Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement

Abstract

Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in the stock market. Investment in the stock market has a big risk associated with it. Minimizing prediction error reduces the investment risk. Machine learning (ML) models typically perform better than statistical and econometric models. Also, ensemble ML models have been shown in the literature to be able to produce superior performance than single ML models. In this work, we compare the effectiveness of tree-based ensemble ML models (Random Forest (RF), XGBoost Classifier (XG), Bagging Classifier (BC), AdaBoost Classifier (Ada), Extra Trees Classifier (ET), and Voting Classifier (VC)) in forecasting the direction of stock price movement. Eight different stock data from three stock exchanges (NYSE, NASDAQ, and NSE) are randomly collected and used for the study. Each data set is split into training and test set. Ten-fold cross validation accuracy is used to evaluate the ML models on the training set. In addition, the ML models are evaluated on the test set using accuracy, precision, recall, F1-score, specificity, and area under receiver operating characteristics curve (AUC-ROC). Kendall W test of concordance is used to rank the performance of the tree-based ML algorithms. For the training set, the AdaBoost model performed better than the rest of the models. For the test set, accuracy, precision, F1-score, and AUC metrics generated results significant to rank the models, and the Extra Trees classifier outperformed the other models in all the rankings.

A Random Forests Approach to Predicting Clean Energy Stock Prices

“…For a 20-day forecast horizon, tree bagging and random forests methods produce accuracy rates of between 85% and 90% while logit models produce accuracy rates of between 55% and 60%. These results are in agreement with other research that shows RFs to have a high stock price predictive accuracy (Ballings et al 2015;Basak et al 2019;Lohrmann and Luukka 2019;Weng et al 2018;Ampomah et al 2020). The positive predictive values and negative predictive values indicate that there is little asymmetry between the up and down prediction classifications.…”

“…The research in this paper shows that RFs produce more accurate clean energy stock price direction forecasts than logit models. These results add to a growing body of research that shows machine learning methods like RFs have considerable stock price direction predictive performance (Ballings et al 2015;Basak et al 2019;Lohrmann and Luukka 2019;Weng et al 2018;Ampomah et al 2020). None of these studies, however, consider clean energy stock prices.…”

A Machine Learning Approach for Micro-Credit Scoring

“…It entails random both element and cut-point choice while dividing a node of a tree. Hence, it differs from other tree-based ensemble approaches because it divides nodes by determining cut-points entirely at random, and it practices on the entire training sample to grow the trees (Ampomah et al 2020). The practice of using the entire initial training samples instead of bootstrap replicas is to decrease bias.…”

Supervised learning techniques to predict compounds in pathway modules based on molecular properties

“…Based on random splitting, the execution time of the ETC is faster. Owing to the computational efficiency, the Extra trees algorithm has massive applications for classification and regression [16], [19], [20].…”