Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review.

Andrés L. Suárez Cetrulo; David Quintana; Alejandro Cervantes

doi:10.9781/ijimai.2023.06.003

Authors

Andrés L. Suárez Cetrulo University College Dublin.
David Quintana Universidad Carlos III de Madrid.
Alejandro Cervantes Universidad Internacional de La Rioja.

DOI:

https://doi.org/10.9781/ijimai.2023.06.003

Keywords:

Concept Drift, Finance, Machine Learning, Metamodel, Regime Change, Systematic Review

Supporting Agencies

We would like to acknowledge the financial support of the Spanish Ministry of Science, Innovation and Universities under grant PGC2018-096849-B-I00 (MCFin).

Abstract

Recent crises, recessions and bubbles have stressed the non-stationary nature and the presence of drastic structural changes in the financial domain. The most recent literature suggests the use of conventional machine learning and statistical approaches in this context. Unfortunately, several of these techniques are unable or slow to adapt to changes in the price-generation process. This study aims to survey the relevant literature on Machine Learning for financial prediction under regime change employing a systematic approach.
It reviews key papers with a special emphasis on technical analysis. The study discusses the growing number of contributions that are bridging the gap between two separate communities, one focused on data stream learning and the other on economic research. However, it also makes apparent that we are still in an early stage. The range of machine learning algorithms that have been tested in this domain is very wide, but the results of the study do not suggest that currently there is a specific technique that is clearly dominant.

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