Computational Aspects of Prospect Theory with Asset Pricing Applications

We develop an algorithm to compute asset allocations for Kahneman and Tversky's (1979) prospect theory. An application to benchmark data as in Fama and French (1992) shows that the equity premium puzzle is resolved for parameter values similar to those found in the laboratory experiments of Kahneman and Tversky (1979). While previous studies like Benartzi and Thaler (1995), Barberis, Huang, and Santos (2001), and Gruuml;une and Semmler (2005) only used myopic loss aversion to explain the equity premium puzzle our paper extends this explanation of the equity premium puzzle by incorporating changing risk aversion. Our extension allows reducing the degree of loss aversion from 2.353 to 2.25, which is the value found by Kahneman and Tversky (1979) while increasing the risk aversion from 1 to 0.894, which is a slightly higher value than the 0.88 found by Kahneman and Tversky (1979). The equivalence of these parameter settings is robust to incorporating the size and the value portfolios of Fama and French (1992). However, the optimal prospect theory portfolios found on this larger set of assets differ drastically from the optimal mean-variance portfolio