{"id":8582,"date":"2024-11-06T11:43:40","date_gmt":"2024-11-06T11:43:40","guid":{"rendered":"https:\/\/revista.isfin.ro\/?p=8582"},"modified":"2024-11-19T11:39:56","modified_gmt":"2024-11-19T11:39:56","slug":"comparative-analysis-of-regression-models-for-stock-price-prediction-linear-support-vector-polynomial-and-lasso","status":"publish","type":"post","link":"https:\/\/revista.isfin.ro\/en\/2024\/11\/06\/comparative-analysis-of-regression-models-for-stock-price-prediction-linear-support-vector-polynomial-and-lasso\/","title":{"rendered":"COMPARATIVE ANALYSIS OF REGRESSION MODELS FOR STOCK PRICE PREDICTION: LINEAR, SUPPORT VECTOR, POLYNOMIAL, AND LASSO"},"content":{"rendered":"
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Authors: \u0218tefan Rusu, Marcel Ioan Bolo\u0219, Marius Leordeanu<\/p>

Vol. 9 \u2022 No. 17 \u2022 2024<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\tView abstract PDF\t\t\t\t\t<\/a>\n\t\t\t\t<\/h6>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Abstract<\/strong><\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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This paper investigates the performance of various regression-based machine learning techniques for short-term stock price prediction. Four regression algorithms \u2013 linear regression, support vector regression (SVR), polynomial regression and LASSO regression \u2013 were applied to Apple Inc\u2019s historical price data for two-years ending on October 1st, 2024, to predict the next day\u2019s closing stock price. Technical indicators such as moving average, relative strength index (RSI) and lag features were included in the regressions to improve prediction performance. Evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R\u00b2) were employed to assess the models\u2019 performance. Results show that linear regression and LASSO regression were the most performant models, reaching R\u00b2 values of more than 0.95, with minimal error values. While SVR yielded the poorest results when accounting for all metrics. Overall, the study highlights the predictive power of simpler regression models over more complex ones in stock price predictions and offers recommendations for model selection.<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Keywords:<\/strong> Stock market, machine learning, predictions, regressions, linear regression, LASSO regression, polynomial regression, support vector regression.<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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JEL Classification:<\/strong> C32; C52; C53; G10<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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DOI: <\/strong>10.55654\/JFS.2024.9.17.09<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Authors: \u0218tefan Rusu, Marcel Ioan Bolo\u0219, Marius Leordeanu Vol. 9 \u2022 No. 17 \u2022 2024 View abstract PDF View article PDF Abstract This paper investigates the performance of various regression-based machine learning techniques for short-term stock price prediction. Four regression algorithms \u2013 linear regression, support vector regression (SVR), polynomial regression and LASSO regression \u2013 were …<\/p>\n

COMPARATIVE ANALYSIS OF REGRESSION MODELS FOR STOCK PRICE PREDICTION: LINEAR, SUPPORT VECTOR, POLYNOMIAL, AND LASSO<\/span> Read More »<\/a><\/p>","protected":false},"author":6,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[184],"tags":[],"yoast_head":"COMPARATIVE ANALYSIS OF REGRESSION MODELS FOR STOCK PRICE PREDICTION: LINEAR, SUPPORT VECTOR, POLYNOMIAL, AND LASSO - Revista de Studii Financiare<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/revista.isfin.ro\/en\/2024\/11\/06\/comparative-analysis-of-regression-models-for-stock-price-prediction-linear-support-vector-polynomial-and-lasso\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"COMPARATIVE ANALYSIS OF REGRESSION MODELS FOR STOCK PRICE PREDICTION: LINEAR, SUPPORT VECTOR, POLYNOMIAL, AND LASSO - Revista de Studii Financiare\" \/>\n<meta property=\"og:description\" content=\"Authors: \u0218tefan Rusu, Marcel Ioan Bolo\u0219, Marius Leordeanu Vol. 9 \u2022 No. 17 \u2022 2024 View abstract PDF View article PDF Abstract This paper investigates the performance of various regression-based machine learning techniques for short-term stock price prediction. 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