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Re-Exploring the Existence of Arbitrage Opportunity with an Agent-based Artificial Stock Market

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  • Ya-Chi Huang

    (Department of International Business, Lunghwa University of Science and Technology, Taiwan)

Abstract

A number of theoretical studies have explained the existence of arbitrage opportunity with so called ¡§noise traders¡¨ (e.g., De Long et al., 1990; Shleifer and Vishny, 1997; Abreu and Brunnermeier, 2002). In fact, noise traders can create the arbitrage opportunity because they are quite influential in these papers. However, by taking endowments into consideration, the large numbers of noise traders in real markets are not necessarily sufficiently influential. Moreover, theoretical papers assume that investors, apart from the noise traders, have perfect foresight, which is also far away from the situation in real financial markets. By relaxing the strong assumptions of a representative agent and perfect foresight in economics, this paper unravels the puzzle of the existence of arbitrage opportunity based on an agent-based artificial stock market.

Suggested Citation

  • Ya-Chi Huang, 2014. "Re-Exploring the Existence of Arbitrage Opportunity with an Agent-based Artificial Stock Market," Journal of Economics and Management, College of Business, Feng Chia University, Taiwan, vol. 10(2), pages 157-180, July.
    • Handle: RePEc:jec:journl:v:10:y:2014:i:2:p:157-180
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    More about this item

    Keywords

    noise traders; arbitrage opportunity; Agent-Based Computationa Modeling; Santa Fe Artificial Stock Market; Genetic Algorithms;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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