Machine Learning
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Multi-resolution subspace for financial trading
Pattern Recognition Letters
Intelligent stock trading system by turning point confirming and probabilistic reasoning
Expert Systems with Applications: An International Journal
Predicting Stock Prices Using a Hybrid Kohonen Self Organizing Map (SOM)
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
A Stock Pattern Recognition Algorithm Based on Neural Networks
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Mining Stock Market Tendency by RS-Based Support Vector Machines
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
The Construction of Stock_s Portfolios by Using Particle Swarm Optimization
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
SBRN '08 Proceedings of the 2008 10th Brazilian Symposium on Neural Networks
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
A Hybrid Neurogenetic Approach for Stock Forecasting
IEEE Transactions on Neural Networks
Hybrid Kansei-SOM model using risk management and company assessment for stock trading
Information Sciences: an International Journal
| Hi-index | 12.05 |
In this paper we propose and analyze a novel method for automatic stock trading which combines technical analysis and the nearest neighbor classification. Our first and foremost objective is to study the feasibility of the practical use of an intelligent prediction system exclusively based on the history of daily stock closing prices and volumes. To this end we propose a technique that consists of a combination of a nearest neighbor classifier and some well known tools of technical analysis, namely, stop loss, stop gain and RSI filter. For assessing the potential use of the proposed method in practice we compared the results obtained to the results that would be obtained by adopting a buy-and-hold strategy. The key performance measure in this comparison was profitability. The proposed method was shown to generate considerable higher profits than buy-and-hold for most of the companies, with few buy operations generated and, consequently, minimizing the risk of market exposure.