Investors Facing Risk: Loss Aversion and Wealth Allocation Between Risky and Risk-Free Assets

This paper studies the impact of loss aversion on decisions regarding the allocation of wealth between risky and risk-free assets. We use a Value-at-Risk portfolio model with endogenous desired risk levels that are individually determined in an extended prospect theory framework. This framework allows for the distinction between gains and losses with respect to a subjective reference point as in the original prospect theory, but also for the influence of past performance on the current perception of the risky portfolio value. We show how the portfolio evaluation frequency impacts investor decisions and attitudes when facing financial losses and analyze the role of past gains and losses in the current wealth allocation. The perceived portfolio value exhibits distinct evolutions in two frequency segments delimitated by what we consider to be the optimal evaluation horizon of one year. Our empirical results suggest that previous research relying on VaR underestimates the aversion of real individual investors to financial losses

Risk and Rationality: Uncovering Heterogeneity in Probability Distortion

It has long been recognized that there is considerable heterogeneity in individual risk taking behavior but little is known about the distributionnof risk taking types. We present a parsimonious characterization of risk taking behavior by estimating a finite mixture regression model for three different experimental data sets, two Swiss and one Chinese, over a large number of real gains and losses. We find two distinct types of individuals: In all three data sets, the choices of roughly 80% of the subjects exhibitnsignificant deviations from linear probability weighting, consistent with prospect theory. 20% of the subjects weight probabilities near linearly and behave essentially as expected value maximizers. Moreover, individuals are cleanly assigned to one type with probabilities close to unity. The reliability and robustness of our classification suggest using a mix of preference theories in applied economic modeling