01762nas a2200253   4500000000100000000000100001008004100002260001200043653001800055653001200073653002100085653001400106653001800120653002200138100003100160700001900191700002400210245011200234856005800346300001200404490000600416520107200422022001401494       2024                            d  c12/202410aConcept Drift10aFinance10aMachine Learning10aMetamodel10aRegime Change10aSystematic Review1 aAndrés L. Suárez-Cetrulo1 aDavid Quintana1 aAlejandro Cervantes00aMachine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review  uhttps://www.ijimai.org/journal/bibcite/reference/3331  a137-1480 v93 aRecent 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.  a1989-1660