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The authors study the explanatory power of investor rationality and irrationality for value and momentum portfolios. We also examine the relationships during financial crisis events, namely, the US subprime mortgage crisis (2007–2009) and the European debt crisis (2011–2013).

This study examines the influence of investors’ rationality and irrationality on the US stock market, using the multiple linear regression model and the stepwise regression model. Technically, the stepwise regression uses the machine-learning technique, with specific testing methods — forward selection, backward selection and stepwise selection — to find the best-fit model, according to Akaike’s Information Criterion (AIC). Thus, in this study, we will show the best model, as tested by the stepwise regression model.

Our empirical results contribute to the importance of reasons and emotions for stock-market returns and conclude that rationality and irrationality simultaneously explain the value and momentum portfolios, as well as the ETF portfolios. Also, the rational and irrational explanatory powers differ, depending on portfolios and different periods. Rational factors usually explain the volatility of the return to a greater extent than irrational factors. Moreover, during a financial crisis, the irrational factors remarkably increase their importance in explaining returns, especially for the ETF portfolios.

We expect this study’s contribution will show not only academic contribution but also benefit many stakeholders in the financial market. Investors and traders can identify various irrational factors of trading — for example, taking a long position during the panic in the market following the indicators in the models. Managers also reconsider the cost of the company by adding irrational factors when computing the equity’s expected return. Similarly, stock exchanges can adequately adjust their circuit breaker during a pessimistic-investor period. Finally, regulators can evaluate a complete picture of the stock market by adding irrational factors into their considerations.

Our study explores friction costs in terms of competition and market structure, considering factors such as market share, industry leverage levels, industry hedging levels, number of peers, and the geographic concentration that influences reinsurance purchase in the Property and Casualty insurance industry in China. Financial factors that influence the hedging level are also included. The data are hand collected from 2008 to 2015 from the Chinese Insurance Yearbook. Using panel data analysis techniques, the results are interesting. The capital structure shows a significant negative relationship with the hedging level. Group has a negative relationship with reinsurance purchases. Assets exhibit a negative relationship with hedging levels. The hedging level has a negative relation with the individual hedging level. Insurers have less incentive to hedge because it provides less resource than leverage. The study also robustly investigates the strategic risk management separately by the financial crises.