Stock portfolio optimization using hill climbing and simple human learning optimization algorithms as a decision support system

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES
MethodsX Pub Date : 2025-06-01 DOI:10.1016/j.mex.2025.103413
Suyash S. Satpute , Amol C. Adamuthe , Pooja Bagane
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Abstract

The goal of this research is to develop a decision support system for stock portfolio optimization using hill climbing and SHLO algorithms based on fundamental analysis of stocks. Portfolio optimization involves constructing a portfolio that maximizes returns while minimizing risk. The novelty in methodology is ‘hybridizing’ nature-inspired algorithms for optimized portfolio selection with two independent modules: intrinsic value of stocks and financial health analysis. This integrated approach aids decision-making by considering multiple dimensions of stock performance. Custom datasets are designed for each input module using historical fundamental data. The DSS output presents an optimized portfolio. Comparison for different risk profiles shows that as risk increases, returns of optimized portfolios decrease from 55 % to 24 %. Results for keeping other inputs the same for varying cardinality show that as cardinality increases, returns decrease. The results show that fundamentally undervalued portfolios outperform growth portfolios by a considerable margin. We conclude that optimized portfolios with varying constraints, >80 % of the time, outperform US market indices.
Key contributions include:
  • Developed a decision support system using intrinsic value and financial health analysis.
  • Novel fitness function for optimization using hill climbing and SHLO.
  • Integrated module outputs with hill climbing and SHLO for portfolio optimization.

Abstract Image

股票投资组合优化采用爬山和简单的人类学习优化算法作为决策支持系统
本研究的目标是在股票基本面分析的基础上,利用爬坡算法和SHLO算法开发一个股票投资组合优化决策支持系统。投资组合优化包括构建收益最大化而风险最小化的投资组合。该方法的新颖之处在于将自然启发的优化投资组合选择算法与两个独立模块“杂交”:股票内在价值和财务健康分析。这种综合方法通过考虑股票表现的多个维度来帮助决策。使用历史基础数据为每个输入模块设计自定义数据集。DSS输出是一个优化的组合。对不同风险状况的比较表明,随着风险的增加,优化投资组合的回报率从55%下降到24%。对于不同的基数保持其他输入相同的结果表明,随着基数的增加,回报减少。结果表明,从根本上被低估的投资组合的表现明显优于成长型投资组合。我们得出的结论是,具有不同约束条件的优化投资组合在80%的情况下优于美国市场指数。主要贡献包括:•利用内在价值和财务健康分析开发了一个决策支持系统。•利用爬坡和SHLO进行优化的新颖适应度函数。•集成模块输出与爬山和SHLO投资组合优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
MethodsX
MethodsX Health Professions-Medical Laboratory Technology
CiteScore
3.60
自引率
5.30%
发文量
314
审稿时长
7 weeks
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