Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)

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Using Three Classifications to Optimize the Stock Portfolio Performance with Weighted Scoring Models

Authors
Yi Peng1, *
1King’s College London, King’s Business School, Bush House, 30 Aldwych, London, WC2B 4B, UK
*Corresponding author. Email: k23089454@kcl.ac.uk
Corresponding Author
Yi Peng
Available Online 31 August 2025.
DOI
10.2991/978-94-6463-823-3_84How to use a DOI?
Keywords
stock portfolio performance; weighted scoring models; Logistic regression; decision tree
Abstract

This essay aims to improve stock portfolio performance by enhancing the traditional weighted scoring model with three classification methods: tree decision, logistic regression, and voting taxonomy. As financial markets evolve rapidly, traditional models face challenges such as unclear stock selection criteria, complex variable relationships, and difficulty adapting to fast-changing market conditions. Moreover, investors have diverse risk preferences, making rigid models less effective. To address these issues, the essay analyzes historical data from Apple and Tesla, focusing on 11 variables including Return on Equity (ROE) and stock returns. The tree decision method evaluates variable importance, logistic regression scores the selected stocks, and the voting classification provides final recommendations—buy, hold, or sell. Results show that this integrated model offers greater clarity, adaptability, and decision-making support compared to traditional methods. By combining these three classification approaches, the new model aligns better with today’s complex market environment and helps investors make more accurate and flexible investment decisions.

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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Volume Title
Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
Series
Advances in Computer Science Research
Publication Date
31 August 2025
ISBN
978-94-6463-823-3
ISSN
2352-538X
DOI
10.2991/978-94-6463-823-3_84How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - Yi Peng
PY  - 2025
DA  - 2025/08/31
TI  - Using Three Classifications to Optimize the Stock Portfolio Performance with Weighted Scoring Models
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 838
EP  - 848
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_84
DO  - 10.2991/978-94-6463-823-3_84
ID  - Peng2025
ER  -