Modelling Trade-Based Manipulation Strategies in Limit-Order Markets

Insider trading (i.e., “informed market manipulation”) use private information to illegally profit. However, it is not necessary to have trading related information to profit in a stock market. If a trader can generate false information in order to mislead other market participants, he or she can make a profit. This is commonly termed “uninformed market manipulation. ” Modelling stock markets provides a means to test market manipulation theories. This thesis presents such market models to describe different aspects of complex behaviour in stock markets and stock manipulation. This research is shown to be the first to characterise trade-based manipulation in a single realistic model of a limit order market. We discuss the characteristics of stock manipulation by considering trading related information, and present a simple framework for manipulation. Realistic market micro-structure models are presented to characterise stock market and trader behaviour. A belief structure of a stock trader is characterised. This model allows us to control and tune trader beliefs explicitly in order to analyse their learning processes and effects on the order book. Models are then used to explain real stock market behaviour. The impacts of heterogeneous trader types on these models are considered. Finally, stock manipulation scenarios are characterised as external processes and introduced to our computational market, thus allowing stock manipulation models to be built. Using these manipulation models, simplified formal explanations for manipulation scenarios are presented. The resulting models are used to address current theories regarding manipulation and in explaining the profitability and detectability of manipulation in liquid and illiquid markets. ii Acknowledgements It is time to look back and pay my debts of gratitude to those wonderful individuals who supported me throughout my PhD journey. I am forever grateful to my primary supervisor Associate Professor Peter Whigham, for being the best supervisor a student could ever dream of. Thank you very much for pushing me forward when I was too slow, for pulling me out when I was stuck, and for walking beside me when I needed it most. It is a privilege to be your student. Many thanks to my co-supervisor Professor Timothy Crack for all the moral support and guidance you gave me to complete this thesis. Thank you very much for all the finance “private tutoring” you gave me and Peter during our discussions. Also I must thank Associate Professor I M Premachandra for introducing me to Otago and for all the support you gave me at the initial phases of my PhD. Continuation of my PhD studies would not have been possible if it was not for the Otago postgraduate scholarship that I received. Many thanks to the School of Business, for funding my travel to the International Congress on Modelling and Simulation 2011, Perth Australia. I make this an opportunity to thank my previous employer Millennium Information Technologies Sri Lanka Pvt. Ltd. for providing valuable knowledge and support to start this research. Many thanks to the teaching, technical support, and administrative staff at the Department of Information Science, for all their support. Special