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Mathematics
Special Issues
Applications of Mathematics Analysis in Financial Marketing
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Applications of Mathematics Analysis in Financial Marketing

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 705

Special Issue Editor

Prof. Dr. Yue Qi

E-Mail Website
Guest Editor
Department of Financial Management, Business School, Nankai University, Tianjin 300071, China
Interests: portfolio selection; portfolio optimization; asset pricing

Special Issue Information

Dear Colleagues,

As artificial intelligence, machine learning, block chains, cloud computing, and big data are explosively and revolutionarily deployed in financial markets, researchers and investors are dissecting mathematics as a foundation for technologies. Historically, financial research has been fundamentally reinforced by mathematical innovations and applications. For instance, Markowitz originated portfolio selection, while Sharpe then originated capital asset pricing models. Moreover, Black, Scholes, and Merton originated option pricing.

The purpose of this Special Issue is to enhance mathematical research and its application in financial marketing and strengthen the research regarding artificial intelligence, machine learning, block chains, cloud computing, and big data.

Artificial intelligence, machine learning, block chains, cloud computing, and big data are dramatically reshaping research and practices for financial marketing. Therefore, we disregard the limitations of specific areas and embrace submissions in all related mathematical, operation-research, statistics, computer-science, and mathematical-finance areas.

We look forward to your submission to this Special Issue.

Prof. Dr. Yue Qi
Guest Editor

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

  • optimization
  • mathematical finance
  • portfolio theory
  • corporate finance modeling
  • prediction of bankruptcy and failure
  • financial market modeling
  • behavioral finance
  • financial econometrics
  • ⁠risk management
  • portfolio optimization
  • Portfolio optimization and asset allocation
  • trading systems
  • market analysis algorithms
  • market simulations
  • digital assets and cryptocurrencies
  • algorithmic trading

 

 

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Published Papers (1 paper)

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Research

33 pages, 6180 KiB  
Article
Multi-Stage Data-Driven Framework for Customer Journey Optimization and Operational Resilience
by Tzu-Chien Wang, Ruey-Shan Guo, Chialin Chen and Chia-Kai Li
Mathematics 2025, 13(7), 1145; https://doi.org/10.3390/math13071145 - 31 Mar 2025
Viewed by 537
Abstract
Optimizing customer journeys is a critical challenge in e-commerce and financial services, attracting attention from marketing, operations research, and business analytics. Traditional customer analytics models, such as rule-based segmentation and regression models, rely heavily on structured transactional data, limiting their ability to capture [...] Read more.
Optimizing customer journeys is a critical challenge in e-commerce and financial services, attracting attention from marketing, operations research, and business analytics. Traditional customer analytics models, such as rule-based segmentation and regression models, rely heavily on structured transactional data, limiting their ability to capture latent behavioral patterns and adapt to multi-channel dynamics. These models often struggle to integrate unstructured data sources, failing to provide adaptive, personalized insights. To address these limitations, this study proposes a multi-stage data-driven framework integrating latent Dirichlet allocation (LDA) for behavioral insights, deep learning for predictive modeling, and heuristic algorithms for adaptive decision-making. Empirical validation using Taiwanese financial institution data shows a 15% improvement in predictive accuracy compared to traditional machine-learning models, significantly enhancing customer lifetime value (CLV) predictions and multi-channel resource allocation. This research highlights the practical value of integrating structured and unstructured data for improving customer analytics. Our framework leverages LDA to extract behavioral patterns from customer interactions, enriching predictive models and enhancing real-time decision-making in financial services. Robustness checks confirm the scalability and adaptability of this approach, offering a data-driven strategy for long-term value optimization in dynamic digital ecosystems. Full article
(This article belongs to the Special Issue Applications of Mathematics Analysis in Financial Marketing)
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