[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120724-en":3,"doc-seo-120724-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120724,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis - A Systematic Review","Recent crises, recessions, and bubbles highlight the non-stationary nature of financial data and the occurrence of abrupt structural changes. Existing studies often rely on conventional machine learning and statistical methods, yet many cannot adapt efficiently to shifts in the underlying price-generation process. This systematic review surveys machine learning approaches for financial prediction under regime change, with a focused emphasis on technical analysis and bridges between data-stream learning and economic research communities, while noting the field remains early-stage with no clearly dominant technique.","Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review  \nAndrés L. Suárez-Cetrulo1, David Quintana2, Alejandro Cervantes3*  \n1Ireland’s Centre for Applied AI (CeADAR), University College Dublin (Ireland)  \n2 Department of Computer Science and Engineering, Universidad Carlos III de Madrid, Avda. Universidad 30, 28911 Leganes  \n(Spain)  \n3 Escuela Superior de Ingeniería y Tecnología, Universidad Internacional de La Rioja (UNIR), Logroño (Spain)  \nReceived 26 May 2022 | Accepted 28 April 2023 | Early Access 23 June 2023  \nAbstract   \nRecent 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.  \nI. Introduction  \nFinancial markets can be described asan evolutionary and nonlinear  \ndynamical complex system [1], [2] . Forecasting in the financial domain has traditionally been performed under the assumption that the underlying data has been created by a linear process [3] . Another line of work to make financial predictions is to use machine learning (ML) . These algorithms have surprised financial experts [4]–[6] because of their success in mapping nonlinear relationships without prior knowledge [7]. Deep learning algorithms (neural networks) and ensembles have been some of the techniques obtaining the best results for stock trend prediction [8]–[13] .  \nDifferent crises, recessions and bubbles, such as the COVID-19 pandemic, or volatile mid-term trends in crypto markets, have made apparent the non-stationary nature and the presence of drastic structural changes in financial markets [14] . During these periods, mean returns, volatility and correlations among assets tend to change quickly [15]. This has brought attention to the problem of concept drift [16] in computational finance [17]. Many recent research works point out that financial assets or companies present different states that may repeat or not overtime or evolve due to inflation, deflation, or changes in supply and demand [18]–[24] .  \nIn finance, a change in the collective behaviour of market participants and their reactions is called a regime change (RC) . As  \n* Corresponding author.  \nE-mail address: [alejandro.cervantesrovira@unir.net](alejandro.cervantesrovira@unir.net)  \ncovered by the marked efficiency hypothesis [25], we cannot observe the individual behaviour of a trader or its intentions. Instead, we can only observe changes in the price dynamics and macro or microeconomic variables and extrapolate the changes that make them modify their behaviour. The execution of these strategies is the actual generative process of the observed time series of prices or trends. The estimation of the hidden processes driving the market into different regimes is often approached using regime-switching models, a typeof time series model where parameters can have different values indifferent cycles [26] .  \nDespite the fact that artificial intelligence has recently become a trend and even a buzzword in many industries, this has not become th","cbCaiexujXEXc6EF","https://ap.wps.com/l/cbCaiexujXEXc6EF","pdf",997078,1,12,"English","en",105,"# Abstract\n# Introduction\n## Non-stationarity and structural changes in financial markets\n## Concept drift in computational finance\n## Regime change and modeling approaches\n## Role of technical indicators and interpretability","[{\"question\":\"Why is financial prediction under regime change challenging?\",\"answer\":\"Financial markets are non-stationary and experience drastic structural changes, causing returns, volatility, and asset correlations to evolve quickly. This creates the concept drift problem in computational finance.\"},{\"question\":\"What does the review focus on?\",\"answer\":\"It surveys relevant literature on machine learning for financial prediction under regime change using a systematic approach, with special emphasis on technical analysis.\"},{\"question\":\"Do the reviewed results indicate a single dominant machine learning technique?\",\"answer\":\"No. Although a wide range of algorithms has been tested, the review does not suggest any specific technique is clearly dominant in this domain.\"}]","Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis - A Systematic Review | PDF",1785731717,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-financial-prediction-under-regime-change-using-technical-analysis-a-systematic-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-financial-prediction-under-regime-change-using-technical-analysis-a-systematic-review/120724/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is financial prediction under regime change challenging?","Question",{"text":75,"@type":76},"Financial markets are non-stationary and experience drastic structural changes, causing returns, volatility, and asset correlations to evolve quickly. This creates the concept drift problem in computational finance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the review focus on?",{"text":80,"@type":76},"It surveys relevant literature on machine learning for financial prediction under regime change using a systematic approach, with special emphasis on technical analysis.",{"name":82,"@type":73,"acceptedAnswer":83},"Do the reviewed results indicate a single dominant machine learning technique?",{"text":84,"@type":76},"No. Although a wide range of algorithms has been tested, the review does not suggest any specific technique is clearly dominant in this domain.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]