PhD ThesisThis thesis aims to investigate the behaviour of financial markets by using agent-based
computational models. By using a special adaptive form of the Strongly Typed Genetic
Programming (STGP)- based learning algorithm and real historical data of stocks, indices and
currency pairs I analysed various stylized facts of financial returns, market efficiency and
stock market forecasts.
This thesis also sought to discuss the following: 1) The appearance of herding in financial
markets and the behavioural foundations of stylised facts of financial returns; 2) The
implications of trader cognitive abilities for stock market properties; 3) The relationship
between market efficiency and market adaptability; 4) The development of profitable stock
market forecasts and the price-volume relationship; 5) High frequency trading, technical
analysis and market efficiency.
The main findings and contributions suggest that: 1) The magnitude of herding behaviour
does not contribute to the mispricing of assets in the long run; 2) Individual rationality and
market structure are equally important in market performance; 3) Stock market dynamics
are better explained by the evolutionary process associated with the Adaptive Market
Hypothesis; 4) The STGP technique significantly outperforms traditional forecasting
methods such as Box-Jenkins and Holt-Winters; 5) The dynamic relationship between price
and volume revealed inconclusive forecasting picture; 6) There is no definite answers as to
whether high frequency trading is harmful or beneficial to market efficiency
