Machine learning methods in finance

¹ Belarusian State University, Digital Economy Department, 220030 Minsk, Belarus
² Saint-Petersburg State Agrarian University, 196601 Saint-Petersburg, Russia
³ Penza State University, Digital Economy Department, 440026 Penza, Russia
⁴ К.G. Razumovsky Moscow State University of technologies and management (the First Cossack University), Moscow, Russian Federation

^* Corresponding author: karachun@bsu.by

Abstract

This article focuses on supervised learning and reinforcement learning. These areas overlap most with econometrics, predictive modelling, and optimal control in finance. We choose to focus on how to cast machine learning into various financial modelling and decision frameworks. This work introduces the industry context for machine learning in finance, discussing the critical events that have shaped the finance industry’s need for machine learning and the unique barriers to adoption. The finance industry has adopted machine learning to varying degrees of sophistication. Some key examples demonstrate the nature of machine learning and how it is used in practice. In particular, we begin to address many finance practitioner’s concerns that neural networks are a “black-box” by showing how they are related to existing well-established techniques such as linear regression, logistic regression, and autoregressive time series models. Neural networks can be shown to reduce to other well-known statistical techniques and are adaptable to time series data.