Measuring the extent of Financial Constraints--论文代写范文精选

对于时间序列和横截面,例如负收益,市场价值等。相比之下,金融约束措施基于会计特征，没有控制变量的存在。能力指数,股价指数和流动性指数，来预测事件的结果是一致的。下面的paper代写范文进行一定的论述。

Abstract 
  Although some widely used financial constraint indexes would imply smooth sailing for the New York Times as of 2008, the high frequency of constraining words in the text foreshadowed its uncertain future. Textual analysis, as a variable added to the traditional mix of finance variables that might be used to gauge the level of financial constraints, has the potential to identify inflection points not captured by variables like firm market capitalization or age. We show that the constraining tone of 10-K documents is a measure of financial constraints distinct from measures based on accounting characteristics. 

  Further, the percent of constraining words, unlike the SA and WW indexes, has a low correlation with market capitalization. When we turn our attention to the ability of various measures of financial constraints to predict events related to the deterioration or improvement in external financing conditions, we find that a more frequent usage of constraining words is strongly related to a higher likelihood of future dividend omission (+10.32%), increases (-6.46%), equity recycling (-23.24%), and underfunded pensions (+2.34%).2 The results are stronger in the cross-section than in the time-series and are also robust to inclusion of firm characteristics, e.g., market value, book-to-market, negative earnings dummy, and past performance. 

  In contrast, measures of financial constraints based on accounting characteristics (KZ index, SA index, and WW index) have limited success in predicting the liquidity events even without the presence of control variables. The inability of the KZ index, SA index, and WW index to predict liquidity events is consistent with the findings of Farre-Mensa and Ljungqvist (2015). The two authors present strong evidence that the three commonly used indexes do a poor job of identifying firms that are plausibly considered financially constrained. Surprisingly, Farre-Mensa and Ljungqvist (2015) find that ‘constrained’ firms identified by the three indexes are able to raise debt when it is in their best interest, continue to obtain bank borrowing after a negative shock to the supply of local bank loans, and even engage in equity recycling. Thus, these ‘financially constrained’ firms do not appear to face inelastic capital supply curves as would be suggested by their index values. Financial constraints can be thought of as a two-tail phenomenon, with some firms facing constraints due to deterioration in their cash flows, while others are unable to finance extraordinary growth. None of our tests directly identify firms that are growing, but at a slower rate than the firm desires, due to the high cost of external capital. That is, we cannot accurately measure how the inability to access reasonably priced external capital constrains a firm’s ability to invest in positive NPV projects.

  Our analysis differs from earlier work on the use of qualitative information to gauge financial constraints along four key dimensions. First, our measure of financial constraints – percentage of constraining words in the 10-K – is objective. That is, we do not assign the financial constraint scores by actually reading the document, but rely on the output of the pre-specified automated parsing algorithm. Since we use the constraining word list, there is no need to read the 10-K to make subjective decisions on whether a particular sentence hints that a firm might be financially constrained. In this way, our measure is not affected by potential misinterpretations or inconsistencies of the classifier. This procedure also makes our measure easier to replicate since we provide our entire constraining word list for other researchers to use. Second, manual categorization, used in prior research, is extremely time consuming which imposes limits on the sample size of the analyzed firms. KZ had a sample of only 49 lowdividend paying manufacturing firms while HP used a random sample of 356 unique firms (1,848 firm-year observations in total). In contrast, in our analysis we use the entire sample of publicly-traded 10-K filers.

  Third, both KZ and HP relied on the notion that disclosure rules force firms to reveal financial constraints, which would require them to be explicit about difficulties in obtaining financing. However, as Fazzari, Hubbard, and Petersen (2000) point out, “Regulation S-K requires the firm to reveal the inability to invest due to financial constraints only when the firm fails to act on a previously announced investment commitment.” As we demonstrate, our less restrictive approach of considering a broad range of constraining words appears to be better at capturing qualitative information about financial constraints. 

  Fourth, our approach is fundamentally different in how we use qualitative information to gauge financial constraints. KZ and HP use qualitative information to rank subsamples of firms according to their financial constraints status, with their subsequent measures based on accounting characteristics used to explain these rankings. In contrast, by quantifying the language of 10-Ks and using financial events to identify constrained firms, we treat qualitative information as a measure of financial constraints in its own right. In a paper complementary to ours, Hoberg and Maksimovic (2014) also use textual analysis of 10-Ks to identify financially constrained firms. The most similar construct to ours is their measure of delayed investment where they search for words like delay, abandon, eliminate, or postpone within 12 words of investment-type words like construction or expansion. Unlike our paper which parses the entire 10-K, they focus this word search within the Liquidity and Capitalization Resource Subsection [CAP+LIQ] in the Management Discussion and Analysis (MD&A) section. 3 The authors report that only 5.5% of their sample use delay-type and investment-type words in close proximity to each other.(paper代写)

  Due to concerns that firms might specifically avoid using delay and related synonyms close to expansion, Hoberg and Maksimovic create a delayed investment score to measure how similar the CAP+LIQ subsection of firms which mention postponing projects is to other firms. They use the methodology of Hanley and Hoberg (2010) to gauge similarity of text between firms. The Hoberg and Maksimovic (2014) paper takes a completely different approach than ours in using 10-K text to identify financially constrained firms. Hoberg and Maksimovic (2014) specifically link words like delay and construction in a 10-K subsection with being constrained while we attempt to measure the level of constraints by the frequency of constraining words within the entire 10-K.4 We believe the tone of managers’ words captures subtle signs that the company will face greater future financial challenges. As shown by numerous papers starting with Antweiler and Frank (2004), Tetlock (2007), and Tetlock, Saar‐Tsechansky, and Macskassy (2008), document text often contains important information for investors. The remainder of the paper is organized as follows. Section II introduces the data and variables. Section III reports empirical results. A brief conclusion follows.(paper代写)

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