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Previous studies indicate that applying solely financial ratios (FR) provides limited SME bankruptcy prediction performance. On the other hand, the application of non-financial features is cost-ineffective for SMEs. Intellectual capital (IC) features provide a meaningful alternative to analyse SMEs' financial performance since companies with higher IC regularly achieve consistently higher sales growth. This paper aims to examine the possibilities of applying intellectual capital features in predicting SME bankruptcy. 14 IC features and 27 FR of 54,003 SMEs from 2016 to 2021 were collected from financial statements. Three groups of XGBoost and CatBoost models were developed – with only IC features, with only FR and combining FR and IC features. The results show that IC features are a practical addition to FR, with the best AUC equal to 89%, and their combination outperforms models using only FR by an average of 2.3%. Moreover, IC features such as capital employed efficiency and structural capital reduced the likelihood of SMEs' bankruptcy. The implication of this study is that SME bankruptcy models perform better using IC features without significantly increasing financial cost or processing time, which can be helpful for financial institutions. Additionally, this can contribute to developing other methods of measuring IC. [ABSTRACT FROM AUTHOR]

Financial distress occurs when a company cannot meet its financial obligations within a specified timeframe, often owing to prolonged poor operational performance. While studies on financial distress prediction (FDP) use financial ratios (FRs) to forecast distress, they neglect to differentiate long‐term (LT) attributes from FRs. To address this gap, our study introduces a novel model that distinguishes between LT and short‐term (ST) accounting attributes in FRs. Using data from Taiwanese public companies (1991–2018), our proposed model employs a stacking ensemble classifier to split LT and ST Altman's ratios. This study addresses three key questions: (1) Do models involving split of LT and ST ratios outperform those that combine them? (2) How reliable and robust are these proposed models? (3) What is the proposed model's impact on distress prediction? The results show a significant outperformance of the existing solution, with higher accuracy, lower Type I and Type II errors, and reduced misclassification costs. These models are reliable in handling imbalanced data, proving suitable for real‐market investigations. Diverse FR contexts from previous Taiwanese studies validate the distinction between LT and ST features, representing robust performance. This model identifies characteristics of correctly and incorrectly predicted distress in companies, providing nuanced insights into complex distress attributes. This study introduces a pioneering model demonstrating superior predictive accuracy, reliability, and robustness by considering the split between LT and ST accounting attributes. It lays a foundation for future studies to extend and refine the proposed model, offering valuable insights into the complex dynamics of FDP. [ABSTRACT FROM AUTHOR]

This study aims to analyze potential differences in the financial performance of acquiring companies before and after acquisitions, focusing on Liquidity Ratios (Current Ratio and Quick Ratio), Profitability Ratios (Return on Assets Ratio and Net Profit Margin Ratio), Solvency Ratios (Debt to Equity Ratio and Debt to Assets Ratio), Activity Ratio (Total Assets Turnover Ratio and Fixed Assets Turnover Ratio), and Market Ratio (Earning per Share Ratio and Price Earnings Ratio). The research methodology involves a Quantitative Descriptive and Comparative approach, along with an Event Study comparing the financial performance of companies three years before and after the acquisition. Statistical tests, including Sample Paired t-Test and Wilcoxon Signed Rank Test, were conducted using the EViews application on seven acquiring companies listed on the IDX from 2017 to 2023. The findings indicate a significant difference in the company's financial performance before and after the acquisition, specifically in terms of Return on Assets, while the other financial ratios showed no significant differences before and after the acquisition is conducted. The implications of this study are expected to provide in-depth knowledge about the significance of differences in the company's financial performance before and after the acquisition, which will serve as a basis for decision making for investors and public in general. [ABSTRACT FROM AUTHOR]

The purpose of this study is to test whether the accrual-based variable or the cash-based variable is more effective in determining firm value by using accrual and cash-based variables. For this purpose, data obtained from the financialstatements of 36 manufacturing firms operating in Borsa Istanbul (BIST) between 2010 and 2020 are used. Generalized Method of Moments (GMM) Dynamic Panel method was applied in the study. As a result of the analysis, it is found that net profit quality, sales quality and cash return on fixed assets have a positive and significant effect on firm value. However, cash debt coverage and working capital cash return have a negative and significant effect on firm value. However, operating cash flow (operating), operating cash flow (financing), cash interest coverage and equity cash flow ratios have no significant effect on firm value. In addition, according to the results of the analysis of the relationship between accrual-based financial ratios and firm value, asset turnover, inventory turnover, earnings per share and asset growth ratios were found to have a significant effect on firm value. In addition, asset turnover and inventory turnover ratios have the largest effect on firm value, respectively. When the relationship between accrual-based and cash-based ratios used in this study and firm value is compared, it is seen that cash-based ratios are more effective in explaining firm value. In addition, it has been determined that the use of accrual and cash-based ratios together in determining firm value is more successful than using them individually. [ABSTRACT FROM AUTHOR]

