Machine learning methods in finance: Recent applications and prospects

Abstract

We study how researchers can apply machine learning (ML) methods in finance. We first establish that the two major categories of ML (supervised and unsupervised learning) address fundamentally different problems than traditional econometric approaches. Then, we review the current state of research on ML in finance and identify three archetypes of applications: i) the construction of superior and novel measures, ii) the reduction of prediction error, and iii) the extension of the standard econometric toolset. With this taxonomy, we give an outlook on potential future directions for both researchers and practitioners. Our results suggest large benefits of ML methods compared to traditional approaches and indicate that ML holds great potential for future research in finance.

Perspective Chapter: Macroeconomic Dynamics through the Lens of the Adaptive Neuro-Fuzzy Inference System

“…Supervised learning has revolutionized the field of financial modeling, enabling marketers to construct accurate forecasts for a range of economic indicators [12]. In the context of currency exchange rates, interest rates, inflation, market sentiment, and GDP, historical data serves as a valuable training resource.…”