This project is within the field of financial technology, in particular on two topics of quantitative investment: conditional market timing and optimal execution.
Successful market timing requires two correct decision: when to get in and when to get out. Three categories of market timing signals include seasonal market timing signal, technical market timing signals, and fundamental market timing signals. The work on the market timing includes:
1) Develop neural network based methods to “learn” the nonlinearity of multi-frequency market timing signals including technical indicators and macroeconomic event signals such for forecasting a complete market timing signals;
2) Develop neural network rule extraction methods for extracting, generating, and visualising the internal logic and knowledge learned by the NN model from the timing signals.
The optimal execution of large orders requires considerations of the liquidity and the corresponding price impact. The price impact, liquidity, and the order size comprise a theoretical surface, which is discrete and non-linear. The optimal execution is to search through the surface to maximize the revenue while minimizing the impact with respect to a stochastic liquidity. The work on the microstructure includes:
1) Estimate, interpolate and smooth the surface of price impact, liquidity, and the order size;
2) Formulate the optimal execution as an optimization problem: find out the best combination of small orders through the surface that maximizes the revenue while minimizing the impact.
The findings are expected to have significant implications for portfolio managers in investment decision-making process.
ABOUT OUR SCHOOL
Based in the central campus at the University of Edinburgh, the Business School seeks to set the agenda across a wide range of business disciplines, with our research areas staffed by teams whose work has real influence in business, government, and the public sector.
The University of Edinburgh was ranked as 4th in the UK for research power (Times Higher Education, 2019), and the Business School has significant research expertise across six subject areas. The research group, Management Science and Business Economics, where this project resides, includes influential subject-matter experts and researchers.
We aim to provide a highly dynamic and supportive research environment for our large and diverse graduate community, ensuring that Business School PhD students have the resources required to make the most of their individual projects. This is evidenced by our positive student feedback in national surveys such as the PRES 2019.
In addition, the Business School has received full research training accreditation both from the Economic and Social Research Council (ESRC) and European Quality Improvement System (EQUIS), and holds the Athena SWAN Bronze Award in recognition of the positive work the School has done to promote diversity and gender equality.
HOW TO APPLY
Applicants must meet the following entry PhD in Financial Technology requirements for this project:
1. The academic entry requirement.
This normally requires a minimum qualification (or expected qualification if you are current Masters’ student) of above-average academic achievement, quantified as a 70% or above overall at the Masters level, with a distinction level dissertation (or UK equivalent) in finance, economics, informatics, physics, mathematics, engineering or another relevant programmes with significant quantitative elements. Students with significant finance and technology industry experience, or with relevant professional qualifications, that also have a minimum of a Bachelor’s degree in the programmes stated above will be given due consideration on a case-by-case basis.
2. The English requirement.
Full details of the English requirements can be found on our School website here: https://www.business-school.ed.ac.uk/phd/entry-requirements. The most commonly approved certificate is an IELTS, for which the minimum accepted score is 7.0 overall with at least 6.0 in each section.
To apply applicants should send their current academic CV, a copy of your Masters’ level transcript (or interim transcript if you are completing your studies) and a cover letter describing why you would be suitable for the project. Eligible applicants will be ranked by a selection panel and applicants will be notified if they have been shortlisted for interview.
Interviews will be scheduled during the week commencing 2nd December.
This opportunity is linked with our industrial partner, Franklin Templeton. Therefore, the successful candidate will be expected to achieve the project under the supervision of Dr Yizhe Dong and Dr Yi Cao, as well as the team at Franklin Templeton.
The project is fully funded, covering tuition fees (at the UK/EU rate) and an enhanced stipend of £20,000 for up to four years.
Please note that all awards are subject to candidates successfully securing admission to the PhD in Financial Technology programme within the University of Edinburgh.
How good is research at University of Edinburgh in Business and Management Studies?
FTE Category A staff submitted: 51.60
Research output data provided by the Research Excellence Framework (REF)
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