This study aims to analyse how financial ratios such as the independence ratio, effectiveness ratio, efficiency ratio, fiscal decentralisation ratio, dependency ratio, and compatibility ratio affect economic growth, directly or indirectly, through capital expenditure as a mediating factor. This research used a quantitative approach; purposive sampling was conducted, and path analysis was applied to explore the relationships between variables. The results show that self-reliance, effectiveness, efficiency, fiscal decentralisation, dependency, and capital expenditure significantly affect economic growth. The independence and effectiveness ratios have a positive impact, indicating that improvements in these variables directly foster economic growth. However, the efficiency and fiscal decentralisation ratios have a negative effect, suggesting that increases in these variables may reduce economic growth. Indirectly, through capital expenditure, the independence, effectiveness, dependency, and compatibility ratios significantly affect economic growth, with the independence ratio being the most dominant. Conversely, the fiscal decentralisation and efficiency ratios did not show significant indirect effects, indicating that capital expenditure is not an effective mediator for these variables. These findings provide insights into how local financial management strategies can influence regional development, offering key policy recommendations for Banten's local government. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Finance Letters / Maliye Finans Yazıları Dergisi is the property of Maliye Finans Yazilari Yayimcilik Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The analysis of the stock market performance ratios is crucial for investors and fund managers. The food and beverage industry in Turkey is the largest sector in the Istanbul Stock Exchange (ISE). It contributes over half of the country’s GDP and is a highly attractive sector. This study aims to rank the top food and beverage companies based on their stock market performance ratios. The criteria weights were determined by using DEMATEL and CRITIC methods, with the help of three experts for DEMATEL. The stock market performances of the companies were evaluated by using three MCDM methods; EDAS, WASPAS, and TOPSIS, with the weights obtained from both DEMATEL and CRITIC. The robustness of the results was tested by applying various combinations of weighting and evaluation methods. According to the DEMATEL, earnings per share had the highest weight while CRITIC found the market value to book value ratio as the most important criterion. The study concluded that the best-ranked companies are CCOLA and TBORG. Also, there is no significant stability in other companies’ rankings. To reveal which methods produced similar rankings, Spearman’s Rank Correlation analysis was conducted: while WASPAS combinations produced similar rankings, all EDAS and TOPSIS combinations gave similar findings. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine the effects of agency cost on auditor choice. This paper also deals with the moderating role of the board's financial expertise (Bfe) and the status of the internal control (Intecon) system on the relationship between agency cost and auditor selection. Design/methodology/approach: This study's sample consists of 1,040 firm-year observations of Iranian nonfinancial companies listed on the Tehran Stock Exchange from 2012 to 2019. The information required for this research is mainly extracted from Comprehensive Database of All Listed Companies (in Iran Stock Exchange). Data from 130 companies were obtained during the research period. This study used logistic regression to test the hypotheses. Findings: The findings indicate that companies with higher agency costs choose the auditor from lower classes. As the proportion of financial expert members on the board increases, the intensity of this relationship will be reduced. Companies with higher agency costs choose the auditor from the lower classes, but the higher the ratio of financial expert board members, the more these companies will choose high-quality auditors. However, findings showed that the status of the Intecon system has no moderating effect on the relationship between agency costs and auditor selection. Originality/value: The results of this study can expand the existing literature on the relationship between auditor selection and agency costs and the factors affecting this relationship, especially the Bfe and Intecon. This research has significant suggestions for regulators, stakeholders, shareholders and analysts in emerging economies that may encounter similar contextual implications. [ABSTRACT FROM AUTHOR]

The article presents an assessment of financial stability of Russian industrial companies based on financial ratios calculated on the data from their accounting statements. The assessment uses a modified methodology of the signals approach, originally designed for early warning of currency crises. Based on the data about operating and bankrupt companies, thresholds crossing which increases the likelihood of bankruptcy are calculated for six financial ratios. The consolidated leading indicator of bankruptcy calculated basing on these thresholds correctly predicts bankruptcies in 91.5% cases and the absence of bankruptcies in 93.7% cases, in 2023. [ABSTRACT FROM AUTHOR]

The study aims to report empirical findings of quantitative research investigating what variables are significant leading proxies of fraudulent financial reporting (FFR) and the performance of the fraud detection model. The paper used financial and non-financial proxies as indicators to detect FFR with the panel data of 2235 observations of listed companies on the Vietnamese Stock Exchange from 2014 to 2020. Based on the materiality principle in auditing, the study divided the profit variance ratio into four material fraud thresholds of over 5%, 10%, 20%, and 50%. Two data mining techniques were employed: random forest for the classification model and an artificial neural network for building the best fraud prediction model. The findings show that the average accuracy of the prediction results of the random forest algorithm (RFA) reaches 91% for a material fraud threshold of 5%; when the materiality of fraud increases to above 50% of profit variance, the predictability is 98%. The average prediction accuracy of an artificial neural network (ANN) for the training set is 99%, and the test set is 97% at different fraud thresholds. These results confirm that RFA and ANN give a high accuracy in predicting fraud, and the determinants of firms committing to FFR are proxies of financial stability, followed by cash in the business and the nature of the industry. Notably, the three most important proxies related to FFR include return on total assets, return on equity, and EBT on total assets. The findings have practical implications: to identify fraudulent firms, creditors, analysts, and other stakeholders should use financial and non-financial ratios and employ data mining techniques instead of traditional fraud detection methods. [ABSTRACT FROM AUTHOR]

