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Mathematics
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Machine Learning and Finance
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Machine Learning and Finance

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

Deadline for manuscript submissions: 31 January 2025 | Viewed by 3614

Special Issue Editors

Dr. Luca Di Persio

E-Mail Website1 Website2
Guest Editor
Department of Computer Science, College of Mathematics, University of Verona, Strada le Grazie 15, 37134 Verona, Italy
Interests: stochastic partial differential equations (SPDEs) in both finite and infinite dimensions; asymptotic expansion of finite/infinite integrals; interacting particle systems; random walk in random media; stochastic mean field games with applications in finance; time series analysis with applications in finance; machine learning and mathematical foundations of neural networks with applications in real markets
Special Issues, Collections and Topics in MDPI journals
Dr. Matteo Garbelli

E-Mail Website
Guest Editor
Department of Computer Science, College of Mathematics, University of Verona, Strada le Grazie 15, 37134 Verona, Italy
Interests: adapted optimal transport; probability theory; mean field games; neural networks applications

Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a comprehensive overview of the latest advancements in machine learning methods for finance, emphasizing the critical role of stochastic analysis techniques and the utilization of real financial data. In an era characterized by increasingly complex financial markets and vast amounts of available data, the integration of machine learning methodologies has become paramount for effectively analyzing and navigating the intricacies of financial systems.

This initiative seeks to foster interdisciplinary collaboration between machine learning experts, finance professionals, and data scientists, aiming to explore innovative approaches for addressing contemporary challenges in finance. Topics of interest include, but are not limited to, predictive modelling for market trends, risk assessment and management strategies, algorithmic trading, credit risk modelling, fraud detection, mean field games applications in finance, sentiment analysis, and portfolio optimization.

We particularly welcome submissions that explore innovative approaches to explainable AI (XAI) within the context of finance. Authors are encouraged to address the interpretability, transparency, and trustworthiness of their machine learning models, providing insights into the decision-making process and the factors driving model predictions.

Through this Special Issue, we aim to provide a platform for researchers and practitioners to exchange ideas, share insights, and contribute to the advancement of knowledge at the intersection of machine learning and finance. By showcasing cutting-edge research and innovative methodologies, we strive to facilitate the development of robust and reliable solutions that can enhance decision-making processes and drive positive outcomes in the financial industry.

Authors are invited to submit original research articles, review papers, and case studies that contribute to advancing machine learning methods in finance. 

We look forward to hearing from you.

Dr. Luca Di Persio
Dr. Matteo Garbelli
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
  • neural networks
  • finance
  • XAI
  • MFGs
  • economics
  • computer science
  • stochastic analysis
  • risk
  • predictive modelling
  • CR modelling
  • fraud detection
  • sentiment analysis

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Published Papers (5 papers)

