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A reexamination of modern finance issues using Artificial Market Frameworks

Iryna Veryzhenko 1, 2
1 SMAC - Systèmes Multi-Agents et Comportements
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : Agent-based mo deling (ABM) is widely used to study economic systems under a complex paradigm framework. Within this research stream, financial markets have received a lot of interest from academics and practitioners these last years, notably in offering an alternative to mathematical finance and financial econometrics. The traditional approach to analyzing such systems uses analytical models. The latter make simplifying assumptions, for example about perfect rationality homogeneity of market participants. These limitations motivate the use of alternative tools. Thus, disciplines such as Computational Economics and Computational Finance have gained attention and earned their place in the scientific arena. In this thesis we present an artificial stock market, called ATOM, and contribute to the understanding of some important issues regarding the construction of an abstract model of stock markets as well as a series of technical issues. In ATOM, we model a wide variety of trading strategies and market rules, that allows us to reexamine several traditional questions in finance within a totally different framework. Firstly, we investigate different conditions under which the statistical properties of an artificial stock market resemble those of real financial markets. To the best of our knowledge, this research is the first to clearly reproduce set of price dynamics at different granularities (intraday and extraday over several simulated years). We argue that generating realistic financial dynamics that reproduce quantitative financial distribution is out-of-reach within the pure zero-intelligent traders framework. Secondly, we increase agents' intelligence to address the problem of portfolio optimization at the level of individual strategies. We show that the higher relative risk aversion helps the agents earn higher Sharp e ratio and final wealth in the long-range. We also investigate the relation between rebalancing frequency and portfolio performance in low- and high-volatility market regimes with different transaction costs. Thirdly, we renew the analysis of classical questions in finance, namely, the relative performance of various investment strategies. For that purpose we compare rational mean-variance portfolio optimization versus "naive diversification". We test the investors' performance, each of them following a specific strategy, scrutinizing their behavior in ecological competitions where populations of artificial investors co evolve. Some investment strategies, followed by artificial traders, are based on different variations of canonical modern Markowitz portfolio theory, others on the "Naive" diversification principles, and others on combinations of sophisticated rational and naive strategies. Finally, we develop a new method for the determination of the upper bound in terms of maximum profit for any investment strategy applied in a given time window. We first describe this problem using a linear programming framework. Thereafter, we propose to embed this question in a graph theory framework as an optimal path problem in an oriented, weighted, bipartite network or in a weighted directed acyclic graph.
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Iryna Veryzhenko. A reexamination of modern finance issues using Artificial Market Frameworks. Multiagent Systems [cs.MA]. Université Panthéon-Sorbonne - Paris I, 2012. English. ⟨tel-00839380⟩

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