Data Analysis and Patterns in Data

The existence of easily accessible big data sets and the ability to extract meaningful information from them will shape the future in many research areas. From the analysis of the history of financial data, coupled with the history of the sentiment extracted from the web, one may try to understand better market reaction to the economic figures announcement.  From the analysis of household expenditure one might be able to better predict responses to regulatory change in mortgage markets, and ultimately understand the stability of the financial system. If one could quickly determine from the huge flow of data in an exchange that a flash crash was in process this would have value.  Large heterogeneous data sets demand development of novel methodology to better describe, and detect patterns.

OMI houses many important sources of data and provides support for researchers doing data driven research.  In addition it has a number of teams with deep and varied experience at extracting information from complex data. Computer scientists extract information from the web, engineers bring machine learning, statisticians bring classical data mining tools and mathematicians bring novel understanding and new tools: for example using rough paths theory to classify points in the data sets and use this focussed classification to concisely and easily extract useful information.

 

Decision Making under Uncertainty, Asset Allocation and Pricing

Having to act in a context of uncertainty, or “take risks” is not something shocking and is at the centre of much of human endeavour. Sometimes risks are hard to quantify and certainly there are no quick fixes in deciding what to do; some are completely unanticipated. In other areas, including parts of finance, the underlying understanding of the context and the large aggregate experience of many players means much can be quantified. Understanding how to make decisions – even when you have full information about risks can be quite challenging. A bank needs to understand counterparty risk over a huge range of different potential scenarios if it is to understand its own risks and satisfy regulators. This raises massive computational tasks even if all the data is there.

 

Electronic Trading

The transition from voice trading of liquid high volume assets like equity and FX to electronic trading occurred some time ago. Now, many institutions face big challenges to move as much as possible of their business onto electronic trading platforms. Computer algorithms execute the orders and make substantive decisions without any human intervention. There is a growing need to develop better quantitative trading algorithms. OMI expertise in this area comes from many directions. There is work on order books, the development of tools to better describe the flow processes of these complex systems and algorithms to learn and act in these dynamic environments.

 

Numerical Methods and High Performance Computing in Finance

Pricing and risk management methodologies currently used by financial firms depend on the ability to implement robust and efficient algorithms. Performance and stability of these algorithms is a key to the commercial success of any participant to the financial markets. OMI researchers push the boundaries of knowledge in the number of directions relevant to this broad area.  Technological advances like the use of GPUs are part of the process of transforming and automating financial services industry.