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Mathematics
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Financial Econometrics and Machine Learning
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Financial Econometrics and Machine Learning

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 4052

Special Issue Editors

Prof. Dr. Chihwa Kao

E-Mail Website
Guest Editor
Department of Economics, University of Connecticut, Storrs, CT 06269, USA
Interests: panel econometrics; spatial econometrics; mathematical finance; economics; econometrics
Dr. Zhonghui Zhang

E-Mail Website
Guest Editor
Institute of Banking and Money, Nanjing Audit University, Nanjing 210017, China
Interests: econometrics; financial econometrics

Special Issue Information

Dear Colleagues,

The primary objective of this Special Issue is to showcase the latest developments in the field of financial econometrics and machine learning and provide a platform for researchers to share their insights, methodologies, and findings. We invite contributions that bridge the gap between econometric theory and machine learning applications, shedding light on the challenges, opportunities, and implications of this integration.

This Special Issue covers a wide range of pertinent topics that are suitable for exploration. These topics encompass, but are not limited to, asset pricing models incorporating machine learning techniques, portfolio optimization and asset allocation using advanced data analytics, volatility modeling and forecasting with machine learning algorithms, high-frequency trading and market microstructure analysis, risk management and credit scoring models utilizing machine learning, financial forecasting and macroeconomic modeling with machine learning, the interpretability and explainability of machine learning models in finance, model validation, and the robustness of machine learning applications in financial econometrics. Both theoretical and empirical contributions that provide novel insights, methodologies, and practical applications in this evolving field are also welcome. 

We look forward to receiving your submissions and to compiling an exceptional collection of articles that will advance our understanding of financial econometrics and machine learning. The ultimate aim is to explore the frontiers of this exciting field and uncover new avenues for knowledge and innovation.

Prof. Dr. Chihwa Kao
Dr. Zhonghui Zhang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • machine learning
  • financial econometrics
  • asset pricing models
  • portfolio optimization
  • volatility modeling
  • investment decision-making
  • derivatives
  • risk management
  • credit analysis
  • financial forecasting
  • model validation
  • robustness
  • random matrix theory

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

26 pages, 1734 KiB  
Article
Explainable Domain Adaptation Learning Framework for Credit Scoring in Internet Finance Through Adversarial Transfer Learning and Ensemble Fusion Model
by Feiyang Xu and Runchi Zhang
Mathematics 2025, 13(7), 1045; https://doi.org/10.3390/math13071045 - 24 Mar 2025
Viewed by 367
Abstract
Adversarial transfer learning is extensively applied in computer vision owing to its remarkable capability in addressing domain adaptation. However, its applications in credit scoring remain underexplored due to the complexity of financial data. The performance of traditional credit scoring models relies on the [...] Read more.
Adversarial transfer learning is extensively applied in computer vision owing to its remarkable capability in addressing domain adaptation. However, its applications in credit scoring remain underexplored due to the complexity of financial data. The performance of traditional credit scoring models relies on the consistency of domain distribution. Any shift in feature distribution leads to a degradation in model accuracy. To address this issue, we propose a domain adaptation framework comprising a transfer learner and a decision tree. The framework integrates the following: (1) feature partitioning through Wassertein relevance metric; (2) adversarial training of the transfer learner using features with significant distributional differences to achieve an inseparable representation of the source and target domains, while the remaining features are utilized for decision tree model training; and (3) a weighted voting method combines the predictions of the transfer learner and the decision tree. The Shapley Additive Explanations (SHAP) method was used to analyze the predictions of the model, providing the importance of individual features and insights into the model’s decision-making process. Experimental results show that our approach improves prediction accuracy by 3.5% compared to existing methods. Full article
(This article belongs to the Special Issue Financial Econometrics and Machine Learning)
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14 pages, 982 KiB  
Article
Online Investor Sentiment via Machine Learning
by Zongwu Cai and Pixiong Chen
Mathematics 2024, 12(20), 3192; https://doi.org/10.3390/math12203192 - 12 Oct 2024
Viewed by 1131
Abstract
In this paper, we propose utilizing machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment and employing the multifold forward-validation method to select the relevant hyperparameters. Our empirical studies provide strong evidence that some machine [...] Read more.
In this paper, we propose utilizing machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment and employing the multifold forward-validation method to select the relevant hyperparameters. Our empirical studies provide strong evidence that some machine learning methods, such as extreme gradient boosting or random forest, show significant predictive ability in terms of their out-of-sample performances with high-dimensional investor sentiment proxies. They also outperform the traditional linear models, which shows a possible unobserved nonlinear relationship between online investor sentiment and risk premium. Moreover, this predictability based on online investor sentiment has a better economic value, so it improves portfolio performance for investors who need to decide the optimal asset allocation in terms of the certainty equivalent return gain and the Sharpe ratio. Full article
(This article belongs to the Special Issue Financial Econometrics and Machine Learning)
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27 pages, 4774 KiB  
Article
A Functional Data Approach for Continuous-Time Analysis Subject to Modeling Discrepancy under Infill Asymptotics
by Tao Chen, Yixuan Li and Renfang Tian
Mathematics 2023, 11(20), 4386; https://doi.org/10.3390/math11204386 - 22 Oct 2023
Viewed by 1749
Abstract
Parametric continuous-time analysis often entails derivations of continuous-time models from predefined discrete formulations. However, undetermined convergence rates of frequency-dependent parameters can result in ill-defined continuous-time limits, leading to modeling discrepancy, which impairs the reliability of fitting and forecasting. To circumvent this issue, we [...] Read more.
Parametric continuous-time analysis often entails derivations of continuous-time models from predefined discrete formulations. However, undetermined convergence rates of frequency-dependent parameters can result in ill-defined continuous-time limits, leading to modeling discrepancy, which impairs the reliability of fitting and forecasting. To circumvent this issue, we propose a simple solution based on functional data analysis (FDA) and truncated Taylor series expansions. It is demonstrated through a simulation study that our proposed method is superior—compared with misspecified parametric methods—in fitting and forecasting continuous-time stochastic processes, while the parametric method slightly dominates under correct specification, with comparable forecast errors to the FDA-based method. Due to its generally consistent and more robust performance against possible misspecification, the proposed FDA-based method is recommended in the presence of modeling discrepancy. Further, we apply the proposed method to predict the future return of the S&P 500, utilizing observations extracted from a latent continuous-time process, and show the practical efficacy of our approach in accurately discerning the underlying dynamics. Full article
(This article belongs to the Special Issue Financial Econometrics and Machine Learning)
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