The Four Horsemen of Machine Learning in Finance

Machine Learning has been used in the financial services industry for over 40 years, yet it is only in recent years that it has become more pervasive across investment management and trading. Machine learning provides a more general framework for financial modeling than its linear parametric predecessors, generalizing archetypal modeling approaches, such as factor modeling, derivative pricing, portfolio construction, optimal hedging with model-free, data-driven approaches which are more robust to model risk and capture outliers. Yet despite their demonstrated potential, barriers to adoption have emerged – most of them artifacts of the sociology of this inter-disciplinary field. Based on discussions with several industry experts and the authors' multi-decadal experience using machine learning and traditional quantitative finance at investment banks, asset management and securities trading firms, this position article identifies the major red flags and sets out guidelines and solutions to avoid them. Examples using supervised learning and reinforcement in investment management & trading are provided to illustrate best practices