Advanced Portfolio Optimization Using Financial AI Tools and Credibility-Based Risk Metrics: A Case Study on the Dow Jones Index

Document Type : Original Article

Authors

1 Iran University of Science and Technology, Tehran, Iran.

2 University of Science and Technology of Iran, University , Tehran, Iran.

Abstract

Quarterly reports Quarterly financial reports are among the most accurate and valuable sources for assessing a company’s performance and strategic direction. In this paper, we propose a novel investment strategy model that uses these reports to improve long-term investment decisions. The core of our approach lies in the integration of artificial intelligence and advanced financial analytics. Specifically, we use NotebookLM, an artificial intelligence-based tool capable of parsing and extracting key insights from unstructured financial texts, and the FinBERT model, a sentiment analysis model tailored to finance, to assess market sentiment and the tone of a company’s disclosures. We use the value-at-risk(VaR) measure to calculate the risk of the proposed model, which assesses and quantifies potential financial losses. Recognizing the limitations of traditional quantitative modelsespecially in volatile or ambiguous market conditions we have used fuzzy number theory in the context of credibility theory. This allows the model to handle imprecision and ambiguity more effectively and reflect the inherent uncertainties of financial markets. To evaluate the practical applicability of our model, we conduct empirical tests using real financial data from companies listed in the Dow Jones Industrial Average (DJIA). The results show that our approach not only improves the accuracy of risk assessment but also enhances portfolio performance compared to conventional models. By integrating sentiment analysis, fuzzy logic, and financial risk metrics, our framework provides a more comprehensive view of corporate risk and long-term value. Ultimately, the proposed model contributes to more informed, flexible, and sustainable investment decisions.

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Main Subjects

References

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Budget and Finance Strategic Research
Volume 6, Issue 4 - Serial Number 24
Winter Quarterly
February 2026
Pages 31-59

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History

  • Receive Date: 06 August 2025
  • Revise Date: 31 August 2025
  • Accept Date: 09 December 2025

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