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Trading in Peer-to-peer (P2P) markets

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Peer-to-peer market trading is becoming more prevalent any many areas that were traditionally the domain of large companies – stocks, foreign exchange, gambling. This project will look at a variety of statistical fundamentals for these areas including the practical application of statistical arbitrage, traditional arbitrage, algorithmic and high-frequency trading.
The project deals with large data issues, data-mining/machine-learning methods, cloud-computing and the dynamics of automated trading via APIs. The project will be heavily computational in either R or python, with potentially compiled languages for computational bottlenecks. The ideal candidate would have a good grasp of practical computing and statistics.

Funding Notes

Multiple sources of scholarship funding are potentially available, including university, research council (EPSRC) and research group. Some are open to international students, some to EU and some UK only.

Applicants should have a good first degree in mathematics, statistics or another discipline (e.g., biology, computer science), with substantial statistical component. A masters-level degree is an advantage.

Further details of the application procedure, including contact details for the Postgraduate Officer, are available at View Website

References

Erichson, N. B. & Donovan, C. R. (2016) Randomized low-rank Dynamic Mode Decomposition for motion detection. Computer Vision and Image Understanding. Vol. 146 pp 40-50.

How good is research at University of St Andrews in Mathematical Sciences?

FTE Category A staff submitted: 30.60

Research output data provided by the Research Excellence Framework (REF)

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