- Agent-based Modeling (1)
- Competition (1)
- Financial Markets (1)
- Fondmanager ; Risikoanalyse ; Wettbewerb ; Online-Publikation (1)
- Frequency of Publication (1)
- High-Frequency Trading (1)
- Kapitalmarkt ; Künstliche Intelligenz ; Mehragentensystem ; Online-Publikation (1)
- Kreditmarkt ; Mehragentensystem ; Kapitalmarkt ; Wettbewerb ; Mathematisches Modell ; Online-Publikation (1)
- financial markets; autocorrelations; artificial intelligence; agent-based modeling (1)
- risk preferences; competition; genetic programming; fund managers; portfolio theory (1)
- Wissenschaftliches Institut für Hochschulsoftware der Universität Bamberg (ihb) (8)
- Lehrstuhl für Volkswirtschaftslehre, insbesondere Wirtschaftspolitik (2)
- Lehrstuhl für Wirtschaftsinformatik, insbesondere Industrielle Informationssysteme (2)
- Lehrstuhl für Volkswirtschaftslehre, insbesondere Finanzwissenschaft (1)
- Using Agent-Based Modeling to Explore the Dynamics of Financial Markets and the Potential for Regulation (2012)
- 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.
- Fund managers: Why the best might be the worst – On the evolutionary vigor of risk-seeking behavior (2011)
- Removing systematic patterns in returns in a financial market model by artificially intelligent traders (2012)
- 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.
- Fund Managers - Why the Best Might be the Worst: On the Evolutionary Vigor of Risk-Seeking Behavior (2013)
- 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.
- Improving the Support for Investment Decisions in Financial Markets Using the System Dynamics Methodology (2010)
- Disclosure Requirements, the Release of New Information and Market Efficiency: New Insights from Agent-based Models (2010)
- Removing systematic patterns in returns in a financial market model by artificially intelligent traders (2011)