similar pattern on the relation between leverage and its determinants across the seven countries. Apart from the common results on capital structure, they also highlight that distinct institutional conditions may pose remarkable effects on leverage. For example, different tax code, bankruptcy law, financial market structure (banking, bond markets and equity market) as well as ownership all have different effects on the capital structure choice. A later study by Wald (1999) also makes an international comparison on capital structure in five developed countries, France, Germany, Japan, the United Kingdom and the United States. What makes his research differ from the work of Rajan and Zingales (1995) is that he includes new determinants (ratio of depreciation on total assets as the non-debt tax shield) and uses different measures of explanatory variables (replace market-to-book ratio with sales growth to proxy for investment opportunity). Most of Wald s findings are similar to that of Rajan and Zingales (1995), except that they find positive signs of investment opportunity on debt ratio, compared with the negative coefficients in Rajan and Zingales(1995) s study. This difference is possibly due to their different measurement. All these studies deepen academic research,as well as practical analysis within the developed world. Nevertheless, studies into emerging-market, which differ from developed countries on a number of dimensions (culture, institutions, legal system and stage of development), is also worth noting. 7

11 Booth, Aivazian, Demirguc-Kunt and Maksimovic (2001), among the first to have an eye on the emerging countries, conduct empirical study to test the explanatory power of capital structure models in 10 developing countries. They posit capital structure choice in developing countries is significantly affected by similar variables but to different degree. To be specific, it is shown that larger firms tend to hold more debt, revealing that larger firms own higher liability capacity and are more diversified to avoid financial distress. Nevertheless, since firms in developing countries usually suffer more from market inefficiencies and institutional constraints, systematic difference seems to exist between the developed and developing countries. For this reason, some of the variables show different signs, for example, growth (measured as market-to-book value of assets by both paper) is proved to be negatively associated with leverage in Rajan and Zingales (1995), yet shows significantly positive relation in half of the emerging countries in Booth, Aivazian, Demirguc-Kunt and Maksimovic (2001) s research. Above all, their work broadens the understanding of firm financial behaviour in different settings, and they also suggest detailed studies on difference derived from institutional factors at the end of their paper. Referring to literature in China, Chen (2004) studies the determinants for firm-level capital structure in China using a balanced panel of 77 public listed companies over the period of and concludes that some of the insights from modern finance theory are portable to China, and certain firm-specific factors that are relevant for explaining capital structure in the western countries are also relevant in China. Nevertheless, there are also difference, for example, growth, which shows negative relation in Wald (1999) s work, exhibits inverse relationship in Chen (2004) study. Interestingly, this is consistent with the evidence from Booth, Aivazian, Demirguc-Kunt and Maksimovic (2001), and this suggests a similar case between China and other developing countries. A more recent study by Qian, Tian and Wirjanto (2009) conducts an investigation on the targeting behavior of public listed firms in China. To obtain a balanced panel data, their sample is limited to 650 firms with a time span of 1999 to They find that firm size, tangibility and ownership structure are positively associated with firm s leverage ratio, while profitability, non-debt tax shields, growth and volatility are negative related to firm s leverage ratio. 8

12 2.2 Target leverage determination Determinants of target leverage Though proved to drive the changes of capital structure (Leary and Roberts, 2005), the target leverage is not observable and usually estimated by a set of proxies for firm-specific features in empirical studies. Prior studies in capital structure used to employ static or cross-sectional method and attempt to find the relationship between observed leverage and a group of firm characteristics such as size, profitability, asset tangibility, investment opportunities and uniqueness. Such model obviously ignores the fluctuations of actual leverage around the optimal leverage with the presence of adjustment costs (Fischer, Heinkel and Zechner, 1989) Numerous studies have been done to specify and estimate the unobservable optimal capital structure using a wide range of observable determinants. Titman and Wessels (1988) conduct an empirical study to identify the factors that determine firms borrowing choice of U.S.A companies, including asset structure, non-debt tax shields, growth, uniqueness, industry classification, size, earning volatility and profitability. Their results emphasize the importance of uniqueness but find no significant effect of non-debt tax shields, volatility, collateral value or future growth. They attribute this inconsistency with theoretical implication to inadequate measurement of optimal leverage determinants. Aiming to investigate the borrowing choice of the G-7 countries, Rajan and Zingales (1995) examine the relationship between leverage ratio and tangibility, growth, firm size, as well as profitability. Their evidence indicates that debt ratio increases with tangibility and size, and decreases with growth and profitability. Table 1 gives predictions on the signs of determinants on the leverage. 9

