Behavioural biases and evolutionary dynamics in an agent-based financial market

This research is devoted to the study of financial market dynamics in a framework which combines agent-based modelling and concepts from behavioural finance. The thesis explores, in an agent-based financial market model, the interlinkage between investor heterogeneity, bounded rationality, behavioural biases and the aggregate market dynamics. We develop a dynamic equilibrium model of a financial market in the presence of heterogeneous, boundedly rational investors. The model combines a performance-driven strategy-switching mechanism of an adaptive belief system (Brock and Hommes, 1998) and an evolutionary finance model (Evstigneev, Hens and Schenk-Hopp´e, 2011). A key feature of this new model is that it contains a combination of passive and active learning dynamics. Passive learning refers to the market force by which wealth accumulates on investment strategies which have done relatively well. Active learning refers to the switching behaviour by which investors actively move their wealth into strategies which have performed well in the recent or distant past. This thesis extends the literature by examining the joint effect of passive and active learning in relation to the evolutionary dynamics of financial markets. By drawing in concepts from behavioural finance, we focus on the micro-level modelling of various heuristics and behavioural biases which may affect investors’ active learning and financial forecasting, such as overconfidence, recency bias, sentiment, etc. We quantify the macro-level market impact of these behavioural elements and study the evolutionary prospects of market dynamics. We show that the interaction between passive and active learning is crucial to understanding the market selection of dominant strategy or the survival of different strategies. Investors’ bounded rationality and behavioural biases in active learning and financial forecasting play an important role in shaping the market dynamics. Our findings point to the causes of the persistence of market inefficiencies and a variety of stylised facts of financial market. The added value of drawing together agent-based modelling and behavioural finance on the study of financial markets dynamics is demonstrated