Financial Market Correlation Analysis and Stock Selection Application Based on TCN-Deep Clustering

Yuefeng Cen (), Mingxing Luo, Gang Cen, Cheng Zhao and Zhigang Cheng
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Yuefeng Cen: School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
Mingxing Luo: School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
Gang Cen: School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
Cheng Zhao: School of Economics, Zhejiang University of Technology, Hangzhou 310014, China
Zhigang Cheng: School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China

Future Internet, 2022, vol. 14, issue 11, 1-14

Abstract: It is meaningful to analyze the market correlations for stock selection in the field of financial investment. Since it is difficult for existing deep clustering methods to mine the complex and nonlinear features contained in financial time series, in order to deeply mine the features of financial time series and achieve clustering, a new end-to-end deep clustering method for financial time series is proposed. It contains two modules: an autoencoder feature extraction network based on TCN (temporal convolutional neural) networks and a temporal clustering optimization algorithm with a KL (Kullback–Leibler) divergence. The features of financial time series are represented by the causal convolution and the dilated convolution of TCN networks. Then, the pre-training results based on the KL divergence are fine-tuned to make the clustering results discriminative. The experimental results show that the proposed method outperforms existing deep clustering and general clustering algorithms in the CSI 300 and S&P 500 index markets. In addition, the clustering results combined with an inference strategy can be used to select stocks that perform well or poorly, thus guiding actual stock market trades.

Keywords: financial time series; clustering; deep learning; temporal convolutional neural networks (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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