[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125051-en":3,"doc-seo-125051-105":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},125051,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","MACHINE LEARNING FOR STOCK MARKET FORECASTING - A REVIEW OF MODELS AND ACCURACY","As financial markets become increasingly complex and dynamic, machine learning (ML) methods have attracted substantial attention for stock market forecasting. The review surveys traditional time-series approaches such as ARIMA and MACD and explains their limits in modeling intricate financial patterns. It then examines advanced ML model families including SVM, ANN, and ensemble methods such as random forests and gradient boosting, focusing on their strengths, weaknesses, and measured predictive accuracy.","OPEN ACCESS  \nFinance & Accounting Research Journal P-ISSN: 2708-633X, E-ISSN: 2708-6348  \nVolume 6, Issue 2, P.No. 112-124, February 2024 DOI: 10.51594/farj.v6i2.783  \nFair East Publishers [Journal Homepage:](Journal Homepage: www.fepbl.com/index.php/farj)[ ](Journal Homepage: www.fepbl.com/index.php/farj)[www.fepbl.com/index.php/farj](Journal Homepage: www.fepbl.com/index.php/farj)  \nMACHINE LEARNING FOR STOCK MARKET FORECASTING: A REVIEW OF MODELS AND ACCURACY  \nDavid Iyanuoluwa Ajiga 1, Rhoda Adura Adeleye2, Tula Sunday Tubokirifuruar3, Binaebi Gloria Bello4, Ndubuisi Leonard Ndubuisi5, Onyeka Franca Asuzu6, &  \nOluwaseyi Rita Owolabi7  \n1Independent Researcher, Chicago, Illinois, USA  \n2Information Technology & Management, University of Texas, Dallas, USA 3Department of Accounting, Ignition Ajuru University of Education, Rivers State, Nigeria 4Kings International School, Port-Harcourt, Rivers State, Nigeria  \n5 Spacepointe Limited, Rivers State, Nigeria  \n6Dangote Sugar Refinery Plc, Lagos, Nigeria  \n7Independent Researcher, Indianapolis Indiana, USA  \n*Corresponding Author: Onyeka Franca Asuzu  \nCorresponding Author Email: [asuzufranca@yahoo.com](asuzufranca@yahoo.com)  \nArticle Received: 20-10-23 Accepted: 01-02-24 Published: 14-02-24  \nLicensing Details: Author retains the right of this article. The article is distributed under the terms of  \nthe Creative Commons Attribution-Non Commercial 4.0 License  \n([http://www.creativecommons.org/licences/by-nc/4.0/](http://www.creativecommons.org/licences/by-nc/4.0/)) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the Journal open access page.  \nABSTRACT  \nAs financial markets become increasingly complex and dynamic, the application of machine learning (ML) techniques for stock market forecasting has garnered significant attention. This paper presents a comprehensive review of various ML models employed in the realm of stock market forecasting, focusing on their methodologies and the accuracy achieved in predicting market trends. The review begins by examining traditional time-series models such as autoregressive integrated moving average (ARIMA) and moving average convergence divergence (MACD) and their limitations in capturing the intricate patterns present in financial data. Subsequently, the discussion transitions to more advanced ML models, including support vector machines (SVM), artificial neural networks (ANN), and ensemble methods like random forests and gradient boosting. Each model's strengths and weaknesses are scrutinized in the context of stock market forecasting. The paper explores the pivotal role of feature selection and  \nengineering in enhancing the predictive power of ML models. Feature sets encompassing financial indicators, macroeconomic variables, sentiment analysis from news articles, and social media data are analyzed for their impact on forecasting accuracy. Additionally, the incorporation of technical indicators and alternative data sources is explored as potential avenues to improve model robustness. A critical aspect of this review is the assessment of accuracy in predicting stock market movements. The evaluation is conducted through a comparative analysis of model performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and accuracy rates. The study also addresses the challenge of model overfitting and proposes strategies to mitigate this issue for more reliable predictions. This review provides a nuanced understanding of the landscape of ML models for stock market forecasting, highlighting the diverse approaches, challenges, and opportunities in the quest for improved accuracy. It contributes valuable insights for researchers, practitioners, and investors seeking to leverage the potential of ML in navigating the complexities of financial markets. Keywords: Machine Learning, Stock Market, Forecasting, Models, Re","cbCaifGswle4ubDn","https://ap.wps.com/l/cbCaifGswle4ubDn","pdf",285480,1,13,"English","en",105,"# Abstract\n# Introduction\n## Motivation for ML in forecasting\n## Importance of accurate forecasting\n## ML advantages over traditional methods\n# Model Review and Accuracy Evaluation\n## Traditional time-series models and limitations\n## Advanced ML models and ensemble approaches\n## Feature selection and engineering\n## Accuracy metrics and validation\n## Overfitting challenges and mitigation strategies","[{\"question\":\"Why are traditional stock market forecasting methods often insufficient?\",\"answer\":\"Financial markets are volatile and unpredictable, and traditional time-series approaches like ARIMA and MACD may not capture complex patterns in financial data effectively.\"},{\"question\":\"Which ML model types are reviewed for stock market forecasting?\",\"answer\":\"The review covers support vector machines (SVM), artificial neural networks (ANN), and ensemble methods such as random forests and gradient boosting.\"},{\"question\":\"How is forecasting accuracy assessed in the review?\",\"answer\":\"Model performance is evaluated using comparative metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), and accuracy rates, alongside discussion of overfitting and mitigation strategies.\"}]","MACHINE LEARNING FOR STOCK MARKET FORECASTING - 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