Financial statement fraud is a scheme in which an employee intentionally causes a misstatement or omission of material information in a company's financial statements. This study aims to analyze the effect of Fraud Hexagon on Financial Statement Fraud and the effect of Green Competitive Advantage on financial statement fraud. This researcher also aims to analyze the role of the Audit Committee as a moderator of the effect of Fraud Hexagon and Green Competitive Advantage on Financial Statement Fraud. Based on the results of the study, it shows that financial targets have a significant effect on financial statement fraud. Accrual Ratio has a significant effect on financial statement fraud. Ineffective monitoring does not have a significant effect on financial statement fraud. Changes in directors do not have a significant effect on financial statement fraud. External pressure has a significant effect on financial statement fraud. Project cooperation has a significant effect on financial statement fraud. Green Competitive advantage does not have a significant effect on financial statement fraud. The Audit Committee strengthens the influence of Financial Targets on Financial Statement Fraud. The Audit Committee strengthens the influence of Accrual Ratio on Financial Statement Fraud. The Audit Committee does not strengthen the influence of Ineffective Monitoring on Financial Statement Fraud. The Audit Committee does not strengthen the influence of Changes in Directors on Financial Statement Fraud. The Audit Committee does not strengthen the influence of External Pressure on Financial Statement Fraud. The Audit Committee strengthens the influence of Project Cooperation on Financial Statement Fraud. The Audit Committee does not strengthen the influence of Green Competitive Advantage on Financial Statement Fraud. [ABSTRACT FROM AUTHOR]

This research aims to analyze the influence Return on Assets (ROA), Operating Costs to Operating Income (BOPO) and Net Interest Margin(NIM) against Financial Sustainability Ratio(FSR) at National Private Commercial Banks for Foreign Exchange in Indonesia during the period 2018 to 2022. The population in this research is all National Private Commercial Banks for Foreign Exchange in Indonesia during the period 2018 to 2022. The sample selection technique used purposive sampling and 15 banks were selected as research data. The data analysis method used is panel data regression analysis using Eviews 10. The research results show that: Return on Assets (ROA) has a positive and significant effect on the Financial Sustainability Ratio (FSR). Operational Costs on Operating Income (BOPO) and Net Interest Margin (NIM) have a negative and significant effect on the Financial Sustainability Ratio (FSR). The R Square value is 0.957730, indicating that the Financial Sustainability Ratio (FSR) is influenced by Return on Assets (ROA), Operational Costs to Operating Income (BOPO) and Net Interest Margin (NIM) of 95.77%. [ABSTRACT FROM AUTHOR]

This article reports on a study aimed at measuring and characterising consumer over-indebtedness in South Africa, despite the existence of financial regulatory measures and legislation. The study employed quantitative analysis to examine the impact of macroeconomic variables on consumer over-indebtedness. The primary conclusion of the study indicated that the debt-to-disposable-income ratio decreased following the enactment of the National Credit Act; nevertheless, the alterations in debt levels cannot be solely ascribed to the enforcement of regulations and legislation. The study findings indicate that over-indebtedness correlates positively with gross domestic product, consumer expenditure, unemployment, and the debt-to-income ratio, while exhibiting a negative correlation with the interest rate. [ABSTRACT FROM AUTHOR]

Copyright of Polish Statistician / Wiadomości Statystyczne is the property of State Treasury - Statistics Poland and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Financial Politic & Economic Reviews / Finans Politik & Ekonomik Yorumlar is the property of Journal of Financial Politic & Economic Reviews / Finans Politik & Ekomomik Yorumlar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the realm of financial decision-making, it is crucial to consider multiple factors, among which lies the pivotal concern of a firm's potential insolvency. Numerous insolvency prediction models utilize machine learning techniques try to solve this critical aspect. This paper aims to assess the financial performance of financial investment firms listed on the Iraq Stock Exchange (ISX) from 2012 to 2022. A Multi-Layer Perceptron predicting model with a parameter optimizer is proposed integrating an additional feature selection process. For this latter process, three methods are proposed and compared: Principal Component Analysis, correlation coefficient, and Particle Swarm Optimization. Through the fusion of financial ratios with machine learning, our model exhibits improved forecast accuracy and timeliness in predicting firms' insolvency. The highest accuracy model is the integrated MLP + PCA model, at 98.7%. The other models, MLP + PSO and MLP + CC, also exhibit strong performance, with 0.3% and 1.1% less accuracy, respectively, compared to the first model, indicating that the first model serves as a powerful predictive approach. [ABSTRACT FROM AUTHOR]

In this paper, we used data from publicly traded restaurant firms between 2000 and 2019 to test the effectiveness of multiple discriminant analysis (MDA) and logistic regression (logit) in predicting the probability of bankruptcy in the restaurant industry. We constructed various financial ratios extracted from the financial information and analyzed them to determine the optimal models. Our results show that liquid ratios (particularly the quick ratio), operating cash flow, and working capital emerge as the most crucial indicators of potential bankruptcy filings for restaurant firms. The results also show that the logit model performs better within the sample. However, both models exhibit similar predictive capacities with out-of-sample data. [ABSTRACT FROM AUTHOR]

This essay contributes to the debate on the relevance of fiscal multipliers in the design of economic policies. The need for a different methodological perspective emerges from the results produced in the Eurozone by various fiscal consolidation programs, which, after being implemented following the sovereign debt crisis of 2011 and then suspended to address the COVID-19 crisis, are now being reintroduced according to the balanced budget rules set forth by the Maastricht Treaty. Building on the method elaborated by Pasinetti, we identify the sustainability area of the debt-to-GDP ratio within which different levels of public deficit can be achieved based on the fiscal multiplier. The analysis reveals that, under certain country-specific conditions, imposing limits on public spending can be counterproductive to the sustainability of public debt. [ABSTRACT FROM AUTHOR]

