Financial Data Research Articles

The economic and financial performance of biogas production: a retrospective analysis of the French case

Purpose While numerous studies have focused on the economic impact of technical changes made to anaerobic digestion plants, there is limited understanding of the overall economic and financial performance of the biogas production units. This study aims to analyse the economic and financial performance of the biogas production sector over a 5-year period using various accounting indicators. Design/methodology/approach The research is based on an examination of financial data of French biogas units over a five-year period (2015–2019). Following a research protocol detailed in the study, 334 units were studied: 192 projects run by farmers, 83 projects run by industrial companies and 59 run by a mixed partnership (farmers, local authorities, and industry). Findings This study reveals that biogas production is performing well both economically and financially, but the performance varies depending on the type of producer: farmers better perform from an economic point of view, while industrialists are looking for financial profitability. Farmers exploit their competitive advantage based on waste disposal. Industrialists offset this disadvantage by a better ability to raise funds to negotiate payment terms and interest rates. Originality/value The originality of the study lies in its business approach. It completes the energy efficiency one which is more usual in energy sector management publications. Additionally, the study spans a five-year period, providing a longitudinal perspective of companies’ economic and financial performance. Furthermore, the data is sourced from reliable government sources.

Financial Inclusion and Climate Resilience: The Role for an AI-Enhanced Digital Wallet in Caribbean SIDS

Caribbean Small Island Developing States (SIDS) are highly vulnerable to extreme weather events and climate change. Caribbean SIDS climate vulnerability is worsened by their high level of financial exclusion. Many people do not have bank accounts and access to electronic fund transfer (EFT). As such, they cannot electronically receive funds before or after a natural disaster to cope with the effects. The financial exclusion problem can be addressed through a digital wallet. A digital wallet is a financial transaction application that securely stores a user’s banking and payment information on a cloud interface and allows the user to perform a transaction while hiding their banking information from a vendor. The biggest concern of users with regards to the use of digital wallets are its convenience and security. While digital wallets offer outstanding convenience of purchasing goods and services, data privacy and fraud risks deter people from adopting mobile payment. Potential fraud risk to digital wallets can be identified with anomaly detection techniques. The research problem investigated in this study is how the implementation of an artificial intelligence (AI) enhanced digital wallet can facilitate financial inclusion in the Caribbean, particularly in the context of disaster preparedness and recovery. Since security is an important aspect of a digital wallet, a sub-objective of this study is to derive an appropriate model for anomaly detection in financial transactions in a digital wallet. This study modifies Liu et al. [1] Gated Transformer Networks (GTN) architecture to allow for univariate time series classification. The corresponding new model is referred to as the Univariate Gated Transformer Network (UGTN). The UGTN is used for anomaly detection in financial data from a digital wallet. This study also provides policy recommendations for the implementation of a digital wallet to facilitate financial inclusion and climate resilience in Caribbean SIDS.

The role of big data in transforming financial management in U.S. healthcare: A conceptual framework

This paper explores the transformative role of big data in financial management within the U.S. healthcare sector, highlighting its capacity to address long-standing financial inefficiencies, enhance decision-making, and improve regulatory compliance. Big data technologies, such as predictive analytics and real-time financial reporting, provide healthcare institutions with the tools to forecast financial trends, optimize budgeting, and mitigate risks. Despite the significant benefits, the adoption of big data in healthcare finance faces challenges, including data privacy concerns, regulatory complexities, and technological limitations. The paper examines these barriers and enablers, offering strategic recommendations for healthcare institutions and policymakers to optimize the use of big data. By improving data governance, upgrading infrastructure, and fostering collaboration across departments, healthcare organizations can maximize the impact of big data on their financial operations. The review concludes with a call for continued policy support and financial incentives to encourage the broader adoption of big data in healthcare finance, ultimately promoting cost efficiency and operational resilience in the sector. Keywords: Big Data, Healthcare Financial Management, Predictive Analytics, Data Governance, Regulatory Compliance, Financial Decision-Making.

