Machine Learning and Data Science Blueprints for Finance

Chapter 5. Supervised Learning: Regression (Including Time Series Models)

Supervised regression–based machine learning is a predictive form of modeling in which the goal is to model the relationship between a target and the predictor variable(s) in order to estimate a continuous set of possible outcomes. These are the most used machine learning models in finance.

One of the focus areas of analysts in financial institutions (and finance in general) is to predict investment opportunities, typically predictions of asset prices and asset returns. Supervised regression–based machine learning models are inherently suitable in this context. These models help investment and financial managers understand the properties of the predicted variable and its relationship with other variables, and help them identify significant factors that drive asset returns. This helps investors estimate return profiles, trading costs, technical and financial investment required in infrastructure, and thus ultimately the risk profile and profitability of a strategy or portfolio.

With the availability of large volumes of data and processing techniques, supervised regression–based machine learning isn’t just limited to asset price prediction. These models are applied to a wide range of areas within finance, including portfolio management, insurance pricing, instrument pricing, hedging, and risk management.

In this chapter we cover three supervised regression–based case studies that span diverse areas, including ...