Chagwiza, WilbertKubjana, TlouGumani, Thanyani Rodney2022-11-242022-11-242022-11-10Gumani, T. R. (2022) Fundamental Analysis for Stocks using Extreme Gradient Boosting. University of Venda. South Africa.<http://hdl.handle.net/11602/2383>.http://hdl.handle.net/11602/2383MSc (Applied Mathematics)Department of Mathematical and Computational SciencesWhen it comes to stock price prediction, machine learning has grown in popularity. Accurate stock prediction is a very difficult activity as financial stock markets are unpredictable and non-linear in nature. With the advent of machine learning and improved computational capabilities, programmed prediction methods have proven to be more effective in stock price prediction. Extreme gradient boosting(XGBoost) is the variant of the gradient boosting machine. XGBoost, an ensemble method of classification trees, is investigated for the prediction of stock prices based on the fundamental analysis. XGBoost outperformed the competition and had higher accuracy. The developed XGBoost model proved to be an effective model that accurately predicts the stock market trend, which is considered to be much better than conventional non-ensemble learning techniques.1 online resource (vii, 42 leaves} : color illustrationsenUniversity of VendaStock PredictionUCTDMachine LearningXG BoostFundamental AnalysisClassification515Machine learningFundamental analysisStock -- PricesStocksDissertationGumani TR. Fundamental Analysis for Stocks using Extreme Gradient Boosting. []. , 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/2383Gumani, T. R. (2022). <i>Fundamental Analysis for Stocks using Extreme Gradient Boosting</i>. (). . Retrieved from http://hdl.handle.net/11602/2383Gumani, Thanyani Rodney. <i>"Fundamental Analysis for Stocks using Extreme Gradient Boosting."</i> ., , 2022. http://hdl.handle.net/11602/2383TY - Dissertation AU - Gumani, Thanyani Rodney AB - When it comes to stock price prediction, machine learning has grown in popularity. Accurate stock prediction is a very difficult activity as financial stock markets are unpredictable and non-linear in nature. With the advent of machine learning and improved computational capabilities, programmed prediction methods have proven to be more effective in stock price prediction. Extreme gradient boosting(XGBoost) is the variant of the gradient boosting machine. XGBoost, an ensemble method of classification trees, is investigated for the prediction of stock prices based on the fundamental analysis. XGBoost outperformed the competition and had higher accuracy. The developed XGBoost model proved to be an effective model that accurately predicts the stock market trend, which is considered to be much better than conventional non-ensemble learning techniques. DA - 2022-11-10 DB - ResearchSpace DP - Univen KW - Stock Prediction KW - Machine Learning KW - XG Boost KW - Fundamental Analysis KW - Classification LK - https://univendspace.univen.ac.za PY - 2022 T1 - Fundamental Analysis for Stocks using Extreme Gradient Boosting TI - Fundamental Analysis for Stocks using Extreme Gradient Boosting UR - http://hdl.handle.net/11602/2383 ER -