A topological based feature extraction method for the stock market

Chen Chang; Hongwei Lin; Chen Chang; Hongwei Lin

doi:10.3934/DSFE.2023013

Data Science in Finance and Economics

2023, Volume 3, Issue 3: 208-229. doi: 10.3934/DSFE.2023013

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A topological based feature extraction method for the stock market

Chen Chang ¹,
Hongwei Lin ^{2
,
,}

1.
Polytechnic Institute, Zhejiang University, China
2.
School of Mathematical Science, Zhejiang University, China

Received: 27 April 2023 Revised: 24 June 2023 Accepted: 03 July 2023 Published: 11 July 2023
JEL Codes: C65

We proposed a topology-based method for pre-processed time series data extracted from stock market data. The topology features are extracted from data after denoising and normalization by using a version of weighted Vietoris-Rips complex. We compare the features from bullish, bearish and normal periods of the Chinese stock market and found significant differences between the features extracted from the groups. Based on the previous research mentioned in the context, we proposed a topology-based stock market index which has the ability to distinguish different stages of the stock market and forewarn stock market crashes.
- topological data analysis,
- stock market,
- feature extraction
Citation: Chen Chang, Hongwei Lin. A topological based feature extraction method for the stock market[J]. Data Science in Finance and Economics, 2023, 3(3): 208-229. doi: 10.3934/DSFE.2023013

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Abstract

We proposed a topology-based method for pre-processed time series data extracted from stock market data. The topology features are extracted from data after denoising and normalization by using a version of weighted Vietoris-Rips complex. We compare the features from bullish, bearish and normal periods of the Chinese stock market and found significant differences between the features extracted from the groups. Based on the previous research mentioned in the context, we proposed a topology-based stock market index which has the ability to distinguish different stages of the stock market and forewarn stock market crashes.

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