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Entropy
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Entropy, Artificial Intelligence and the Financial Markets
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Entropy, Artificial Intelligence and the Financial Markets

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 877

Special Issue Editor

Prof. Dr. Gil Cohen

E-Mail Website
Guest Editor
Department of Management, Western Galilee Academic College, P.O. Box 2125, Acre 2412101, Israel
Interests: investment; financial markets; corporate finance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Financial markets are evolving rapidly, reshaping global investment opportunities. AI is revolutionizing finance by enhancing decision-making, improving predictive accuracy, and optimizing risk management. This transformation fosters efficiency, transparency, and accessibility across markets.

One key contribution of AI is its ability to enrich the investment process with valuable information. Traditional models struggle with processing vast, unstructured data, but AI extracts insights from alternative sources such as news sentiment, social media, and economic indicators, improving market predictions and asset valuations. By reducing noise and identifying hidden patterns, AI enhances informational efficiency, enabling more informed investment decisions.

AI also plays a crucial role in managing entropy in financial systems. Entropy representing uncertainty and randomness in markets can lead to inefficiencies and volatility. AI-driven models help to quantify and mitigate entropy by detecting trends, forecasting risks, and refining asset pricing. This allows investors to navigate uncertainty more effectively and optimize capital allocation.

We request research papers exploring AI’s impact on financial forecasting, risk analysis, and decision-making. Studies on AI’s role in asset pricing, anomaly detection, and algorithmic trading across various financial instruments are highly encouraged. Both theoretical and empirical contributions that offer fresh insights into AI and finance are welcome.

Prof. Dr. Gil Cohen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI and financial innovations
  • AI and business information
  • AI and trading
  • AI and market forecasts
  • AI and risk analysis
  • AI and fintech
  • AI real estate information and pricing
  • AI and technical analysis

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Published Papers (1 paper)

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Research

17 pages, 917 KiB  
Article
Artificial Intelligence Models for Predicting Stock Returns Using Fundamental, Technical, and Entropy-Based Strategies: A Semantic-Augmented Hybrid Approach
by Gil Cohen, Avishay Aiche and Ron Eichel
Entropy 2025, 27(6), 550; https://doi.org/10.3390/e27060550 - 23 May 2025
Viewed by 648
Abstract
This study examines the effectiveness of combining semantic intelligence drawn from large language models (LLMs) such as ChatGPT-4o with traditional machine-learning (ML) algorithms to develop predictive portfolio strategies for NASDAQ-100 stocks over the 2020–2025 period. Three different predictive frameworks––fundamental, technical, and entropy-based––are tested [...] Read more.
This study examines the effectiveness of combining semantic intelligence drawn from large language models (LLMs) such as ChatGPT-4o with traditional machine-learning (ML) algorithms to develop predictive portfolio strategies for NASDAQ-100 stocks over the 2020–2025 period. Three different predictive frameworks––fundamental, technical, and entropy-based––are tested through examination of novel combinations of ML- and LLM-derived semantic metrics. The empirical results reveal a considerable divergence in optimal blending methods across the methodologies; namely, the technical methodology exhibits the best performance when using only ML predictions, with around 1978% cumulative returns with monthly rebalancing. In contrast, the fundamental methodology achieves its full potential when it is based primarily on LLM-derived semantic insights. The Entropy methodology is improved by a balanced combination of both semantic and ML signals, thus highlighting the potential of LLMs to improve predictive power by offering interpretative context for complex market interactions. These findings highlight the strategic importance of tailoring the semantic–algorithmic fusion to suit the nature of the predictive data and the investment horizon, with significant implications for portfolio management and future research in financial modeling. Full article
(This article belongs to the Special Issue Entropy, Artificial Intelligence and the Financial Markets)
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