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Research

27 pages, 666 KiB  
Article
Ownership of Cash Value Life Insurance among Rural Households: Utilization of Machine Learning Algorithms to Find Predictors
by Wookjae Heo, Eun Jin Kwak, John Grable and Hye Jun Park
Mathematics 2024, 12(16), 2467; https://doi.org/10.3390/math12162467 - 9 Aug 2024
Viewed by 324
Abstract
This study examines the determinants of life insurance ownership with a focus on rural areas and farming households in the United States. Utilizing data from online surveys conducted in 2019 and 2021, this paper explores how psychological factors, financial knowledge, and household characteristics [...] Read more.
This study examines the determinants of life insurance ownership with a focus on rural areas and farming households in the United States. Utilizing data from online surveys conducted in 2019 and 2021, this paper explores how psychological factors, financial knowledge, and household characteristics influence life insurance ownership. Traditional indicators like wealth, income, and age were evaluated alongside less frequently discussed variables such as farm loans and rural residency. Machine learning techniques, including neural networks, Support Vector Machine modeling, Gradient Boosting, and logistic regression, were employed to identify the most robust predictors of life insurance demand. The findings reveal that farming-associated factors, particularly holding a farm loan and living in a farming household, significantly predict life insurance ownership. The study also highlights the complexity of life insurance demand, showing that financial education and management practices are critical determinants. This research underscores the need for tailored financial risk management strategies for rural and farming households and contributes to a nuanced understanding of life insurance demand in varying contexts. Full article
(This article belongs to the Special Issue Machine Learning and Finance)
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22 pages, 4075 KiB  
Article
Methodology for Smooth Transition from Experience-Based to Data-Driven Credit Risk Assessment Modeling under Data Scarcity
by Hengchun Li, Qiujun Lan and Qingyue Xiong
Mathematics 2024, 12(15), 2410; https://doi.org/10.3390/math12152410 - 2 Aug 2024
Viewed by 329
Abstract
Credit risk refers to the possibility of borrower default, and its assessment is crucial for maintaining financial stability. However, the journey of credit risk data generation is often gradual, and machine learning techniques may not be readily applicable for crafting evaluations at the [...] Read more.
Credit risk refers to the possibility of borrower default, and its assessment is crucial for maintaining financial stability. However, the journey of credit risk data generation is often gradual, and machine learning techniques may not be readily applicable for crafting evaluations at the initial stage of the data accumulation process. This article proposes a credit risk modeling methodology, TED-NN, that first constructs an indicator system based on expert experience, assigns initial weights to the indicator system using the Analytic Hierarchy Process, and then constructs a neural network model based on the indicator system to achieve a smooth transition from an empirical model to a data-driven model. TED-NN can automatically adapt to the gradual accumulation of data, which effectively solves the problem of risk modeling and the smooth transition from no to sufficient data. The effectiveness of this methodology is validated through a specific case of credit risk assessment. Experimental results on a real-world dataset demonstrate that, in the absence of data, the performance of TED-NN is equivalent to the AHP and better than untrained neural networks. As the amount of data increases, TED-NN gradually improves and then surpasses the AHP. When there are sufficient data, its performance approaches that of a fully data-driven neural network model. Full article
(This article belongs to the Special Issue Machine Learning and Finance)
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11 pages, 791 KiB  
Article
Novel Machine Learning Based Credit Card Fraud Detection Systems
by Xiaomei Feng and Song-Kyoo Kim
Mathematics 2024, 12(12), 1869; https://doi.org/10.3390/math12121869 - 15 Jun 2024
Viewed by 1042
Abstract
This research deals with the critical issue of credit card fraud, a problem that has escalated in the last decade due to the significant increase in credit card usage, largely driven by advances in international trade, e-commerce, and FinTech. With global losses projected [...] Read more.
This research deals with the critical issue of credit card fraud, a problem that has escalated in the last decade due to the significant increase in credit card usage, largely driven by advances in international trade, e-commerce, and FinTech. With global losses projected to exceed USD 400 billion in the next decade, the urgent need for effective fraud detection systems is apparent. Our study leverages the power of machine learning (ML) and presents a novel approach to credit card fraud detection. We used the European cardholders dataset for model training, addressing the data imbalance issue that often hinders the effectiveness of the learning process. As a key innovative element, we introduce compact data learning (CDL), a powerful tool for reducing the size and complexity of the training dataset without sacrificing the accuracy of the ML system. Comparative experiments have shown that our CDL-adapted feature reduction outperforms various ML algorithms and feature reduction methods. The findings of this research not only contribute to the theoretical foundations of fraud detection but also provide practical implications for the financial sector, which can benefit immensely from the enhanced fraud detection system. Full article
(This article belongs to the Special Issue Machine Learning and Finance)
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16 pages, 2953 KiB  
Article
Stochastic Patterns of Bitcoin Volatility: Evidence across Measures
by Georgia Zournatzidou, Dimitrios Farazakis, Ioannis Mallidis and Christos Floros
Mathematics 2024, 12(11), 1719; https://doi.org/10.3390/math12111719 - 31 May 2024
Viewed by 600
Abstract
This research conducted a thorough investigation of Bitcoin volatility patterns using three interrelated methodologies: R/S investigation, simple moving average (SMA), and the relative strength index (RSI). The paper jointly employes the above techniques on volatility range-based estimators to effectively capture the unpredictable volatility [...] Read more.
This research conducted a thorough investigation of Bitcoin volatility patterns using three interrelated methodologies: R/S investigation, simple moving average (SMA), and the relative strength index (RSI). The paper jointly employes the above techniques on volatility range-based estimators to effectively capture the unpredictable volatility patterns of Bitcoin. R/S analysis, SMA, and RSI calculations assess time series data obtained from our volatility estimators. Although Bitcoin is known for its high volatility and price instability, our analysis using R/S analysis and moving averages suggests the existence of underlying patterns. The estimated Hurst exponents for our volatility estimators indicate a level of persistence in these patterns, with some estimators displaying more persistence than others. This persistence underscores the potential of momentum-based trading strategies, reinforcing the expectation of additional price rises after declines and vice versa. However, significant volatility often interrupts this upward movement. The SMA analysis also demonstrates Bitcoin’s susceptibility to external market forces. These observations indicate that traders and investors should modify their risk management approaches in accordance with market circumstances, perhaps integrating a combination of momentum-based and mean-reversion tactics to reduce the risks linked to Bitcoin’s volatility. Furthermore, the existence of robust patterns, as demonstrated by our investigation, presents promising opportunities for investing in Bitcoin. Full article
(This article belongs to the Special Issue Machine Learning and Finance)
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18 pages, 466 KiB  
Article
Ensemble Approach Using k-Partitioned Isolation Forests for the Detection of Stock Market Manipulation
by Hugo Núñez Delafuente, César A. Astudillo and David Díaz
Mathematics 2024, 12(9), 1336; https://doi.org/10.3390/math12091336 - 27 Apr 2024
Viewed by 916
Abstract
Stock market manipulation, defined as any attempt to artificially influence stock prices, poses significant challenges by causing financial losses and eroding investor trust. The prevalent reliance on supervised learning models for detecting such manipulations, while showing promise, faces notable hurdles due to the [...] Read more.
Stock market manipulation, defined as any attempt to artificially influence stock prices, poses significant challenges by causing financial losses and eroding investor trust. The prevalent reliance on supervised learning models for detecting such manipulations, while showing promise, faces notable hurdles due to the dearth of labeled data and the inability to recognize novel manipulation tactics beyond those explicitly labeled. This study ventures into addressing these gaps by proposing a novel detection framework aimed at identifying suspicious hourly manipulation blocks through an unsupervised learning approach, thereby circumventing the limitations of data labeling and enhancing the adaptability to emerging manipulation strategies. Our methodology involves the innovative creation of features reflecting the behavior of stocks across various time windows followed by the segmentation of the dataset into k subsets. This setup facilitates the identification of potential manipulation instances via a voting ensemble composed of k isolation forest models, which have been chosen for their efficiency in pinpointing anomalies and their linear computational complexity—attributes that are critical for analyzing vast datasets. Evaluated against eight real stocks known to have undergone manipulation, our approach demonstrated a remarkable capability to identify up to 89% of manipulated blocks, thus significantly outperforming previous methods that do not utilize a voting ensemble. This finding not only surpasses the detection rates reported in prior studies but also underscores the enhanced robustness and adaptability of our unsupervised model in uncovering varied manipulation schemes. Through this research, we contribute to the field by offering a scalable and efficient unsupervised learning strategy for stock manipulation detection, thereby marking a substantial advancement over traditional supervised methods and paving the way for more resilient financial markets. Full article
(This article belongs to the Special Issue Machine Learning and Finance)
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