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Leveraging enhanced egret swarm optimization algorithm and artificial intelligence-driven prompt strategies for
Zhendai Huang1, Zhen Zhang2, Cheng Hua1
1College of Computer Science and Engineering, Jishou University, Jishou, 416000, China.
Scientific Reports
|November 4, 2024
Summary
This study introduces a structured prompt framework for generative AI in stock selection, enhancing interpretability for investors. A novel algorithm, NBESOA, optimizes portfolios for higher Sharpe Ratios under strict constraints.
Area of Science:
- Quantitative Finance
- Artificial Intelligence in Finance
- Portfolio Optimization
Background:
- Efficient investment portfolio construction is crucial in finance, involving asset selection and allocation.
- Generative Artificial Intelligence (AI) and Large Language Models (LLMs) offer advanced capabilities but often lack interpretability.
- Existing AI tools require iterative fine-tuning, hindering direct application for investors.
Purpose of the Study:
- To develop a structured prompt framework for generative AI to enable direct and interpretable stock selection.
- To propose a novel optimization algorithm for the Mean-Variance Portfolio Selection problem with Transaction Costs and Cardinality Constraints (MVPS-TCCC).
- To evaluate the performance of AI-recommended portfolios using real stock market data.
Main Methods:
- Creation of representative scenarios and experimental cases to test prompt framework influence on AI responses.
- Development of the Nonlinear-Activated Beetle Antennae Search strategy combined with Egret Swarm Optimization Algorithm (NBESOA).
- Application of NBESOA to solve the MVPS-TCCC problem using real stock market data and AI-generated recommendations.
Main Results:
- The structured prompt framework enhances the interpretability of generative AI outputs for stock selection.
- NBESOA demonstrated superior performance in optimizing portfolio configurations compared to other algorithms.
- NBESOA achieved higher Sharpe Ratios under stringent transaction costs and cardinality constraints, approaching the efficient frontier.
Conclusions:
- The proposed prompt framework offers a practical approach to leveraging generative AI for interpretable stock selection.
- NBESOA provides an effective method for solving complex portfolio optimization problems.
- This research bridges the gap between advanced AI capabilities and practical investment decision-making.
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