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Mathematics
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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 March 2027 | Viewed by 7145

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 (4 papers)

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Research

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74 pages, 7687 KB  
Article
ForExAI: Time Series Inference and News Article Analysis Reveal Profitable Foreign Exchange Signals
by Beakal Lemeneh, Eli Hadad, Allen George Ajith, Yanbo Hou, Charlie Zha, Ganesh Scarozza, Zakaria Baannou, Ermiyas Liyeh, Anthony Tomasic and Dennis Shasha
Mathematics 2026, 14(13), 2319; https://doi.org/10.3390/math14132319 - 1 Jul 2026
Viewed by 941
Abstract
Forecasting foreign exchange rates over long time periods depends on economic fundamentals. Short-term predictions, by contrast, depend largely on emotions, governmental announcements, the flow of capital, and media commentary. This paper proposes a suite of methods, collectively referred to as ForExAI, to [...] Read more.
Forecasting foreign exchange rates over long time periods depends on economic fundamentals. Short-term predictions, by contrast, depend largely on emotions, governmental announcements, the flow of capital, and media commentary. This paper proposes a suite of methods, collectively referred to as ForExAI, to predict foreign exchange rates based on time series analysis and news article analysis. The time series analysis is based on classical statistical time series techniques, such as ARIMA, as well as machine learning methods using neural networks. Separately, ForExAI uses Large Language Models to analyze news articles based on two kinds of prompts: (i) expert-written based on econometric considerations, (ii) existing prompts documented in the literature. Our findings on time series of exchange rates indicate that there are signals in the time series that can be captured even by simple methods like ARIMA(1,1,1), as well as novel machine learning methods on the time series of foreign exchange rate trades. Further, an adaptation of the Kelly criterion can increase cumulative profits. Finally, an ensemble approach often delivers slightly lower profit, but also lower volatility, leading to a higher Sharpe ratio. Regarding news article analysis, shorter prompts yield far better results than complex ones derived from expert knowledge. This work shows a method for hyperparameter tuning a collection of models that forecast complex time series, as well as the relative virtues of different versions of the Kelly criterion. The results make a pragmatic contribution as well. Because we measure profits ignoring transaction costs, our work is not directly actionable by traders, but the insights could be useful. In addition, our results point to further areas of research for traders. Full article
(This article belongs to the Special Issue Applications of Mathematics Analysis in Financial Marketing)
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17 pages, 338 KB  
Article
Multi-Criteria Financial Screening Under Data Uncertainty: An LLM-Extraction and Min–Max TOPSIS Approach for SMEs
by Vinicius Minatogawa, Mitsuyoshi Fukushi, Jose Garcia, Jorge Rojas, Jose Gornall, Alfredo Angulo and Jefferson Pinto
Mathematics 2026, 14(12), 2217; https://doi.org/10.3390/math14122217 - 20 Jun 2026
Viewed by 395
Abstract
Small and medium enterprises routinely face a paradox in financial monitoring: their accounting documents exist, but the cost of converting heterogeneous PDFs into timely financial signals is prohibitive without dedicated analytical staff or specialized software. This paper presents a two-layer artifact, designed under [...] Read more.
Small and medium enterprises routinely face a paradox in financial monitoring: their accounting documents exist, but the cost of converting heterogeneous PDFs into timely financial signals is prohibitive without dedicated analytical staff or specialized software. This paper presents a two-layer artifact, designed under Design Science Research, that bridges this gap using only public-web large language models (LLMs) and a parsimonious multi-criteria decision routine. Layer 1 implements a structured LLM-driven workflow that extracts account–value pairs from annual tax balance sheets without code, APIs, or fine-tuning. Layer 2 reconstructs auditable accounting aggregates and ranks yearly financial condition through TOPSIS with min–max normalization—a deliberate replacement for classical vector normalization, which fails when profitability indicators are negative, as routinely occurs in distress years. To avoid size effects and algebraic redundancy, the decision matrix uses only three criteria spanning liquidity, profitability, and solvency. The artifact is demonstrated in a four-year case study of an anonymized construction SME (2021–2024), with accountant-verified document-level match rates of 0.810, 0.998, 0.950, and 0.909. Equal weighting is the only weighting configuration used; a supplementary entropy-based dispersion diagnostic yields the same ordinal ranking—2024 > 2023 > 2021 > 2022—and 10,000 Monte Carlo replications, with uncertainty injected at the reconstructed-aggregate level, confirm that the extreme ranks are invariant across all runs. The contribution is methodological and practical: a transparent, low-infrastructure pipeline that brings first-pass financial screening within reach of SMEs operating under severe data and budget constraints. Full article
(This article belongs to the Special Issue Applications of Mathematics Analysis in Financial Marketing)
33 pages, 6180 KB  
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
Cited by 7 | Viewed by 4411
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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Review

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30 pages, 561 KB  
Review
On Intensively Criticizing and Envisioning the Research on Multiple-Objective Portfolio Selection from the Perspective of Capital Asset Pricing Models
by Yue Qi, Jianing Huang and Yixuan Zhu
Mathematics 2026, 14(2), 216; https://doi.org/10.3390/math14020216 - 6 Jan 2026
Cited by 1 | Viewed by 519
Abstract
Nobel Laureate Markowitz originates portfolio selection as the birth of modern finance. Nobel Laureate Sharpe implements portfolio selection and originates capital asset pricing models. Nobel Laureate Fama also implements portfolio selection and originates zero-covariance capital asset pricing models. After these feats, researchers have [...] Read more.
Nobel Laureate Markowitz originates portfolio selection as the birth of modern finance. Nobel Laureate Sharpe implements portfolio selection and originates capital asset pricing models. Nobel Laureate Fama also implements portfolio selection and originates zero-covariance capital asset pricing models. After these feats, researchers have gradually realized additional objectives and have promisingly extended portfolio selection into multiple-objective portfolio selection. However, there hardly exists research to leap from multiple-objective portfolio selection to multiple-objective capital asset pricing models (as initiated by Markowitz and Sharpe in finance). Moreover, the extension is basically confined to the branches of mathematics, operations research, optimization, and computer sciences. Many researchers sufficiently review multiple-objective portfolio selection. However, the reviews are extensive. Instead, we intensively criticize and envision the research on multiple-objective portfolio selection from the perspective of capital asset pricing models by crystallizing the research limitations and heralding future directions. Specifically, we emphasize seven research limitations for multiple-objective portfolio optimization, multiple-objective capital asset pricing models, and multiple-objective zero-covariance capital asset pricing models. We also generalize from common three-objective portfolio selection to k-objective portfolio selection. Visually, we orchestrate figures to delineate the complexity. Theoretically, this paper heralds challenging but encouraging future directions. Pragmatically, this paper proposes a formulation for the multiple-objective nature of practical convolution in finance. Full article
(This article belongs to the Special Issue Applications of Mathematics Analysis in Financial Marketing)
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