We are not able to resolve this OAI Identifier to the repository landing page. If you are the repository manager for this record, please head to the Dashboard and adjust the settings.
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
Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.