Reference dependence, loss aversion, and risk seeking for losses together comprise the preference-based component of prospect theory that sets its value function apart from the standard risk-aversion model. Using an elasticity analysis, we show that this distinctive preference component serves to underpin negative-feedback trading propensities, but cannot manifest itself in behavior directly or holistically at the individual-choice level. We then propose and demonstrate that the market interaction between prospect-theory investors and regular CRRA investors allows this preference component to dominate in equilibrium behavior and hence helps to reestablish the intuitive link between prospect-theory preferences and negative-feedback trading patterns. In the model, the interaction also reconciles the contrarian behavior of prospect-theory investors with asymmetric volatility and short-term return reversal. The results suggest that prospect-theory preferences can lead investors to behave endogenously as contrarian noise traders in the market interaction process.

We examine the effects of implementing a U.S. approach to the enforcement of mandatory disclosure in China. Using a hand-collected sample of comment letters (CLs) issued by the Shanghai Stock Exchange over the period of 2013 to 2018, we show that stock price reactions to CL receipts and replies are negative and significant. Using textual analysis to match issues raised by regulators to targeted firms’ changes in disclosure, we show that these firms do address CL issues point by point, but do not experience significant improvements in their information environments. Our article highlights the importance of incentives rather than regulation/enforcement in reducing information asymmetry.

As the skewed return distribution is a prominent feature in nonlinear portfolio selection problems which involve derivative assets with nonlinear payoff structures, Value-at-Risk (VaR) is particularly suitable to serve as a risk measure in nonlinear portfolio selection. Unfortunately, the nonlinear portfolio selection formulation using VaR risk measure is in general a computationally intractable optimization problem. We investigate in this paper nonlinear portfolio selection models using approximate parametric Value-at-Risk. More specifically, we use first-order and second-order approximations of VaR for constructing portfolio selection models, and show that the portfolio selection models based on Delta-only, Delta–Gamma-normal and worst-case Delta–Gamma VaR approximations can be reformulated as second-order cone programs, which are polynomially solvable using interior-point methods. Our simulation and empirical results suggest that the model using Delta–Gamma-normal VaR approximation performs the best in terms of a balance between approximation accuracy and computational efficiency.

We develop a continuous-time asset allocation model to investigate the effects of mean-reverting stock returns on investors with Prospect Theory (PT) preferences. Our semi-analytical solution facilitates a comprehensive exploration of how the stock investment of PT investors may differ when accounting for mean reversion. We find that incorporating mean reversion attenuates the distinct V-shaped demand pattern in relation to contemporaneous prices, which is more pronounced when mean reversion is absent, by significantly reducing PT investors’ stock demand following price increases. This shift leads to a stock demand profile that demonstrates an inverse relationship with stock prices. In line with this change, we also show that combining PT utility with mean reversion predicts short-term contrarian behavior and the disposition effect more reliably than benchmark models that incorporate either PT utility or mean-reverting returns alone.