The Financial Mathematics Computation Cluster (FMC2) is a research collaboration between Industry, University College Dublin, Dublin City University, and NUI Maynooth. FMC2 aims to create a globally-leading centre of financial research which will provide a critical underpinning for the future development of, and employment growth in, the international financial services sector in Ireland.

The research cluster brings together complementary expertise in financial mathematics, financial economics and computer science to create a multi-disciplinary research centre with the critical mass to be globally competitive. The cluster will also embed significant linkages with a range of industrial partners, ensuring the direct industry-relevance of its research activities.

In order to provide a clear focus for the research activities of FMC2, we initially concentrate on the development of theory and methods for the task of asset management. This area is chosen as it is one of the primary activities within the IFSC, it offers high value-add, and has substantial growth potential. In addition to its practical significance, activities in the asset management encapsulate a wide variety of important scientific research questions in mathematical finance and computation.

The academic principal investigators involved in FMC2 are Professor Anthony Brabazon, Professor Gregory Connor, Professor John Cotter, Dr. David Edelman, Prof. Paolo Guasoni and Dr. Michael O’Neill.

There are two, complementary, strands to the research activities of the cluster.

The first concentrates on how we should construct and manage portfolios of assets and covers such important issues as optimal asset allocation, risk management of the resulting investment portfolios, performance measurement of fund managers, algorithmic trading and efficient execution dealing. The cluster will also develop new software tools for these tasks.

The second stream of research focusses on increasing our understanding of risk in financial and other investment markets (including property) and the development of better metrics and software tools to manage this risk. This stream will also examine pension risk in order to develop better methods to manage long term pension investment risk.