Neural Network Based Multiagent System for Simulation of Investing Strategies

Recent years of empirical research have collected enough evidences that for efficient markets the process of lower-wealth accumulation by capital investment is approximated by log-normal and high-wealth range by Pareto wealth distribution. This research aims to construct a simple neural network (NN) based multiagent system of heterogeneous agents' targeted to get on the efficiency frontier by combining investments to the real life index funds and nonrisky financial assets, diversifying the risk and maximizing the profits. Each agent is represented by the different stock trading strategy according to his portfolio, saving and risk aversion preferences. The goal is, following empirical evidences from the real investment markets, to find enough proofs that NN-based multiagent system, in principle, has the same fundamental properties of real investment markets described by the log-normal, Pareto wealth and Levy stock returns distributions and can be used further to simulate even more complex social phenomena.

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