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Investment Data Science with Generative AI
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Investment Data Science with Generative AI

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Technology and Innovation".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 7828

Special Issue Editor

Dr. Xianrong (Shawn) Zheng

E-Mail Website
Guest Editor
Information Technology & Decision Sciences Department, Old Dominion University, Norfolk, VA 23529, USA
Interests: AI; cloud computing; FinTech
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on “Investment Data Science with Generative AI”. Generative Artificial Intelligence can generate text, images, and videos responding to prompts. Generative AI is the latest innovation in artificial intelligence. It will play an important role in finance and investment. With a Large Language Model (LLM) like ChatGPT, extracting sentiment values from textual information, such as news, reports, and social media, becomes convenient. For example, ChatGPT can be used for sentiment analysis, corporate culture analysis, Federal Reserve opinion analysis, etc.

Yet, how to tap the full potential of generative AI to make investment decisions is a research topic that merits more investigation. As such, this Special Issue calls for papers on Investment Data Science with Generative AI. It welcomes research articles that present novel theories, algorithms, systems, and applications of generative AI for investment and encourages submissions from multiple disciplines, including artificial intelligence, computer science, information systems, finance, statistics, etc. Topics of interest include, but are not limited to, data science for investment, generative AI for investment, large language models, algorithmic trading, robo-advisors, computational finance, and financial forecasting.

Dr. Xianrong (Shawn) Zheng
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 250 words) can be sent to the Editorial Office for assessment.

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. Journal of Risk and Financial Management 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 1600 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

  • data science for investment
  • generative AI for investment
  • large language model
  • algorithmic trading
  • robo-advisors
  • computational finance
  • financial forecasting

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Published Papers (3 papers)

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Research

15 pages, 1846 KB  
Article
Tracking the Unseen: AI-Driven Dashboards for Real-Time Detection of Calendar Anomalies in Cryptocurrency Markets
by Dima Alberg and Elroi Hadad
J. Risk Financial Manag. 2025, 18(12), 712; https://doi.org/10.3390/jrfm18120712 - 12 Dec 2025
Viewed by 1245
Abstract
This study introduces a novel AI-powered Business Intelligence Dashboard System (AIBIDS) designed to detect and visualize calendar-based anomalies in cryptocurrency returns. Focusing on Bitcoin as a case study, the system integrates unsupervised machine learning algorithms to identify periods of abnormal market behavior across [...] Read more.
This study introduces a novel AI-powered Business Intelligence Dashboard System (AIBIDS) designed to detect and visualize calendar-based anomalies in cryptocurrency returns. Focusing on Bitcoin as a case study, the system integrates unsupervised machine learning algorithms to identify periods of abnormal market behavior across multiple temporal resolutions. The proposed system leverages a star-schema OLAP data warehouse, enabling real-time anomaly detection, dynamic visualization, and drill-down exploration of market irregularities. Empirical results confirm the presence of pronounced calendar effects in Bitcoin returns, such as heightened anomalies during Q1 and Q4, and reveal model-specific sensitivities to local versus global volatility. Our novel platform offers a practical, scalable innovation for investors, analysts, and regulators seeking to monitor cryptocurrency markets more effectively, and contributes to the emerging FinTech literature on AI-driven anomaly detection and behavioral market dynamics. Full article
(This article belongs to the Special Issue Investment Data Science with Generative AI)
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19 pages, 292 KB  
Article
Unpacking Alpha in Innovation-Driven ETFs: A Comparative Study of Artificial Intelligence and Blockchain Funds
by Davinder K. Malhotra
J. Risk Financial Manag. 2025, 18(12), 673; https://doi.org/10.3390/jrfm18120673 - 26 Nov 2025
Cited by 1 | Viewed by 3003
Abstract
This paper evaluates the performance and portfolio role of Artificial Intelligence (AI) and Blockchain exchange-traded funds (ETFs) based on monthly returns from 2010 to 2025. The findings show that both AI and Blockchain ETFs generate positive alpha and high standalone returns but also [...] Read more.
This paper evaluates the performance and portfolio role of Artificial Intelligence (AI) and Blockchain exchange-traded funds (ETFs) based on monthly returns from 2010 to 2025. The findings show that both AI and Blockchain ETFs generate positive alpha and high standalone returns but also display considerable drawdown risk. Their weak correlations with each other and with broad indices highlight diversification benefits, particularly when combined with U.S. benchmarks. Portfolio optimization reveals that Global Minimum Variance (GMV) and Tangency portfolios ascribe lower weights to these ETFs, while Risk Parity portfolios have a more balanced exposure, helping to diversify risks. Efficient frontier analysis highlights that the inclusion of AI and Blockchain ETFs improves the attainable risk–return profiles, even if they are not a dominant allocation. The findings stress that AI and Blockchain ETFs are suitable as satellite holdings. When applied judiciously, they offer the potential to improve diversification and risk-adjusted performance; however, concentrated bets subject investors to undue downside risks. Positioning portfolios around broad-based indices and overlaying modest thematic tilts emerges as a prudent approach to capturing innovation-driven upsides without compromising long-term portfolio resilience. Full article
(This article belongs to the Special Issue Investment Data Science with Generative AI)
24 pages, 415 KB  
Article
ChatGPT as a Financial Advisor: A Re-Examination
by Minh Tam Tammy Schlosky and Sterling Raskie
J. Risk Financial Manag. 2025, 18(12), 664; https://doi.org/10.3390/jrfm18120664 - 23 Nov 2025
Cited by 1 | Viewed by 2694
Abstract
Building on prior research, we revisited the 21 personal finance scenarios using OpenAI’s newer ChatGPT-4o to observe whether its financial guidance has meaningfully evolved. Our qualitative analysis relied on expert assessments to examine both the content and tone of the model’s advice, considering [...] Read more.
Building on prior research, we revisited the 21 personal finance scenarios using OpenAI’s newer ChatGPT-4o to observe whether its financial guidance has meaningfully evolved. Our qualitative analysis relied on expert assessments to examine both the content and tone of the model’s advice, considering how prompt engineering influenced ChatGPT outputs. We observed that ChatGPT-4o often produced more thorough suggestions and paid closer attention to tax implications—though it still overlooked some important details. It also showed more creative thinking in certain situations. However, some of the same shortcomings persisted: Generalizations remained too broad with respect to certain topics, legal references were occasionally misleading, and emotional empathy continued to feel artificial, even with carefully crafted prompts. We also extended our analysis to the newest ChatGPT model (ChatGPT-5). We found that the recommendations generated by ChatGPT-5 were quite similar to those generated by ChatGPT-4o, but the accuracy in the numerical problems was better under ChatGPT-5. While not a replacement for financial professionals, ChatGPT appears to be maturing into a more useful supporting tool for both advisors and clients. Our findings not only suggest cautious optimism but also underscore the need for careful oversight when using such tools in personal financial decision-making. Full article
(This article belongs to the Special Issue Investment Data Science with Generative AI)
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