Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635011
Title: An investigation of the behaviour of financial markets using agent-based computational models
Author: Manahov, Viktor
ISNI:       0000 0004 5353 7151
Awarding Body: University of Newcastle Upon Tyne
Current Institution: University of Newcastle upon Tyne
Date of Award: 2014
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Abstract:
This 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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.635011  DOI: Not available
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