Stock market decision support modeling with tree-based AdaBoost ensemble machine learning models

Ernest Kwame Ampomah, Zhiguang Qin, Gabriel Nyame, Francis Effirm Botchey


Forecasting stock market behavior has received tremendous attention from investors, and researchers for a very long time due to its potential profitability. Predicting stock market behavior is regarded as one of the extremely challenging applications of time series forecasting. While there is divided opinion on the efficiency of markets, numerous empirical studies which are widely accepted have shown that the stock market is predictable to some extent. Statistical based methods and machine learning models are used to forecast and analyze the stock market. Machine learning (ML) models typically perform better than those of statistical and econometric models. In addition, performance of ensemble ML models is typically superior to those of individual ML models. In this paper, we study and compare the efficiency of tree-based AdaBoost ensemble ML models (namely, AdaBoost-DecisionTree (Ada-DT), AdaBoost-RandomForest (Ada-RF), AdaBoost-Bagging (Ada-BAG), and Bagging-ExtraTrees (Bag-ET)). Ten stock data sets randomly collected from three different stock exchanges (NYSE, NASDAQ, and NSE) are used for the study. Forty technical indicators are computed and used as input features. The performance of the models is evaluated using accuracy, precision, recall, F-measure, specificity. And AUC metrics. Also, Kendall W test of concordance is used to rank the performance of the different models. The experimental results show that AdaBoost- ExtraTree (Ada-ET) model is the highest performer among the tree-based AdaBoost ensemble models studied.

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