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JRFM
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Quantitative Finance in the Era of Big Data and AI
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Quantitative Finance in the Era of Big Data and 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: 31 December 2026 | Viewed by 3528

Special Issue Editor

Dr. Ruipeng Liu

E-Mail Website
Guest Editor
Department of Finance, Deakin Business School, Deakin University, 221 Burwood Highway, Melbourne, VIC 3125, Australia
Interests: financial markets; long memory volatility modelling; multifractal processes; risk measurements and management; climate finance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of quantitative finance is undergoing a profound transformation, driven by the explosive growth of big data and the rapid advancements in artificial intelligence (AI). In conjunction with the First International Online Conference on Risk and Financial Management—Big Data, Artificial Intelligence, and Machine Learning in Finance (IOCRF2025), we are pleased to announce a call for papers for the Special Issue of JRFM, titled "Quantitative Finance in the Era of Big Data and AI", which explores how innovations are reshaping financial markets, modeling, risk management, and decision-making.

This Special Issue invites original, high-quality research that examines the intersection of quantitative finance, big data analytics, and AI technologies. We welcome submissions that address, but are not limited to, the following topics:

  • Machine Learning and AI in Financial Modeling: Innovative AI methods such as deep learning, reinforcement learning, and natural language processing applied to asset pricing, portfolio optimization, and algorithmic trading.
  • Big Data in Financial Markets: The role of alternative data (social media, satellite imagery, and market sentiment) and their integration into traditional financial models for improved predictive accuracy.
  • Regulatory and Ethical Implications: The impact of AI and big data on financial regulations, ethical concerns, and transparency of decision-making algorithms.
  • Computational Finance: Efficient computational methods for handling large-scale data in high-frequency trading and real-time financial analysis.

We invite academics and practitioners to submit manuscripts that contribute to advancing our understanding of how big data and AI can revolutionize the field of quantitative finance.

Dr. Ruipeng Liu
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

  • algorithmic trading
  • artificial intelligence
  • AI ethics
  • big data
  • financial modeling
  • forecasting
  • risk management
  • regulation
  • machine learning
  • quantitative finance

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Published Papers (3 papers)

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Research

16 pages, 436 KB  
Article
Why Market Prices May Not Be the Best Benchmark for Automated Valuation Models: Empirical Evidence of Ex Ante Unobservability of Gender-Associated Price Discrepancy in the Auckland House Market
by Chung Yim Yiu and Ka Shing Cheung
J. Risk Financial Manag. 2026, 19(3), 171; https://doi.org/10.3390/jrfm19030171 - 28 Feb 2026
Viewed by 407
Abstract
Automated Valuation Models (AVMs) are typically trained by learning to replicate observed housing transaction prices. This paper argues that such benchmarking is theoretically debatable. Market transaction prices are not direct measures of underlying property value but are realised outcomes of exchange processes that [...] Read more.
Automated Valuation Models (AVMs) are typically trained by learning to replicate observed housing transaction prices. This paper argues that such benchmarking is theoretically debatable. Market transaction prices are not direct measures of underlying property value but are realised outcomes of exchange processes that involve buyer-specific attributes that are unobservable prior to sale. Using residential housing transactions from Auckland, New Zealand, and buyers’ gender inferred from unstructured purchaser name data via artificial intelligence-based natural language processing, we provide empirical evidence that buyer attributes systematically affect transaction prices. Specifically, gender composition is shown to influence the discrepancy between AVM estimates and transaction prices, while no corresponding effect is found when AVMs are compared with capital values, which are the Council’s appraisals for rating purposes. This asymmetry reflects the shared information set of AVMs and professional appraisals, as both are based only on property and market information available prior to sale and do not incorporate buyer identity. The findings provide initial evidence for valuers to address the latest professional requirements of using AVMs. Full article
(This article belongs to the Special Issue Quantitative Finance in the Era of Big Data and AI)
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39 pages, 6278 KB  
Article
Towards Generative Interest-Rate Modeling: Neural Perturbations Within the Libor Market Model
by Anna Knezevic
J. Risk Financial Manag. 2026, 19(1), 82; https://doi.org/10.3390/jrfm19010082 - 21 Jan 2026
Viewed by 483
Abstract
This study proposes a neural-augmented Libor Market Model (LMM) for swaption surface calibration that enhances expressive power while maintaining the interpretability, arbitrage-free structure, and numerical stability of the classical framework. Classical LMM parametrizations, based on exponential decay volatility functions and static correlation kernels, [...] Read more.
This study proposes a neural-augmented Libor Market Model (LMM) for swaption surface calibration that enhances expressive power while maintaining the interpretability, arbitrage-free structure, and numerical stability of the classical framework. Classical LMM parametrizations, based on exponential decay volatility functions and static correlation kernels, are known to perform poorly in sparsely quoted and long-tenor regions of swaption volatility cubes. Machine learning–based diffusion models offer flexibility but often lack transparency, stability, and measure-consistent dynamics. To reconcile these requirements, the present approach embeds a compact neural network within the volatility and correlation layers of the LMM, constrained by structural diagnostics, low-rank correlation construction, and HJM-consistent drift. Empirical tests across major currencies (EUR, GBP, USD) and multiple quarterly datasets from 2024 to 2025 show that the neural-augmented LMM consistently outperforms the classical model. Improvements of approximately 7–10% in implied volatility RMSE and 10–15% in PV RMSE are observed across all datasets, with no deterioration in any region of the surface. These results reflect the model’s ability to represent cross-tenor dependencies and surface curvature beyond the reach of classical parametrizations, while remaining economically interpretable and numerically tractable. The findings support hybrid model designs in quantitative finance, where small neural components complement robust analytical structures. The approach aligns with ongoing industry efforts to integrate machine learning into regulatory-compliant pricing models and provides a pathway for future generative LMM variants that retain an arbitrage-free diffusion structure while learning data-driven volatility geometry. Full article
(This article belongs to the Special Issue Quantitative Finance in the Era of Big Data and AI)
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19 pages, 415 KB  
Article
Informed Trading Through the COVID-19 Pandemic: Evidence from the Bitcoin Market
by Timotheos Mavropoulos, Oguz Ersan and Ender Demir
J. Risk Financial Manag. 2026, 19(1), 59; https://doi.org/10.3390/jrfm19010059 - 10 Jan 2026
Viewed by 1442
Abstract
We investigate informed trading in the Bitcoin market throughout the COVID-19 pandemic. Compared to the pre-pandemic period, we find that informed trading is significantly higher in the affective first stage of the pandemic, before reverting to its pre-COVID-19 level during the later stage [...] Read more.
We investigate informed trading in the Bitcoin market throughout the COVID-19 pandemic. Compared to the pre-pandemic period, we find that informed trading is significantly higher in the affective first stage of the pandemic, before reverting to its pre-COVID-19 level during the later stage of the pandemic. Furthermore, information asymmetry tends to increase in daily COVID-19-related news: confirmed cases and deaths. Our findings are robust to alternative parameters and model specifications. The main implication for traders is that they should be extra cautious in timing their trading decisions during such events, as these tend to encourage informed trading. Full article
(This article belongs to the Special Issue Quantitative Finance in the Era of Big Data and AI)
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