13 Table 1 Effects predicted by trade-off theory (TOT) and pecking order theory (POT) variable TOT POT Empirical evidence (-)Titman and Wessels (1988) size + - (+)Harris and Raviv (1990);Wald(1999); Booth, Aivazian, Demirguc-Kunt and Maksimovic(2001); Flannery and Rangan (2006) (+)Jensen and Meckling (1976); tangibility + - Harris and Raviv (1990); Rajan and Zingales (1995);Wald(1999) (-)Titman and Wessels (1988);Harris and Raviv (1991); profitability + - Rajan and Zingales (1995); Shyam-Sunder and Myers (1999) non-debt tax shield - (-)DeAngelo and Masulis (1980) growth - + (+)Hovakimian,Opler and Titman (2001) (-)Myers (1984);Harris and Raviv (1991); Booth, Aivazian, Demirguc-Kunt and Maksimovic(2001); Fama and French(2002); Effects of determinants on target leverage In the following part, I will discuss the effects of traditional firm-specific characteristics which are thought to be the key factors that influence the capital structure of firms. Asset tangibility Suggested by Titman and Wessels(1988) and Rajan and Zingales (1995), asset tangibility is positively related to debt-to- asset ratio. The rational is as follows. Tangible assets can be used as the collateral not only to raise debt, but also to negotiate borrowing. In other words, firms can borrow at lower interest rates if the debt is secured with such assets, thus reducing default risk. Moreover, increasing liability generates considerable monitoring from creditors, namely more useful information will be disclosed to investors, hence firms are less subject to information asymmetry,which in turn makes them less reliable on debt 10

14 when external financing is in need. As a result, more tangible assets could reduce debt ratio on the other hand. (Harris and Raviv, 1990) Firm size Firm size is an important factor that influences leverage, though the relationship between firm size and debt ratio is uncertain theoretically. In the trade-off theory framework, larger firms tend to be more diversified and fail less often, or less likely to go bankruptcy, therefore they usually have better debt capacity than smaller firms; furthermore, larger firms are believed to have easier access to the capital markets. (Lian and Zhong, 2007) On the other hand, Rajan and Zingales (1995) argue that size may be inversely related to the debt ratio because large firms tend to release more information to public than smaller ones, consequently larger firms have less incentive to fund with debt, and they even favor equity financing instead. Positive sign for the relation is documented by many empirical studies (Titman and Wessels, 1998 and Booth, Aivazian, Demirguc-Kunt and Maksimovic, 2001). By contrast, the empirical evidence by Rajan and Zingales (1995) provides a negative sign between size and leverage. Non-debt tax shields It is believed that firms prefer debt because of corporate tax shield from interest payment. Nevertheless, DeAngelo and Masulis (1980) suggest an often overlooked but major feature: the non-debt tax shield (NDTS). They argue that non-debt tax shields items could serve as a substitute for interest expense in term of reducing corporate tax payments. For this reason, the presence of non-debt tax shields, such as depreciation, tax credit and pension funds is likely to induce a less strong tax incentive to adopt debt and maintain lower debt ratio. As a consequence, a positive relationship is expected between the amount of non-debt tax shields and debt ratio. This inverse relationship is confirmed by Wald (1999) and Huang and Song (2006). Growth Another crucial factor determining capital structure is growth opportunities. Titman and Wessels (1988) illustrate that firms with great growth could incur higher agency costs, namely the conflicts between shareholders and creditors. 11

