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
A Hybrid Neurogenetic Approach for Stock Forecasting
IEEE Transactions on Neural Networks
Improving trading systems using the RSI financial indicator and neural networks
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Expert Systems with Applications: An International Journal
Information Systems Frontiers
Hybrid Kansei-SOM model using risk management and company assessment for stock trading
Information Sciences: an International Journal
The trading on the mutual funds by gene expression programming with Sortino ratio
Applied Soft Computing
Hi-index | 12.05 |
The stock market is considered as a high complex and dynamic system with noisy, non-stationary and chaotic data series. So it is widely acknowledged that stock price series modeling and forecasting is a challenging work. A significant amount of work has been done in this field, and in them, soft computing techniques have showed good performance. Generally most of these works can be divided into two categories. One is to predict the future trend or price; another is to construct decision support system which can give certain buy/sell signals. In this paper, we propose a new intelligent trading system based on oscillation box prediction by combining stock box theory and support vector machine algorithm. The box theory believes a successful stock buying/selling generally occurs when the price effectively breaks out the original oscillation box into another new box. In the system, two SVM estimators are first utilized to make forecasts of the upper bound and lower bound of the price oscillation box. Then a trading strategy based on the two bound forecasts is constructed to make trading decisions. In the experiment, we test the system on different stock movement patterns, i.e. bull, bear and fluctuant market, and investigate the training of the system and the choice of the time span of the price box. The experiments on 442 S&P500 components show a promising performance is achieved and the system dramatically outperforms buy-and-hold strategy.