{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/predicting-the-direction-of-stock-market","title":"Predicting the direction of stock market prices using random forest","arxiv_id":"1605.00003","date":"2016-04-29","proceeding":null,"authors":["Luckyson Khaidem","Snehanshu Saha","Sudeepa Roy Dey"],"abstract":"Predicting trends in stock market prices has been an area of interest for\nresearchers for many years due to its complex and dynamic nature. Intrinsic\nvolatility in stock market across the globe makes the task of prediction\nchallenging. Forecasting and diffusion modeling, although effective can't be\nthe panacea to the diverse range of problems encountered in prediction,\nshort-term or otherwise. Market risk, strongly correlated with forecasting\nerrors, needs to be minimized to ensure minimal risk in investment. The authors\npropose to minimize forecasting error by treating the forecasting problem as a\nclassification problem, a popular suite of algorithms in Machine learning. In\nthis paper, we propose a novel way to minimize the risk of investment in stock\nmarket by predicting the returns of a stock using a class of powerful machine\nlearning algorithms known as ensemble learning. Some of the technical\nindicators such as Relative Strength Index (RSI), stochastic oscillator etc are\nused as inputs to train our model. The learning model used is an ensemble of\nmultiple decision trees. The algorithm is shown to outperform existing algo-\nrithms found in the literature. Out of Bag (OOB) error estimates have been\nfound to be encouraging. Key Words: Random Forest Classifier, stock price\nforecasting, Exponential smoothing, feature extraction, OOB error and\nconvergence.","url_abs":"http://arxiv.org/abs/1605.00003v1","url_pdf":"http://arxiv.org/pdf/1605.00003v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"predicting-the-direction-of-stock-market","repo_url":"https://github.com/eric-kruger/RForest_EquitiesPredict","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"predicting-the-direction-of-stock-market","repo_url":"https://github.com/jmartinezheras/reproduce-stock-market-direction-random-forests","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"predicting-the-direction-of-stock-market","repo_url":"https://github.com/wpla/Khaidem.etal.2016_Analysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"predicting-the-direction-of-stock-market","repo_url":"https://github.com/Siddaarth-Babu/Prediction-of-Stock-Market-using-RF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"predicting-the-direction-of-stock-market","repo_url":"https://github.com/osman-haider/stock-market-prices-prediction-using-RF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1605.00003","atlas_url":"https://app.syntology.ai/?focus=1605.00003","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.00003"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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