15 Shareholders in fast-growing firms may turn down valuable investment to avoid the situation that gains transfer to creditors firstly, accordingly giving rise to asset substitution and underinvestment. Besides, firms with greater growth options possess more valuable but intangible assets than tangible assets, implying that bankruptcy costs are higher for these firms. As a result, trade-off theory predicts a negative relation between leverage and growth options. In contrast, the pecking order theory posits that all else being equal, encouraging prospects generate higher needs for financing needs. Moreover, the higher the level of information asymmetry with respect to investment opportunities, the higher preference for debt relative to equity. Rajan and Zingales (1995) and Booth, Aivazian, Demirguc-Kunt and Maksimovic (2001) all document negative signs on the association between growth and leverage. Profitability There is no uniform conclusion on the relation between leverage and profitability by previous theories. Serving as a desirable discipline device, debt forces managers to pay out profits rather than waste on perquisite or build business empires. Cutting free cash flow is regarded the main contribution of debt to limit agency problems. Therefore, more profitable firms will have higher debt-to-asset ratio. On the other hand, as the typical pecking order theory hypothesis of Myers and Majluf (1984) indicates, firms prefer retained earnings as their main financing source when it is available, followed by debt financing, and turn to new equity issues at last. Higher profitability allows firms to follow this pecking order, thus a negative association between profitability and leverage would be expected. Interestingly, most of the empirical evidence suggests an inverse relationship between profitability and debt ratio (Rajan and Zingales, 1995 and Booth, Aivazian, Demirguc-Kunt and Maksimovic, 2001). Hypothesis 1: Listed companies in China have optimal debt-to-asset ratio which is significantly affected by a set of firm-specific characteristics, as predicted by trade-off theory and pecking order theory. 12

16 2.3 Partial adjustment towards target leverage According to trade-off theory, a firm has a target leverage ratio and makes issuing and repurchasing decisions aiming to keep its leverage at the target level. Studies by Rajan and Zingales (1995) and Titman and Wessels (1988) both employ a static framework to determine the target leverage ratio but do not answer the question of whether leverage tends to revert to a target. To identify this issue, a series of paper work on the adjustment behavior of firms towards the optimal leverage. Jalilav and Harris (1984) are among the first to analyze the dynamic target behavior of firms, though with limited sample of 108 manufacturing firms from1963 to They employ a partial adjustment process of firms to long-run financial targets. After that, Fischer, Heinkel and Zechner (1989) posits that the existence of deviation from target level is originated from adjustment costs, which consists of two parts: the fixed cost and institutional cost. The former includes legal fees, accounting fees as well as evaluation fees, and the latter depends on corporate performance and financial market development. Hovakimian, Opler and Titman (2001), who examine the borrowing choice of all firms from the Compustat tape from 1979 to 1997 in a dynamic framework, point out that firms would set a time-varying rather than a constant target debt ratio, towards which managers make financial decisions. Moreover, Leary and Roberts (2005) conduct a duration analysis specifically on the rebalancing behavior on nonfinancial and nonutility firms from annual Compustat tape during They conclude that firms in their sample adjust at a frequency of once a year to keep the actual debt ratio within a target range. Furthermore, the seemingly persistent effects of shock are actually generated by adjustment costs. Unfortunately, adjustment cost can t be observed, therefore, speed of adjustment acts as an important device to measure the unobserved adjustment costs and to analyze target behavior at the same time. Regularly used by existing literature (Fama and French, 2002, Flannery and Rangan, 2006 and Huang and Ritter, 2009), the typical partial adjustment model: L t -L t-1 =λ (L t *-L t-1 ) +ε t 13

