Stock Classification Using PCA and K-means for Value Investing

DOI

10.2991/978-94-6463-823-3_66How to use a DOI?

Keywords

Stock Classification; Principal Component Analysis; K-means Clustering; Value Investing; Financial Data Analysis

Abstract

Value investing relies on identifying stocks with strong financial fundamentals and long-term growth potential. However, with the increasing volume and complexity of financial data, traditional stock classification methods may struggle to efficiently differentiate stocks based on their intrinsic value. To address this challenge, this paper presents a stock classification approach for value investing by leveraging Principal Component Analysis (PCA) and K-means clustering. Dimensionality reduction and the extraction of crucial financial factors influencing stock performance are achieved through the use of PCA, while K-means clustering categorizes stocks based on their financial characteristics. By analyzing stock index data from the A-share market over the past four years, the study identifies meaningful financial patterns and classifies stocks into distinct groups. The results demonstrate that the classification remains stable across multiple years, providing investors with a structured method to differentiate stocks based on their financial attributes. This classification approach can serve as a reference for value investors, aiding in portfolio construction and risk assessment.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Runqi Zhang
PY  - 2025
DA  - 2025/08/31
TI  - Stock Classification Using PCA and K-means for Value Investing
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 675
EP  - 683
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_66
DO  - 10.2991/978-94-6463-823-3_66
ID  - Zhang2025
ER  -