Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Optimal linear combinations of neural networks
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combination of multiple classifiers for the customer's purchase behavior prediction
Decision Support Systems - Special issue: Agents and e-commerce business models
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
A fusion model of HMM, ANN and GA for stock market forecasting
Expert Systems with Applications: An International Journal
Forecasting the volatility of stock price index
Expert Systems with Applications: An International Journal
Combining heterogeneous classifiers for stock selection: Research Articles
International Journal of Intelligent Systems in Accounting and Finance Management
Constructing ensembles of classifiers by means of weighted instance selection
IEEE Transactions on Neural Networks
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
Stock market trading rule discovery using two-layer bias decision tree
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
Save the best for last? The treatment of dominant predictors in financial forecasting
Expert Systems with Applications: An International Journal
Automated trading with performance weighted random forests and seasonality
Expert Systems with Applications: An International Journal
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The problem of predicting stock returns has been an important issue for many years. Advancement in computer technology has allowed many recent studies to utilize machine learning techniques such as neural networks and decision trees to predict stock returns. In the area of machine learning, classifier ensembles (i.e. combining multiple classifiers) have proven to be a method superior to single classifiers. In order to build a better model for predicting stock returns effectively and efficiently, this study aims at investigating the prediction performance that utilizes the classifier ensembles method to analyze stock returns. In particular, the hybrid methods of majority voting and bagging are considered. Moreover, performance using two types of classifier ensembles is compared with those using single baseline classifiers (i.e. neural networks, decision trees, and logistic regression). These two types of ensembles are 'homogeneous' classifier ensembles (e.g. an ensemble of neural networks) and 'heterogeneous' classifier ensembles (e.g. an ensemble of neural networks, decision trees and logistic regression). Average prediction accuracy, Type I and II errors, and return on investment of these models are also examined. Our results indicate that multiple classifiers outperform single classifiers in terms of prediction accuracy and returns on investment. In addition, heterogeneous classifier ensembles offer slightly better performance than the homogeneous ones. However, there is no significant difference between majority voting and bagging in prediction accuracy, but the former has better stock returns prediction accuracy than the latter. Finally, the homogeneous multiple classifiers using neural networks by majority voting perform best when predicting stock returns.