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«Using 10-K Text to Gauge Financial Constraints Andriy Bodnaruk Mendoza College of Business University of Notre Dame Notre Dame, IN 46556-5646 ...»

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Using 10-K Text to Gauge Financial Constraints

Andriy Bodnaruk

Mendoza College of Business

University of Notre Dame

Notre Dame, IN 46556-5646

574.631.4597 voice


Tim Loughran

Mendoza College of Business

University of Notre Dame

Notre Dame, IN 46556-5646

574.631.8432 voice


Bill McDonald

Mendoza College of Business

University of Notre Dame

Notre Dame, IN 46556-5646

574.631.5137 voice


August 25, 2015

Key words: Financial constraints; textual analysis; dividend omissions; equity recycling;

underfunded pensions.

JEL Classifications: G31, G32, D92.

We thank Matt Cain, Andreas Chouliaras, Mara Faccio, Paul Gao, Jarrad Harford (the editor), Chris Henderson, Gerard Hoberg, Fred Mittelstaedt, an anonymous referee, and seminar participants at the 2014 FMA Europe Conference, University of Lugano, University of Mannheim, University of Nebraska–Lincoln, and Emory University for helpful comments.

Using 10-K Text to Gauge Financial Constraints Abstract Measuring the extent to which a firm is financially constrained is critical in assessing capital structure. Extant measures of financial constraints focus on macro firm characteristics such as age and size – variables highly correlated with other firm attributes. We parse 10-K disclosures filed with the SEC using a unique lexicon based on constraining words. We find that the frequency of constraining words exhibits very low correlation with traditional measures of financial constraints and predicts subsequent liquidity events—like dividend omissions or increases, equity recycling, and underfunded pensions—better than widely-used financial constraint indexes.

I. Introduction Miller (1988), in a retrospective look at the Modigliani-Miller propositions, emphasizes that the complement of “irrelevance” is most important, stating that “showing what doesn’t matter can also show, by implication, what does.” Thus, as surmised by Hennessy and Whited (2007), the relevance of corporate finance is, to a great extent, determined by financing frictions. The nature and substance of market frictions has been considered at length (see, for example, Battacharya (1979), Townsend (1979), Myers and Majluf (1984)). Whether identified market imperfections are of first or second order importance in financial decisions is an empirical question that relies critically on the ability to identify financially constrained firms — firms for which there is a wedge between the internal and external costs of funds.

Numerous methods for measuring financial constraints have been proposed. While most of them assign firms a financial constraint status based purely on a firm’s accounting variables, two important papers, Kaplan and Zingales (1997) (hereafter KZ) and Hadlock and Pierce (2010) (hereafter HP) also incorporate textual disclosures in construction of their measures. KZ and HP examine 10-K text to identify cases where managers discuss difficulties in obtaining external financing, liquidity problems, or forced reduction in investment and subjectively classify firms by financial constraint status on the basis of the number and severity of the disclosed constraints.

They then use accounting characteristics to predict where the firm will fall within their classification.

In manually reading the 10-K text for constraining tone, KZ and HP do not list all of the specific words used to identify the constraining sentences. Due to the time intensive nature of their method, their analyses were limited to relatively small samples of firms. With advances in textual analysis, why not have computers parse the text for constraining tone? This would greatly

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empirical results. The missing piece for researchers is a list of constraining words.

The main contribution of our paper is the creation of a constraining word list to assist other researchers in identifying whether or not a firm is financially constrained. The list contains 184 constraining words. Like the Loughran and McDonald (2011) creation of six word lists (negative, positive, uncertain, litigious, strong modal, and weak modal), we examine tens of thousands of words that appear in at least 5% of all 10-K filings. We only select words which would be most likely considered constraining in the majority of occurrences.

Specifically, we parse 10-K disclosures filed with the Securities and Exchange Commission (SEC) to measure a document’s tone as indicated by the percentage of constraining words.

