[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120386-en":3,"doc-seo-120386-105":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},120386,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",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 time series and frequent structural breaks in market dynamics. Conventional machine learning and statistical methods are widely used, yet many struggle to adapt quickly to changes in how prices are generated. This systematic review surveys key research on regime-change-aware financial prediction, with special focus on technical analysis. It discusses how work links data-stream learning and economic research, while concluding that no single approach dominates and the field remains early-stage.","Regular Issue  \nMachine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review  \nAndrés L. Suárez-Cetrulo1, David Quintana2, Alejandro Cervantes3*  \n1 Ireland’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 (Spain)  \n3 Escuela Superior de Ingeniería y Tecnología, Universidad Internacional de La Rioja (UNIR), Logroño (Spain)  \n* Corresponding author: [alejandro.cervantesrovira@unir.net](alejandro.cervantesrovira@unir.net)  \nReceived 26 May 2022 | Accepted 28 April 2023 | Published 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 covered 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 the main t","cbCaiu5sdAwbdoRQ","https://ap.wps.com/l/cbCaiu5sdAwbdoRQ","pdf",397206,1,12,"English","en",105,"# Abstract\n# Introduction\n## Non-stationarity and structural change\n## Concept drift and repeating market states\n## Regime change and regime-switching modeling\n## Role of technical indicators and explainability\n## Time-varying correlations in market data","[{\"question\":\"Why is regime change important for financial prediction?\",\"answer\":\"Regime changes reflect shifts in collective market behavior that alter price dynamics and macro/microeconomic variables. Such shifts can quickly change returns, volatility, and correlations, making prior assumptions unreliable.\"},{\"question\":\"What is the focus of this systematic review?\",\"answer\":\"The review surveys relevant literature on machine learning for financial prediction under regime change, emphasizing methods that use technical analysis. It also highlights efforts bridging data-stream learning and economic research.\"},{\"question\":\"Does the review identify a single dominant machine learning technique?\",\"answer\":\"No. Although many algorithm families have been tested, the findings do not indicate a clearly dominant approach in this domain, and the research stage is described as early.\"}]","Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis - A Systematic Review | PDF",1785729771,30,{"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/120386/",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 regime change important for financial prediction?","Question",{"text":75,"@type":76},"Regime changes reflect shifts in collective market behavior that alter price dynamics and macro/microeconomic variables. Such shifts can quickly change returns, volatility, and correlations, making prior assumptions unreliable.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the focus of this systematic review?",{"text":80,"@type":76},"The review surveys relevant literature on machine learning for financial prediction under regime change, emphasizing methods that use technical analysis. It also highlights efforts bridging data-stream learning and economic research.",{"name":82,"@type":73,"acceptedAnswer":83},"Does the review identify a single dominant machine learning technique?",{"text":84,"@type":76},"No. Although many algorithm families have been tested, the findings do not indicate a clearly dominant approach in this domain, and the research stage is described as early.","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"]