Agent-based modeling bridges theory of behavioral finance and financial markets

Hiroshi Takahashi, Takao Terano

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

This chapter describes advances of agent-based models to financial market analyses based on our recent research. We have developed several agent-based models to analyze microscopic and macroscopic links between investor behaviors and price fluctuations in a financial market. The models are characterized by the methodology that analyzes the relations among micro-level decision making rules of the agents and macro-level social behaviors via computer simulations. In this chapter, we report the outline of recent results of our analysis. From the extensive analyses, we have found that (1) investors' overconfidence behaviors plays various roles in a financial market, (2) overconfident investors emerge in a bottom-up fashion in the market, (3) they contribute to the efficient trades in the market, which adequately reflects fundamental values, (4) the passive investment strategy is valid in a realistic efficient market, however, it could have bad influences such as instability of market and inadequate asset pricing deviations, and (5) under certain assumptions, the passive investment strategy and active investment strategy could coexist in a financial market.

Original languageEnglish
Title of host publicationMulti-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies
Subtitle of host publicationIntelligent Techniques for Ubiquity and Optimization
PublisherIGI Global
Pages134-154
Number of pages21
ISBN (Print)9781605668987
DOIs
Publication statusPublished - 2010 Dec 1

ASJC Scopus subject areas

  • Computer Science(all)

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    Takahashi, H., & Terano, T. (2010). Agent-based modeling bridges theory of behavioral finance and financial markets. In Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization (pp. 134-154). IGI Global. https://doi.org/10.4018/978-1-60566-898-7.ch008