AI-Driven Automated Stock Market Forecasting: Mathematical Models and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 24
Special Issue Editor
2. Department of Economics and Economic Policy, Economy I Doctoral School, Faculty of Theoretical and Applied Economics, The Bucharest University of Economic Studies, 010374 Bucharest, Romania
Interests: mathematical models and applications; artificial intelligence; economics; management; finance; auditing; accounting; business counseling; financial analysis; control and evaluation; intellectual capital; corporate governance; sustainability; sustainable development; business environment; business process management; quality management; human resources management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue focuses on the broad topic of “AI-Driven Automated Stock Market Forecasting: Mathematical Models and Applications” and includes novel research on the emerging challenges that are currently occurring in these days business environment, having as major target revolutionizing the stock market. There are pivotal aspects that draw specialists’ and researchers’ attention and interest today when centering on offering a better and more in-depth understanding of the most recent trends on the marketplace, namely: the artificial intelligence (AI) stock trading, as a valuable game changer for investors; the impact of AI in enhancing predictive behavior and developing mathematical models capable to forecast future stock trends; the power of creating and developing AI stock prediction software that would depend on the rules of statistical analysis; and the role of multifactor prediction models for the stock market analysis as well as the trading applications.
Theoretical and empirical articles on the topic of AI-driven automated stock market forecasting, with a particular emphasis on mathematical models and applications are welcome, to cover the specific objectives of the journal. Moreover, connections with sustainability assessment instruments and methods are encouraged, since navigating the complex landscape of AI-driven trading ought to consider the aspects embodied by the Sustainable Development Goals.
Contributions focusing on but not limited to the implications of AI-driven automated stock market forecasting, use of AI in stock trading, mathematical modeling stock market, machine learning efficient algorithms, AI-driven financial market analysis, automated training, the stock market’s AI revolution, effectiveness of AI in stock market, emerging trends in AI-based stock market predictions, predictive maintenance and AI, preductive analytics and AI, AI stock management, are encouraged.
We are interested in conceptual, theoretical, methodological, empirical, and systematic review studies.
Prof. Dr. Cristina Raluca Gh. Popescu
Guest Editor
Manuscript Submission Information
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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
- accounting
- artificial intelligence
- artificial intelligence-driven stock market predictions
- artificial intelligence-driven demand forecasting
- artificial intelligence-driven predictive analytics in finance
- artificial intelligence in procurement
- artificial intelligence in stock market
- artificial intelligence stock prediction software
- artificial intelligence powered trading system
- advanced techniques
- algorithmic strategies
- algorithmic trading
- asset management
- auditing
- business counseling
- business environment
- business excellence
- business performance
- business process management
- customer behavior
- deep learning
- diversification artificial intelligence-driven portfolio management
- dynamic growth
- human intuition
- financial modeling and forecasting
- forecast markets
- fraud detection
- machine learning
- mathematical techniques
- mitigate risks
- portfolio management
- predictive analytics in finance
- procurement analytics
- profitability
- real time data and automation
- risk management
- smart finance
- sustainable development goals
- statistical techniques
- stock market index
- stock price predictions
- trading efficiency
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