Milan Lovric

Overconfident investors in the LLS agent-based artificial financial market

Agent-based artificial financial markets are bottom-up models of financial markets which explore the mapping from the micro level of individual investor behavior into the macro level of aggregate market phenomena. It has been recently recognized in the literature that such (agentbased) models are potentially a very suitable tool to generate or test various behavioral hypotheses. One of the psychological biases that received a lot of attention in financial studies, both mainstream and behavioral, is the phenomena of investor overconfidence. This paper studies overconfident investors in the agent-based artificial financial market based on the Levy, Levy, Solomon (2000) model. Overconfidence is modeled as miscalibration, i.e. as underestimated risk of expected returns. We find that overconfident investors create less frequent but more extreme bubbles and crashes when compared to the unbiased efficient market believers of the original model. When investors are modeled to exhibit a biased self-attribution, they quickly move to the state of high overconfidence and remain there. With an unbiased self-attribution, on the other hand, investor overconfidence varies greatly, but around a moderate level of overconfidence.

Modeling investor sentiment and overconfidence in an agent-based stock market

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.

Evaluating Off-Peak Pricing Strategies in Public Transportation with an Activity-Based Approach

Public transportation authorities across the world are implementing various peak and off-peak pricing strategies to manage travel demand and improve the overall system performance. In this study, an activity-based demand framework was used to evaluate two off-peak pricing strategies currently in use in Singapore. These strategies consisted of a free prepeak travel on mass rapid transit (MRT) and an off-peak discount for an integrated transit (public buses and MRT). Smart card data collected before and after the implementation of the first policy were used to calibrate the behavioral models involved, to capture travelers’ preferences and choices properly. To evaluate both pricing strategies, a comprehensive set of key performance indicators was considered and included the changes in peak ridership, average trip fare, operator’s revenue, the number of public transportation trips, and mode share. The results indicate that off-peak discount pricing strategies are a viable policy option for spreading demand peaks and that they are more effective during the afternoon peak period. This study also demonstrates the capabilities and the advantages of an agent-based modeling platform, SimMobility, as a tool for policy analysis.

Modeling loss aversion and biased self-attribution using a fuzzy aggregation operator

In this paper we use an agent-based stock market to study how investor performance and market predictions influence investor sentiment and confidence. Investor sentiment is modeled using a generalized average operator, which has been proposed in the fuzzy literature as an index of optimism. Our simulations show the impact of loss aversion on investor optimism, and the emergence of investor overconfidence through biased self-attribution. Computational models of financial markets show potential for studying the dynamics of investor psychology with respect to various market feedbacks, while the fuzzy aggregation operator used provides a convenient way of modeling those psychological effects.