Novel Deep Learning Approaches to Portfolio Allocation and Risk Management
Lead Research Organisation:
University College London
Department Name: Computer Science
Abstract
The first part of my research focuses on the review of heuristic portfolio optimisation techniques by carrying out a number of empirical out of sample studies on several cross-asset universes. The results of this performance evaluation are then used towards the main research goal, which is the application of machine learning techniques to identify market regimes where a particular asset allocation strateguy is closest to the mean-variance optimum, as described by Harry Markowitz. Latest research in empirical finance, information theory and biodiversity accumulated a list of mathematical tools that can now be used to accurately describe leading characteristics of an asset universe at each point in time. I am particularly interested in studyingthe effectiveness of deep learning in detecting and exploiting non-linear interactions among these metrics and whether they possess enough forecasting power to accurately predict relative performance of each portfolio allocation strategy. Finally, my research proposes a novel portfolio allocation procedure, which dynamically selects a strategy with the best risk-adjusted performance in the future investment period, taking into account transaction costs.
Organisations
People |
ORCID iD |
Denise Gorse (Primary Supervisor) | |
Damian Kisiel (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
NE/W502716/1 | 01/04/2021 | 31/03/2022 | |||
2097518 | Studentship | NE/W502716/1 | 01/10/2018 | 30/03/2023 | Damian Kisiel |