Machine Learning-Based Financial Big Data Analysis and Forecasting: From Preprocessing to Deep Learning Models

Abstract: Machine learning technology has revolutionized the financial sector by allowing faster and more accurate analysis and forecasting of large-scale financial data. This paper focuses on how machine learning ( ML ), especially deep learning models, can help to deal with high dimensional, noisy, and non-stationary financial data. Essential methodologies such as data preprocessing, feature engineering, and dimensionality reduction are imperative for preparing the raw financial data to ML algorithms. Methods such as outlier detection, normalisation for preprocessing, and feature (variable) selection for dimensionality reduction improve the models accuracy and efficiency. The paper also examines how deep learning models, such as Recurrent Neural Networks ( RNN ) and Long Short-Term Memory ( LSTM ) networks, can overcome the issues of autoregressive integrated moving average ( ARIMA ) models for financial time series prediction. An in-depth comparison of the machine learning models, ranging from supervised to unsupervised methods, is also provided to discuss their pros and cons in the financial domain, including popular applications such as credit scoring, fraud detection, and market risk prediction. The study finally concludes by discussing how optimisation methods such as hyperparameter tuning and cross-validation are imperative for ML models in complex financial scenarios to ensure their generalisation capability and avoid overfitting.

Microprudential Regulations and Bank Lending in Nigeria

Objectives: The current study aimed at investigating the relationship between microprudential regulations and bank lending in Nigeria. Theoretical framework: The theoretical underpinning of the study is drawn on the theory of risk-return tradeoff which maintains that potential return rises with an increase in risk. Method: Financial data of 13 banks over a period of 15 years were sourced from the repository of Central Bank of Nigeria, and were analyzed using the Panel (Least Squares) Multiple regression technique. Results and Discussions: The result of analyses revealed a high R-squared value of 0.75, an indication that microprudential regulation accounted for 75% variation in profitability in the Nigeria banking institution. Also, the F-statistic of 101.31 and a p-value of 0.000, indicated that the Ordinary Least Square regression model is statistically significant at the 1% level. Overall, microprudential regulation impact on lending and profitability in Nigeria. Research Implications: More so, the study’s relevance is evident in its applications to the principles and practice of lending, fund mobilization and other finance related policies and decisions in the banking sector. Originality/ Value: The outcome of the current study is valid and can be used for statistical inference. The conceptual framework of the study is a novel model that demonstrates how the variables in the study are interconnected. Furthermore, the model specification presents a viable template for financial speculations and calculations.

Boosting Financial Market Prediction Accuracy With Deep Learning and Big Data

The accuracy of financial market trend prediction directly impacts investors' decisions and financial institutions' risk management, making it a focal point of research in the financial domain. While traditional statistical methods lay the foundation for market forecasting, they still face limitations in handling complex, nonlinear, and non-stationary financial time series data with accuracy and adaptability. Time series analysis plays a pivotal role in financial market prediction, revealing historical patterns and future trends within the data. Addressing the limitations of existing methods, this study proposes a deep learning-based CEEMDAN-CNN-LSTM model. Leveraging the CEEMDAN decomposition method to capture the multi-frequency components of time series, CNN extracts spatial features, and LSTM learns temporal dependencies. Experimental results demonstrate that the performance of the CEEMDAN-CNN-LSTM model surpasses traditional models on standardized evaluation metrics such as RMSE, MAE, and MAPE across four major financial datasets.

