Are Financial Constraints Priced? Evidence from Textual Analysis

Matthias Buehlmaier is Principal Lecturer in Finance at the University of Hong Kong, and Toni M. Whited is Dale L. Dykema Professor of Business Administration at the University of Michigan. This post is based on their recent article, forthcoming in the Review of Financial Studies.

In our paper, Are Financial Constraints Priced? Evidence from Textual Analysis, forthcoming in the Review of Financial Studies, we develop a new measure of financial constraints based on the narrative portions of company annual reports and use this measure to revisit the question of whether financial constraints affect stock returns. Financial constraints arise from frictions such as information asymmetries that make external funds more costly than internal funds, sometimes prohibitively so. An example of a financially constrained firm is a rapidly growing company that has good investment projects but faces difficulties obtaining all of the necessary outside financing to fund its growth. Although financial constraints are easy to understand on this conceptual level, it remains an empirical challenge to quantify them and thus to understand their implications. For example, the academic literature has produced many measures of financial constraints based on accounting data, but these measures are likely noisy, as accounting statements contain no direct information on potential investment projects or desired financing needs.

In contrast, our analysis looks for financial constraints in firms’ annual reports because this is where they are directly discussed. Our actual analysis of the text of these reports is intuitive. First, we use either machine or human reading of the annual reports to isolate cases in which a firm appears to be financially constrained or unconstrained. This step produces what we call a training sample. Second, using this sample, we estimate the probability that a firm is constrained or unconstrained as a function of the words in the annual report. Third, we use this fitted probability model to predict the financial constraints status for our entire sample of U.S. firms.

This approach allows us to build upon the idea that financial constraints are not one-dimensional, as a company might face constraints when securing one type of external finance but not another. We quantify this richness by including firms that face different kinds of financial market frictions when we construct our training samples. We end up with three distinct measures of financial constraints, each reflecting a different source of financial frictions. The first is a general measure that is not specific about the source of constraints, the second captures equity issuance frictions, and the third captures debt issuance frictions.

We find that all three measures do a good job of capturing firm characteristics typically associated with financial constraints. For example, constrained firms are small, have low cash flow, and pay out fewer dividends. Moreover, our measures appear to capture characteristics that differ from other widely used measures. This result makes sense, inasmuch textual analysis is fundamentally different from analyzing accounting data.

After these initial sanity checks, we investigate the relation of our financial constraints measures to stock returns. To this end, we build stock portfolios by sorting stocks on our financial constraints measures. For all measures, we find that returns are higher for financially constrained firms. This result indicates that investors need compensation for taking on financial risk. Moreover, we show that firms constrained in debt markets have the highest stock returns. To bolster these findings, we regress these the returns of portfolios of financially constrained firms on well-known risk factors. Even when we control for risk, returns increase with financial constraints.

We examine next whether this risk premium is concentrated only in small stocks. This issue is a concern because small stocks can be illiquid and difficult to trade. However, we find the opposite, with the largest returns in the portfolios of constrained large-cap and constrained mid-cap stocks, but not in portfolios of constrained small-cap stocks. Thus, illiquid stocks do not drive our results, so trading strategies that implement our results should not be prohibitively costly to construct. In this regard, we also find that the portfolio returns do not decrease meaningfully if we explicitly account for trading costs.

To investigate financial constraints further, we construct a zero-cost financial constraints factor portfolio. We then average out size quantiles to ensure we are detecting variation in financial constraints and not size. Regressing this portfolio on the Fama-French five factors yields an annualized risk-adjusted return of 7.2% for our debt-based financial constraints measure for the top market capitalization percentile.

Of all three measures, the constraints measure for debt appears to be most important for financial constraints risk. The annualized risk-adjusted excess stock returns for a zero-cost arbitrage portfolio are 6.5% for the debt measure, 3.7% for the general constraints measure, and 3.0% for the equity measure. This finding implies that the equity market is not overly concerned about a firm’s capacity to raise money via equity issuance and instead prices a firm’s ability to raise money in debt markets. This finding is also one of our main contributions above and beyond the extant research on the risk from financial constraints, which does not distinguish between frictions in markets for different securities. One important exception is evident in portfolios of small firms, where equity-constraints risk matters. This result is consistent with the notion that tapping equity markets is important for publicly listed small firms who had an IPO or SEO in the not so distant past.

Our result that debt financial constraints matter for stock returns makes intuitive sense for two reasons. First, it is well known that equity issuances are rare events. Second, the least risky firms are those that can issue debt easily in times when equity issuance is costly. These firms have lower returns because they use this financial flexibility to smooth their real investment policies, thereby lowering systematic risk.

In the end, our ability to find more definitive and richer results on the relation between financial constraints and returns can be attributed to using better measures of financial constraints. These measures are essentially based on new data, where these data are words. As finance is a highly data-driven field, using textual analysis promises to enrich many different areas of inquiry. Moreover, using textual analysis in combination with traditional analysis of accounting variables may also prove useful for the measurement of difficult-to-observe corporate characteristics.

The complete article is available here.

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