The nature of statistical learning theory
The nature of statistical learning theory
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Regression neural network for error correction in foreign exchange forecasting and trading
Computers and Operations Research
StockMarket Forecasting Using Hidden Markov Model: A New Approach
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Intelligent stock trading system by turning point confirming and probabilistic reasoning
Expert Systems with Applications: An International Journal
The application of echo state network in stock data mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
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The stock market is considered as a high complex and dynamic system. Many machine learning and data mining technologies are used for stock analysis, but it still leaves an open question about how to integrate these methods with the plentiful knowledge and techniques accumulated in stock investment which are critical to the successful stock analysis. In this paper, we propose an intelligent stock trading system by combining support vector machine (SVM) algorithm and box theory of stock. The box theory believes a successful stock buying/selling generally occurs when the price effectivley breaks out the original oscillation box into another new box. In the system, support vector machine algorithm is utilized to make forecasts of the top and bottom of the oscillation box. Then a trading strategy based on the box theory is constructed to make trading decisions. The different stock movement patterns, i.e, bull, bear and fluctuant market, are used to test the feasibility of the system. The experiments on S&P500 components show a promising performance is achieved.