Journal Abbreviation: Financ. Stat. J. | Financial Statistical Journal (FSJ, eISSN: 2578-1960) is a prestigious journal dedicated to advancing the frontiers of knowledge at the intersection of finance and statistics with an open access model. We are committed to publishing high-quality, original research articles and review articles that contribute to the understanding and application of statistical techniques in financial contexts. The journal encompasses a broad range of topics that include, but are not limited to, the application of statistical methods, data analysis, econometrics, mathematical methods, simulation, and computation as they relate to finance. We aim to provide a platform for scholars, researchers, and practitioners to share their insights and findings, fostering a deeper understanding of financial phenomena through rigorous statistical analysis. |
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Financial Statistical Journal | $500 |
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Vol 8, No 2 (2025)
Table of Contents
Financial drivers and governance structures are two of the many aspects that affect earnings management. Firms can be encouraged to display consistent financial performance by financial drivers such as net income, total assets, annual revenue, and cash flow volatility. The effectiveness of governance procedures, particularly audit quality, is crucial in preventing earnings manipulation. The trustworthiness of financial accounts is increased by high-quality audits, especially those carried out by Big 4 audit companies. Because of their solid reputations and strict procedures, these companies are linked to less profit manipulation. Moreover, audit effectiveness and objectivity are strongly impacted by auditor independence and audit tenure. This study examines selected Nigerian deposit money banks, including Zenith Bank, Polaris Bank, First Bank, Access Bank, Union Bank, United Bank for Africa (UBA), and Wema Bank, from 2013 to 2023. The study’s goal is to close the gap between raw data and strategic decision-making by analyzing financial data from these institutions. This analysis is required to understand how financial measurements and audit characteristics affect the dependability of financial reporting. To assess the connections between their operational efficiency, profitability, and financial sustainability, important variables like Earnings Per Share (EPS), Cash Flow from Operations, Annual Revenue, Total Assets, Net Income, Audit Tenure, Revenue-to-Asset Ratio, and Net Income Margin are examined. The study uses multiple regression analysis as a methodological tool to investigate the relationships between independent variables, such as auditor independence, affiliation with the Big 4 audit firms, and financial metrics, such as cash flow operations and total assets, and dependent variables, such as Discretionary Accruals (DA), Earnings Quality (EQ), and Earnings Per Share (EPS). Bayesian Model Averaging (BMA) emphasized strong predictors and addressed model uncertainty. The accuracy and relevancy of the data were guaranteed by their sourcing from audit firm databases and publicly accessible financial reports. Earnings Per Share was found to be significantly predicted by annual revenue and audit tenure. Longer audit tenures and higher yearly revenues have a favorable impact on EPS. The significance of these variables in explaining variations in EPS was highlighted by the BMA method, which validated the findings’ robustness. No significant variables for categorical earnings quality classifications were found using multinomial regression.
Announcements
New research advances precision matrix estimation with multi-target linear shrinkage |
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A groundbreaking study published on the Financial Statistical Journal of EnPress Press has introduced a novel approach to estimating large precision matrices using a multi-target linear shrinkage estimator. This innovative method, proposed by Yuang Xue and Lihong Wang from the School of Mathematics at Nanjing University, China, aims to enhance the accuracy and efficiency of precision matrix estimation, particularly in high-dimensional settings. For more information, please refer to the original article published on the Financial Statistical Journal of EnPress Press: Xue Y, Wang L. Multi-target linear shrinkage estimation of large precision matrix. Financial Statistical Journal. 2024; 7(2): 9912. https://doi.org/10.24294/fsj9912 |
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Online Access to the articles of Vol.7, No.1, 2024 |
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![]() We are pleased to announce that all the articles on vol. 7, No. 1, 2024, are now accessible online. This comprehensive collection is ready for your reading and research needs. |
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Posted: 2024-07-08 | |
Polish Quality Award for prof. Radosław Wolniak |
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Posted: 2024-05-08 | More... |
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