Financial Research Research Articles

A Financial Research on Transport Trade Services Group

A Financial Research on Transport Trade Services Group

Finance Research and the UN Sustainable Development Goals – An analysis and forward look

The relationship between academic finance research and the United Nations Sustainable Development Goals (UN SDGs) has gained increasing attention over recent years. This study conducts a comprehensive analysis of the interplay between the UN SDGs and scholarly output in financial journals from 2010 to 2022. Utilizing a framework that combines fuzzy matching, bibliometric analysis, and topic modelling techniques, the research traces the evolution of finance research and its alignment with the UN SDGs. Our findings indicate that academic finance research is unevenly spread across the SDG’s. Key areas around the natural world, sustainability and climate change are underresearched. The implications of our study are significant for both academic scholars and policymakers, suggesting a need for strategic redirection in research priorities to better support global sustainability initiatives in the future.

Event study methodology trends in the stock market: A systematic review based on bibliometric analysis

An event study is a statistical method used to assess the impact of an event. Most often the event study method is utilized in financial research to analyze the impact of events such as merger announcements, stock splits, earnings announcements, and economic shocks on the financial condition of an economy or industry or company. This study provides a systematic review based on bibliometric analysis using R software by using the Scopus database as a resource. The study used keywords to discover 2325 research articles from the Scopus database between 1984 and 2024. Additionally, this paper provides results with a clear explanation of publication trends, pertinent authors, relevant journals with impact factors, and global collaborations in the area of event study methods in the stock market. Along with the bibliometric results, the systematic review is carried out with highly cited and relevant publications about event study methodology on the stock market.

Understanding researchers' perceptions and experiences in finance research replication studies: A pre-registered report

A fundamental principle of responsible scientific research is replication. Replication provides independent verification of previously reported findings and improves our understanding of observed phenomena by removing researcher bias, strengthening existing evidence and questioning past conclusions. Replication is an essential component of the self-correcting nature of science but, for too long the scientific community has overlooked the benefits of replication due to its perceived lack of novelty. However, a shift in attitudes has been making its way through various scientific disciplines, with finance now having its turn. In this study we survey the authors of Pacific-Basin Finance Journal replication studies to explore their motivations, experiences and seek their feedback on the specific process utilized by the journal to undertake the important task of facilitating the publication of replications.JEL classification: G00.

Peran Bank dalam Pembiayaan UMKM dan Dampaknya terhadap Perekonomian Lokal

The role of banks in financing Micro, Small, and Medium Enterprises (MSMEs) and its impact on local economies is a crucial area of study in contemporary financial research. This study delves into the multifaceted relationship between banks, MSMEs, and local economies, employing a quantitative methodology coupled with a literature review technique. Through meticulous examination of existing literature and quantitative analysis of secondary data, this research aims to elucidate the extent of banks' involvement in MSME financing and its repercussions on local economic dynamics. By scrutinizing various financial reports, government publications, and academic sources, the study will discern patterns and trends in MSME financing patterns, evaluate the efficacy of banking policies supporting MSMEs, and assess the consequential effects on local economies, including growth indicators like employment rates and income levels. The findings are anticipated to offer valuable insights for banks, policymakers, and stakeholders, guiding them towards more informed decision-making processes and strategies to foster sustainable MSME growth and bolster local economic resilience.

New trends and directions in local government finance research: A bibliometric analysis

Local government finance has a critical impact on regional sustainability and governance. This study seeks to conduct comprehensive analyses of evolving trends and future directions in municipal finance research. It aims to address the insufficient longitudinal studies and fragmented understanding of how municipal finance practices and policies evolve by providing systematic evidence on dynamic trends and potential future trajectories. The study employed bibliometric analysis using Scopus and Google Scholar databases and Publish or Perish (PoP) software to analyze literature dynamics and associated topics and research trends and improve understanding of local government financial research. The results show a strong focus on local governance. Key themes included transparency, accountability, and financial sustainability, highlighting the critical need for good governance and fiscal prudence. The promising directions are technology (e-government) and comprehensive actions on the economic side through public-private partnerships and financial sustainability. Critical themes are fiscal stress, budgeting, and local government finance. However, new trends in sustainability, public-private partnerships, financial resilience, municipal bonds, and land finance are emerging as significant research areas. It highlights the field’s adaptability to evolving social and economic conditions. Through a thorough synthesis of these insights, stakeholders are better positioned to gain a deeper understanding of the current landscape of local government finance studies and their broader implications within a constantly changing global environment. AcknowledgmentsThe authors would like to express their sincere gratitude to the Dean of the Faculty of Economics and Business, Hasanuddin University, for their invaluable support and assistance in this research. Their unwavering commitment to advancing academic research and community service has been the key to completing this study.

