Novel Deep Learning Approaches to Portfolio Allocation and Risk Management

我的研究的第一部分集中在对启发式投资组合优化技术的回顾上，通过对几个跨资产领域进行一些样本外的实证研究。然后，绩效评估的结果被用于主要研究目标，即应用机器学习技术来识别特定资产配置策略最接近均值-方差最优的市场机制，正如Harry Markowitz所描述的那样。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，现在可以用来准确描述资产领域在每个时间点的主要特征。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分配策略的相对表现。最后，本文提出了一种新颖的投资组合配置方法，该方法在考虑交易成本的前提下，动态地选择在未来投资期内具有最佳风险调整绩效的策略。

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.