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Mathematics
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Computational Intelligence Algorithms in Economics and Finance
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Computational Intelligence Algorithms in Economics and Finance

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1376

Special Issue Editors

Prof. Dr. Maurizio Naldi

E-Mail Website
Guest Editor
Department of Law, Economics, Politics and Modern languages, LUMSA University, 00193 Rome, Italy
Interests: machine learning applications; blockchain applications; network and service economics; healthcare management systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Loretta Mastroeni

E-Mail Website
Guest Editor
Department of Economics, Roma Tre University, Via Silvio D’Amico 77, 00145 Rome, Italy
Interests: quantitative finance; risk management in energy and commodity markets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational intelligence, intended as a broad set of techniques that extract information from massive datasets, has gained a prominent role in many fields of applications and Economics and Finance are no exceptions. This is particularly true in contexts where analyses were previously conducted through models validated on small datasets. The wide availability of massive datasets, e.g., on trading operations, has revolutionized the ways of conducting analysis. The applications of CI techniques include short- and long-term forecasting, risk classification, optimal resource allocation, and optimal pricing of assets and services. The tasks called for include classification, regression, and optimization.

For this Special Issue, we welcome innovative contributions and applications of Machine Learning and Computational Intelligence Algorithms in all areas of Economics and Finance, including financial applications that address climate and environmental risk assessment. The primary acceptance criterion for submission will be the high quality and originality of the contribution.

We especially welcome innovative contributions related to, but not limited to, the following main topics:

  • Machine learning
  • Deep learning
  • Neural networks
  • Genetic algorithms
  • Credit risk
  • Market risk
  • Liquidity risk
  • Climate risk
  • Asset and derivative pricing
  • Network modelling
  • Portfolio optimization
  • Systemic risk
  • Insurance

Prof. Maurizio Naldi
Dr. Loretta Mastroeni
Guest Editors

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. Mathematics is an international peer-reviewed open access semimonthly 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

  • computational intelligence
  • machine learning
  • deep learning
  • data analytics
  • business analytics
  • risk analysis
  • ML applications in economics and finance

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Published Papers (1 paper)

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Research

22 pages, 5798 KiB  
Article
Nested Sentiment Analysis for ESG Impact: Leveraging FinBERT to Predict Market Dynamics Based on Eco-Friendly and Non-Eco-Friendly Product Perceptions with Explainable AI
by Aradhana Saxena, A. Santhanavijayan, Harish Kumar Shakya, Gyanendra Kumar, Balamurugan Balusamy and Francesco Benedetto
Mathematics 2024, 12(21), 3332; https://doi.org/10.3390/math12213332 - 23 Oct 2024
Viewed by 803
Abstract
In the current era, the environmental component of ESG is recognized as a major driver due to the pressing challenges posed by climate change, population growth, global warming, and shifting weather patterns. The environment must be considered a critical factor, and as evidenced [...] Read more.
In the current era, the environmental component of ESG is recognized as a major driver due to the pressing challenges posed by climate change, population growth, global warming, and shifting weather patterns. The environment must be considered a critical factor, and as evidenced by existing research, it is regarded as the dominant component within ESG. In this study, the ESG score is derived primarily from the environmental score. The increasing importance of the environmental, social, and governance (ESG) factors in financial markets, along with the growing need for sentiment analysis in sustainability, has necessitated the development of advanced sentiment analysis techniques. A predictive model has been introduced utilizing a nested sentiment analysis framework, which classifies sentiments towards eco-friendly and non-eco-friendly products, as well as positive and negative sentiments, using FinBERT. The model has been optimized with the AdamW optimizer, L2 regularization, and dropout to assess how sentiments related to these product types influence ESG metrics. The “black-box” nature of the model has been addressed through the application of explainable AI (XAI) to enhance its interpretability. The model demonstrated an accuracy of 91.76% in predicting ESG scores and 99% in sentiment classification. The integration of XAI improves the transparency of the model’s predictions, making it a valuable tool for decision-making in making sustainable investments. This research is aligned with the United Nations’ Sustainable Development Goals (SDG 12 and SDG 13), contributing to the promotion of sustainable practices and fostering improved market dynamics. Full article
(This article belongs to the Special Issue Computational Intelligence Algorithms in Economics and Finance)
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