Björn-Christopher Witte Livres

This article explores the influence of competitive conditions on the evolutionary fitness of different risk preferences. As a practical example, the professional competition between fund managers is considered. To explore how different settings of competition parameters, the exclusion rate and the exclusion interval, affect individual investment behavior, an evolutionary model based on a genetic algorithm is developed. The simulation experiments indicate that the influence of competitve conditions on investment behavior and attitudes towards risk is significant. What is alarming is that intense competitive pressure generates riskseeking behavior and undermines the predominance of the most skilled.

This study seeks to explore, how market efficiency changes, if ordinary traders receive fundamental news more or less often. We show that longer temporal information gaps lead to fewer but larger shocks and a reduction of the average noise level on the dynamics. The consequences of these effects for market efficiency are ambiguous. Longer temporal information gaps can deteriorate or improve market efficiency. The concrete result depends on the stability of the market together with the interval in which the length of the gap is incremented.

The unpredictability of returns counts as a stylized fact of financial markets. To reproduce this fact, modelers usually implement noise terms − a method with several downsides. Above all, systematic patterns are not eliminated but merely blurred. The present article introduces a model in which systematic patterns are removed endogenously. This is achieved in a reality-oriented way: Intelligent traders are able to identify patterns and exploit them. To identify and predict patterns, a very simple artificial neural network is used. As neural network mimic the cognitive processes of the human brain, this method might be regarded as a quite accurate way of how traders identify patterns and forecast prices in reality. The simulation experiments show that the artificial traders exploit patterns effectively and thereby remove them, which ultimately leads to the unpredictability of prices. Further results relate to the influence of pattern exploiters on market efficiency.