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Mathematics
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New Advances in Computational Finance and Computational Intelligence in Finance
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New Advances in Computational Finance and Computational Intelligence in Finance

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

Deadline for manuscript submissions: closed (30 June 2025) | Viewed by 13071

Special Issue Editors

Dr. Vasilios N. Katsikis

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Guest Editor
Department of Economics, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, Zografou, Greece
Interests: linear and multilinear algebra; numerical linear algebra; neural networks; intelligent optimization; mathematical finance
Special Issues, Collections and Topics in MDPI journals
Dr. Xinwei Cao

E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, Swansea University, Swansea SA18EN, Wales, UK
Interests: portfolio optimization; big data; fintech management and decision making; fraud detection
Dr. Shuai Li

E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, Swansea University, Swansea SA18EN, Wales, UK
Interests: neural networks; nonlinear optimization; optimal control; robotic planning
Special Issues, Collections and Topics in MDPI journals
Dr. Dimitris Balios

E-Mail Website
Guest Editor
Department of Economics, Division of Business Economics and Business Administration - Finance, National and Kapodistrian University of Athens, Sofokleous, 1, 10559 Athens, Greece
Interests: accounting analytics; earnings management; financial accounting; auditing; accounting standards
Prof. Dr. Theodore E. Simos

grade E-Mail Website
Guest Editor
1. Laboratory of Applied Mathematics for Solving Interdisciplinary Problems of Energy Production, Ulyanovsk State Technical University, Severny Venetz Street 32, 432027 Ulyanovsk, Russia
2. Digital Industry REC, South Ural State University, 76, Lenin Avenue, 454080 Chelyabinsk, Russia
3. Section of Mathematics, Department of Civil Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Interests: numerical analysis; scientific computing; applied numerical analysis; computational chemistry; computational material sciences; computational physics; parallel algorithm and expert systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will focus on the broad topics of "Computational Finance" and "Computational Intelligence in Finance," presenting original research on the application of computational methods and machine intelligence techniques for modeling in finance.

We welcome submissions advancing cutting-edge research and novel ideas for computational methods with practical applications in finance, as well as computational intelligence methods as an alternative to statistical and econometric approaches to financial market analysis.

Contributions primarily focused on the following topics are encouraged:

  • Artificial intelligence, machine learning and big data in finance and data mining for financial data analysis;
  • Financial forecasting and trading algorithms;
  • Genetic algorithms, heuristics and metaheuristics in finance and portfolio optimization algorithms;
  • Fuzzy logic in financial modeling and quantum computing for finance;
  • Accounting analytics, earnings management, blockchain-based accounting, big data analytics in auditing and cloud accounting and big data;
  • Computational risk management and computing and financial management;
  • Digital assets and cryptocurrencies and asset pricing models;
  • Market analysis algorithms, market simulations, algorithmic trading and hedging strategies;
  • Dynamical analysis of financial markets, behavioral finance models and financial markets and firm dynamics.

Dr. Vasilios N. Katsikis
Dr. Xinwei Cao
Dr. Shuai Li
Dr. Dimitris Balios
Prof. Dr. Theodore E. Simos
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

  • fuzzy logic in financial modeling
  • quantum computing for finance
  • artificial intelligence in finance
  • machine learning and big data in finance
  • financial forecasting
  • trading algorithms
  • heuristics and metaheuristics in finance
  • genetic algorithms in finance
  • data mining for financial data analysis
  • digital assets and cryptocurrencies
  • asset pricing models, libration and simulation
  • computational risk management
  • computing and financial management
  • accounting analytics
  • earnings management
  • blockchain-based accounting
  • big data analytics in auditing
  • cloud accounting and big data
  • portfolio optimization algorithms
  • market analysis algorithms
  • market simulations
  • algorithmic trading
  • hedging strategies
  • dynamical analysis of financial markets
  • behavioral finance models
  • financial markets and firm dynamics

