Agent-based stock markets as bottom-up models of financial markets allow us to study the link between individual investor behavior and aggregate market phenomena, and as such are a useful tool for investigating the implications of behavioral finance and investor psychology. In this paper we want to disentangle between the effects of investor sentiment and investor overconfidence. While investor optimism or pessimism influences the expectations of future returns, overconfidence is related to the precision of those expectations and is modeled as miscalibration. In an artificial stock market based on the LLS model, we find that more optimistic investors create more pronounced booms and crashes in the market, when compared to the unbiased efficient market believers of the original model. In the case of extreme optimism, the optimistic investors end up dominating the market, while in the case of extreme pessimism, the market reduces to the benchmark model of rational informed investors. The overconfidence of investors is found to exacerbate the effects of investor sentiment.

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Keywords agent -based modeling, artificial stock market, behavioral finance, fuzzy aggregation, investor sentiment, optimism index, overconfidence
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Series ERIM Article Series (EAS)
Journal Human Systems Management
Spronk, J, Lovric, M, & Kaymak, U. (2010). Modeling investor sentiment and overconfidence in an agent-based stock market. Human Systems Management, 29(2), 89–101. doi:10.3233/HSM-2010-0718