Thesis (Ph.D.) - Cairo University - Faculty of Economics and Political science - Department of Social Science Computing

In this thesis, we develop two artificial financial market models; a single-asset model and a multi-stocks model.The financial markets are populated with agents following two heterogeneous trading beliefs,the technical and the fundemental prediction rules.In the single asset framework, agents switch between trading rules with respect to their past performance.The agents are lss averse over asset price fluctuations. loss aversion behaviour depends on the past perfrmance of the trading strategies in terms of an evolutionary fitness measure. We propsed a novel pplication of the prospect theory to agent-based modelling,and by simulation, the effect of evolutionary fitness measure on adaptive belief system is investigated.for comparison, we syudy pricing dynamics of financial market populated with chartists perceive losses and gains symmetrically.one of our contributions is validating the agent-based models using real financial data of the Egyptian stock exchange.However, in the multi stocks model, agents switch between multiple stocks with respect to the attractiveness of each individual stock. loss aversion behaviour depends on the attractiveness of individual stocks in terms of any evolutionary fitness measure.We propse a novel application of the prospect theory to agent-based modelling, and by simulation, the effect of evolutionary fitness measure on the switching behaviour among multiple stock is investigated.we finf that the proposed frameworks can explain important stylized facts in financial time series, such as random walk price behaviour, bubbles and crashes, fat-tailed return distributions, power-law tails in the distribution of returns,excess volatility, volatiltiy clustering,and power - law autocorrelation in absolute return. in addition to this, we find that loss aversion improves market qualiy and market stability

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