Behavioral economics and its applications in finance

Traditional theories in economics state that people make their decisions in order to maximize their utility function and all the relevant constraints and preferences are included and weighted appropriately. In other words, in standard models, it is usually assumed that decision makers are fully rational. However, some studies in behavioral economics and finance suggest that individuals deviate from standard models. Behavioral economic models try to make standard models more realistic by modifying these assumptions. This thesis focuses on some applications of behavioral economics in three chapters. Chapter 1 focuses on individuals’ deviations from standard preferences. Based on standard models, individuals have the same preferences about future plans at different points in time and the discounting factor between any two time periods is independent of when utility is evaluated. However, robust laboratory experiments show choice reversal behavior in humans and animals. The aim of chapter 1 is to find an approach for measuring the decision makers’ awareness of choice reversal by analyzing demand for commitment. We use the data from an experimental study by Casari (2009) to measure the awareness of the selfcontrol problem. Also, the welfare implications of introducing a commitment device are studied in this chapter. The results show that decision makers are partially aware of their self-control problems. Moreover, introducing a costless commitment device can increase the total welfare of the studied population. This increase depends on individuals’ awareness of future choice reversal. The aim of chapter 2 is to analyze stock price movements as a result of fundamental or technical shocks under a heterogeneous agents model (HAM). In this study, it is assumed that the market involves heterogeneous agents that have different rules for trading and that prices are endogenously determined through interactions between these agents. I use the numerical simulation method to examine changes in the prices as the result of fundamental shocks. The result of this chapter indicates that increasing heterogeneity in technical trading strategies could lead to more price oscillations, which is consistent with the excess volatility in stock prices. The aim of chapter 3 is to predict stock price movements under a new HAM. I use the HAM framework proposed in the previous chapter. The value added by this chapter is estimating stock prices in a heterogeneous agent environment where chartists use different moving average trading strategies. I use monthly data from S&P 500 from 1990 until 2012 and discuss the forecasting ability of the model. The results of this chapter show that the presented model has a better one-step ahead, out-of-sample forecasting power compared with Boswijk et al. (2007) and Chiarella et al. (2012)