资本结构选择的决定因素[外文翻译].doc

本科毕业论文（设计）外 文 翻 译原文：The Determinants of Capital Structure ChoiceThis paper analyzes the explanatory power of some of the recent theories of optimal capital structure. The study extends empirical work on capital structure theory in three ways. First, it examines a much broader set of capital structure theories, many of which have not previously been analyzed empirically. Second, since the theories have different empirical implications in regard to different types of debt instruments, the authors analyze measures of short-term, long-term, and convertible debt rather than an aggregate measure of total debt. Third, the study uses a factor-analytic technique that mitigates the measurement problems encountered when working with proxy variables.In recent years , a number of theories have been proposed to explain the variation in debt ratios across firms. The theories suggest that firms select capital structures depending on attributes that determine the various costs and benefits associated with debt and equity financing. Empirical work in this area has lagged behind the theoretical research, perhaps because the relevant firm attributes are expressed in terms of fairly abstract concepts that are not directly observable.The basic approach taken in previous empirical work has been to estimate regression equations with proxies for the unobservable theoretical attributes. This approach has a number of problems. First, there may be no unique representation of the attributes we wish to measure. There are often many possible proxies for a particular attribute, and researchers, lacking theoretical guidelines, may be tempted to select those variables that work best in terms of statistical goodness-of-fit criteria, thereby biasing their interpretation of the significance levels of their tests. Second, it is often difficult to find measures of particular attributes that are unrelated to other attributes that are of interest. Thus, selected proxy variables may be measuring the effects of several different attributes. Third, since the observed variables are imperfect representations of the attributes they are supposed to measure, their use in regression analysis introduces an errors-in-variable problem. Finally, measurement errors in the proxy variables may be correlated with measurement errors in the dependent variables, creating spurious correlations even when the unobserved attribute being measured is unrelated to the dependent variable.This study extends empirical work on capital structure theory in three ways. First, it extends the range of theoretical determinants of capital structure by examining some recently developed theories that have not, as yet, been analyzed empirically. Second, since some of these theories have different empirical implications with regard to different types of debt instruments, we analyze separate measures of short-term, long-term, and convertible debt rather than an aggregate measure of total debt. Third, a technique is used that explicitly recognizes and mitigates the measurement problems discussed above.This technique, which is an extension of the factor-analytic approach to measuring unobserved or latent variables, is known as linear structural modeling . Very briefly, this method assumes that, although the relevant attributes are not directly observable, we can observe a number of indicator variables that are linear functions of one or more attributes and a random error term. There is, in this specification, a direct analogy with the return-generating process assumed to hold in the Arbitrage Pricing Theory. While the identifying restrictions imposed on our model are different, the technique"for estimating it is very similar to the procedure used by Roll and Ross to test the APT.Our results suggest that firms with unique or specialized products have relatively low debt ratios. Uniqueness is categorized by the firms' expenditures on research and development, selling expenses, and the rate at which employees voluntarily leave their jobs. We also find that smaller firms tend to use significantly more short-term debt than larger firms. Our model explains virtually none of the variation in convertible debt ratios across firms and finds no evidence to support theoretical work that predicts that debt ratios are related to a firm's expected growth, non-debt tax shields, volatility, or the collateral value of its assets. We do, however, find some support for the proposition that profitable firms have relatively less debt relative to the market value of their equity.In this section, we present a brief discussion of the attributes that different theories of capital structure suggest may affect the firm's debt-equity choice. These attributes are denoted asset structure, non-debt tax shields, growth, uniqueness, industry classification, size, earnings volatility, and profitability. The attributes, their relation to the optimal capital structure choice, and