This study focuses on the deterioration of the predictive power and the analysis of the predictive stability of business failure prediction models, an aspect not sufficiently analysed in previous research. Insolvency prediction is considered with three temporal horizons (1 year, 3 years and 5 years prior to failure). The Genetic Programming (GP) tool has been used to achieve prediction models with high performance and stability over time, considering a long post-learning period in the stability analysis. In addition, novel scenarios representative of actual model use are proposed and considered, as well as metrics to assess the deterioration of the models’ predictive power. The optimised GP prediction models (in the three temporal horizons) present a higher performance with respect to external references and, more importantly in relation to the objective of our study, the selected GP models substantially improve on the stability reported in previous studies, meeting the pursued requirements of degree of deterioration (less than 5%) and stability (Pearson’s coefficient of variation less than 5%). Thus, the predictions of the GP models after the learning are very stable (period 2008–2019), to a certain extent immune, with respect to their environment, responding adequately in both procyclical and countercyclical modes, all of which is particularly relevant as this period includes a strong recession and a strong recovery. This should help to increase the reliability of business failure prediction models. Moreover, the relevance of including variables other than the usual financial ratios as predictors of failure is confirmed. [ABSTRACT FROM AUTHOR]

Embedding value investment in portfolio optimization models has always been a challenge. In this paper, we attempt to incorporate it by employing principal component analysis (PCA) to filter out dominant financial ratios from each sector and thereafter, use the portfolio optimization model incorporating second-order stochastic dominance (SSD) criteria to derive an optimal investment. We consider 11 financial ratios corresponding to each sector representing four categories of ratios, namely liquidity, solvency, profitability, and valuation. PCA is then applied over 10 years to extract dominant ratios from each sector in two ways, one from the component solution and the other from each category on the basis of their communalities. The two-step sectoral portfolio optimization (SPO) model is then utilized to build an optimal portfolio. The strategy formed using the formerly extracted ratios is termed PCA-SPO(A) and the latter PCA-SPO(B). The results obtained from the proposed strategies are compared with those from mean-variance, minimum variance, SPO, and nominal SSD models, with and without financial ratios. The empirical performance of proposed strategies is analyzed in two ways, viz., using a rolling window scheme and on different market scenarios for the S &P BSE 500 (India) and S &P 500 (U.S.) indices. We observe that the proposed strategy PCA-SPO(B) outperforms all other models in terms of downside deviation, CVaR, VaR, Sortino, Rachev, and STARR ratios over almost all out-of-sample periods. This highlights the importance of value investment where ratios are carefully selected and embedded quantitatively in the portfolio selection process. [ABSTRACT FROM AUTHOR]

This study compares the analysis of the financial statements of the brewing sector in Italy and Spain due to its growth in both Mediterranean countries and its relationship with other sectors of activity of great importance in these countries. The web transparency of the sustainability indicators of the brewing sector in both countries is also analyzed, following the new regulatory framework, EU Directive 2022/2426, on sustainability information, in order to analyze, in an integrated way, the financial and sustainability information which they report for a sustainable development of the sector, in line with the Sustainable Development Goals and the European Green Deal. The methodology used involved compositional data, which are reliable at an accounting and statistical level; such data allow us to value the financial health of the sector and its relationship with the web exploration of the communication of its environmental, social, and corporate governance indicators. The results indicate a solvency of the sector in the short term, with poor margins, especially in the pandemic, which recovered in 2021 due to the sector's resilience. On the other hand, there is a clear need to study the costs and margins of the sector in depth to improve the quality of the beers and to project the sector. The web analysis reveals acceptable transparency at the environmental level and poor transparency at the social and corporate governance level, with differences between the two countries and the population under study. [ABSTRACT FROM AUTHOR]

Copyright of Contaduría y Administración is the property of Facultad de Contaduria y Administracion-Universidad Nacional Autonoma de Mexico and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective of this study is to ascertain the financial ratios, specifically the Cash Ratio, Return on Investment (ROI), Return on Equity (ROE), and Net Profit Margin (NPM), of Financial Distress in Manufacturing Companies within the Consumer Goods Industry Sector that are listed on the Indonesian Stock Exchange. This study employs quantitative data. The findings and analysis presented in this study are as follows: The multiple linear regression test results indicate that the t-count value is 10.625, more significant than the t-table value of 2.03951. Additionally, the significance level of the t-count is 0.001, which is less than the significance level of 0.05. Therefore, we may infer that the t-count value is statistically significant. The ROI multiple linear regression test results indicate that the t-count value of 2.886 is statistically significant with a p-value of 0.007, compared to a t-table value of 2.03951 and a significance level of 0.05. Therefore, we can infer that the t-count value of 2.886 is significant. The t-value is 2.03951, and the significance level is 0.007, less than 0.05. The multiple linear regression analysis shows that the t-count for ROE is -2.897, which has a significance level of 0.007. Comparing this with the t-table value of 1.69552 at a significance level of 0.05, we may infer that the t-count value of 2.897 is greater than the t-table value of 1.69552, and the significance level of 0.007 is less than 0.05. The multiple linear regression analysis shows that the t-count value for NPM is -1.095, with a significance level of 0.282. Comparing this to the t-table value of 1.69552 at a significance level of 0.05, we may conclude that the t-count value of 1.095 is less than the t-table value and is not statistically significant. The F-table test yielded a result of 2.67 with an error rate of 5%. The acquired F-count was 50.596, more significant than the F-table value of 2.67, indicating a significant difference with a significance level of 0.002, less than the threshold of 0.05.

