TY - JOUR
KW - Concept Drift
KW - Finance
KW - Machine Learning
KW - Metamodel
KW - Regime Change
KW - Systematic Review
AU - Andrés L. Suárez-Cetrulo
AU - David Quintana
AU - Alejandro Cervantes
AB - 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.
IS - Regular issue
M1 - 1
N2 - 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.
PY - 2024
SE - 137
SP - 137
EP - 148
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review
UR - https://www.ijimai.org/journal/bibcite/reference/3331
VL - 9
SN - 1989-1660
ER -