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

• Published in: Informatica (Ljubljana) Vol. 44; no. 4; pp. 477 - 489
• Main Authors: Ampomah, Ernest Kwame, Qin, Zhiguang, Nyame, Gabriel, Botchey, Francis Effirm
• Format: Journal Article
• Language: English
• Published: Ljubljana Slovenian Society Informatika / Slovensko drustvo Informatika 01.12.2020
• Subjects: Accuracy; Algorithms; Bagging; Classifiers; Decision trees; Economic forecasting; Efficiency; Empirical analysis; Forecasting; Investments; Machine learning; Mathematical models; Neural networks; Performance evaluation; Profitability; Securities markets; Social networks; Statistical methods; Stock exchanges; Support vector machines; Test sets; Time series; New York
• ISSN: 0350-5596; 1854-3871
• DOI: 10.31449/inf.v44i4.3159

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 ensemble ML models (namely, Bagging classifier, Random Forest (RF), Extra trees classifier (ET), AdaBoost of Bagging (ADAofBAG), AdaBoost of RandomForest (ADAofRF), and AdaBoost of ExtraTrees (ADA_of_ET)). Stock data randomly collected from three different stock exchanges were used for the study. Forty technical indicators were computed and used as input features. The data set was spilt into training and test sets. The performance of the models was evaluated with the test set using accuracy, precision, recall, F1-score, specificity and AUC metrics. Kendall W test of concordance was used to rank the performance of the different models. The experimental results indicated that AdaBoost of Bagging (ADAofBAG) model was the highest performer among the tree-based ensemble models studied. Also, boosting of the bagging ensemble models improved the performance of the bagging ensemble models.