{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T05:02:02Z","timestamp":1769749322052,"version":"3.49.0"},"reference-count":21,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T00:00:00Z","timestamp":1759017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["BDCC"],"abstract":"<jats:p>Stock price prediction remains a challenging problem due to the inherent volatility and complexity of financial markets. This study proposes a multi-model machine learning framework for one-day-ahead stock price prediction using thirty-six features derived from technical indicators. Empirical analysis is conducted on data from Apple, Tesla, and NVIDIA, employing nine classification algorithms, including support vector machines, random forests, extreme gradient boosting, and logistic regression. Results indicate that momentum-based indicators are the most influential predictors. While support vector machines achieve the highest accuracy for Apple, extreme gradient boosting performed best for NVIDIA and Tesla. In addition, explainable AI techniques are applied to interpret individual model predictions, thereby enhancing transparency and trust in the results. The study contributes to financial analytics research by providing a comparative evaluation of diverse machine learning methods and highlighting key indicators critical for short-term stock price forecasting.<\/jats:p>","DOI":"10.3390\/bdcc9100248","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T11:40:52Z","timestamp":1759146052000},"page":"248","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Multi-Model Machine Learning Framework for Daily Stock Price Prediction"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4229-0109","authenticated-orcid":false,"given":"Bharatendra","family":"Rai","sequence":"first","affiliation":[{"name":"Department of Decision and Information Sciences, Charlton College of Business, University of Massachusetts Dartmouth, 285 Westport Road, North Dartmouth, MA 02747, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4307-5814","authenticated-orcid":false,"given":"Leili","family":"Soltanisehat","sequence":"additional","affiliation":[{"name":"Department of Decision and Information Sciences, Charlton College of Business, University of Massachusetts Dartmouth, 285 Westport Road, North Dartmouth, MA 02747, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1086\/294743","article-title":"The Behavior of Stock-Market Prices","volume":"38","author":"Fama","year":"1965","journal-title":"J. Bus."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"75","DOI":"10.2469\/faj.v51.n1.1861","article-title":"Random walks in stock market prices","volume":"51","author":"Fama","year":"1995","journal-title":"Financ. Anal. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6800","DOI":"10.1016\/j.eswa.2008.08.008","article-title":"Development and performance evaluation of FLANN based model for forecasting of stock markets","volume":"36","author":"Majhi","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1016\/j.ins.2010.01.014","article-title":"A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting","volume":"180","author":"Cheng","year":"2010","journal-title":"Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.eswa.2015.07.063","article-title":"An intelligent short term stock trading fuzzy system for assisting investors in portfolio management","volume":"43","author":"Chourmouziadis","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.jbef.2015.03.003","article-title":"Trading System based on the use of technical analysis: A computational experiment","volume":"6","author":"Bergo","year":"2015","journal-title":"J. Behav. Exp. Financ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.eswa.2017.12.026","article-title":"A novel data-driven stock price trend prediction system","volume":"97","author":"Zhang","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_8","first-page":"288","article-title":"The complete guide to using candlestick charting; how to earn high rates of return\u2014Safely","volume":"24","author":"Northcott","year":"2009","journal-title":"Atl. Publ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"309","DOI":"10.2307\/1907042","article-title":"Can stock market forecasters forecast?","volume":"1","author":"Cowles","year":"1933","journal-title":"Econometrica"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1257\/jel.45.4.936","article-title":"The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis","volume":"45","author":"Menkhoff","year":"2007","journal-title":"J. Econ. Lit."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.jfineco.2008.07.002","article-title":"Technical analysis: An asset allocation perspective on the use of moving averages","volume":"92","author":"Zhu","year":"2009","journal-title":"J. Financ. Econ."},{"key":"ref_12","first-page":"14072","article-title":"Applying a GA kernel on optimizing technical analysis rules for stock picking and portfolio composition","volume":"38","author":"Gorgulho","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1016\/j.eswa.2011.07.051","article-title":"A novel model by evolving partially connected neural network for stock price trend forecasting","volume":"39","author":"Chang","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7046","DOI":"10.1016\/j.eswa.2015.05.013","article-title":"Evaluating multiple classifiers for stock price direction prediction","volume":"42","author":"Ballings","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.eswa.2014.07.040","article-title":"Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques","volume":"42","author":"Patel","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.jfds.2016.03.002","article-title":"A hybrid stock trading framework integrating technical analysis with machine learning techniques","volume":"2","author":"Dash","year":"2016","journal-title":"J. Financ. Data Sci."},{"key":"ref_17","unstructured":"Ayalon, Y. (2025). Technical Analysis Indicators 101: A Practical Guide to Technical Analysis Indicators, Independently Published. ISBN-13: 979-8292402558."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e1301","DOI":"10.1002\/widm.1301","article-title":"Hyperparameters and tuning strategies for random forest","volume":"9","author":"Probst","year":"2019","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Putatunda, S., and Rama, K. (2018, January 28\u201330). A Comparative Analysis of Hyperopt as Against Other Approaches for Hyper-Parameter Optimization of XGBoost. Proceedings of the 2018 International Conference on Signal Processing and Machine Learning (SPML \u201818), Shanghai, China.","DOI":"10.1145\/3297067.3297080"},{"key":"ref_20","first-page":"100631","article-title":"Key technical indicators for stock market prediction","volume":"20","author":"Mostafavi","year":"2025","journal-title":"Mach. Learn. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"100398","DOI":"10.1016\/j.joitmc.2024.100398","article-title":"Technical indicator empowered intelligent strategies to predict stock trading signals","volume":"10","author":"Saud","year":"2024","journal-title":"J. Open Innov. Technol. Mark. 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