17 Where L t *-L t-1 is the deviation between the target leverage ratio and actual leverage at time t-1, L t -L t-1 denotes the change between leverage at time t and time t-1, and λ captures the adjustment speed. Table 2 presents the estimation techniques and results by influential papers centering on the speed of adjustment. Table 2 Estimation techniques and results on speed of adjustment(soa) Paper Estimation techniques Market leverage Results Book leverage Fama and French(2002) Flannery and Rangan(2006) Lemmon, Roberts and Zender(2008) OLS regression 7% to 15% 10% to 18% OLS regression 13.30% N/A Firm-fixed effects 38.00% N/A Panel regression with instrumental variables 34.40% N/A Pooled OLS regression N/A 17% Firm-fixed effects N/A 39% GMM N/A 25% Huang and Ritter(2009) Long differencing OLS regression with firm-fixed effects 23.20% 17% Hovakimian and Li(2011) Pooled OLS regression N/A 13.20% Firm-fixed effects N/A 35.70% Diversified results of estimated speed towards optimal debt ratio are reported by previous literature. Using OLS regression with Fama-MacBeth time-series standard errors, Fama and French (2002) computer speed of adjustment ranging from 7% to 18% per year, which is suspiciously slow. Flannery and Rangan (2006) report different speed of adjustment (SOA) depending on various estimation techniques, and they also demonstrate that by applying firm-specific effects, the estimation is much higher, reaching 38% per year by market value of debt ratio. Adopting the system GMM 14

18 method, Lemon, Roberts and Zender (2008) estimate speed of adjustment (SOA) with book leverage, and obtain a moderate rate of 25% per year. A later study by Huang and Ritter (2009), using long differencing OLS regression with firm-fixed effects, estimates the adjustment speed: 17% for book leverage and 23.2% for market leverage. A more recent paper by Hovakimian and Li (2011) report the results of a full-sample OLS regression of 35.7% and 13.2% with and without firm-fixed effects, respectively. In China, Tong (2004) first introduces the dynamic adjustment framework with panel data of 249 listed firms covering He comes to the conclusion that China s companies conduct a partial adjustment procedure at a speed of 27.75%. As he posits, with underdeveloped market economy and financial system, listed companies in China are faced with higher adjustment cost, which could account for the slower targeting pace. Focusing on 620 public listed companies in China, Wang, Zhou and Fang (2007) estimate the speed of adjustment towards target debt ratio as 27.3% and 41.4% with pooled OLS and firm-fixed effects, respectively. Qian, Tian and Wirjanto (2009) choose a sample of 3900 firm-year observations for 650 firms over the period of 1999 to They apply generalized methods of moments (GMM) and estimate that listed companies in China rebalance to the equilibrium level of debt at a rate of 18.5%. Hypothesis 2: The adjustment towards optimal capital structure in China is slower than that in most developed countries which reflects the existence of excessive adjustment cost. 15

19 3. Methodology specification and data 3.1 Database and Variable Definition Data Source In this research, to examine the partial adjustment process of China s public listed companies, I use the data taken from the Compustat tape for a period of 12 years from 2000 to 2011 within a panel framework. The advantage of panel data is obvious: it allows for a much larger sample and improves the creditability of estimation. In the meantime, regression with both firm-specific (fixed-effect) and time specific factors correspond to the real situation. Financial firms (SIC code: ) and regulated utilities (SIC code: ) are excluded. It is due to that their financing decisions are out of macro policy by government or byproduct of regulation (Fama and French, 2002). After eliminating observations which fail to computer dependent and independent variables, I have 2139 firms and firm-year observations Variable Definition Dependent variable: Firm leverage is defined as the fraction of debt on total assets. However, there is disagreement on using whether book value or market value. Yet I have a preference on book value, and it is due to the following reasons: 1) To facilitate firm management, it is more flexible for CFOs to concern book value of leverage, since market leverage fluctuates a lot and it is too costly to adhere to market-based target debt ratio; 2) Possible measurement problem derived from the situation in China, that is, more than often, listed companies in China have a large number of untradeable shares, as a result, it is not plausible to calculate market value of firm with shares outstanding and share price. (Huang and Song, 2006) I define the dependent variable as the total liability (Compustat item 181) scaled by total assets (Compustat item 6), following Fama and French (2002). 16

