Applications of Machine Learning in Finance

A Special Issue of International Journal of Financial Studies (ISSN 2227-7072).

Deadline for manuscript submissions: 10 June 2027 | Viewed by 727

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Guest Editor
School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, China
Interests: machine learning; deep generative; learning; financial statistics; financial econometrics; financial risk management

Special Issue Information

Dear Colleagues,

Machine learning is rapidly transforming financial research by providing flexible, data-driven tools for modeling complex, nonlinear, and high-dimensional financial systems. At the same time, finance offers a distinctive environment in which prediction, inference, interpretability, and economic reasoning must be jointly considered. This Special Issue, “Applications of Machine Learning in Finance”, aims to bring together high-quality theoretical, methodological, and empirical studies that advance the use of machine learning in financial studies.

We welcome contributions that integrate machine learning with financial econometrics, including asset pricing, volatility modeling, risk measurement, portfolio optimization, credit risk, systemic risk, financial networks, and stress testing. Particular interest is given to studies that combine modern machine learning and deep learning methods with econometric identification, causal inference, panel data models, high-frequency data analysis, and robust statistical inference. We also encourage the submission of research on machine-learning-based financial market prediction, including stock, bond, commodity, cryptocurrency, derivative, and future markets, as well as studies using alternative data, textual information, news sentiment, social media, ESG data, and transaction-level records.

Beyond prediction accuracy, we are especially interested in papers that address interpretability, model uncertainty, robustness, generalization under market regime changes, and responsible AI in financial decision-making. Empirical applications that generate new financial insights, improve risk management, support regulatory technology, or inform investment and corporate financial decisions are highly encouraged. Both original research articles and review papers are welcome.

Prof. Dr. Hanwen Ning
Guest Editor

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Keywords

  • machine learning in finance
  • financial econometrics
  • financial market prediction
  • asset pricing
  • volatility forecasting
  • risk management
  • portfolio optimization
  • causal inference in finance
  • alternative financial data
  • explainable AI in finance

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Published Papers (2 papers)

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Research

28 pages, 361 KB  
Article
Analyst Logical Inconsistency and Stock Price Crash Risk: Evidence from Large Language Models
by Yingge Ma, Hu Zhang and Zihuan Gao
Int. J. Financ. Stud. 2026, 14(9), 227; https://doi.org/10.3390/ijfs14090227 - 28 Aug 2026
Viewed by 121
Abstract
This paper examines whether and how analyst logical inconsistency affects the stock price crash risk in China’s A-share market. The sample includes 3784 listed non-financial companies. The final sample comprises 12,460 firm-year observations from 2016 to 2023. Specifically, we employ the open source [...] Read more.
This paper examines whether and how analyst logical inconsistency affects the stock price crash risk in China’s A-share market. The sample includes 3784 listed non-financial companies. The final sample comprises 12,460 firm-year observations from 2016 to 2023. Specifically, we employ the open source Qwen1.5-14B-Chat model, an instruction-tuned generative large language model, to measure analyst logical inconsistency, classify the sentiment expressed in analyst reports, and determine the differences between earnings forecasts and textual tone. Using a panel fixed-effect model for basic regression, and applying two-stage least squares and propensity score matching to deal with endogenous problems, we find that analyst logical inconsistency significantly increases the stock price crash risk. The results remain robust to alternative variable definitions, additional control variables, alternative sample periods, and more stringent fixed-effects specifications. The mechanism test shows that the analyst logical inconsistency increases the stock price crash risk through three channels: increased financial risk, reduced investment efficiency, and degraded information disclosure quality. Heterogeneity analysis also shows that this positive impact is strongest in companies with high media coverage, good corporate reputation and low ESG performance. Our research results are helpful to the study of information intermediaries and stock price crash risk by introducing measures for the quality of analyst reports based on large language models, and provide useful suggestions for regulators and investors in emerging markets. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Finance)
28 pages, 2677 KB  
Article
Reassessment of Factors Affecting China’s Quantity-Based Monetary Policy Effectiveness: An Interpretable Machine Learning Approach
by Li Sun, Nian Jiang and Aojun Wang
Int. J. Financ. Stud. 2026, 14(8), 215; https://doi.org/10.3390/ijfs14080215 - 14 Aug 2026
Viewed by 326
Abstract
Effective transmission of monetary policy serves as the foundational institutional guarantee for sustaining macroeconomic stability, smoothing cyclical economic fluctuations, and promoting high-quality development. Nevertheless, the underlying determinants driving the time-varying effectiveness of China’s quantity-based monetary policy have not been systematically and empirically delineated [...] Read more.
Effective transmission of monetary policy serves as the foundational institutional guarantee for sustaining macroeconomic stability, smoothing cyclical economic fluctuations, and promoting high-quality development. Nevertheless, the underlying determinants driving the time-varying effectiveness of China’s quantity-based monetary policy have not been systematically and empirically delineated in the prevailing literature. This paper first constructs a precise measurement indicator for the effectiveness of China’s quantity-based monetary policy from the output-transmission dimension, which is defined as the response of domestic real output (excluding the contribution of net exports) to orthogonalized exogenous M2 growth shocks. On this basis, the gradient-boosting decision tree (GBDT) model is integrated with the Shapley Additive Explanations (SHAP) framework to quantitatively identify the core determinants that govern the policy effectiveness across different economic cycles and structural transformation stages. The estimation results document clear stage-wise heterogeneity in the drivers of China’s quantity-based monetary policy effectiveness: population-aging and macroeconomic-policy indicators stand out as the dominant explanatory factors over 2002–2008, while economic-structure indicators assume the leading role in shaping policy effectiveness during 2009–2015. The 2016–2022 period is further characterized by the joint dominance of demographic aging and economic-structure dimensions. Within this latest phase, the old-age dependency ratio, real–virtual economy structural misalignment, and distorted aggregate supply configuration exert statistically significant negative marginal contributions to the model-predicted effectiveness of monetary policy, whereas the total fertility rate and potential output growth rate yield positive and economically meaningful contributions. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Finance)
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