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. In algorithmic trading, academic members are experts in the areas of algorithmic and high-frequency trading. There is extensive work on limit order books, optimal execution,  and market making, where the main tools are drawn from market microstructure, stochastic optimal control, and machine learning.

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, mathematicians bring novel understanding and new tools, and financial economists provide a robust framework of how markets functions and a deep understanding of the incentives of the main stakeholders in the economy.

 

Decision Making under Uncertainty, Asset Allocation and Pricing

Having to act in a context of uncertainty, or “take risks” is at the centre of much of human endeavour. Risks are hard to quantify and it is not always straightforward to make a decision under uncertainty.  In finance, risks stem from the randomness of a future outcome (e.g., unexpected changes in: prices, demand, supply, etc.) and from assuming that a model is a correct representation of a financial system. In both cases, deciding what is an optimal financial strategy or policy, requires a deep understanding of how key financial variables are interconnected to understand the system and to make predictions.