Novel Deep Learning Approaches to Portfolio Allocation and Risk Management

我的研究的第一部分侧重于启发式投资组合优化技术的回顾，通过对几个跨资产领域进行了一些实证的样本外研究。这种绩效评估的结果随后用于主要研究目标，即应用机器学习技术来识别市场机制，其中特定的资产配置策略最接近Harry Markowitz所描述的均值方差最优。在经验金融学、信息论和生物多样性方面的最新研究积累了一系列数学工具，现在可以用来准确地描述每个时间点的资产范围的主要特征。我特别感兴趣的是研究深度学习在检测和利用这些指标之间的非线性相互作用方面的有效性，以及它们是否具有足够的预测能力来准确预测每个投资组合配置策略的相对表现。最后，本文提出了一种新的投资组合配置方法，该方法在考虑交易成本的情况下，动态选择未来投资期内风险调整后绩效最佳的投资组合策略。

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.