Stock Vulnerability and Resilience
提出一种统计方法，利用因子属性预测个股在市场下跌中的脆弱性或韧性，该方法允许因子影响非线性变化，并在五次最大市场下跌中验证了预测效果。
The authors propose a parsimonious yet flexible statistical method for predicting the relative vulnerability or resilience of individual stocks to market drawdowns. The authors’ approach compares a stock’s unique circumstances—as reflected in popular factor attributes—to the circumstances of stocks that have proven vulnerable or resilient to previous market drawdowns. Unlike other approaches, the authors’ method allows the influence of each factor attribute to vary across stocks in a nonlinear, conditional way. The authors test their explicit method for predicting stock vulnerability and resilience out of sample using the five largest market drawdowns since the global financial crisis. The nonlinear composite scores the authors derive are reliably better predictors of cross-sectional return than any of the individual factor attributes or an ex post linear combination of factor attributes.