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EDITORIAL article

Front. Appl. Math. Stat.

Sec. Mathematical Finance

Volume 11 - 2025 | doi: 10.3389/fams.2025.1573606

This article is part of the Research Topic Applied Quantitative Methods for Corporate Finance and Investments View all 5 articles

Editorial: Applied Quantitative Methods for Corporate Finance and Investments

Provisionally accepted
  • Department of Finance, Bucharest Academy of Economic Studies, Bucharest, Romania

The final, formatted version of the article will be published soon.

    consumption scale, and their interaction are significant. To enhance these effects, the paper suggests strengthening financial support, improving infrastructure, encouraging consumption, and promoting coordinated regional financial development, with tailored policies to address varying levels of digital financial concentration across cities.The paper of Zhang and Yin uses machine learning algorithms to predict the green innovation performance of micro-entities in China, examining the impact of internal drivers, external regulations, and financial capabilities on corporate green innovation. Key findings show that the gradient ascent algorithm best predicts performance, with financial indicators and operational capacity playing crucial roles in driving green innovation. Internal mechanisms, such as financial growth, have a positive effect, while certain regulatory tools, like administrative penalties and pollution control investments, incentivize green innovation. However, excessive environmental taxes can hinder progress. The study suggests governments shape policies for micro-entities, enforce regulations, improve financing mechanisms, and adjust tax structures. Enterprises, on the other hand, should optimize internal resources, comply with regulations, and increase transparency to enhance green competitiveness and encourage sustainable development.Osterrieder and Seigne discuss the gap in academic research regarding the execution phase of share buybacks. The paper explores various complexities like financial anomalies, market biases, and advanced computational techniques such as genetic algorithms and neural networks. There are revealed inefficiencies in how buybacks are executed, which has significant implications for both practitioners and policymakers. For practitioners, this knowledge gap could lead to financial losses, distorted markets, and damage to corporate reputation, urging them to adopt better strategies. For policymakers, there is a need to update regulatory frameworks to ensure fairness, transparency, and efficiency in buyback executions, especially as technological advancements continue to reshape the market landscape.The research of Oudat et al. aimed to explore the relationship between capital, liquidity, and operational risks and financial performance indicators (return on assets and return on equity) in United Arab Emirates (UAE) banks, considering the impact of bank size. The study analyzed data from 10 banks over seven years (2015)(2016)(2017)(2018)(2019)(2020)(2021). Findings revealed a positive correlation between bank size and return on equity, with larger banks generally yielding higher returns. Capital risk positively influenced return on assets, while liquidity and operational risks had no significant impact. Regression analysis showed that both capital risk and bank size were predictive of return on assets, but liquidity and operational risks did not predict return on equity. The study advises UAE banks to focus on managing capital risk to improve return on assets and consider expanding operations to increase return on equity. However, bank expansion should be managed carefully due to potential operational and liquidity risks. This research provides valuable insights into UAE bank's risk management and performance, helping policymakers and bank executives enhance industry efficiency.Finally, as financial markets become more complex and interconnected, the future of corporate finance and investments lies in the continuous integration of innovative quantitative tools. Financial institutions and corporations must stay ahead of the curve by adopting these methods, whether through in-house expertise or external consultants. Academic research and collaboration between the finance industry and quantitative disciplines will drive further advancements, ensuring that businesses remain competitive and resilient in an increasingly volatile global marketplace. Also, the ability to harness the power of quantitative methods will not only enhance financial performance but also drive innovation and create new opportunities in the corporate finance and investment sectors.

    Keywords: Corporate Finance, Investments, Quantitative Methods, Capital structure, Firm risk, Company performance, Stock return

    Received: 09 Feb 2025; Accepted: 13 Feb 2025.

    Copyright: © 2025 Gherghina and Simionescu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ștefan Cristian Gherghina, Department of Finance, Bucharest Academy of Economic Studies, Bucharest, Romania

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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