their observable indicators are discussed below.Six measures of financial leverage are used in this study. They are long-term, short-term, and convertible debt divided by market and by book values of equity. Although these variables could have been combined to extract a common "debt ratio" attribute, which could in turn be regressed against the independent attributes, there is good reason for not doing this. Some of the theories of capital structure have different implications for the different types of debt, and, for the reasons discussed below, the predicted coefficients in the structural model may differ according to whether debt ratios are measured in terms of book or market values. Moreover, measurement errors in the dependent variables are subsumed in the disturbance term and do not bias the regression coefficients.Data limitations force us to measure debt in terms of book values rather than market values. It would, perhaps, have been better if market value data were available for debt. However, Bowman demonstrated that the cross-sectional correlation between the book value and market value of debt is very large, so the misspecification due to using book value measures is probably fairly small. Furthermore, we have no reason to suspect that the cross-sectional differences between market values and book values of debt should be correlated with any of the determinants of capital structure suggested by theory, so no obvious bias will result because of this misspecification.The variables discussed in the previous sections were analyzed over the 1974 through 1982 time period. The source of all the data except for the quit rates is the Annual Compustat Industrial Files. The quit-rate data are from the U.S. Department of Labor, Bureau of Labor Statistics, "Employment and Earnings" publication. These data are available only at the four-digit (SIC code) industry level for manufacturing firms.From the total sample, we deleted all the observations that did not have a complete record on the variables included in our analysis. Furthermore, since many of the indicator variables are scaled by total assets or average operating income, we were forced to delete a small number of observations that included negative values for one of these variables. These requirements may bias our sample toward relatively large firms. In total, 469 firms were available.Sectiondiscussed a number of attributes and their indicators that may in theory affect a firm's capital structure choice. Unfortunately, the theories do not specify the functional forms describing how the attributes relate to the indicators and the debt ratios. The statistical procedures used to estimate the model require that these relations be linear.The model we estimate is an application of the LISREL system developed by K. Joreskog and D. Sorbom. It can be conveniently thought of as a factor-analytic model consisting of two parts: a measurement model and a structural model that are estimated simultaneously. In the measurement model, unobservable firm-specific attributes are measured by relating them to observable variables, e.g., accounting data. In the structural model, measured debt ratios are specified as functions of the attributes defined in the measurement model.The parameters of our model can be estimated by fitting the covariance matrix of observable variables implied by the specification of the model () to the covariance matrix (S) of these variables observed from the sample. In the LISREL system, this is done by minimizing the function, F = log(det ) + tr(S-1) - log(det S) - (p + q), with respect to the vector of parameters of the matrices referred to above. This fitting function is derived from maximum-likelihood procedures and assumes that the observed variables are conditionally multinormally distributed.Our estimates of the parameters of the measurement model are presented in Tables II and III. The estimates are generally in accord with our a priori ideas about how well the indicator variables measure the unobserved attributes. Both the direction and the magnitude, as well as the statistical significance, of the estimates suggest that these indicators capture the concepts we wish to consider as determinants of capital structure choice.The estimates of the structural coefficients are presented in Table IV. These coefficients specify the estimated impact of the unobserved attributes on the observed debt ratios. For the most part, the coefficient estimates for the longterm and short-term debt ratios were of the predicted sign. However, many of the estimated coefficients are fairly small in magnitude and are statistically insignificant. In particular, the attributes representing non-debt tax shields, asset structure, and volatility do not appear to be related to the various measures of leverage. Moreover, the estimated models explain virtually none of the crosssectional variation in the convertible debt ratios.An examination of the