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

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Research

21 pages, 3919 KiB  
Article
Comparative Analysis of Resampling Techniques for Class Imbalance in Financial Distress Prediction Using XGBoost
by Guodong Hou, Dong Ling Tong, Soung Yue Liew and Peng Yin Choo
Mathematics 2025, 13(13), 2186; https://doi.org/10.3390/math13132186 - 4 Jul 2025
Viewed by 188
Abstract
One of the key challenges in financial distress data is class imbalance, where the data are characterized by a highly imbalanced ratio between the number of distressed and non-distressed samples. This study examines eight resampling techniques for improving distress prediction using the XGBoost [...] Read more.
One of the key challenges in financial distress data is class imbalance, where the data are characterized by a highly imbalanced ratio between the number of distressed and non-distressed samples. This study examines eight resampling techniques for improving distress prediction using the XGBoost algorithm. The study was performed on a dataset acquired from the CSMAR database, containing 26,383 firm-quarter samples from 639 Chinese A-share listed companies (2007–2024), with only 12.1% of the cases being distressed. Results show that standard Synthetic Minority Oversampling Technique (SMOTE) enhanced F1-score (up to 0.73) and Matthews Correlation Coefficient (MCC, up to 0.70), while SMOTE-Tomek and Borderline-SMOTE further boosted recall, slightly sacrificing precision. These oversampling and hybrid methods also maintained reasonable computational efficiency. However, Random Undersampling (RUS), though yielding high recall (0.85), suffered from low precision (0.46) and weaker generalization, but was the fastest method. Among all techniques, Bagging-SMOTE achieved balanced performance (AUC 0.96, F1 0.72, PR-AUC 0.80, MCC 0.68) using a minority-to-majority ratio of 0.15, demonstrating that ensemble-based resampling can improve robustness with minimal impact on the original class distribution, albeit with higher computational cost. The compared findings highlight that no single approach fits all use cases, and technique selection should align with specific goals. Techniques favoring recall (e.g., Bagging-SMOTE, SMOTE-Tomek) are suited for early warning, while conservative techniques (e.g., Tomek Links) help reduce false positives in risk-sensitive applications, and efficient methods such as RUS are preferable when computational speed is a priority. Full article
(This article belongs to the Special Issue New Advances in Computational Finance and Computational Intelligence in Finance)
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31 pages, 5220 KiB  
Article
A Generative Adversarial Network-Based Investor Sentiment Indicator: Superior Predictability for the Stock Market
by Shiqing Qiu, Yang Wang, Zong Ke, Qinyan Shen, Zichao Li, Rong Zhang and Kaichen Ouyang
Mathematics 2025, 13(9), 1476; https://doi.org/10.3390/math13091476 - 30 Apr 2025
Cited by 5 | Viewed by 597
Abstract
Investor sentiment has a profound impact on financial market volatility; however, it is difficult to accurately capture the complex nonlinear relationships among sentiment proxies with the existing methods. In this study, we propose a novel investor sentiment indicator, SGAN, which uses [...] Read more.
Investor sentiment has a profound impact on financial market volatility; however, it is difficult to accurately capture the complex nonlinear relationships among sentiment proxies with the existing methods. In this study, we propose a novel investor sentiment indicator, SGAN, which uses generative adversarial networks (GANs) to extract the nonlinear latent structure from eight sentiment proxies from February 2003 to September 2023 in the Chinese A-share market. Unlike traditional linear dimensionality reduction methods, GANs are able to capture complex market dynamics through adversarial training, effectively reducing noise and improving prediction accuracy. The empirical analyses show that SGAN significantly outperforms existing methods in both in-sample and out-of-sample prediction capabilities. The GAN-based investment strategy achieves impressive annualized returns and provides a powerful tool for portfolio construction and risk management. Robustness tests across economic cycles, industries, and U.S. markets further validate the stability of SGAN. These findings highlight the unique advantages of GANs as sentiment-driven financial forecasting tools, providing market participants with new ways to more accurately capture sentiment-shifting trends and develop effective investment strategies. Full article
(This article belongs to the Special Issue New Advances in Computational Finance and Computational Intelligence in Finance)
