Purpose The purpose of this paper is to investigate if the volatility of stock prices in the days surrounding the Chapter 11 bankruptcy process predicts a firm’s likelihood to successfully restructure and emerge from bankruptcy. Design/methodology/approach The authors use a sample of Chapter 11 cases between 1980 and 2016 that have available stock price data surrounding the bankruptcy filing dates. Following Goyal and Wang (2013), the KMV–Merton model is utilized to estimate the probability that a firm successfully emerges from its restructuring process. In order to interpret the market’s assessment about a firm, the authors use the analogy of a European call option to derive the assessment of the firm’s prospects as the probability that it will emerge from bankruptcy. This estimated probability of emergence is compared to actual outcomes of bankruptcy cases and tested for significance using various regression techniques. Findings This study exploits the information found in stock prices surrounding the bankruptcy process and finds that volatility after, but not before, filing for bankruptcy significantly predicts a firm’s likelihood to emerge. In addition, the market-based probability of emergence has better predictive power on the recovery rates of unsecured creditors than measures based on financial statements. Originality/value Predictors of bankruptcy have been extensively studied by scholars over the decades, with early studies focusing on accounting-based measures and recent studies incorporating market-driven variables. However, in recent years, studies have begun to assess bankrupt firms’ ability to reorganize and successfully emerge from bankruptcy. This study contributes to the recent literature investigating market-based predictors of successful emergence.

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Firms seeking to apply hedge accounting treatment under the Accounting Standards Codification Topic 815 must demonstrate higher hedge effectiveness, for which the regression analysis is commonly used as a testing method. An autoregressive distributed lag (ARDL) model is adopted in this article to examine the hedge effectiveness in the presence of a long-run cointegrating relationship between spot and futures prices while spot contracts are traded far less frequently. Using precious metal market data, our study empirically demonstrates that a hedge ratio estimated with a conventional OLS model tends to be downwardly biased. It is also shown that whether this omitted-variable bias is observable depends on the liquidity in a futures market.