Sunwoda's Financial Analysis Based on The Harvard Analytical Framework

In order to comply with the strategy of sustainable development, China hopes to further accelerate the rapid development of the new energy vehicle industry and supporting industries. Power battery is the core component of new energy vehicles, and the selection of sunwoda in the battery industry for financial analysis can provide a useful reference for the same type of enterprises.This paper selects sunwoda as an analysis case, applies the Harvard analytical framework to sunwoda's financial analysis, and conducts in-depth discussion and analysis of sunwoda's financial status and existing problems. The analysis found that sunwoda is currently in the growth period,adopting a growth-oriented development strategy to gradually expand the scale of the enterprise, with many projects underconstruction and rapid growth in scale. However, behind the seemingly perfect financial data, there are problems such as low profitability, intensified market competition,and insufficient future development capabilities.In view of these problems,corresponding improvement suggestions are put forward,hoping to help the development of sunwoda.

Unlocking financial innovation through strategic investments in information management: a systematic review

PurposeThe purpose of this study is to explore strategic investment in information management and its crucial role in driving financial innovation. By examining the integration of advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Blockchain, Big Data Analytics, Cloud Computing, Large Language Models (LLMs), Robotic Process Automation (RPA), Internet of Things (IoT), Cybersecurity Technologies, and Quantum Computing, this research aims to highlight how these technologies enhance decision-making, operational effectiveness, risk management, and compliance within the financial sector.MethodologyThe study employs a comprehensive literature review of existing research to analyze the impact of strategic investment in information management on financial innovation. Key technologies are identified and their applications in finance are discussed. The methodology includes synthesizing findings from various sources to present a cohesive understanding of the relationship between information management, technology, and financial innovation.ResultsThe results indicate that strategic investment in information management significantly enhances financial innovation by leveraging advanced technologies. AI and ML improve predictive analytics and customer personalization, Blockchain ensures secure transactions and transparency, Big Data Analytics enables data-driven decision-making, and Cloud Computing provides scalable solutions. LLMs enhance natural language processing capabilities, RPA automates repetitive tasks, IoT facilitates real-time monitoring, Cybersecurity Technologies protect financial data, and Quantum Computing offers potential breakthroughs in financial modeling and encryption.ImplicationThe implications of this study suggest that financial institutions should prioritize strategic investments in information management and the adoption of advanced technologies to stay competitive and resilient in the evolving financial landscape. Effective information management practices enable better decision-making, improved operational efficiencies, enhanced risk management, and regulatory compliance, ultimately fostering financial innovation.ContributionThis study contributes to the existing body of knowledge by providing a detailed analysis of the role of strategic investment in information management and its impact on financial innovation. It highlights the importance of integrating advanced technologies in financial practices and offers insights into how these technologies can be leveraged to achieve innovative solutions and improvements in the financial sector. The findings serve as a valuable resource for financial institutions, policymakers, and researchers interested in the intersection of technology and finance.

Enhancing User Experience and Sustainability in Open Banking Using PLS-SEM

A framework empowering third-party providers to access individual financial data has revolutionized the financial services landscape. Despite the user-centric nature of FinTech solutions, research on post-adoption behavior remains relatively scarce. Given the pivotal role of FinTech in the modern financial industry, understanding the factors driving sustained adoption and continued usage is crucial. This study aims to explore the key determinants influencing users’ decisions to remain engaged with Open Banking solutions. We propose a model examining the impacts of various factors on user satisfaction and their intention to continue using Open Banking. Partial least squares structural equation modeling (PLS-SEM) was employed to validate this model. Our findings reveal that user satisfaction with the usage experience is the sole predictor of continued use intention, explaining 62% of the variance in continuance use intention (R2 = 0.62). Additionally, user satisfaction is influenced by perceived usefulness (β = 0.33, p < 0.001), trust (β = 0.24, p = 0.003), and convenience (β = 0.25, p = 0.006), accounting for 70.5% of the variance in satisfaction (R2 = 0.705). The practical implications of this study suggest that Open Banking providers should prioritize enhancing user satisfaction by improving the perceived usefulness and convenience of their applications while fostering trust to maintain user engagement. Furthermore, our findings emphasize that convenience and trust are critical levers that Open Banking providers can utilize to ensure long-term customer loyalty and adoption, particularly in developing economies.

