Financial constraints from firms’ statements--论文代写范文精选

隐含在论点中的是假设金融约束的低股利是有用的指标。使用基于这样的方法,评估公司是否在经济上受到影响。基于他们的情形,只有15%表明公司没有基金所需的投资。下面的essay代写范文进行详述。

Abstract 
Existing proxies aim to infer financial constraints from firms’ statements about their funding situation or changes in investment plans, their actions (such as not paying a dividend), or their characteristics (such as being young, or small, or having low leverage, or not having a credit rating). The literature is divided on which of these best captures financial constraints and as a result, empirical studies tend to employ a range of measures for robustness. Judged by Google Scholar citations, the KZ index is the most popular measure of financial constraints. It has its origins in an influential debate between Fazarri, Hubbard, and Petersen (FHP, 1988) and Kaplan and Zingales (1997). 

Augmenting Hayashi’s (1982) Q-investment model, FHP find a significant sensitivity of investment to cash flow in a sample of 422 firms over the period 1970 to 1984. Based on the finding that cash flow sensitivities are especially large among the 49 sample firms that pay no or low dividends, FHP conclude that significant cash flow sensitivities reflect empirically important financial constraints. Implicit in their argument is the assumption that low dividends are a useful indicator of financial constraints. Using a text-based approach that has proved popular, Kaplan and Zingales (1997) challenge FHP’s conclusions. They assess whether a firm is financially constrained by reading the 10-Ks (annual reports) of the 49 supposedly constrained low-dividend firms in the FHP sample. Based on their reading, only 15% of firm-years show evidence of firms being unable to fund their desired investments. Moreover, estimated cash flow sensitivities are greatest not among these arguably constrained firms but among the firms that, based on their 10-Ks, are the least constrained. 

The implications are that neither absence of dividends nor significant cash flow sensitivities are useful indicators of financial constraints. The actual KZ index is due to Lamont, Polk, and Saa-Requejo (2001). These authors estimate an ordered logit model relating the degree of financial constraints according to Kaplan and Zingales’ (1997) classification to five readily available accounting variables: cash flow, market value, debt, dividends, and cash holdings, each scaled by total assets. The model is estimated on the 49 firms in FHP’s sample and the estimated regression coefficients are used to construct an index. The resulting KZ index loads positively on market to book and leverage and negatively on cash flow, dividends, and cash. 

A higher index value suggests a firm that is more constrained. Subsequent authors have used Lamont, Polk, and Saa-Requejo’s coefficient estimates to create an index for samples other than FHP’s 49 firms, assuming implicitly that the coefficient estimates are stable across samples and over time. The convention in the literature is to classify, each year, the top tercile of firms as constrained and the bottom tercile as unconstrained. (Implicit in this approach is the assumption that the prevalence of financial constraints varies neither over time nor over the business cycle.) Hadlock and Pierce (2010) update Kaplan and Zingales’ (1997) text-based approach by 6 searching the 10-Ks of 356 randomly selected firms over the period 1995-2004 for evidence of firms identifying themselves as financially constrained.4 

They use this classification to create their own index of financial constraints, based on size (with a negative loading), size-squared (positive), and age (negative). As with the KZ index, subsequent users of the HP index proceed by applying Hadlock and Pierce’s coefficients to their own samples. Another popular approach is to treat firms without a credit rating as constrained.5 The empirical literature offers two main motivations for this. First, unrated firms are assumed to have no access to the public debt markets (Faulkender and Petersen (2006)) and so must borrow on less competitive terms from intermediaries such as banks. （essay代写)

Second, the rating process may reduce information asymmetries between the firm and investors, which implies that unrated firms are more opaque than rated firms and so more likely to be rationed by lenders (Whited (1992)). Whited and Wu (2006) follow a different approach. Their index is based on the coefficients obtained from a structural model. The index is effectively measured as the projection of the shadow price of raising equity capital onto the following variables: cash flow to assets (with a negative loading); a dummy capturing whether the firm pays a dividend (negative); long-term debt to total assets (positive); size (negative); sales growth (negative); and industry sales growth (positive). Rather than re-estimating the structural model on their own samples, users of the WW index then extrapolate out of sample using Whited and Wu’s reported coefficient estimates.6（essay代写)

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