In the Path of the Storm: Does Distress Risk Cause Industrial Firms to Risk-Shift?*

Abstract

We study whether industrial firms risk-shift in response to distress risk increases induced through hurricane strikes. Using new proxies capturing deliberate managerial decisions about the risk of a firm’s operating segment portfolio, differences tests suggest that hurricane strikes prompt moderately, but not highly, distressed firms to skew their asset mixes toward riskier segments by shutting down low-risk, high-average-Q segments. In turn, the moderately distressed firms observe abnormally high failure rates after a hurricane strike. Employing covenant violation data, we offer further evidence that creditor control prevents highly distressed firms from raising their risk. Our conclusions extend those of other studies by suggesting that moderate distress risk levels can lead the managers of industrial firms to not only engage in risk-taking, but, in fact, in risk-shifting.

CEO Compensation Incentives and Playing It Safe: Evidence from FAS 123R

“…To overcome these problems, we follow Aretz et al (2019) and construct an imputed managerial risk-taking proxy as the standard deviation of the portfolio stock returns based on the industries in which a firm operates. 7 As industry volatility is less influenced by firmspecific information and disclosure practices, the imputed proxy better reflects managerial decisions to alter firm risks through changing its operating portfolio.…”

“…rate information (Baik, Farber & Lee, 2011). These managerial behavioral biases and selective disclosure increases the volatility of stock returns (e.g., Jiang, Xu & Yao, 2009, Rajgopal & Venkatachalam, 2011 The approach in Aretz et al (2019) builds on Armstrong & Vashishtha (2012), with several important differences which influenced our choice in favour of the former method. First, Aretz et al (2019) uses a finer definition of industries at the four-, rather than two-digit SIC level used in Armstrong & Vashishtha (2012).…”

“…These managerial behavioral biases and selective disclosure increases the volatility of stock returns (e.g., Jiang, Xu & Yao, 2009, Rajgopal & Venkatachalam, 2011 The approach in Aretz et al (2019) builds on Armstrong & Vashishtha (2012), with several important differences which influenced our choice in favour of the former method. First, Aretz et al (2019) uses a finer definition of industries at the four-, rather than two-digit SIC level used in Armstrong & Vashishtha (2012). Second, firm portfolio volatilities are computed based on weekly returns over the past year in Aretz et al (2019) and based on monthly returns over the past five years in Armstrong & Vashishtha (2012).…”

“…First, Aretz et al (2019) uses a finer definition of industries at the four-, rather than two-digit SIC level used in Armstrong & Vashishtha (2012). Second, firm portfolio volatilities are computed based on weekly returns over the past year in Aretz et al (2019) and based on monthly returns over the past five years in Armstrong & Vashishtha (2012). Thus, the former approach captures the most recent managerial decisions to adjust its operating structure and hence, risk.…”

“…Moreover, using volatility measure computed over a year also relieves the concern of a high skewness of risk measures computed over long periods. Hence, the risk proxy in Aretz et al (2019) is likely to capture the underlying firm risk more timely and accurately.…”