Financial Performance Analysis of PT. Sewindo Consultans for the Years 2021-2022

This research aims to analyze the financial performance of PT. Sewindo Consultans for the years 2021-2022. The research method used is descriptive research with a quantitative approach. The data used in the research are the financial data from the income statement and balance sheet. Secondary data was used in this research. Financial ratio analysis was used as the method of data analysis. The research findings indicate that: Liquidity ratios, which measure the company's ability to meet its short-term obligations through the current ratio and quick ratio, are in a "poor" condition. Solvency ratios, which measure the extent to which the company's assets are financed by debt through the debt to assets ratio and debt to equity ratio, are in a "poor" condition. Activity ratios, which measure how effectively the company utilizes its assets through the fixed assets turnover ratio, are in a "poor" condition, while the total assets turnover ratio is in a "good" condition. Profitability ratios, which measure the company's ability to generate profit through the return on investment and return on equity indicators, are in a "poor" condition. In general, the results of the liquidity, solvency, activity, and profitability ratio analysis of PT. Sewindo Consultans' financial performance for 2021-2022, as seen from various indicators used, show that almost all of them are below industry standards, indicating that the company is in a "poor" condition.

Application of Big Data in Digital Currency and Blockchain Finance

With the rise of emerging economic models, the cryptocurrency system, centered on the internet and electronization, is gradually returning to its core functions — storage, data processing, and related services. The deep value of massive data lies in rapid mining to uncover its inherent patterns, which is not only crucial for the operation, regulation, and monetary policy implementation of cryptocurrencies but also serves as an essential support for the transition from paper money to digital currencies. Given this, this research plans to conduct a detailed exploration of the application of big data in the field of cryptocurrency and blockchain finance, aiming to contribute to the continuous progress of China’s financial industry.

Digital Advanced Financial Accounting Learning Analysis

Advanced Financial Accounting learning is classified as a learning whose material is quite dense and complicated because there is a calculation of accounting financial data and requires a deeper understanding. Therefore, in learning Advanced Financial Accounting, it is not only necessary to have material delivered from within the classroom, but also to deepen the material from various other sources or applications supported by digital technology. This study aims to find out whether the use of digital technology in advanced financial accounting learning can increase the effectiveness of student learning. The method used in this study is a qualitative descriptive method with data collection techniques using questionnaires and interviews. The research involved 8th semester Open University students from UPBJJ-UT Semarang (Salut Cempaka Pati), Faculty of Economics, Accounting Study Program who took the Advanced Financial Accounting course with the name of Accounting 8 class as many as 29 students and 1 lecturer in the Advanced Financial Accounting course. The results of this study show that the use of digital technology can increase the effectiveness of student learning which can be seen from several aspects such as the frequent use of digital technology, the growth of learning motivation from students in learning Advanced Financial Accounting, the ease of using digital technology, and increasing student understanding in the development of Advanced Financial Accounting materials.

Financial resilience and adaptation: Analyzing the impact of the COVID-19 pandemic on businesses rated successful

AbstractThe aim of our research is to fill the research gap on how successful businesses have been affected by the challenges posed by COVID-19 regarding the resilience and adaptability of firms. Since 2016, the Budapest Stock Exchange (BSE), with the collaboration of EY, selects a number of unlisted companies (BSE50) per year that have Hungarian roots and are considered successful based on their operations and ability to increase sales. We examined the impact of COVID-19 on profitability, liquidity, indebtedness and change in turnover or EBITDA based on four years (2018–2021) of financial data from 252 non-financial, unlisted companies. In addition to the descriptive statistics, a two-sample asymptotic z-test was used to compare the periods before and during COVID-19. We divided the BSE50 companies into Dynamics and Laggards based on the change in turnover between 2019 and 2020. The Dynamics of the pandemic have emerged from the challenging period of COVID-19 as real champions, taking advantage of their low level of indebtedness by making efficient use of loan possibilities. Even the Laggards were able to rebalance and consolidate their financial position by the second year of the pandemic. Our study is novel because we used data on Hungarian, non-financial, unlisted companies with successful management to examine the impact of COVID-19 on hard-hit firms as well as on firms in the winning branch.