This research aims to analyze the financial performance of local government in the Toba Regency. The data source uses secondary data in the form of Toba Regency government financial reports for 2020-2022. Data were analyzed using a quantitative descriptive approach. The financial performance of the Toba Regency government is measured using eight financial ratios. The results of the research show that the financial performance of the Toba Regency government in terms of regional financial independence, regional financial dependence, and the degree of fiscal decentralization obtained meagre results. The level of effectiveness of local original revenue (LOR) and the effectiveness of local taxes obtained very effective results. The level of LOR efficiency and regional tax efficiency obtained very efficient results. The degree of contribution of regionally owned enterprises (ROEP) resulted in low contribution results. Overall, the Toba Regency needs to improve at implementing and managing finances. This research can be a reference for the Toba Regency government in improving financial performance through eight financial ratios so that budget use is effective and efficient.

This study uses fuzzy–rough analysis to investigate the influence of Environmental, Social, and Governance (ESG) ratings, along with critical financial and growth ratios, on the stock returns of blue-chip companies in Taiwan. The growing importance of ESG factors in investment decisions underscores the need to understand their impact on stock performance. By integrating the fuzzy–rough set theory, which accommodates uncertainty and imprecision in data, we analyze the complex relationships between ESG ratings, traditional financial metrics (such as ROE, return on equity), and stock returns. Our findings provide insights into how ESG considerations, alongside financial indicators, drive the returns of Taiwan's blue-chip stocks. Three public-listed companies were evaluated using this approach, and the results are consistent with the actual stock performance. This research contributes to the field by offering a robust methodological approach to assess the nuanced effects of ESG factors on financial performance, thus aiding investors and management teams in making informed decisions. [ABSTRACT FROM AUTHOR]

This study examines the correlation between fundamental financial metrics and stock returns of Indonesia's LQ45 Index over a 10-year period (2014-2023). The study aims to provide actionable insights for investors and companies by identifying the most critical financial metrics in predicting stock price movements. By analyzing data from 16 consistently listed Indonesian companies, the study focuses on stocks that have consistently maintained their position in the index over the past decade, a gap that has been underexplored in prior research. The study employs various statistical tests to validate the regression model, with hypothesis testing conducted through t-test and Ftests to determine the significance of financial ratios on stock returns. The analysis shows that Return on Equity, Price to Earnings Ratio (PER), Price to Book Value (PBV), Free Cash Flow per Share to Price (PFCF), and Dividend Payout Rate (DPR) have a significant positive effect on stock return. The research recommends that retail investors prioritize the PFCF ratio when evaluating potential stock investments. Transparency in financial reporting is essential, with companies encouraged to provide accurate and comprehensive financial statements, including clear reporting of key financial metrics, to build investor confidence. [ABSTRACT FROM AUTHOR]

Going concern of entity is of much concern to stakeholders, especially investors of financial institutions since the failure of financial institutions affects a wide range of sectors. The study sought to develop a model to predict going concern of financial institutions. The data were collected from the financial statements of selected banks in Ghana and Nigeria. The binary logistic regression model was employed as the data analysis technique. The model used financial ratios as predictors of going concern. The result revealed that working capital and earnings before interest and tax on total assets are crucial financial predictors for going concern in financial institutions. The implication is that management should maintain adequate working capital and improve upon its earning capacity to sustain the operations of financial institutions. [ABSTRACT FROM AUTHOR]

Traditional financial performance measures should be extended to provide additional information to stakeholders. One such extension is the economic value added (EVA). It shows residual profit above the cost of financing, both creditors and equity financing. This paper elaborates on the impact of selected financial ratios on EVA to total assets and EVA to capital employed using the 20-year aggregated data of non-financial business entities operating in Croatia. It answers the research question of which of the selected financial ratios impacts the above-mentioned EVA-based ratios. Applying dynamic panel data modeling using the generalized method of moments technique resulted in the derivation of two models. The human capital efficiency ratio was statistically significant in both models, positively affecting EVA/total assets and EVA/capital employed. In contrast, the debt ratio and net profit margin were significant only in the second model, where EVA/capital employed was a dependent variable. The research results indicate that the debt ratio affects EVA/capital employed negatively while the net profit margin has a positive effect, confirming the existing research. Total liabilities/earnings before interest, taxes, depreciation and amortization, and total asset turnover were not found to be significant in either of the two models. [ABSTRACT FROM AUTHOR]

