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Related Experiment Videos

A feature fusion based forecasting model for financial time series.

Zhiqiang Guo¹, Huaiqing Wang², Quan Liu¹

¹Key Laboratory of Fiber Optic Sensing Technology and Information Processing, School of Information Engineering, Wuhan University of Technology, Wuhan, China.

|June 28, 2014

Summary

This study introduces a novel stock market prediction model using independent component analysis, canonical correlation analysis, and support vector machines. The model effectively forecasts stock prices, outperforming existing methods on major indices.

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Area of Science:

Quantitative Finance
Machine Learning
Econometrics

Background:

Stock market prediction is a challenging research area with numerous proposed models.
Feature selection is crucial for pre-processing data and reducing noise in prediction models.

Purpose of the Study:

To develop an advanced stock market forecasting model.
To enhance prediction accuracy by integrating multiple feature extraction and combination techniques.

Main Methods:

Feature extraction using Independent Component Analysis (ICA) on historical prices and technical variables.
Feature combination and intrinsic feature extraction via Canonical Correlation Analysis (CCA).
Stock price forecasting using a Support Vector Machine (SVM) classifier.

Main Results:

The proposed model demonstrated superior predictive performance compared to two benchmark models.
Effective extraction of intrinsic features improved the overall prediction accuracy.
Successful application to both the Shanghai Stock Market Index and the Dow Jones Index.

Conclusions:

The integrated approach of ICA, CCA, and SVM offers a robust method for stock market prediction.
The model's enhanced feature engineering significantly contributes to improved forecasting accuracy.
This methodology provides a valuable tool for investors and researchers in financial markets.