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17 pages, 361 KiB  
Article
Contextual Analysis of Financial Time Series
by Nadezhda Yarushkina, Aleksey Filippov and Anton Romanov
Mathematics 2025, 13(1), 57; https://doi.org/10.3390/math13010057 - 27 Dec 2024
Cited by 1 | Viewed by 697
Abstract
The evaluation of the financial state of small and medium-sized companies is a pressing issue today. This article introduces a novel method to evaluate a company’s financial state, implemented as a module within a decision support system. This component uses fuzzy logic and [...] Read more.
The evaluation of the financial state of small and medium-sized companies is a pressing issue today. This article introduces a novel method to evaluate a company’s financial state, implemented as a module within a decision support system. This component uses fuzzy logic and knowledge engineering methods. The article describes an ontological model that provides the framework for data analysis and financial time-series modeling. The ontological framework simplifies the representation of the trends in the financial indicators under analysis. Integrating an ontology and a set of fuzzy rules makes it possible to develop control systems based on fuzzy inference. This approach provides the analysis and interpretation of results. The experimental results validate the accuracy and effectiveness of the proposed method. Full article
(This article belongs to the Special Issue New Advances in Computational Finance and Computational Intelligence in Finance)
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14 pages, 566 KiB  
Article
A Weights Direct Determination Neural Network for International Standard Classification of Occupations
by Dimitris Lagios, Spyridon D. Mourtas, Panagiotis Zervas and Giannis Tzimas
Mathematics 2023, 11(3), 629; https://doi.org/10.3390/math11030629 - 26 Jan 2023
Cited by 3 | Viewed by 2177
Abstract
Multiclass classification is one of the most popular machine learning tasks. The main focus of this paper is to classify occupations according to the International Standard Classification of Occupations (ISCO) using a weights and structure determination (WASD)-based neural network. In general, WASD-trained neural [...] Read more.
Multiclass classification is one of the most popular machine learning tasks. The main focus of this paper is to classify occupations according to the International Standard Classification of Occupations (ISCO) using a weights and structure determination (WASD)-based neural network. In general, WASD-trained neural networks are known to overcome the drawbacks of conventional back-propagation trained neural networks, such as slow training speed and local minimum. However, WASD-based neural networks have not yet been applied to address the challenges of multiclass classification. As a result, a novel WASD for multiclass classification (WASDMC)-based neural network is introduced in this paper. When applied to two publicly accessible ISCO datasets, the WASDMC-based neural network displayed superior performance across all measures, compared to some of the best-performing classification models that the MATLAB classification learner app has to offer. Full article
(This article belongs to the Special Issue New Advances in Computational Finance and Computational Intelligence in Finance)
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14 pages, 786 KiB  
Article
Portfolio Insurance through Error-Correction Neural Networks
by Vladislav N. Kovalnogov, Ruslan V. Fedorov, Dmitry A. Generalov, Andrey V. Chukalin, Vasilios N. Katsikis, Spyridon D. Mourtas and Theodore E. Simos
Mathematics 2022, 10(18), 3335; https://doi.org/10.3390/math10183335 - 14 Sep 2022
Cited by 22 | Viewed by 2193
Abstract
Minimum-cost portfolio insurance (MCPI) is a well-known investment strategy that tries to limit the losses a portfolio may incur as stocks decrease in price without requiring the portfolio manager to sell those stocks. In this research, we define and study the time-varying MCPI [...] Read more.
Minimum-cost portfolio insurance (MCPI) is a well-known investment strategy that tries to limit the losses a portfolio may incur as stocks decrease in price without requiring the portfolio manager to sell those stocks. In this research, we define and study the time-varying MCPI problem as a time-varying linear programming problem. More precisely, using real-world datasets, three different error-correction neural networks are employed to address this financial time-varying linear programming problem in continuous-time. These neural network solvers are the zeroing neural network (ZNN), the linear-variational-inequality primal-dual neural network (LVI-PDNN), and the simplified LVI-PDNN (S-LVI-PDNN). The neural network solvers are tested using real-world data on portfolios of up to 20 stocks, and the results show that they are capable of solving the financial problem efficiently, in some cases more than five times faster than traditional methods, though their accuracy declines as the size of the portfolio increases. This demonstrates the speed and accuracy of neural network solvers, showing their superiority over traditional methods in moderate-size portfolios. To promote and contend the outcomes of this research, we created two MATLAB repositories, for the interested user, that are publicly accessible on GitHub. Full article
