The nature of statistical learning theory
The nature of statistical learning theory
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Support Vector Machine for Regression and Applications to Financial Forecasting
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Real-time prediction of order flowtimes using support vector regression
Computers and Operations Research
Applied Intelligence
A dynamic holding strategy in public transit systems with real-time information
Applied Intelligence
Time series prediction using support vector machines: a survey
IEEE Computational Intelligence Magazine
Expert Systems with Applications: An International Journal
A multiple-kernel support vector regression approach for stock market price forecasting
Expert Systems with Applications: An International Journal
Fast support vector regression based on cut
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Conservative and aggressive rough SVR modeling
Theoretical Computer Science
A heuristic weight-setting algorithm for robust weighted least squares support vector regression
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
A fast data preprocessing procedure for support vector regression
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
A time-dependent enhanced support vector machine for time series regression
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Recently, Support Vector Regression (SVR) has been introduced to solve regression and prediction problems. In this paper, we apply SVR to financial prediction tasks. In particular, the financial data are usually noisy and the associated risk is time-varying. Therefore, our SVR model is an extension of the standard SVR which incorporates margins adaptation. By varying the margins of the SVR, we could reflect the change in volatility of the financial data. Furthermore, we have analyzed the effect of asymmetrical margins so as to allow for the reduction of the downside risk. Our experimental results show that the use of standard deviation to calculate a variable margin gives a good predictive result in the prediction of Hang Seng Index.