20 Independent variables: Existing literature have chosen a wide range of determinants, yet in this empirical analysis, I would focus on five variables: asset tangibility, profitability, growth, non-debt tax shield and firm size, following Rajan and Zingales (1995), Flannery and Rangan (2006) and Lemmon, Roberts and Zender (2008). Table 3presents the variables and measurement of them. Table 3 Variable Definition Variables Symbol Definition Prediction Dependent Variable current leverage ratio lev i, t ratio of book value of total liability to total asset of firm i at time t Independent Variables lagged leverage levi,t-1 ratio of book value of total liability to total asset of firm i at time t-1 profitability profit ratio of earnings before interest (EBIT) to total assets +(TOT)/-(POT) growth growth the annual growth of total assets -(TOT)/+(POT) non-debt tax shields NDTS ratio of depreciation and amortization to total assets -(TOT) size size natural logarithm of total assets +(TOT)/-(POT) asset tangibility tang ratio of net plant, property and equipment and inventories +(TOT)/-(POT) to total assets lev=total liability(item [181])/book value of total assets (item [6]) size =ln(total assets) (item [6]) profit= earnings before interest and taxes (item [18]+item [15]+ item [16])/total asset (item [6]) tang=(net plant, property and equipment(item [8]) + inventories-total(item[3]))/total assets (item [6]) NDTS= depreciation and amortization(item [14])/total assets (item [6]) growth= (total assets i,t -total assets i,t-1 )/total assets i,t (item [6]) TOT denotes prediction by trade-off theory; POT denotes prediction by pecking order theory 17

21 Compared with the explanatory variables chosen by Flannery and Rangan(2006), I drop some variables, for example, credit rating which is not widely recognized in China. The ratio of R&D expense is also dropped because domestic accounting standards rarely require firms to report such item. In the meantime, I change measurement of some variables, say, replace market-to-book ratio with asset growth (suggested by Fama and French, 2002) to proxy for investment opportunity because of data limitation. Following Lemmon, Roberts and Zender (2008), I measure asset tangibility as the ratio of tangible assets to total assets and expect it to be positively related to leverage. The measure of profitability used in the regressions is the ratio of the earnings before interest and taxes (EBIT) to total assets and use the growth of total assets as a proxy for growth opportunities following Fama and French (2002) I use the ratio of depreciation and amortization to total assets as a proxy for non-debt tax shields, following Titman and Wessels (1988) and Fama and French (2002), and a negative sign is expected. In line with Wald (1999), the natural logarithm of total assets is used as a measure of the firm s size and both the positive and negative results are expected Descriptive Statistics and Correlation between Variables Table 4 presents the summary statistics of both the dependent and explanatory variables in my sample over the time period (2000 to 2011). All data is deflated by GDP and use 2005 as the base year. The item overall denotes the statistic difference through all observations, while between implies the statistic difference from firm to firm, and within item indicates yearly changes of variables. N means the number of whole firm-year observations is 14508, n presents the number of firms in my sample is 2139, and T-bar exhibits the number of available time-based observations for variables are for each firm on average. From the table, I can observe the average debt ratio of China s listed companies is 47.48%, relatively higher compared with the mean book leverage of Leary, Roberts and Zender (2008), who report 27% for book leverage and 28% for market leverage. In addition, firm-specific characteristics vary less within firms, or in a more stable manner (the majority of the between standard deviation are greater than that 18

22 of within ), which provides evidence that long-run target leverage exists. Table 4 Summary of statistics My sample includes all China's listed firms taken from Compustat tapes during 2000 to There are firm-year observations with 2139 firms in all. Financial or utility firms are excluded; observations with negative value of total assets are also dropped. All variables are winsorized at 1% and 99% to avoid the bias caused by extreme values. Variable Mean S.D Min Max Observation lev overall N = between n = 2139 within T-bar= growth overall N = between n = 2139 within T-bar= NDTS overall N = between n = 2139 within T-bar= profit overall N = between n = 2139 within T-bar= tang overall N = between n = 2139 within T-bar= size overall N = between n = 2139 within T-bar=

