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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 238

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

Published Papers (1 paper)

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Research

18 pages, 465 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://0-doi-org.brum.beds.ac.uk/10.3390/math12091336 (registering DOI) - 27 Apr 2024
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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