Meeting Minutes - 11 March 2009

At this meeting Milan presented his paper "Overconfident Investors in the LLS Agent-Based Artificial Financial Market," as a preparatory talk for the upcoming IEEE SSCI CIFEr 2009 conference.

This paper is a part of the relatively new research stream where agent-based models of financial markets are used to study various topics of behavioral finance. It has been recognized in the literature that such models could be very suitable to make a link between the behavioral biases of individual investors and the aggregate market phenomena, such as the dynamics of the market prices.

In this paper we focus on overconfident investors, and model overconfidence as miscalibration, in such a way that investors are too certain in their predictions of future returns of a risky asset. In the methodological sense, this paper follows an incremental approach, where an existing model (Levy, Levy, Solomon, 2000) is modified to study the consequences of introducing a variation in investor behavior, namely the overconfidence bias.

We find that overconfident investors create less frequent, but more extreme bubbles and crashes in the market, compared to the original model. Furthermore, more overconfident investor introduce more excess volatility of the market price (over the volatility of the fundamental price), and also reduce the trading volume (as they are highly invested in stock during bubbles). Since this is a rising market, overconfident investors tend to take a larger share in the total wealth of all the market participants.

Furthermore, we study the emergence of overconfidence through biased self-attribution. Investors, who attribute successful predictions to their own skill and unsuccessful predictions to bad luck, learn quickly to be overconfident, and remain at such a high level of overconfidence. For unbiased self-attribution, the level of overconfidence varies greatly depending on the success of predictions.

During the meeting we had a fruitful discussion about the implications of these results, and there were also very interesting suggestions for the future research. In the light of the recent developments of the real-world financial markets, it would be particularly interesting to study the consequences of investor overconfidence in a declining market.

Meeting Minutes - 14 April, 2008

During this meeting Katalin has summarized a working paper on a taxonomy proposed for artificial stock markets (ASMs). The taxonomy is aimed to give a guideline for analyzing, comparing, replicating and designing ASMs. The main factors proposed are extracted from the market microstructure literature, and representation aspects are added based on the literature on ASMs. Two main groups of factors are distinguished that are used to describe real and artificial stock markets: organizational aspects and behavioral aspects. The first group is related to market organization and it contains static, well-defined aspects, such as: the type of stocks, orders, participants, trading sessions, and exchange mechanism. The second group is related to the hardly observable and varying aspects of markets such as price formation and the behavior of the various market participants. An overview of 15 various ASMs has been given using the taxonomy. The variety of various trading strategies implemented within ASMs has been illustrated. An interesting discussion point is whether and to what degree are the represented traders in these ASMs "agents", and to what degree are they "autonomous".