23 Table 5 provides the correlation matrix between firm debt ratio and its determinants Table 5 Correlation matrix of leverage and firm-specific characteristics lev size tang profit NDTS growth VIF lev 1 size tang profit NDTS growth The correlation matrix suggests positive correlation of tangibility with debt ratio, the same as that of size with debt ratio. Inversely, over the total sample period, both profitability and growth are negatively associated with leverage. Interestingly, non-debt tax shields (NDTS) is positively related to leverage, which contrasts the prediction of the trade-off theory (TOT). Also, the positive signs of size and tangibility on leverage, and the negative sign of growth on leverage are in line with the trade-off theory (TOT). Yet the negative correlation between leverage and profit confirm the prediction by pecking order theory (POT) that higher profit provide more sufficient internal funds, thus reducing dependence on external financing, say debt and equity. Despite the majority of explanatory variables are not highly correlated, the correlation coefficient of NDTS and tangibility is , and may give rise to multicollinearity. Whereas, the multicollinearity test (VIF value) presents that this is not a problem. 20

24 3.2 Model Design Aimed to distinguish between observed leverage from optimal level, I adopt the following equation, which allows for a time-varying estimation of target leverage * ratio (lev i, t ) for firm i at time t, suggested by Flannery and Rangan (2006): lev i, t * =βx i, t-1 (1) Where lev * i, t indicates firm i s optimum leverage at time t, while x i, t-1 proxy for a set of firm-specific variables deemed to determine the target leverage for firm i at time t-1. Here, x i, t-1 denotes size, asset tangibility, growth, profitability and NDTS. In an ideal world, firms would maintain the target debt ratio to maximize firm value, put it in another way, the observed leverage should equal the target leverage (lev i, t =lev * i, t ), therefore the change of leverage between the current period and the previous period should be equal, that is lev i, t -lev i, t-1 =lev * i, t -lev i, t-1. Accordingly, I make an inference that external shocks or accidents could lead actual leverage to deviate from the desired level in the short run, whereas firms tend to adjust towards the optimal leverage in the long run. However, the existence of adjustment costs becomes impediment of immediate convergence to optimum level, and only allows for a partial adjustment towards target level from period to period. lev i,t -lev i,t-1 =α(lev i,t * -lev i,t-1 )+μ i (2) α is introduced as the adjustment factor to represent the rate of convergence to the target level. Alternatively, an individual firm covers a fraction αof deviation between the actual debt ratio and the target debt ratio, which is predicted to be positive and vary from 0 to 1 according to trade-off theory. The possible cases are as follows: α=0the adjustment is too costly to induce management to revert to the optimal capital structure, firms would rather stay in a disequilibrium situation. 0<α<1 firms make incomplete adjustment to cover the deviation from the target. α=1 there s no friction for adjustment and firms make a complete and immediate 21

25 Yearly Change Of Book Leverage adjustment and the observed leverage is exactly the same as the target (lev i, t =lev i, t * ). Substituting (1) into (2) and rearranging, we have lev i, t = (1-α)lev i,t-1 +αβx i, t-1 +μ i,t (3) Figure 1 illustrates the mean change of book leverage during the sample period. Figure 1 Mean Change in Leverage 5.00% 3.00% 2.37% 1.00% 0.25% 1.20% 1.45% 1.52% 0.95% 0.60% 0.66% -1.00% % -1.14% -3.00% -2.13% -3.07% -2.08% -5.00% Figure 1 generalizes the leverage changes occur every year throughout the sample, though unclear at the moment that whether these changes are out of the adjustment to desired leverage. In the meantime, I observe the year of 2007 is the turning point of leverage change. Referring to financial crisis since 2007, I assume that the economic downturn induces firms to cut the amount of debt to avoid financial distress. 22

