The most basic of asset pricing theories is that riskier assets should command higher expected returns. Clearly, financial distress is a risk characteristic, but it’s one that presents a dilemma, as there has not been a linear relationship between it and stock returns.
For example, John Birge and Yi Zhang, authors of the April 2017 study “Default Risk Premia and a Non-Linear Asset Pricing Model,” found that, when a company is not near ﬁnancial distress, investors require a return premium for holding extra default risk. However, when a ﬁrm is close to, or in, ﬁnancial distress, a negative relationship exists between default risk and return.
They also found that the weakest companies tend to be small—the relationship between risk and return in these weak-credit-risk stocks is so negative that when the 2.5% of the weakest firms are screened out, the size premium disappears entirely.
These results are consistent with what Pengjie Gao, Christopher Parsons and Jianfeng Shen found in the study “Global Relation Between Financial Distress and Equity Returns,” which was published in the January 2018 issue of The Review of Financial Studies. The study examined the distress risk anomaly—the tendency for stocks with high credit risk to perform poorly—among 38 countries over two decades (January 1992 through June 2013).
Their measure of credit risk was the complete Moody’s KMV database of monthly Expected Default Frequencies.
There is a strong, negative relationship between default probabilities and equity returns that is concentrated among low-capitalization stocks in developed countries in North America and Europe.
The returns of small, distressed firms were worse, by 35-50 basis points per month, and persist up through a year after portfolio formation. Importantly, these relative results are caused by the poor performance of distressed firms rather than the good performance of the firms with the least credit risk.
Risk-based explanations provide a poor account of the patterns—the smaller, riskier credits have higher betas, leverage and volatility.
Gao, Parsons and Shen found that the anomaly is concentrated among small, illiquid stocks, where limits to arbitrate can protect mispricings. They also found evidence pointing to a behavioral interpretation, suggesting stocks of companies in financial distress are temporarily overpriced. For example, they found the distress anomaly is concentrated in periods directly preceded by aggregate market gains fueling investor overconfidence.
Gao, Parsons and Shen’s findings are consistent with research showing that many anomalies exist only in high-sentiment regimes, when investor overconfidence is playing an important role.
Interestingly, the authors found the anomaly in which small-cap stocks with the weakest credits have the worst returns was not present in emerging markets—only two of 17 emerging markets they examined displayed the anomaly.
While the great debate about market efficiency—and whether markets are driven by risk-based explanations or behavioral ones—continues, the evidence suggests the answer isn’t black or white. There appears to be room in which both types of explanation play important roles.
In the case of default risk, we find that, for larger companies with default risk—but which are not yet in financial distress—credit risk is rewarded. However, for smaller firms—where the risk of default has become heightened and more imminent—the risk relationship breaks down, and we see a negative relationship between risk and future returns.
The explanation for the anomaly is that when the ﬁrm is exposed to high default risk, with an asset value close to or even below the company’s total obligations, equity holders have potential upside beneﬁts but little downside risk, because the call option gives them rights—but not obligations—to keep the remaining asset value. In effect, the asset becomes like a lottery ticket, a type of investment preferred by many individuals. Thus, we have an anomaly explained by the well-documented investor preference, or taste, for lottery tickets (assets with positively skewed distributions and fat tails).