Advances in Machine Learning for Computational Finance Workshop

This workshop brings together researches from machine learning, computational finance, academic finance and the financial industry to discuss problems in finance where machine learning may solve challenging problems and provide an edge over existing approaches. The aim of the workshop is to promote discussion on recent progress and challenges as well as on methodological issues and applied research problems. The emphasis will be on practical problem solving involving novel algorithmic approaches.

Topics of the workshop include (but not limited to):

• Optimisation methods
• Reinforcement learning
• Supervised and semi-supervised learning
• Kernel methods
• Bayesian estimation
• Wavelets
• Evolutionary computing
• Recurrent and state space models
• SVMs
• Neural networks
• Boosting
• Multi-agent simulation
• High frequency data
• Trading strategies and hedging techniques
• Execution models
• Forecasting
• Volatility
• Extreme events
• Credit risk
• Portfolio management and optimisation
• Option pricing

Organizers

• David R. Hardoon - University College London
• John Shawe-Taylor - University College London
• Philip Treleaven -University College London
• Laleh Zangeneh - University College London

Pragoramme Committie

• Nicolò Cesa-Bianchi - Università degli Studi di Milano
• Ran El-Yaniv - Technion - Israel Institute of Technology
• Samet Gogus - Barclaycard
• Yuri Kalnishkan - Royal Holloway, University of London
• Jasvindor Kandola - Merrill Lynch
• Donald Lawrence - University College London
• Giuseppe Nuti - Deutsche Bank
• Sandor Szedmak - University of Southampton
• Chris Watkins - Royal Holloway, University of London