Where and how machine learning plays a role in climate finance research

ABSTRACT The financial sector, by mobilizing capital, is fundamental to adapt and mitigate the impact of climate change in the economy. This has led to the emergence of a new research field, climate finance, where experts are starting to harness Machine Learning (ML) as a tool to solve new problems, due to the need to use big datasets and to model complex non-linear relationships. We propose a review of the academic literature that goes beyond the existing bibliometric studies in the field, with the aim of identifying relevant application domains of this technology to inform ML experts where and how their modeling expertise may add value in climate finance. To achieve this, we first assemble a corpus of texts from three scientific databases and use Latent Dirichlet Allocation (LDA) for topic modeling, to uncover seven research areas which we label as: natural hazards, biodiversity, agricultural risk, carbon markets, energy economics, Environmental, Social and Governance (ESG) factors & investing, and climate data. Second, we perform an analysis of publication trends, which confirms that ML is growing both in breadth and depth in climate finance, in particular topics related to energy economics, ESG factors and climate data. Interestingly, some methods stand out in each area, based on data characteristics and modeling requirements.

Computational Reproducibility in Finance: Evidence from 1,000 Tests

Abstract We analyze the computational reproducibility of more than 1,000 empirical answers to 6 research questions in finance provided by 168 research teams. Running the researchers’ code on the same raw data regenerates exactly the same results only 52% of the time. Reproducibility is higher for researchers with better coding skills and those exerting more effort. It is lower for more technical research questions, more complex code, and results lying in the tails of the distribution. Researchers exhibit overconfidence when assessing the reproducibility of their own research. We provide guidelines for finance researchers and discuss implementable reproducibility policies for academic journals.

Resolving Tensions Between Heterogeneous Investors in a Startup

Legal scholars highlight the tensions that exist between different classes of shareholders in startups. We model a startup owned by undiversified investors with heterogeneous capital contributions and risk preferences. A social planner runs the firm on behalf of all investors. We compare investors’ expected utility with a hypothetical first-best decentralized benchmark. The startup’s optimal investment policy is procyclical and a time-varying weighted average of shareholders’ optimal investment policies. The optimal contracts issued to investors are tailor-made, interdependent, and include equity claims resembling preferred stock with heterogeneous payout caps, leading to a complex capitalization table as more investors join the startup. This paper was accepted by Will Cong, finance. Funding: This work was supported by the Cambridge Endowment for Research in Finance and Keynes Fellowship. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.01724 .

To be an eco- and tech-friendly society: Impact research of green finance on AI innovation

Green finance can effectively support industrial upgrading and technology development, such as artificial intelligence (AI). In this paper, we attempt to pursue the possibility of a win-win situation for achieving an eco- and tech-friendly society. By asking where and how we could develop green finance and AI innovation simultaneously, this paper conducts an empirical investigation on the influence of green finance on the development of AI in China from 2011 to 2020. Based on the panel data from the 30 provinces, we introduce spatial measurement, policy effect, heterogeneity, and threshold analyses to present deeper insights into the impact of green finance on AI innovations. Indeed, green finance could promote the progress of AI innovation. China's green finance pilot policy is verified to promote the progress of local AI innovations. Furthermore, the spatial spillover effect and regional heterogeneity are observed as well. The promotion effect is most significant in the western area, where the green finance index is relatively low. Besides, the threshold analysis also considers how to increase the marginal effect of green finance in different areas. Finally, several policy recommendations are proposed, which contribute to providing specific directions for the policymakers to improve AI innovation and achieve sustainable development at the same time.

