Don't miss our weekly PhD newsletter | Sign up now Don't miss our weekly PhD newsletter | Sign up now

  Applications of financial technology in portfolio construction and optimization.


   Ulster Business School

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
  Prof Daniel Broby  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

The research will focus on how to improve the risk reward characteristics of mean-variance portfolios. It will involve investigating the risk-stability of asset portfolios and how to optimize portfolios using big data sets and financial technology. It will require a familiarity with time series and Modern Portfolio Theory.

The PhD Student will work with two Fintech academics who have developed a novel robust co-movement statistic for portfolio optimization. The Smyth-Broby co-movement statistic mitigates the noise associated with measuring aspects of asset co-movement better than existing alternatives.

The PhD studentship falls within that Accounting, Finance and Economics Research Group (AFERG). This was established to foster an inclusive research environment that facilitates, supports and encourages research and collaboration both in a multi-discipline environment. AFERG will co-supervise with the School of Computing due to the technical requirements of the subject. This is comprised of two focused research groups in Pervasive Computing and Artificial Intelligence.

The studentship will be based in the new £363m High Tech Belfast Campus. Belfast and will contribute to Ulster University’s focus on Financial Technology. The researcher will be able to take advantage of the NI-High Performance Computing facility, branded as Kelvin2.   

We are particularly interested in proposals from candidates with a solid grounding in finance, statistics, and computational methods. The scope of the research can extend to the application of optimization techniques using automated technologies, big data and AI, often referred to as Robo-Advisory.

Although focused on the finance discipline, we emphasise the multi-disciplinary nature of this studentship. The research can therefore embrace techniques from Artificial Intelligence, Machine Learning, Analytics, Explanatory Statistics, and Computational Methods. Students will be expected to undertake advanced research, publish in scholarly journals and contribute to the growing body of knowledge on portfolio construction and asset price co-movement.

Business & Management (5)

References

Cheng, E., Struck, C.C., 2021. Antinoise in us equity markets. Quantitative Finance 21, 2069–2087.
Gerber, S., Markowitz, H.M., Ernst, P.A., Miao, Y., Javid, B., Sargen, P., 2022. The gerber statistic: a robust co-movement measure for portfolio optimization. The Journal of Portfolio Management 48, 87–102.
Jobson, J.D., Korkie, B., 1980. Estimation for markowitz efficient portfolios. Journal of the American Statistical Association 75, 544–554.
Kendall, M.G., 1938. A new measure of rank correlation. Biometrika 30, 81–93.
Ledoit, O., Wolf, M., 2004. Honey, i shrunk the sample covariance matrix. The Journal of Portfolio Management 30, 110–119.
Markowitz, H.M., 1968. Portfolio selection, in: Portfolio selection. Yale university press. 7
Michaud, R.O., 1989. The markowitz optimization enigma: Is ‘optimized’optimal? Financial analysts journal 45, 31–42.
Smyth, W. and Broby, D., 2022. An enhanced Gerber statistic for portfolio optimization. Finance Research Letters, 49, p.103229.

How good is research at Ulster University - Belfast Campus in Business and Management Studies?


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

Click here to see the results for all UK universities
Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.

 About the Project