High frequency finance
High-frequency data is the real-time record of all trading activities and
their associated characteristics observed in an electronic exchange system.
These "tick-by-tick" data sets provide deeper insights into the price formation
process at the micro level and have been widely used to study various market
microstructure issues, such as price discovery, order choice behaviour of
market participants and optimal order placement strategy.
Our research in this area includes:
- developing real-time trading platforms
- deriving new financial econometric models for real-time data, with a special focus
on the asymmetric behaviour between the supply and demand sides of the market
and the information set traders refer to before submitting their orders
Agent-based computational economics
Our research in computational economics is focused on agent-based computational economics (ACE).
This includes the use of artificially intelligent agents in the study of self-organising systems and
risk and market-based institutions, and the design of real time trading. This also has the potential
to provide policy makers and financial institutions with a powerful interactive tool to find answers
for 'what-if' questions and to do 'wind tunnel tests' for market and policy design.
Our work is oriented to the non-Gaussian characteristics of financial markets and operational
aspects of financial markets, financial engineering, portfolio and risk management. Computational
methods include adaptive and reinforced learning techniques, heuristic optimisation and evolutionary