The fact that COVID-19 pandemic affected economic situation is known widely, but many companies actually can use ratios for measuring financial situation. If we look deeper, financial ratios can give actual fact about the financial performance periodically. The aim of this research is to find out whether liquidity ratios, Solvability ratios, activity ratios, profitability ratios, and tax ratios have an effect on financial performance. This research was conducted on companies listed on the Indonesia Stock Exchange in 2019 - 2023 with a sample of 117 companies for five years. Thus, the number of samples used in this research was 585. The independent variables in this research were liquidity ratios, Solvability ratios, activity ratios, profitability ratios, and tax ratios, while the dependent variable was financial performance. The data analysis method uses the difference test analyze method with SPSS version 26 software. The results of different tests for liquidity ratios show that there is no significant difference between the years before the COVID-19 pandemic, during the COVID-19 pandemic and after the COVID-19 pandemic. The results of different tests for the Solvability ratios, activity ratios, profitability ratios, and tax ratios showed that there was no significant difference between the years before the COVID-19 pandemic, during the COVID-19 pandemic and after the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The current era is known as the “age of information,” and the capital market is built on information as the economy’s primary engine. The system of financial statements of corporations, which is the most significant source of information used in the capital market, produces an information system called accounting. Fraud and manipulation in these financial statements raise corporate risk, erode investor confidence, and cast doubt on the objectivity of accounting experts. Owing to the significance of fraud, this study aims to offer a way to foretell the likelihood of fraud in the financial statements of businesses admitted to the Tehran Stock Exchange between 2014 and 2021. 180 enterprises listed on the stock exchange make up the statistical sample (532 years of companies - suspected fraud years and 908 years - of non-fraudulent companies). According to the independent auditor’s assessment, the existence of dormant assets and items, the doubting of the assumption of continuity of activity, the presence of tax discrepancies with other tax areas, and the dearth of adequate performance tax reserves led to the selection of the companies suspected of fraud. 96 financial ratios have been compiled by examining the theoretical foundations and research. In this research, the supervised methods of support vector machine, K-nearest neighbor, Bayesian network, neural network, decision tree, logistic regression, random forest and the hybrid method (bagging) have been used. The results of the research showed that the performance evaluation criteria of precision, accuracy, sensitivity, and F-Measure and efficiency (ROC) and the accuracy result of the confusion matrix in the combined method (bagging) were 72.45, 61.21, 64.74, 62.93, 73.50, and 72.45 percent, respectively, which indicates the better performance and greater ability of this method to predict the possibility of fraud in financial statements compared to other proposed methods. [ABSTRACT FROM AUTHOR]

This paper investigates how well the Beneish and Spathis models can predict fraudulent financial reporting. The coefficients of these two models were adjusted using the logistic regression and the newly adjusted models were investigated for the prediction of fraudulent financial reporting. This research seeks to design a suitable native model to predict possible fraud in financial statements. The statistical population included 99 manufacturing companies listed on the Tehran Stock Exchange (1089 observations) during the years 2009-2019. The results show that the Beneish and Spathis models are not good at predicting fraudulent financial reporting, but their adjusted versions can predict it with an accuracy of 72% and 64%, respectively. The prediction accuracy rate of the extracted model based on the best explanatory variables is 79%, which shows that it is possible to predict and discover fraudulent financial reporting. [ABSTRACT FROM AUTHOR]

A higher U.S. government debt‐to‐output (D‐O) ratio does not forecast higher surpluses or lower returns on Treasurys in the future. Neither future cash flows nor discount rates account for the variation in the current D‐O ratio. The market valuation of Treasurys is surprisingly insensitive to macro fundamentals. Instead, the future D‐O ratio accounts for most of the variation because the D‐O ratio is highly persistent. Systematic surplus forecast errors may help account for these findings. Since the start of the Global Financial Crisis, surplus projections have anticipated a large fiscal correction that failed to materialize. [ABSTRACT FROM AUTHOR]

This study aims to analyze the level of accuracy of financial distress prediction models and to test the ability of liquidity risk ratio, credit risk and capital ratio in predicting the possibility of financial distress in rural banks (BPR) in Indonesia. The data used is sourced from secondary data and collected from BPR's financial statements published on the Financial Services Authority (OJK) website during the 2014-2023 period. The population in this study is all rural banks as many as 1,402 rural banks and the number of samples is 312 rural banks spread throughout Indonesia. Determination of samples by the Slovin method by proportionate stratified random sampling technique. The results of the study that the liquidity risk ratio, credit risk and capital ratio in predicting financial distress can be used with an accuracy rate of 95.90%. Liquidity risk ratio and credit risk ratio have a positive and significant effect, capital ratio and primary ratio have a negative and significant effect, while capital adequacy ratio has a positive and significant effect on the possibility of financial distress in rural banks in Indonesia. [ABSTRACT FROM AUTHOR]

Financial Performance is a series of financial activities in a certain period which are reported in financial reports including the profit and loss report and balance sheet. The aim of this research is to find out the comparison of the financial performance of manufacturing companies on the Indonesia Stock Exchange (BEI) and the Malaysia Stock Exchange in 2023. The financial ratios used in this research are Return on Assets (ROA), Debt to Equity Ratio ( DER), Current Ratio (CR), Total Assets Turnover (TATO), and Price Earnings Rasio (PER). The statistical analysis used in this research is the paired sample t-test and the Wilcoxon signed rank test. The results of previous research show that there is an influence on all the financial ratios studied. [ABSTRACT FROM AUTHOR]

