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.
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.
The present cumulative dissertation comprises five contributions in the area of agent-based modeling of financial markets. The contributions can be outlined as follows:
• Contribution 1 focuses the influence of the frequency of publication of fundamental information on market dynamics and efficiency.
• Contribution 2 investigates the question of Contribution 1 with respect to three existing financial market models. The goal is to stress test the robustness of the results and to create a more profound understanding of relevant variables.
• Contribution 3, analyzes the influence of competitive conditions on the risk-preference of agents. The study is based on an evolutionary model which uses a genetic algorithm. As a practical example, the professional competition between fund-managers is considered.
• In Contribution 4, a model is developed in which intelligent agents are able to identify systematic patterns in prices and to exploit them. This behavior produces a chaotic evolution of prices – a central statistical property of financial markets. Artificial intelligence is achieved by means of an Artificial Neuronal Network.
• Contribution 5 has been created on behalf of the Bank of England. The goal is to identify the influence of high frequency trading on market dynamics. The model used in this study is characterized by a relatively realistic mechanism of trading and is validated in a quantitative way against eight statistical indicators.