Financial Computing and Data Science

PhD Research

State-of-the-art financial IT, computational finance and financial engineering and the transformations these can effect, are crucial to international competitiveness in financial services, whether investment banking, investment funds or retail banking.

Challenging and original research projects are undertaken on the financial computing PhD, with support from the highest standard of academic advisors. Students follow specialist lines of research at a doctorate level and apply their work with innovative technologies during placements arranged with our industry partners.

Computer science related research topics are most common in the research undertaken at the Centre, but some students' areas of interest fall under other departments at UCL. For example, students looking for a mathematics or economics PhD may find that their research is a good fit for the Centre.

Research interests at the Centre include:

Machine learning

The automatic recognition of complex patterns and design of algorithms to make intelligent computing decisions based on empirical data. Machine learning addresses the problem that the range of options for all possible inputs is too complex to describe within programming languages, so that programs must automatically describe programs. Artificial intelligence is a closely related field, as are probability theory and statistics, data mining, pattern recognition, adaptive control, and theoretical computer science.

Algorithmic trading

The use of computer programs in electronic financial markets to control aspects of trading orders and guide investment strategy. Timing, price, quantities and even automatic generation and execution of orders can be managed by algorithms. Commonly used by pension funds, mutual funds, and other investor driven traders, algorithmic trading is used to manage market impact and risk, and to provide liquidity to the market. It capitalises on the the speed with which complex decisions can be made with data received electronically.

Systemic Risk & Financial Stability

Network models of systemic risk, financial stabilty and regulation, counterparty risk, central clearing of OTC derivatives, impact of regulation on financial stability, capital requirements and liquidity requirements.

Quantitative Risk Management

Market risk modeling, credit risk, counterparty risk, CVA, liquidity risk, operational risk, risk measures, large-scale portfolio risk modeling.

Stochastic calculus

Atype of mathematics used to model systems that behave randomly. A well-known stochastic process to which stochastic calculus is applied is the Wiener process, used for modelling Brownian motion. In financial mathematics and economics it is applied to stock prices and bond interest rates, as they develop across time.

Portfolio theory

Offers optimisation methods that employ mathematical modelling to maximize investment returns and reduce risk, particularly by diversifying an investor’s portfolio. The degree of success for portfolio optimisation depends on the correlation between assets and how they move in relation to each other.

Artificial intelligence

Financial AI systems include the use of programs to explain market behaviours and spot subtle patterns in world markets, knowledge-based systems that offer real-time, market-level advice, neural networks that can ‘learn’ from their mistakes, and other advanced analytical techniques in time-series analysis and portfolio generation. AI can be applied to picking stocks and is very useful to banks and institutions requiring deep transactional analysis to detect unusual patterns and suspicious transactions in data.

Students at the Centre are currently researching:

algorithmic trading, artificial intelligence, artificial neural networks, Bayesian reasoning, Brownian motion, computational finance, data mining, financial engineering, financial forecasting, financial time series, genetic algorithms, information retrieval, integrated trading systems, intelligent portfolio management, linear dynamical systems, machine learning, mathematical finance, modelling commodity markets, modelling investment risk, Monte Carlo methods, network, grid and parallel computing, neurodynamics, nonparametric methods, portfolio theory, probability trading, quantile regression, quantitative finance problems, semiparametric methods, software engineering, state space models, statistical asymptotic theory, Stochastic calculus, systematic trading, time series analysis, trading models

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