Working Paper: Improved Stock Price Forecasting Algorithm based on Feature-weighed Support Vector Regression by using Grey Correlation Degree

TL;DR

This paper enhances stock price forecasting accuracy by integrating grey correlation analysis with support vector regression, effectively weighting impact factors from behavioral and technical data.

Contribution

It introduces a novel feature-weighted SVR model using grey correlation analysis to improve stock prediction accuracy.

Findings

Forecast accuracy significantly improved with the new method.

The model effectively combines behavioral and technical factors.

Results outperform traditional SVR models.

Abstract

With the widespread engineering applications ranging from artificial intelligence and big data decision-making, originally a lot of tedious financial data processing, processing and analysis have become more and more convenient and effective. This paper aims to improve the accuracy of stock price forecasting. It improves the support vector machine regression algorithm by using grey correlation analysis (GCA) and improves the accuracy of stock prediction. This article first divides the factors affecting the stock price movement into behavioral factors and technical factors. The behavioral factors mainly include weather indicators and emotional indicators. The technical factors mainly include the daily closing data and the HS 300 Index, and then measure relation through the method of grey correlation analysis. The relationship between the stock price and its impact factors during the trading day, and this relationship is transformed into the characteristic weight of each impact factor. The weight of the impact factors of all trading days is weighted by the feature weight, and finally the support vector regression (SVR) is used. The forecast of the revised stock trading data was compared based on the forecast results of technical indicators (MSE, MAE, SCC, and DS) and unmodified transaction data, and it was found that the forecast results were significantly improved.