PENGARUH LEVERAGE, UKURAN PERUSAHAAN, DAN KOMISARIS INDEPENDEN TERHADAP PENGHINDARAN PAJAK PADA PERUSAHAAN SUB SEKTOR PERDAGANGAN BESAR DAN ECERAN YANG TERDAFTAR DI BURSA EFEK INDONESIA (BEI) TAHUN 2019-2023

This study aims to determine the effect of leverage, company size, and independent commissioners as independent variables on tax avoidance as the dependent variable. The object of this research is all wholesale and retail trade subsector companies listed on the Indonesia Stock Exchange in 2019-2023. The number of samples obtained was 17 companies using purposive sampling technique. This study uses secondary data in the form of financial statement data of wholesale and retail trade subsector companies listed on the Indonesia Stock Exchange. The data analysis technique uses multiple regression analysis methods processed with SPSS. The results showed (1) the leverage variable has no effect on tax avoidance with a significance value of 0.125 > from 0.05. (2) the firm size variable affects tax avoidance with a significance value of 0.003 < of 0.05. (3) the independent commissioner variable has no effect on tax avoidance with a significance value of 0.724 > from 0.05.

Pengaruh Kredit Macet, Tingkat Efisiensi dan Likuiditas Terhadap Profitabilitas: Studi pada Perbankan Umum Konvensional yang Terdaftar di Bursa Efek Indonesia Periode 2020-2022

The purpose of this study was to determine how the influence of Bad Debt, Efficiency Level and Liquidity on Profitability both partially and simultaneously. The data used in this study are secondary data in the form of financial statement data on conventional public banking companies listed on the Indonesia Stock Exchange for the period 2020-2022. The research methods used are descriptive and associative methods with a quantitative approach. The sampling technique used in this study used a non-probability sampling approach with purposive sampling method. The data analysis techniques used are multiple correlation coefficient test, coefficient of determination, multiple linear regression test, partial test (t test) and simultaneous test (f test). Based on the results of the study, it shows that from the partial test (t test) Bad Credit (NPL) shows a result of 0.294 where tcount 1.065 < t table 2.024 with a significance value of 0.294, then the Efficiency Level shows a result of 0.000 where tcount 22.440 < t table 2.024 with a significance value of 0. 000, then Liquidity shows a result of 0.002 where tcount 3.396 < ttable 2.024 with a significance value of 0.002. the conclusion is that Bad Debt partially has no effect on Profitability while the Efficiency Level, Liquidity partially has an effect on Profitability.

Perilaku Sticky Cost Terhadap Beban Usaha pada Aktivitas Penjualan Kue Karawo di UMKM Li Maya Gorontalo

Sticky costs refer to the phenomenon where business expenses do not decrease proportionally with a decline in sales volume but tend to rise more quickly as sales volume increases. This research aims to analyze the behavior of sticky costs on operating expenses in the cake sales activities of the Li Maya SME in Gorontalo. The research methodology involves analyzing the financial data of the Li Maya SME, conducting interviews, and direct observation of operational activities. The results show that some cost components, such as permanent employee salaries, exhibit sticky characteristics, meaning they do not decrease significantly when sales decline. Conversely, these costs increase as sales activity rises.

Company clustering based on financial report data using k-means

Stock investment is the act of providing funds or assets to obtain future payments for gifts given. In its application, novice investors often make mistakes, one of which is not knowing the health condition of the company they want to target. By applying the machine learning clustering method based on company financial report data, it was found that 2 clusters were formed. This can show the current condition of the company so that it can be a consideration for investors, such as clusters of companies that have a profit trend that is always stable and increasing, or clusters of companies that are in the process of developing their business and groups of companies that have large amounts of debt from year to year.

