AI Applications in Financial Markets and Computational Finance

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: closed (31 December 2025) | Viewed by 2023

Special Issue Editors

Department of Economics and Finance, City University of Hong Kong, Tat Chee Avenue, Kowloon 999077, Hong Kong
Interests: computational finance; machine learning; big data; investment and risk management; operations research
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Guest Editor
Alliance Bernstein, 18 Westlands Road, Quarry Bay, Hong Kong
Interests: investment; portfolio management; risk management; machine learning; deep learning

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the transformative impact of artificial intelligence (AI) on financial markets and computational finance. It concentrates on innovative AI methodologies, their practical applications, and the implications for market efficiency and risk management. The Special Issue encompasses a wide range of topics, including algorithmic trading, predictive analytics, portfolio optimization, and regulatory compliance, among others, reflecting the interdisciplinary nature of AI in finance.

  • Algorithmic Trading: Examining AI-driven trading strategies and their effects on market dynamics.
  • Predictive Analytics: Utilizing machine learning for forecasting market trends, asset prices, and economic indicators.
  • Portfolio Optimization: Exploring AI techniques for enhancing investment strategies and asset allocation.
  • Risk Management: Investigating AI applications in identifying, assessing, and mitigating financial risks.
  • Fraud Detection: Analyzing AI methods for detecting anomalies and preventing fraudulent activities.
  • Sentiment Analysis: Leveraging natural language processing to gauge market sentiment from news and social media.
  • Regulatory Compliance: Assessing how AI can assist in adhering to evolving regulatory frameworks.
  • Blockchain and AI Integration: Exploring the synergies between AI and blockchain technologies in finance.
  • Robo-Advisors: Investigating the role of AI in automated investment advisory services.

The goal is to provide a comprehensive overview of current trends, challenges, and future directions in the integration of AI technologies within financial systems.

This Special Issue will serve as a valuable supplement to existing research by highlighting cutting-edge research that bridges theoretical frameworks and practical applications. It will emphasize empirical studies and case analyses that demonstrate the efficacy of AI tools in real-world settings, fostering a deeper understanding of their potential and limitations. By synthesizing insights from both academia and industry, the Special Issue aims to inform practitioners and scholars alike, encouraging further exploration into the evolving landscape of AI in financial markets.

Dr. Wei Li
Dr. Ninghui Liu
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 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

  • artificial intelligence
  • machine learning
  • big data
  • large language model (LLM)
  • computational finance
  • quantitative investment
  • fintech
  • risk management

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Published Papers (1 paper)

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Research

26 pages, 1137 KB  
Article
A Hybrid Framework for Multi-Stock Trading: Deep Q-Networks with Portfolio Optimization
by Soroush Shahsafi and Farnoosh Naderkhani
J. Risk Financial Manag. 2026, 19(2), 132; https://doi.org/10.3390/jrfm19020132 - 9 Feb 2026
Viewed by 1442
Abstract
This paper presents a hybrid framework for multi-stock trading that combines the decision-making ability of Deep Q-Networks (DQN) with the allocation precision of portfolio optimization models. Realistic markets are noisy and non-stationary, and complex action spaces can hinder reinforcement learning (RL) performance. The [...] Read more.
This paper presents a hybrid framework for multi-stock trading that combines the decision-making ability of Deep Q-Networks (DQN) with the allocation precision of portfolio optimization models. Realistic markets are noisy and non-stationary, and complex action spaces can hinder reinforcement learning (RL) performance. The DQN generates buy/sell signals based on market conditions. The framework passes buy-listed assets to an optimizer, which computes portfolio weights. Five allocation strategies are examined: naïve 1/N, Markowitz Mean–Variance, Global Minimum Variance, Risk Parity, and Sharpe Ratio Maximization. Empirical evaluations on emerging-market exchange-traded funds (ETFs), as well as additional tests on U.S. equities, show that even the baseline DQN outperforms traditional technical indicators. Furthermore, integrating any of the optimization approaches with DQN yields measurable improvements in return-risk performance metrics. Among the hybrid frameworks, DQN combined with Sharpe Ratio Maximization delivers the most consistent gains. The findings highlight the value of decomposing stock selection from capital allocation and demonstrate the effectiveness of the proposed DQN-optimization framework on our testbed. Full article
(This article belongs to the Special Issue AI Applications in Financial Markets and Computational Finance)
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