In response to the evolving landscape of data security and the rising threat of anomalies in various domains, this study presents an innovative approach for anomaly detection in data streams and extends its application to financial network security. Our research initially focuses on identifying relevant correlation ratios for anomaly detection in the commercial data traffic of some companies listed during the 2014–2020 period. Utilising genetic algorithms (GA), gray wolf optimisation (GWO) for dimensionality reduction, we process 96 different financial streams. Subsequently, we employ the support vector machine (SVM) classifier to categorise companies into anomaly classes based on their financial data. The SVM classifier's initial performance, considering all financial ratios, falls short of our expectations. Our research thus provides a comprehensive approach to anomaly detection, showcasing the versatility and effectiveness of our methods in financial data stream analysis and network security. By combining the strengths of metaheuristic methods in data analysis and advanced deep learning techniques in network security, our work offers comprehensive solutions for anomaly detection in both domains. To enhance the classification accuracy and streamline the ratios, we introduce metaheuristic algorithms. GWO, in particular, stands out, with a fitness function of 0.2940 and an accuracy of 70.06% after 31 iterations. This algorithm successfully extracts 9 crucial financial ratios. These GWO-extracted ratios are then integrated into the SVM classifier, resulting in an anomaly detection model with outstanding accuracy, precision, error reduction, and efficiency, achieving 75.83%, 66.80%, 33.2%, and 80.3%, respectively. [ABSTRACT FROM AUTHOR]

Existing literature on relationship between financial development (FD) and economic growth (EG) has presented dichotomous observations: a linear relationship by many of the studies and yet an inverted U-shaped relationship observed by number of renowned researchers. Most of the studies used narrow measures like credit to GDP growth ratio for measurement of financial development. Moreover, many of the researches have taken only the post-Global Financial Crisis data or neglected the data pre-1990s and most of the researches related to developing countries are on single-country data. Therefore, this relationship requires re-estimation to arrive at some conclusive results. This paper explores the relationship between financial development and economic for both developed and developing countries based on broader measures of financial development (financial access, financial depth, and financial efficiency). Panel Corrected Standard Error regression model is used to evaluate the relationship for 23 developed and 35 developing countries. Our results observe contrasting facts for developed and developing countries. For developed countries, we observed an inverted U-shaped relationship between FD and EG. The contributing sub-components of FD are financial depth and efficiency. However, financial access is observed to have a U-shaped relationship. However, for developing countries, we don't observe an inverted U-shaped relationship but linear relationship between FD and EG. The contributing sub-components are financial depth and financial efficiency. [ABSTRACT FROM AUTHOR]

The object of the study is the estimation of the risk of enterprise bankruptcy. The work is aimed at developing a new model for estimating the risk of enterprise bankruptcy. Estimating the risk of bankruptcy is critical to assessing a company’s financial health. It serves as a key indicator that enables management teams to proactively mitigate potential risks and develop strategies to strengthen the company’s financial position over time. It is possible to enhance our prior bankruptcy prediction model by eliminating the Neural Arithmetic Logic Unit (NALU) block and refining the fuzzifier block to assess if the new architecture can effectively simulate approximate arithmetic for discovering complex financial ratios and relationships. The new model uses our bespoke «neuro-flexible» mechanism that incorporates a fuzzifier block as its initial layer, transforming each financial parameter into a fuzzy representation without any NALU blocks down the line. This approach allows the model to process undefined or missing inputs, enhancing its robustness in varied financial scenarios. The fuzzified values are then processed through linear layers with Mish activation, known for superior generalization performance. Key improvements include optimal categorization of raw numbers through embedding vectors and significant acceleration in learning speed. Experiments conducted using PyTorch on an Apple M1 processor demonstrated a substantial average prediction performance of 72 %, indicating the efficacy of the proposed enhancements in bankruptcy estimation. Bankruptcy risk is important for assessing a company’s financial health. It helps management teams reduce risks and strengthen the company’s finances. By predicting bankruptcy risk, companies can take steps to avoid financial problems and stay in business. [ABSTRACT FROM AUTHOR]

The aim of this study is to research the financial determinants of bankruptcies for firms belonging exclusively to the economic activity 'hotels and similar accommodation'. The study adopts the stance, that estimations and models focused on specific sectors can be a more precise assessment of firm failure in contrast to generic models that sample firms from broader sectors. The dataset covers the years 2010-2020 and uses available financial and bankruptcy information from two credible data sources for over 5,500 hotels which includes 13 bankruptcy events. A multi-period logistic regression model with clustered robust standard errors is applied, a very competent econometric methodology not applied before in hotel failure studies in this exact form. This type of setting is considered a close-to-reality econometric experiment examining the whole economic activity. A stepwise procedure led to three influential financial ratios with the main results revealing that the likelihood of hotel bankruptcy is positively related to leverage and size in terms of the natural logarithm of total assets and negatively related to EBITDA to total liabilities. By extending the bankruptcy horizon further by one and two years, information content appears fairly robust and all variables retain their sign, as leverage remains stable across all horizons, EBITDA to liabilities remain significant for the one additional period, while sizes' statistical significance becomes intermittent. Comparisons are performed also in terms of sensitivity and variable relevance with two earlier Greek studies for the whole economy and the hotel sector, and in both cases sensitivity is reduced and some variables are rendered not relevant. Implications stemming from the empirical results, suggest that leverage is the most influential factor increasing the probability of bankruptcy, so liabilities should be continuously controlled. Size in terms of asset acquisition should be mitigated too, as the 'too big to fail' principle seems not to hold. Also, a balance of liabilities coverage is recommended. The comparisons in terms of sensitivity, variable relevance and the opposite sign for size implies that bankruptcy modeling per economic activity is more appropriate, stressing the need for an ad hoc variable selection that includes size also, as a predefined set of variables may not always be the most optimal set. [ABSTRACT FROM AUTHOR]