Market Factors, Not Quality, Influence Reimbursement for Pancreaticoduodenectomy in an Era of Price Transparency.

The Centers for Medicare and Medicaid Services (CMS) price transparency rule tries to facilitate cost-conscious decision-making. For surgical services, such as pancreaticoduodenectomy (PD), factors mediating transparency and real-world reimbursement are not well described. The Leapfrog Survey was used to identify United States hospitals performing PD. Financial and operational data were obtained from Turquoise Health and CMS Cost Reports. Chi-square tests and modified Poisson regression evaluated associations with reimbursement disclosure. Two-part logistic and gamma regression models estimated effects of hospital factors on commercial, Medicare, and self-pay reimbursements for PD. Of 452 Leapfrog hospitals, 295 (65%) disclosed PD hospital or procedure reimbursements. Disclosing hospitals were larger (beds >200: 81.0% vs. 71.3%, p = 0.04), reported higher net margins (0.7% vs. -2.1%, p = 0.04), more likely for-profit (26.1% vs. 6.4%, p < 0.001), and teaching-affiliated (82.0% vs. 65.6%, p < 0.001). Nonprofit status conferred hospitalization reimbursement increases of $8683-$12,329, while moderate market concentration predicted savings up to $5066. Teaching affiliation conferred reimbursement increases of $4589-$16,393 for hospitalizations and $644 for procedures. Top Leapfrog volume ratings predicted an increase of up to $7795 for only Medicare hospitalization reimbursement. Nondisclosure of hospital and procedural reimbursements for PD remains a major issue. Transparency was noted in hospitals with higher margins, size, and academic affiliation. Factors associated with higher reimbursement were non-profit status, academic affiliation, and more equitable market share. Reimbursement inconsistently tracked with PD quality or volume measures. Policy changes may be required to incentivize reimbursement disclosure and translate transparency into increased value for patients.

Financial Statement Audits and Data Breaches

Financial statement audits for public companies require auditors to test the internal controls over the client’s information systems that are material to the financial reporting process. Given the increasingly integrated nature of corporate data and control systems, a standard audit may have a positive effect on client firms’ other information systems, such as those that help prevent data breaches. In this paper, I provide evidence on whether and how auditors help prevent data breaches. Using plausibly exogenous improvements in auditing due to regulatory inspections and auditors’ cross-client learning, I find a decreased likelihood of data breaches among these auditors’ clients. Institutional insights gathered from interviews with accounting firm partners and industry professionals suggest two mechanisms for the role of auditors in preventing data breaches: the provision of relevant information and incentives for internal controls. Collectively, the findings provide evidence that improving accounting information systems can have a positive impact on nonaccounting systems. This paper was accepted by Ranjani Krishnan, accounting. Funding: I gratefully acknowledge financial support from Columbia Business School, the University of Chicago Booth School of Business, the Deloitte Foundation, and the Bradley Fellowship awarded by the Stigler Center for the Study of the Economy and the State. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.01357 .

Government Subsidies, Market Competition and Firms' Technological Innovation Efficiency

Based on the classification of Chinese listed companies, this paper selects the financial statement data of Chinese listed companies from 2008 to 2022 to conduct an empirical study, and the empirical results confirm that Government subsidies can promote enterprises to enhance the efficiency of technological innovation; Market competition can encourage enterprises to enhance the efficiency of technological innovation and The impact of government subsidies on the efficiency of technological innovation of non-state enterprises is more significant, while other information remains unchanged. The impact of government subsidies on the technological innovation efficiency of non-state-owned enterprises is more significant. Through empirical research, this paper understands the impact of government subsidies and market competition on the technological innovation efficiency of enterprises, which is conducive to providing a reference for the government to formulate enterprise subsidy policies.