A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
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An Improved Cluster Labeling Method for Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Domain described support vector classifier for multi-classification problems
Pattern Recognition
International Journal of Electronic Finance
Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network
Expert Systems with Applications: An International Journal
Fast support-based clustering method for large-scale problems
Pattern Recognition
Dynamic Dissimilarity Measure for Support-Based Clustering
IEEE Transactions on Knowledge and Data Engineering
Expert Systems with Applications: An International Journal
Dynamic pattern denoising method using multi-basin system with kernels
Pattern Recognition
A novel model by evolving partially connected neural network for stock price trend forecasting
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
Equilibrium-Based Support Vector Machine for Semisupervised Classification
IEEE Transactions on Neural Networks
Hi-index | 12.05 |
Recently many statistical learning techniques have been applied to the prediction of financial variables. The aim of this paper is to conduct a comprehensive study of the applications of statistical learning techniques to predict the trend of the return of high-frequency Korea composite stock price index (KOSPI) 200 index data using the information from the one-minute time series of spot index, futures index, and foreign exchange rate. Through experiments, it is observed that the spot index change is better predictable with high-frequency time series data and the futures index information significantly improves the prediction accuracy of the return trends of the spot index for high-frequency index data, while the information of exchange rate does not. Also, dimension reduction process before training helps to increase the accuracy and dramatically for some classifiers. In addition, the trained classifiers with which a virtual trading strategy is applied to, noticeable better profits can be achieved than just a buy-and-hold-like strategy.