PhD Position opening on Neuroeconomics, Reinforcement learning and the equity premium puzzle

PhD Position opening on Neuroeconomics, Reinforcement learning and the equity premium puzzle.

Supervisors: Drs Eleni Vasilaki & Trevor Cohn, Machine Learning group, Department of Computer Science.

The ML group was founded in 1998. The scientific focus of the group is developing formalisms for data analysis, with a particular focus on probabilistic modelling. The engineering focus of the group is on algorithm development for modelling data in computational biology, language, neuroscience and large unstructured data sources. This project is part of a funded partnership with the departments of Automatic Control and Psychology under the theme of Neuroeconomics. It involves a close collaboration between Computer Science and Management departments.

Project description: Humans often make decisions based on their desire to maximize profit or reward. Such decisions take place within changing environments, where optimal choices in the past may differ from those in the present. For example, choosing a tracker-rate mortgage might have been at some time in the past a better option than a fixed-rate but today this may have changed. These choices are typically made under uncertain situations and involve a degree of risk. Though the specifics of decision-making mechanisms are still not fully understood, it is evident that fundamentally the human brain is able to identify information sequences that could also correlate with reward.

This project aims to develop a data driven framework for understanding decision-making types of investors, and the key ingredients of making successful investment decisions. We ask the question whether the choices of successful investors have a higher component of sophisticated principles versus the unsuccessful investors, and whether different mixtures of models can account for different investor strategies. We anticipate that the results would be of immediate interest to finance institutions that may want to use our models to extract information about their clients' profiles in order to provide customized financial training or making decisions about investor loans.

Candidate's profile: The ideal candidate should have degree in Computer Science, Mathematics, Physics, Engineering or similar, a very strong mathematical background, excellent programming skills and interest in financial problems. The PhD topic requires development and application of Artificial Intelligence techniques for financial data analysis.

Scholarship information: The position covers tuition fees at UK/EU rate, provides annual maintenance at the standard RCUK rate (£13,726 for 2013-14), and a contribution towards research and travel expenses of £1,000 p.a. Awards are open to UK, EU and international applicants.
International applicants will be required to prove that they have sufficient funds to cover the difference between the UK/EU and Overseas tuition fees. For exceptional international candidates there may be opportunities for additional fee waivers.

Preliminary enquiries should be addressed to Dr Eleni Vasilaki or Dr Trevor Cohn.
Email: or .

Application Procedure: Please submit an application for a PhD at the department of Computer Science including a CV and a motivation letter.

For more information see: http://www.shef.ac.uk/dcs/postgrad/resdegrees .

Application deadline: 1 May 2013.

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