Machine learning in accounting and finance research: a literature review

Machine learning in accounting and finance research: a literature review

Green and sustainable finance research in BRICS countries: Review and research agenda

This bibliometric analysis investigates the Green and Sustainable Finance (GSF) research landscape in BRICS countries. Our findings reveal a significant surge in GSF research publications, with a marked increase from 2009 to 2017 (nine articles) to 2018 to 2024 (398 articles), showcasing exponential growth that surpasses linear trends. The journal "Sustainability" emerges as the leading source with 83 publications, while "Resources Policy" garners the highest citations (1108). Lee C-C stands out as the most cited author (1013 citations), and Chinese researchers, including Wang Y, Wang X, Zhang Y, and Wang Z, exhibit prolific publication records. China leads in GSF research output among the top 20 influential countries, with all BRICS nations making a significant contribution. This study reveals seven crucial thematic areas encompassing GSF research, delving into diverse aspects such as financial policies, sustainability, ecological-economic dynamics, and the interplay of green finance and innovation for sustainable economic development.

New Paradigm for Economic and Financial Research With Generative AI: Impact and Perspective

New Paradigm for Economic and Financial Research With Generative AI: Impact and Perspective

Research on Agricultural Supply Chain Finance Innovation Strategies Based on Blockchain

Comprehensively promoting rural revitalization and accelerating the construction of an agricultural power are important measures for the comprehensive construction of a modern national strategy. Under this strategic guidance, agricultural supply chain finance is particularly crucial as it is vital for the development of small and medium-sized enterprises and farmers. However, ordinary farmers, small and medium-sized agricultural enterprises, and core enterprises all face financing and credit risks throughout the supply chain. These challenges constrain their development and growth. This thesis first analyzes the current development status of agricultural supply chain finance, then identifies the risk issues affecting the development of agricultural supply chain finance through analysis of the relevant entities involved in the practice of agricultural supply chain. Finally, it proposes a solution approach through the innovative strategy of "dual-chain" combining blockchain with agricultural supply chain finance to address these problems. This thesis provides theoretical support for the development of agricultural supply chain finance and offers practical significance for the innovation research of agricultural supply chain finance under blockchain technology.

The Role of Accounting and Audit in Sustainable Development

Abstract The involvement of accountants and auditors in sustainable development has become more and more present in the literature specialised forums. Sustainability accounting works in the same direction with corporate practices and the evolution of best practices for financial standards. Accountants and auditors contribute to the standardization of uniform procedures for all the nations. Our study will present the responsibilities and work area of accountants and auditors in the economic sector for sustainability. The traditional role of accountants and auditors is evolving towards a more comprehensive spectrum that also includes a sustainability role. We considered the analysis of specialized literature to identify the involvement of sustainability in different fields, sustainability being a subject of interdisciplinary interest.. The current role of accountants and auditors in promoting sustainable development by examining actions set by accounting regulatory authorities and representatives of the accounting profession through a qualitative analysis. Our research aims to enrich the specialized literature in academic financial research. The actual case study will present the involvement of accountants and auditors in the area of sustainable development that desire to achieve the standards proposed by the UN. The importance of work performed by accountants and auditors for international businesses for sustainable development will be also presented. Although it is really important to keep in mind that the actual paper has boundaries that could provide new interesting opportunities for research areas.

Financial Development and Economic Growth: A Bibliometric Analysis

This research conducts a bibliometric analysis of 662 documents from the Scopus database (1986-2022), using tools like Harzing’s Publish or Perish, Tableau Public, Biblioshiny - RStudio, Microsoft Excel, and VOSviewer for data visualization. It highlights historical trends, growth, and future prospects in finance, identifying key authors, publications, countries, and institutions. China, Turkey, and Malaysia lead in publications, with the University of South Africa being the most productive institution. The study offers insights into future research in green finance, FinTech, and sustainability, emphasizing the evolution of concepts like “financial inclusion” and “fintech.”

Direct interaction in digital interactive media and stock performance: Evidence from Panorama.

