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Mathematics
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Applications of Quantitative Analysis in Financial Markets
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Applications of Quantitative Analysis in Financial Markets

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 11830

Special Issue Editor

Prof. Dr. Yongjie Zhang

E-Mail Website
Guest Editor
College of Management and Economics, Tianjin University, Tianjin 300072, China
Interests: financial big data; financial engineering; asset pricing

Special Issue Information

Dear Colleagues,

We invite researchers, academics, and practitioners to submit original research articles to a Special Issue of Mathematics entitled “Applications of Quantitative Analysis in Financial Markets”. The application of quantitative analysis in financial markets has grown significantly in recent years. With the use of advanced technologies, financial investors are increasingly relying on quantitative analysis to make informed investment decisions, manage risks, and optimize their portfolios’ performance. Governments are also guiding the development of financial markets through quantitative analysis, such as encouraging green finance and corporate social responsibility. These have led to an increased demand for research and analysis in this area. Based on the above background, we propose the following topics to consider:

  • Portfolio optimization;
  • Statistical analysis of financial data;
  • Big data analytics in finance;
  • Risk management and asset pricing;
  • Environmental, social, and governance (ESG) factors in investment decision making;
  • Modeling and simulation for green finance.

Prof. Dr. Yongjie Zhang
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

  • quantitative analysis
  • financial markets
  • investment decision
  • big data
  • asset pricing
  • risk management
  • ESG
  • green finance
  • portfolio optimization

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Published Papers (4 papers)