Copyright of Innovar: Revista de Ciencias Administrativas y Sociales is the property of Universidad Nacional de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims primarily to demonstrate the importance of four types of financial ratios, namely profitability ratios, turnover rates, activity cost ratios, and financial leverage ratios, as well as to explain their impact on cash dividend distribution policies for a sample of Iraqi banks traded on the Iraqi Stock Exchange for the period (2010 - 2017). The number of banks was chosen from the Iraq Stock Exchange (13 banks), and the data for the aforementioned time period was taken according to the bulletins and financial statements issued by the official website of the market. After conducting statistical analysis of the data using the statistical package program (SPSS), where the sample size used was (104) observations, and the number of independent variables was (9), which represent the financial ratios, it was found that there is a significant impact of these financial ratios on determining profit distribution policies, and the study recommended to the specialists In the financial field, using these ratios and focusing on them in the financial analysis aspect. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to show that the methodology of classifying firms of the same sector according to the similarity of their classical financial ratios results in serious problems of incoherence and, consequently, it is necessary to find a viable alternative. With this objective in mind, simple examples are used to show that the appearance of multiple unsolvable problems make it impossible to accept the validity of grouping firms according to homogeneous groups based on these ratios using the usual clustering techniques. Once this fundamental fact has been verified, the proposal of classifying firms based on the ratios created in compositional data methodology and fuzzy clustering is shown to offer a viable alternative for this purpose. The paper ends with a complete analysis using real data. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the financial risk, debt, and efficiency of state-owned enterprises (SOEs) in Indonesia's construction industry and compare these aspects with those of private companies through financial ratio analysis and efficiency analysis approaches. Four SOEs from the construction sector were evaluated and compared to five private companies with financial data ranging from 2015 to 2022. Financial ratio analysis was applied to assess debt and financial risk, while efficiency analysis utilized data envelopment analysis (DEA) and paired t-tests to validate differences between the two groups of companies. This study reveals that the financial ratio performance of state-owned companies is relatively poor, with low profitability, critical liquidity, and a high debt ratio. Debt, as a source of capital in financing construction projects, causes companies to face a greater debt risk. This study also validates that SOEs have lower efficiency compared to private companies. In response to current challenges, SOEs should prioritize enhancing liquidity through faster receivable collections, debt restructuring, capital infusions, and divestment, reducing non-essential investments, focusing on asset recycling, and improving project efficiency. [ABSTRACT FROM AUTHOR]

This study examines the relationship between risk management and performance based on the size of commercial banks in Tanzania. Specifically, the study aims to determine the effect of liquid asset/Total asset ratio on Return on asset (ROA). Data employed were extracted from audited financial statement report. The explanatory variables were liquid asset/Total asset ratio while the dependent variable was financial performance measured by return on asset. Panel data of 23 commercial banks for the period of five years (2017 to 2021) was employed. Fixed and random model was adopted in analyzing the data. Findings highlight that the impact of liquid asset/Total asset on performance varies among commercial banks, with large banks showing insignificant positive relationship, while medium and small banks exhibit weak but significant negative relationship. This implies that maintaining more liquid assets compared to total assets provides a certain level of risk management capability though it may lead to relatively small returns to small and medium commercial banks. As far as large banks are concerned the relationship does not lead low return Therefore, the study recommends that banks should establish optimal size of liquidity to maximize returns and invest excess liquidity in higher-yielding assets or where additional income can be generated without significantly increasing risk. Regulatory authorities may need to consider different liquidity risk management policies depending on the size of the bank, such as tailoring the policies to ensure that the proportion of liquid assets is appropriate and reflect size of the bank. Consequently, it may help small banks and medium avoid excessive liquid assets which can negatively impact their financial performance. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of this study is to find out the financial performance of PT Industri Jamu dan Farmasi Sido Muncul Tbk in the 2019-2023 period to see what the Company's capabilities are like using financial ratio analysis. This research uses secondary data such as financial statements and company reports. The analysis tools used in this study are liquidity ratio, activity ratio, solvency ratio and profitability ratio. Based on the results of this study, PT Industri Jamu dan Farmasi Sido Muncul Tbk in the 20192023 period experienced fluctuations in its ratio value. Especially in 2020-2021, the results of the calculation of the liquidation ratio in 2020 experienced a significant decrease compared to the previous year. Meanwhile, the solvency ratio increased in 2020 and gradually decreased in the following years. Similar to the solvency ratio, the value of the profitability ratio also experienced a significant increase in 2020-2021. For the calculation of the activity ratio, the value of Total Asset Turnover, in the 2019-2023 period, no one has reached the existing industry standard figure of 5 times, resulting in the Company being in a bad position. However, for the calculation of other ratio values, despite fluctuations affected by the Covid-19 pandemic in 20202021, the Company's ratio value is still in a good position because it is still above industry standards. [ABSTRACT FROM AUTHOR]