26 3.3 Estimation Strategy Given the sample of 2139 firms covering a time span of 12 years from 2000 to 2011, I plan to apply several estimation strategies for the equation (3). (1) A basic ordinary least squares(ols) tests, assuming there is no difference between firms, only to form an outline of partial adjustment procedure in China (Fama and French, 2002) (2) Pooled OLS with year dummy variables to absorb the unobservable time-specific effects on targeting behavior(flannery and Rangan, 2006) (3) Instrumental variable panel regression to get rid of serial correlation between the lagged leverage and the error term, employing lagged value of lev i,t-1 and X i,t-1 as the instruments(flannery and Rangan, 2006) (4) Fixed effects panel regression to control for the time-invariant firm-specific factors. (Lemmon, Roberts and Zender, 2008, Hovakimian and Li, 2011) (5) Instrumental variable (lagged value of lev i,t-1 and X i,t-1 are used as instruments) panel regression with both year dummy variables and firm fixed effects, taking endogeneity problem, omitted variables and time effect into consideration at the same time(flannery and Rangan, 2006) What s more, robustness tests of equation (3) are conducted to examine the stability over both time period and firm size. 23

27 4. Empirical results In this part, I report and discuss the empirical results on the partial adjustment model towards debt target. Table 6exhibits the empirical results of equation (3) for the whole sample of 2139 firms and over the time span of All intercepts reported in Table 3 are statistically significant, which suggests that firms in China conduct active adjustment of leverage in each period. In the following part, more detailed analysis is provided to give a full picture of the adjustment procedure of China s listed firms with regard to the two hypotheses I put forward earlier. Hypothesis 1: Listed companies in China have optimal debt-to-asset ratio which is significantly affected by a set of firm-specific characteristics, as predicted by trade-off theory and pecking order theory. My regression reports statistically significant effects of determinants on firm leverage. Accordingly, these results provide reliable support that listed firms in China have target leverage in mind, thus hypothesis 1 is confirmed. In general, the empirical analysis supports both the trade-off theory (TOT) and pecking order theory (POT). There are two estimates consistent with the prediction of pecking order theory. First of all, significantly positive relation between growth and debt ratio is consistent to POT, yet contrast to TOT. I would interpret this result as in firms with great growth opportunities, shareholders are reluctant to issue new equity in case of dilution, hence they rely more on debt financing. Moreover, investors also favor firms with better prospects; therefore fast-growing firms have better access to external financing. Secondly, relationship between profitability and leverage is negative and statistically significant, which is consistent with pecking order theory (POT). This can be interpreted that more profitable firms enjoy better financial statue and have more stable income. Nevertheless when in need of funds for investment, firms rely more on internal financing, for example, retained earnings rather than debt or equity 24

28 issue. The estimated coefficients of the rest of variables are consistent with the prediction of trade-off theory (TOT). Initially, firm size exerts significantly positive effects on leverage, which is consistent with the prediction that larger firms suffer less information asymmetry between firm managers and investors, facilitating external, especially debt financing. In addition, larger firms are more diversified. And this lowers the risk of running into bankruptcy. The economies of scale also make larger firms to lose less in financial distress than smaller firms do. Moreover, I find coefficients of non-debt tax shield are negative and significant at 1% level in all cases. This evidence is strongly consistent with the trade-off theory, positing that firms with more non-debt tax shields benefit from tax deduction, which indirectly offset the tax-advantage of debts. Lastly, though not significant when applying panel data regression, tangibility poses positive effects on leverage targeting behavior. The results support the trade-off theory, which points out firms with more tangible assets are able to reduce loss when liquating firms in financial distress, besides, more tangible assets reduce the risk of default, therefore limiting the agency costs between debt-holders and shareholders. Consequently, both the trade-off theory(tot) and pecking order theory(pot) possess explanatory power in explaining firms financial behavior, it s not clear which wins the race in deciding capital structure. 25