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Review of Quantitative Finance and Accounting

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04-01-2021 | Original Research

Distress risk puzzle and analyst forecast optimism

Authors: K. C. Kenneth Chu, W. H. Sophia Zhai

Published in: Review of Quantitative Finance and Accounting | Issue 2/2021

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Abstract

A general consensus in the literature is that financial analysts make optimistic forecasts. That is, they tend to underreact to negative but overreact to positive information. In this study, we invoke this idea to provide an explanation for the distress risk puzzle, the phenomenon that high distress risk firms deliver anomalously low subsequent returns. We find that analysts underestimate the implication of the poor performance of higher distress risk firms, and thus make EPS and sales forecasts that are generally more optimistic than those for the lower distress risk firms. Because market respond to the analyst forecasts, investors initially overvalue the high distress risk firms; later on, when those firms report less than expected performance, analysts revise their forecasts downwards that in turn cause the high distress risk firms to earn low future returns composing of both immediate-forecast-revision responses and post-forecast-revision price drifts. We further document that (quarter) earnings announcements convey a substantial amount of information that roughly drives more than 60% of the analyst forecast revisions and 30% of the revision-related market responses.

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Note only financial analysts, there is evidence indicating that other professional market participants are making over-optimistic assessments for poor firms. For example, Cao et al. (2017) find that auditors seem to significantly underreact to payout decreases (i.e., negative signals) but react appropriately to payout increases (i.e., positive signals) in their going-concern decisions for financially distressed clients.

By using three independent distress risk measures for the analyses, our paper provides counterevidence for the query that the distress risk puzzle may be merely special case results due to the use of some distress risk measures such as the O-SCORE (e.g. Kim 2013).

As robustness check, we follow an anonymous reviewer’s suggestion to construct another default risk measure by the method in Campbell et al. (2008). We repeat the main analysis by the new risk measure and results are qualitatively the same. To save space, we do not include the results in the paper but are available upon request.

Another criticism of using O-SCORE as distress risk measure is that O-SCORE is highly correlated with the firm’s accruals (Kim 2013). In other words, the distress risk puzzle may be just a manifesto of the accrual anomaly (Sloan 1996) that motives our study to using also probability of default and credit rating as extra measures of distress risk to provide evidence that the distress risk puzzle is not simply equivalent to the accrual anomaly.

We also measure the size-adjusted CRET that show similar decreasing trends along the three sets of DR groups: from − 10.91 to − 26.57% for osgrp, from − 11.24 to − 15.65% for pdgrp, and from − 7.96 to − 24.81% for ratgrp.

We do not consider the forecast errors and revisions announced in the 11th month and 12th fiscal month because the number of observations in these 2 months normally decrease to less than 10% the observations of other fiscal months.

We assume analysts are basing on known (financial) information of the firm to make their forecasts, so we use the financial variables of the firms as of their last fiscal year-ends to construct their distress risk measures and other control variables.

A puzzling observation is that firm size has effectively no explanatory power to sales forecast errors, suggesting that size may have different implications to the EPS and sales forecast errors. We leave this question to future research.

We thank and follow an anonymous reviewer’s suggestion to include return skewness as a control for investors’ preference for returns (Conrad et al. 2014).

Teoh and Wong (2002) study the effect of analyst optimism on the performance of equity issuers. They find evidence that analysts are over optimistic in subsequent years for issuers reporting higher accruals, thus causing the underperformance of the issuers after the issue years. They fit analyst forecast errors by excess and expected accruals, and then regress the subsequent returns of issuers on the fitted errors and residuals. Our paper uses fitted forecast revisions as the explanatory variable so that we can observe directly how revisions of the biased forecasts drive the returns.

By including both EPS and sales forecast revisions as the control variables, we can see whether sales forecast revisions contribute to the price drifts as well. To the best of our knowledge, the post-sales-forecast-revision price drifts are not studied in prior literature.

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Title: Distress risk puzzle and analyst forecast optimism
Authors: K. C. Kenneth Chu
W. H. Sophia Zhai
Publication date: 04-01-2021
Publisher: Springer US
Published in: Review of Quantitative Finance and Accounting / Issue 2/2021
Print ISSN: 0924-865X
Electronic ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-020-00950-5

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