(This article belongs to the Special Issue New Advances in Computational Finance and Computational Intelligence in Finance)
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20 pages, 2442 KiB  
Article
Exploiting Mean-Variance Portfolio Optimization Problems through Zeroing Neural Networks
by Spyridon D. Mourtas and Chrysostomos Kasimis
Mathematics 2022, 10(17), 3079; https://doi.org/10.3390/math10173079 - 26 Aug 2022
Cited by 12 | Viewed by 2534
Abstract
In this research, three different time-varying mean-variance portfolio optimization (MVPO) problems are addressed using the zeroing neural network (ZNN) approach. The first two MVPO problems are defined as time-varying quadratic programming (TVQP) problems, while the third MVPO problem is defined as a time-varying [...] Read more.
In this research, three different time-varying mean-variance portfolio optimization (MVPO) problems are addressed using the zeroing neural network (ZNN) approach. The first two MVPO problems are defined as time-varying quadratic programming (TVQP) problems, while the third MVPO problem is defined as a time-varying nonlinear programming (TVNLP) problem. Then, utilizing real-world datasets, the time-varying MVPO problems are addressed by this alternative neural network (NN) solver and conventional MATLAB solvers, and their performances are compared in three various portfolio configurations. The results of the experiments show that the ZNN approach is a magnificent alternative to the conventional methods. To publicize and explore the findings of this study, a MATLAB repository has been established and is freely available on GitHub for any user who is interested. Full article
(This article belongs to the Special Issue New Advances in Computational Finance and Computational Intelligence in Finance)
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14 pages, 778 KiB  
Article
Adopting Nonlinear Activated Beetle Antennae Search Algorithm for Fraud Detection of Public Trading Companies: A Computational Finance Approach
by Bolin Liao, Zhendai Huang, Xinwei Cao and Jianfeng Li
Mathematics 2022, 10(13), 2160; https://doi.org/10.3390/math10132160 - 21 Jun 2022
Cited by 17 | Viewed by 2166
Abstract
With the emergence of various online trading technologies, fraudulent cases begin to occur frequently. The problem of fraud in public trading companies is a hot topic in financial field. This paper proposes a fraud detection model for public trading companies using datasets collected [...] Read more.
With the emergence of various online trading technologies, fraudulent cases begin to occur frequently. The problem of fraud in public trading companies is a hot topic in financial field. This paper proposes a fraud detection model for public trading companies using datasets collected from SEC’s Accounting and Auditing Enforcement Releases (AAERs). At the same time, this computational finance model is solved with a nonlinear activated Beetle Antennae Search (NABAS) algorithm, which is a variant of the meta-heuristic optimization algorithm named Beetle Antennae Search (BAS) algorithm. Firstly, the fraud detection model is transformed into an optimization problem of minimizing loss function and using the NABAS algorithm to find the optimal solution. NABAS has only one search particle and explores the space under a given gradient estimation until it is less than an “Activated Threshold” and the algorithm is efficient in computation. Then, the random under-sampling with AdaBoost (RUSBoost) algorithm is employed to comprehensively evaluate the performance of NABAS. In addition, to reflect the superiority of NABAS in the fraud detection problem, it is compared with some popular methods in recent years, such as the logistic regression model and Support Vector Machine with Financial Kernel (SVM-FK) algorithm. Finally, the experimental results show that the NABAS algorithm has higher accuracy and efficiency than other methods in the fraud detection of public datasets. Full article
(This article belongs to the Special Issue New Advances in Computational Finance and Computational Intelligence in Finance)
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