Harnessing machine learning for enhanced quantitative financial strategies: Applications and case studies

Abstract

Machine learning algorithms have long been an essential tool for quantitative finance, as they play an important role in the asset pricing, portfolio optimisation and financial risk management. This work discusses how to apply Random Forest, Support Vector Machine (SVM), and Neural Networks to further develop and enhance the quantitative investment research. With automatic feature selection, reconstruction of high-dimensional data and resist against over-fitting, Random Forest has been an efficient ensemble approach with remarkable prediction performance. For this reason, Random Forest is leveraged for the stock price prediction. SVM, particularly known for its robustness in high-dimensional classification task, has a non-linear optimisation ability that can be employed to capture the inherent non-linear dependencies in options prices. Neural Networks, as a learning approach capable of discovering complex relationships, is employed for the portfolio optimisation, with a promise for further improving the risk-adjusted portfolio return. The work is conducted through practical case studies to simulate how Random Forest, SVM and Neural Networks enable us to further improve the financial performance, and to illustrate its advantage, disadvantage and practical application in quantitative finance.