Commonly used constraining words from our list are required, obligations, requirements, permitted, comply, and imposed. Our conjecture is that managers anticipating financial challenges will use a more constraining tone in 10-K filings to communicate their concerns to shareholders, thereby lowering their exposure to subsequent litigation. In the context of IPOs, Hanley and Hoberg (2012) find that strong disclosure in the IPO prospectus lowers the probability of being sued. Clearly, part of the use of constraining words by managers is with an eye towards lowering litigation exposure.1 Our paper thus expands on KZ and HP’s approach of using qualitative information to gauge firms’ financial constraints. Whereas KZ and HP use qualitative analysis of a firm’s disclosures as an intermediate step in deriving accounting based indexes of financial constraints, we use qualitative information to directly construct a measure of financial constraints and use the


1 As might be expected, there is a positive correction (0.386) between the frequency of our constraining words in the 10-K text and the frequency of litigious words from the Loughran and McDonald (2011) word list.

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improvement of external financing conditions, events which we label “liquidity events.” Our four ex post liquidity events include dividend omissions, dividend increases, equity recycling (i.e., paying out equity proceeds to shareholders in the form of share buybacks and dividends), and underfunded pension plans. Note that the events include instances of being financially constrained (e.g., dividend omissions) and cases identifying firms that are clearly less constrained (e.g., equity recycling). Being financially constrained typically does not have definitive endpoints. Thus, we are simply trying to capture the likelihood of being in a particular state over a reasonable time frame.

Our choice of liquidity events is deeply rooted in the financial constraints literature. Starting with Fazzari et al. (1988) and Kaplan and Zingales (1997), the literature argues that firms would pay out dividends only when their internally generated funds exceed their investment needs.

Indeed, Campello, Graham, and Harvey (2010), surveying corporate CFOs, found that during the recent financial crisis, constrained firms in the U.S. planned to drastically reduce or eliminate dividend payments whereas unconstrained firms did not. Firms with high levels of cash dividends or share repurchases relative to equity issuance are unlikely to be financially constrained. Finally, Rauh (2006) notes that capital expenditures decrease when a firm has to make mandatory contributions for its defined pension benefit plan.

HP (2010) use a combination of total assets and firm age to measure financial constraints.

Whited and Wu (2006) (hereafter WW) create a six component index; two of WW’s components, total assets and dividend dummy, are directly linked with larger and older firms.

Thus for both indexes, large and old firms have a lower likelihood of being financially

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firms can quickly become financially constrained.

As an example, consider the New York Times. As of June 2008, the New York Times had a large market value (over $2 billion), total assets of $3.5 billion, positive trailing cash flows, and was relatively old. As a result, it had extremely low values for some of the traditionally used indexes of financial constraints (i.e., the firm would not be identified as financially constrained).

Yet, within 12 months, the New York Times completely eliminated its dividends, did not engage in equity recycling, experienced a 63% raw decline in its stock price, and continued to have an underfunded employee pension plan.

Interestingly, the Times’ 10-K filed on February 26, 2008 contained 1.05% constraining words (which put it in the top 1.5 percentile of all firms in that year). This was the firm’s highest constraining word percentage of any year in our sample. Its high constraining count was caused by discussions in the New York Times 10-K concerning all the debt, legal, employee, and environmental constraints facing the firm. For example, the company notes that 47% of its workers were unionized (“As a result, we are required to negotiate the wages, salaries, benefits, staffing levels…”); the document also includes discussions about credit agencies (“To maintain our investment-grade ratings, the credit rating agencies require us to meet certain financial performance ratios”); a mandatory contract with a major paper supplier (“The contract requires us to purchase annually the lesser of a fixed number of tons…”); obligations (“The Company would have to perform the obligations of the National Edition printers under the equipment and debt guarantees if the National Edition printers defaulted under the terms of their equipment leases or debt agreements”); and underfunded defined benefit pension plans (“As of December

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unfunded status of our postretirement plans”) (constraining words are in italics).

So 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


2  All economic effects are estimated as marginal differences in the dependent variable (divided by the sample mean) related to a one standard deviation increase in the percentage of constraining words. 

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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 low

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(1,848 firm-year observations in total). In contrast, in our analysis we use the entire sample of publicly-traded 10-K filers.

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