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A Hybrid Approach Based on Improved Relevance Vector Regression and Feature Extraction Algorithm for Stock Trend Forecasting

Shaolong Sun, Han Qiao, Yunjie Wei, Shouyang Wang


The trend forecasting of stock market has long been a hot spot of financial studies. Forecasting and analysis of stock price trends are crucial to confirm whether the closing prices of the future would decrease or increase. In this paper, a new optimized relevance vector regression (ORVR) based on the empirical orthogonal function (EOF) feature extraction algorithm is proposed to forecast the stock price trends. Firstly, the EOF method is utilized to extract the main features of the input datasets. Secondly, the gravitational search algorithm (GSA) is employed to determine the optimal width parameter and improve the forecasting accuracy of the RVR method. Finally, the level accuracy and directional accuracy of the proposed hybrid approach are evaluated by means of the actual stock data of CNPC and SG in China. The empirical results show that the proposed hybrid approach can significantly improve forecasting performance and outperform the other benchmark models in terms of the directional accuracy and the level accuracy.


Stock trend forecasting, Feature extraction, Relevance vector machine, Gravitational search algorithm, Intelligent optimization.

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