Stock classification and investment analysis based on K-means clustering

Huihui Huang; Qiangqiang Xu; Weigong Feng

doi:10.1117/12.2636207

6 May 2022 Stock classification and investment analysis based on K-means clustering

Huihui Huang, Qiangqiang Xu, Weigong Feng

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 121761M (2022) https://doi.org/10.1117/12.2636207
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

With massive data, complex information, and the increasingly expanding huge stock pool, it is always difficult for investors to quickly and accurately choose stocks with good inherent value and predictable constant growth. Therefore, stock analysis before investment is vital. In this paper, by applying classical K-means clustering algorithm in data mining, 100 US stocks randomly selected from Yahoo Finance are first classified into different performance groups based on their historical trading data in the recent one year, then detailed analysis of the typical stocks in each performance group is given from the perspective of technical analysis, attempting to find some important features for simple and rapid evaluation of stock investment value, in addition, corresponding investment advice is also proposed based on some statistical analysis index.

Citation Download Citation

Huihui Huang, Qiangqiang Xu, and Weigong Feng "Stock classification and investment analysis based on K-means clustering", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 121761M (6 May 2022); https://doi.org/10.1117/12.2636207

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