Stock-market prediction using machine-learning technique aims at developing effective and efficient models that can provide a better and higher rate of prediction accuracy. Numerous ensemble regressors and classifiers have been applied in stock market predictions, using different combination techniques. However, three precarious issues come in mind when constructing ensemble classifiers and regressors. The first concerns with the choice of base regressor or classifier technique adopted. The second concerns the combination techniques used to assemble multiple regressors or classifiers and the third concerns with the quantum of regressors or classifiers to be ensembled. Subsequently, the number of relevant studies scrutinising these previously mentioned concerns are limited. In this study, we performed an extensive comparative analysis of ensemble techniques such as boosting, bagging, blending and super learners (stacking). Using Decision Trees (DT), Support Vector Machine (SVM) and Neural Network (NN), we constructed twenty-five (25) different ensembled regressors and classifiers. We compared their execution times, accuracy, and error metrics over stock-data from Ghana Stock Exchange (GSE), Johannesburg Stock Exchange (JSE), Bombay Stock Exchange (BSE-SENSEX) and New York Stock Exchange (NYSE), from January 2012 to December 2018. The study outcome shows that stacking and blending ensemble techniques offer higher prediction accuracies (90–100%) and (85.7–100%) respectively, compared with that of bagging (53–97.78%) and boosting (52.7–96.32%). Furthermore, the root means square error (RMSE) recorded by stacking (0.0001–0.001) and blending (0.002–0.01) shows a better fit of ensemble classifiers and regressors based on these two techniques in market analyses compared with bagging (0.01–0.11) and boosting (0.01–0.443). Finally, the results undoubtedly suggest that an innovative study in the domain of stock market direction prediction ought to include ensemble techniques in their sets of algorithms.

使用机器学习技术进行的股票市场预测旨在开发有效且高效的模型，从而可以提供更高和更高的预测准确性。使用不同的组合技术，许多集成回归器和分类器已应用于股票市场预测。但是，在构造集成分类器和回归器时会想到三个不稳定的问题。首先要考虑采用的基本回归器或分类器技术。第二个涉及用于组装多个回归器或分类器的组合技术，第三个涉及要集合的回归器或分类器的数量。随后，审查这些前述问题的相关研究数量有限。在这个研究中，我们对集成技术进行了广泛的比较分析，例如增强，装袋，混合和超级学习者（堆叠）。使用决策树（DT），支持向量机（SVM）和神经网络（NN），我们构造了二十五（25）个不同的组合回归器和分类器。我们从2012年1月开始比较了加纳证券交易所（GSE），约翰内斯堡证券交易所（JSE），孟买证券交易所（BSE-SENSEX）和纽约证券交易所（NYSE）的股票数据的执行时间，准确性和错误指标。到2018年12月。研究结果表明，与套袋（53–97.78％）和升压（52.7–96.32）相比，堆叠和混合合奏技术分别提供更高的预测精度（90–100％）和（85.7–100％）。 ％）。此外，其根均方根是通过堆栈记录的（0.0001–0）。001）和混合（0.002-0.01）显示出在市场分析中基于这两种技术的集成分类器和回归器比套袋（0.01-0.11）和增强（0.01-0.443）更好。最后，结果无疑表明，在股票市场方向预测领域的创新研究应在其算法集中包括集成技术。