Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review
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 the...
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| Format: | Article |
| Language: | English |
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Universidad Internacional de La Rioja (UNIR)
2025-01-01
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| Series: | International Journal of Interactive Multimedia and Artificial Intelligence |
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| Online Access: | https://www.ijimai.org/journal/bibcite/reference/3331 |
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| author | Andrés L. Suárez-Cetrulo David Quintana Alejandro Cervantes |
| author_facet | Andrés L. Suárez-Cetrulo David Quintana Alejandro Cervantes |
| author_sort | Andrés L. Suárez-Cetrulo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-215c3c36e777450a99b4a221a999c790 |
| institution | Kabale University |
| issn | 1989-1660 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universidad Internacional de La Rioja (UNIR) |
| record_format | Article |
| series | International Journal of Interactive Multimedia and Artificial Intelligence |
| spelling | doaj-art-215c3c36e777450a99b4a221a999c7902025-01-03T15:20:35ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16602025-01-019113714810.9781/ijimai.2023.06.003ijimai.2023.06.003Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic ReviewAndrés L. Suárez-CetruloDavid QuintanaAlejandro CervantesRecent 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.https://www.ijimai.org/journal/bibcite/reference/3331concept driftfinancemachine learningmetamodelregime changesystematic review |
| spellingShingle | Andrés L. Suárez-Cetrulo David Quintana Alejandro Cervantes Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review International Journal of Interactive Multimedia and Artificial Intelligence concept drift finance machine learning metamodel regime change systematic review |
| title | Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review |
| title_full | Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review |
| title_fullStr | Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review |
| title_full_unstemmed | Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review |
| title_short | Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review |
| title_sort | machine learning for financial prediction under regime change using technical analysis a systematic review |
| topic | concept drift finance machine learning metamodel regime change systematic review |
| url | https://www.ijimai.org/journal/bibcite/reference/3331 |
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