Machine Learning
Machine Learning
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers and Operations Research - Special issue: Emerging economics
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
An introduction to variable and feature selection
The Journal of Machine Learning Research
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Flexible neural trees ensemble for stock index modeling
Neurocomputing
International Journal of Electronic Finance
The weighted majority algorithm
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
Dynamic adaptive ensemble case-based reasoning: application to stock market prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Predicting stock returns by classifier ensembles
Applied Soft Computing
Adaptive stock trading with dynamic asset allocation using reinforcement learning
Information Sciences: an International Journal
Lung cancer cell identification based on artificial neural network ensembles
Artificial Intelligence in Medicine
A Study on Feature Selection for Trend Prediction of Stock Trading Price
ICCIS '13 Proceedings of the 2013 International Conference on Computational and Information Sciences
| Hi-index | 12.05 |
Seasonality effects and empirical regularities in financial data have been well documented in the financial economics literature for over seven decades. This paper proposes an expert system that uses novel machine learning techniques to predict the price return over these seasonal events, and then uses these predictions to develop a profitable trading strategy. While simple approaches to trading these regularities can prove profitable, such trading leads to potential large drawdowns (peak-to-trough decline of an investment measured as a percentage between the peak and the trough) in profit. In this paper, we introduce an automated trading system based on performance weighted ensembles of random forests that improves the profitability and stability of trading seasonality events. An analysis of various regression techniques is performed as well as an exploration of the merits of various techniques for expert weighting. The performance of the models is analysed using a large sample of stocks from the DAX. The results show that recency-weighted ensembles of random forests produce superior results in terms of both profitability and prediction accuracy compared with other ensemble techniques. It is also found that using seasonality effects produces superior results than not having them modelled explicitly.