The Impact of Large-stake Lotteries on Parameters in Prospect Theory via Maximum Likelihood Estimation

Abstract

This study investigates how the number of large-stake lotteries in a questionnaire affects parameter estimation in Prospect Theory (PT) and Cumulative Prospect Theory (CPT). Using binary-choice data from Japanese university students, parameters are estimated via maximum likelihood estimation combined with a genetic algorithm. Twelve models are constructed by combining alternative value functions and probability weighting functions. The results show that exponential value functions yield stable parameter estimates across different questionnaire designs, whereas power value functions are sensitive to the number of large-stake lotteries. Fewer large-stake lotteries lead to higher estimated risk aversion in gains and higher risk seeking in losses. These findings highlight the importance of functional form selection and questionnaire design in empirical applications of prospect theory.