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Research

36 pages, 4070 KiB  
Article
Microeconomic Shock Propagation Through Production Networks in China
by Yihan Liao
Mathematics 2025, 13(3), 359; https://doi.org/10.3390/math13030359 - 23 Jan 2025
Viewed by 721
Abstract
The question of whether microeconomic shocks induce aggregate fluctuations constitutes a central issue in economic research. This paper introduces a general equilibrium model with production networks to explore the propagation mechanisms of microeconomic shocks. A novel triangular production network structure is introduced, and [...] Read more.
The question of whether microeconomic shocks induce aggregate fluctuations constitutes a central issue in economic research. This paper introduces a general equilibrium model with production networks to explore the propagation mechanisms of microeconomic shocks. A novel triangular production network structure is introduced, and simulations are performed using China’s input-output table to analyze the propagation of these shocks within the Chinese economy. The model demonstrates that the first-order effects of microeconomic shocks propagate downstream along the industrial chain, while the second-order effects of microeconomic productivity shocks propagate both upstream and downstream along the chain. The first-order propagation mechanism of microeconomic shocks involves changes in prices within the affected sector and its downstream sectors. Additionally, the second-order effects of microeconomic shocks rely on the reallocation of factors. The simulation results indicate that China’s production network matrix is triangular, and that the financial sector plays a crucial role in amplifying the effects of microeconomic shocks. Government should prioritize supporting upstream fundamental sectors to mitigate the adverse impacts of external shocks on economic fluctuations and to address systemic financial risks. Full article
(This article belongs to the Special Issue Applications of Quantitative Analysis in Financial Markets)
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17 pages, 1967 KiB  
Article
Machine Learning for Sustainable Portfolio Optimization Applied to a Water Market
by María Antonia Truyols-Pont, Amelia Bilbao-Terol and Mar Arenas-Parra
Mathematics 2024, 12(24), 3975; https://doi.org/10.3390/math12243975 - 18 Dec 2024
Viewed by 1533
Abstract
This study introduces a novel methodology that integrates the Black–Litterman model with Long Short-Term Memory Neural Networks (BL–LSTM). We use predictions from the LSTM as views in the Black–Litterman model. The resulting portfolio performs better than the traditional mean-variance (MV) and exchange-traded funds [...] Read more.
This study introduces a novel methodology that integrates the Black–Litterman model with Long Short-Term Memory Neural Networks (BL–LSTM). We use predictions from the LSTM as views in the Black–Litterman model. The resulting portfolio performs better than the traditional mean-variance (MV) and exchange-traded funds (ETFs) used as benchmarks. The proposal empowers investors to make more insightful decisions, drawing from a synthesis of historical data and advanced predictive techniques. This methodology is applied to a water market. Investing in the water market allows investors to actively support sustainable water solutions while potentially benefiting from the sector’s growth, contributing to achieving SDG 6. In addition, our modeling allows for companies’ environmental, social, and governance (ESG) scores to be considered in the portfolio construction process. In this case, investors’ decisions take into account companies’ socially responsible behavior in a broad sense, including aspects related to decent work, respect for indigenous communities and diversity, and the absence of corruption, among others. Therefore, this proposal provides investors with a tool for promoting sustainable investment practices. Full article
(This article belongs to the Special Issue Applications of Quantitative Analysis in Financial Markets)
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36 pages, 688 KiB  
Article
The Impact of Employee Stock Ownership Plans on Capital Structure Decisions: Evidence from China
by Fu Cheng, Chenyao Huang and Shanshan Ji
Mathematics 2024, 12(19), 3118; https://doi.org/10.3390/math12193118 - 5 Oct 2024
Viewed by 1458
Abstract
The determination of the capital structure is a critical component of a company’s financial decision-making process. The question of how to optimize a firm’s capital structure to increase its value has been a significant topic of interest within the financial community. The employee [...] Read more.
The determination of the capital structure is a critical component of a company’s financial decision-making process. The question of how to optimize a firm’s capital structure to increase its value has been a significant topic of interest within the financial community. The employee stock ownership plan (ESOP) has developed rapidly in China’s capital market over the past decade, providing a suitable context for studying the impact of employee equity incentives on capital structure decisions. This paper employs cross-sectional ordinary least squares regression models and unbalanced panel fixed effect models to investigate the impact of employee stock ownership plans (ESOPs) on firms’ capital structure decisions. The analysis is conducted on a sample of Chinese A-share listed companies on the Shanghai and Shenzhen Stock Exchanges. The research considers both static capital structure choice and dynamic capital structure adjustment. We find that the implementation of an ESOP reduces the level of corporate debt and accelerates the dynamic adjustment of capital structure, suggesting that employee equity incentives play a role in optimizing firms’ capital structure decisions. We also find that the impact of ESOPs on the dynamic adjustment of capital structure is asymmetric. Specifically, the implementation of ESOPs markedly accelerates the downward adjustment of capital structure, yet has no impact on the upward adjustment of capital structure. Further analysis demonstrates that the impact of ESOPs on capital structure decisions is contingent upon the macroeconomic environment, industry characteristics, corporate governance, and ESOP contract designs. First, the optimization of ESOPs on capital structure decisions is more pronounced in an economic boom environment, in a poor market climate, or in competitive industries. Second, the reduction effect of ESOPs on corporate debt is more pronounced in non-state-owned companies, high-tech companies and those with lower ownership concentration. In contrast, the acceleration effect of ESOPs on capital structure adjustment is more pronounced in state-owned companies, non-high-tech companies and those with higher ownership concentration. Ultimately, ESOPs financed by loans from a firm’s major shareholders—or with a longer lock-up period, smaller shareholding size or executive subscription ratio—demonstrate a more pronounced optimization effect on capital structure decisions. This paper not only contributes to the existing literature on the relationship between equity incentives and capital structure decisions, but also provides guidance for listed companies on the reasonable design of their ESOPs and the optimization of their capital structure decisions. Full article
(This article belongs to the Special Issue Applications of Quantitative Analysis in Financial Markets)
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28 pages, 1373 KiB  
Article
Optimizing Cryptocurrency Returns: A Quantitative Study on Factor-Based Investing
by Phumudzo Lloyd Seabe, Claude Rodrigue Bambe Moutsinga and Edson Pindza
Mathematics 2024, 12(9), 1351; https://doi.org/10.3390/math12091351 - 29 Apr 2024
Viewed by 6925
Abstract
This study explores cryptocurrency investment strategies by adapting the robust framework of factor investing, traditionally applied in equity markets, to the distinctive landscape of cryptocurrency assets. It conducts an in-depth examination of 31 prominent cryptocurrencies from December 2017 to December 2023, employing the [...] Read more.
This study explores cryptocurrency investment strategies by adapting the robust framework of factor investing, traditionally applied in equity markets, to the distinctive landscape of cryptocurrency assets. It conducts an in-depth examination of 31 prominent cryptocurrencies from December 2017 to December 2023, employing the Fama–MacBeth regression method and portfolio regressions to assess the predictive capabilities of market, size, value, and momentum factors, adjusted for the unique characteristics of the cryptocurrency market. These characteristics include high volatility and continuous trading, which differ markedly from those of traditional financial markets. To address the challenges posed by the perpetual operation of cryptocurrency trading, this study introduces an innovative rebalancing strategy that involves weekly adjustments to accommodate the market’s constant fluctuations. Additionally, to mitigate issues like autocorrelation and heteroskedasticity in financial time series data, this research applies the Newey–West standard error approach, enhancing the robustness of regression analyses. The empirical results highlight the significant predictive power of momentum and value factors in forecasting cryptocurrency returns, underscoring the importance of tailoring conventional investment frameworks to the cryptocurrency context. This study not only investigates the applicability of factor investing in the rapidly evolving cryptocurrency market, but also enriches the financial literature by demonstrating the effectiveness of combining Fama–MacBeth cross-sectional analysis with portfolio regressions, supported by Newey–West standard errors, in mastering the complexities of digital asset investments. Full article
(This article belongs to the Special Issue Applications of Quantitative Analysis in Financial Markets)
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