correlation matrix of the sample data (Table V) provides some insights about the robustness of our results. Particularly noteworthy is the high negative simple correlation between OI/TA and the various debt ratios. This relation can potentially create a problem in interpreting the correlation between variables scaled by either OI or TA and the debt ratio measures.The best examples of this are the indicators of non-debt tax shields. For instance, the simple correlation between NDT/TA and the different measures of leverage is strongly negative. While this correlation is predicted by the DeAngelo and Masulis model, it should be noted that the large negative correlation may be due to the large positive correlation between OI/TA and NDT/TA caused by their common denominators. In the estimated structural model, where we control for the profitability attribute that is measured by OI/TA and OI/S, the coefficient estimate for the non-debt tax shield attribute is not statistically significant. Moreover, if we replace the denominators of the non-debt tax shield indicators with OI,the simple correlations are still just as strong but are reversed. For example, NDT/OI is strongly negatively correlated with OI/TA and strongly positively correlated with the measures of leverage. Using indicators scaled by OI for the non-debt tax shield attribute leads to positive coefficient estimates that are sometimes marginally statistically significant in the structural equations. While this result is inconsistent with the DeAngelo and Masulis model, it is most likely caused by the way the variables used as indicators are scaled.This paper introduced a factor-analytic technique for estimating the impact of unobservable attributes on the choice of corporate debt ratios. While our results are not conclusive, they serve to document empirical regularities that are consistent with existing theory. In particular, we find that debt levels are negatively related to the "uniqueness" of a firm's line of business. This evidence is consistent with the implications of Titman that firms that can potentially impose high costs on their customers, workers, and suppliers in the event of liquidation have lower debt ratios.The results also indicate that transaction costs may be an important determinant of capital structure choice. Short-term debt ratios were shown to be negatively related to firm size, possibly reflecting the relatively high transaction costs small firms face when issuing long-term financial instruments. Since transaction costs are generally assumed to be small relative to other determinants of capital structure, their importance in this study suggests that the various leverage-related costs and benefits may not be particularly significant. In this sense, although the results suggest that capital structures are chosen systematically, they are in line with Miller's argument that the costs and benefits associated with this decision are small. Additional evidence relating to the importance of transaction costs is provided by the negative relation between measures of past profitability and current debt levels scaled by the market value of equity. This evidence also supports some of the implications of Myers and Majluf and Myers .Our results do not provide support for an effect on debt ratios arising from non-debt tax shields, volatility, collateral value, or future growth. However, it remains an open question whether our measurement model does indeed capture the relevant aspects of the attributes suggested by these theories. One could argue that the predicted effects were not uncovered because the indicators used in this study do not adequately reflect the nature of the attributes suggested by theory. If stronger linkages between observable indicator variables and the relevant attributes can be developed, then the methods suggested in this paper can be used to test more precisely the extant theories of optimal capital structure.Source: Sheridan Titman; Roberto Wessels,1988.“The Determinants of Capital Structure Choice”. The Journal of Finance, Vol. 43, No. 1. (Mar., 1988), pp. 1-19.译文：资本结构选择的决定因素本文分析了最近一些优化资本结构理论的解释能力。这项研究用三种方法扩展了对资本结构理论的实证研究。第一，它检验了更为广泛的资本结构理论集合，其中有许多还没有实证分析。第二，因为理论方面对于不同类型的债务工具有不同的经验影响，作者分析了短期、长期和可转换债券的措施，而不是债务总额措施。第三，研究采用因子分析技术，减轻了使用代理变数测量时遇到的问题。近年来,有一些理论被提出来解释整个企业的负债比率变化。这些理论认为，企业资本结构选择的根据属性，决定了成本和与债务和股权融资相关的利益。在这方面的工作经验已经落后于理论研究，也许是因为公司相关属性都用相当抽象的概念来表示，而不是直接观察。其基本方法在以往的实证工作被用来与不可观察的理论属性代理估计回归方程，这个方法有一些问题。首先，可能没有我们想测量的具有独特代表性的属性。往往很可能代表了特殊属性，研究人员，缺乏理论的指导方针，可能受到诱惑而选择那些在统计拟合优度标准方面工作最好的变量，从而偏置了他们对他们测试显著性水平意义的解释。第二，往往很难找到与其他感兴趣的属性无关的特殊属性的措施。因此，选择代理变量可以测量多种不同属性的效果。第三，他们应该衡量的属性的不完全代表，他们在回归分析中的应用中介绍了一个变量错误问题。最后，在代理变量中的测量误差可能与因变量的测量误差相关，建立假性相关与因变量无关，即使是在未观察的属性被测量到的时候。这项研究在三个方面扩充了对资本结构理论工作的经验。首先，它扩展了一些最近开发的理论研究到目前为止还没有实证分析的资本结构的决定因素的理论范围。第二，由于部分理论考虑到不同类型的债务工具的经验的影响，我们分析分成短期、长期和可转换债券不同的措施，而不是一个总债务总额的措施。第三，一种技术被明确承认并用来减轻上述讨论的测量问题。这种技术，是因子分析的方法测量到的或潜在变量的推广，被称为线性结构建模。很简单，此方法假设，虽然相关属性不能直接观测，但我们可以观察大量一个或多个属性和随机误差项的线性