Job Description

0923-410

Full Time, Fixed Term (24 months)

Applications are invited for the post of Postdoctoral Research Fellow in the Department of Economics.

We are looking for a highly motivated individual to join the research project:

Information Content and Dissemination in High-Frequency Trading

The main goal of this project is to study information dissemination through the interaction of machines and humans in financial markets. Machines in the form of algorithms are nowadays an integral part of any financial market. In the last decades, there has been emerging interest in algorithms to make predictions and decisions in financial markets. The project intends to add a new dimension to it by studying the interaction between machines and humans.

The aim of the project is to address the following questions:

1. How do human participants react to the presence of algorithms in the market?

2. How is information dissemination within electronic markets affected by the presence of algorithms? Are human and algorithmic traders able to detect the information disseminated in the market? How can they learn from each other?

3. How does the presence of algorithms affect the dynamics of electronic financial markets? How unstable and manipulatable a market becomes as the proportion of algorithms increases?

4. How well can an algorithm learn from historical data? Can such a trained algorithm adapt to learn from new data from experiments? How well can an algorithm learn to account for its actions when such actions impact the environment?

To answer these questions, a considerable innovation of the project is the use of economic experiments where participants can be either humans or algorithms trained on high frequency datasets.

The project is empirically focussed and requires programming knowledge of Python. Moreover, data management skills and high level of competence in econometrics and machine learning are desired. Experience in economic experiments would also be desirable. Most of all, the successful candidate will have a great enthusiasm to conduct research on this topic.

This is a project funded by the Leverhulme Trust. The successful candidate will work full time on this project.

The successful candidate will work closely with the research team: Professors Francesco Feri (RHUL), Michael Naef (Durham) and Alessio Sancetta (RHUL). They are expected to engage as full member of the research team contributing to the analysis of high frequency data, developing algorithms, designing and coding economic experiments, analysing causal relations, providing with the development of the project\xe2\x80\x99s output, including the preparation of outputs for journal submission.

The successful candidate needs to have much of the following essential experience:

1. Data analysis of high frequency data sets; experience with the Lobster database is a plus;

2. Modelling structural causal relations; knowledge of FAVAR, ICA, Pearl\xe2\x80\x99s do operators is a plus;

3. Knowledge of high dimensional econometric literature and state of the art machine learning estimation methods;

4. Designing economic experiments;

5. Setting up and coding economic experiment using major online platforms;

6. Econometric analysis of experimental data including diff-diff methods and the relevant literature on causality to evaluate treatment effects;

6. Knowledge of Python is a must, and possibly Matlab; experience with high performance coding and modules such as numba and multiprocessing is a plus.

7. Ability to keep accurate records, strong communication skills, experience with interacting with research users and with researchers from related disciplines, and ability to work independently under regular supervision as well as within a team setting.

The post is based in Egham, Surrey, where the University is situated in a beautiful, leafy campus near to Windsor Great Park and within commuting distance from London. Meetings with the team will take place at Royal Holloway or virtually. Work on data analysis can be done from a location of choice subject to appropriate data security. Economic experiments will be carried out in a dedicated lab of the Egham Campus. There might be the possibility to use facilities in Central London based on project requirements.

For an informal discussion about the post, please contact Prof. Alessio Sancetta by email on .

For queries on the application process the Human Resources Department can be contacted by email at:

Please quote the reference: 0923-410

Closing Date: Midnight, 21 September 2o23

Interview Date: To Be Confirmed

Further details:

Royal Holloway, University of London