Media information plays an essential role in the stock market. Recent financial research has verified that media information could shock stock price by influencing investors' expectation. Now, a new type of interactive media, called Digital Interactive Media (DIM), is popular in Chinese stock market and becomes the main channel for investors to understand listed companies. Unlike general news media or investor forums, DIM enables direct interaction between listed companies and investors. In the modern society where digital economy is booming, media information would largely affect investors' decisions. Therefore, it is urgent to use natural language processing (NLP) technology to deconstruct the massive questions and answers (Q&A) interactive information in DIM and extract valuable factors that affect stock prices and stock performances to explore the influence mechanism of digital interactive information on stock performances. This paper firstly uses web crawling technology to obtain approximately 110000 Q&A text information from the digital interactive platform ('Panoramic Network') from 2015 to 2021. Then we use big data text analysis technology and emotional quantification technology to extract valuable influencing factors from the massive text. A Multiple Linear Regression (MLR) model was created to explore specific influence mechanism of digital interactive information on stock price performance. The empirical results show that the emotions implicit in investors' questions do not significantly impact stock performance. However, the emotions and attitudes of the answers by listed companies can significantly affect corresponding stock prices, which indirectly confirms the Proximate Cause Effect of behavioral finance. This effect is particularly evident in the stock prices on the current trading day and the next trading day. In the Robustness Test, this paper replaces dependent variable and adds relevant control variables, and the conclusion remains valid. In the Endogeneity Test, this paper selects sample data before the launch of Panorama Network in 2014 as a comparison, and uses a Difference-in-Difference (DID) model to prove the significant impact of the launch of Panorama Network on Chinese stock market. In the Heterogeneity Test, the paper classifies the market value, region, and industry of listed companies and regressed the sub samples, once again confirming the reliability of the empirical conclusions. The results of Robustness Test, Endogeneity Test, and Heterogeneity Test conducted in this paper all support empirical conclusions.

Reviewer Acknowledgements for Accounting and Finance Research, Vol. 13, No. 2, 2024

Reviewer Acknowledgements for Accounting and Finance Research, Vol. 13, No. 2, 2024

Return-forecasting Signals in Australia, Brazil and United States Equity Markets

This paper offers an in-depth analysis of the role various signals play in equity indices within Australia, Brazil, and the United States. Utilizing statistical methods, particularly single-variate regression analysis, I examine the relationship between the monthly return rates of these indices alongside various signals. A noteworthy finding of my research is that certain trading signals exhibit statistical significance within the context of individual nations, yet this significance does not translate across borders. This difference could be attributed to distinct economic factors inherent to each country, such as varying inflation rates and market microstructures. This study sheds light on multiple financial literatures by offering a comparative cross-sectional perspective on equity markets, and it laid the foundation for more nuanced financial research in the future. Despite the significant insights gained from this study, it also acknowledges the potential for further enhancement of the dataset, emphasizing the quest for a deeper understanding of these complicated relationships.

Machine learning methods for financial forecasting and trading profitability: Evidence during the Russia–Ukraine war

PurposeThis study aims to evaluate the effectiveness of machine learning models to yield profitability over the market benchmark, notably in periods of systemic instability, such as the ongoing war between Russia and Ukraine.Design/methodology/approachThis study made computational experiments using support vector machine (SVM) classifiers to predict stock price movements for three financial markets and construct profitable trading strategies to subsidize investors’ decision-making.FindingsOn average, machine learning models outperformed the market benchmarks during the more volatile period of the Russia–Ukraine war, but not during the period before the conflict. Moreover, the hyperparameter combinations for which the profitability is superior were found to be highly sensitive to small variations during the model training process.Practical implicationsInvestors should proceed with caution when applying machine learning models for stock price forecasting and trading recommendations, as their superior performance for volatile periods – in terms of generating abnormal gains over the market – was not observed for a period of relative stability in the economy.Originality/valueThis paper’s approach to search for financial strategies that succeed in outperforming the market provides empirical evidence about the effectiveness of state-of-the-art machine learning techniques before and after the conflict deflagration, which is of potential value for researchers in quantitative finance and market professionals who operate in the financial segment.