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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]

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 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]

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 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]

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]

The objective of this study is to analyze the intrinsic value of sharia stocks in the property sector listed on the Sharia Securities List (DES), utilizing two distinct valuation methodologies: free cash flow to equity (FCFE) and relative valuation. The stocks selected for analysis include PT Metropolitan Kentjana Tbk (MKPI), PT Mega Manunggal Property Tbk (MMLP), and PT Pollux Properties Indonesia Tbk (POLL). In this study, the free cash flow to equity (FCFE) method was employed to ascertain the intrinsic value of the shares. This was achieved through the consideration of free cash flow, which was subsequently deducted to account for expenses pertaining to working capital and investment. In contrast, the relative valuation method involved the comparison of the intrinsic value of the shares with financial ratios such as the price-to-earnings ratio (PER) and the price-to-book value (PBV). The aforementioned approach was informed by a fundamental analysis, which involved the examination of macroeconomic conditions, industry conditions, and company conditions. The data was obtained from property companies listed on the Indonesia Sharia Stock Index (ISSI) for the period 2019-2022. The results indicated that the intrinsic value of MKPI stock, as determined by the FCFE method, was Rp. 24,134. This value suggests that the stock is overvalued, given that the market price was Rp. 27,200. The PER method yielded the following results: The intrinsic value of the stock is Rp. 59,461, indicating that it is undervalued. The PBV method yields an intrinsic value of IDR 7,515, indicating that the stock is overvalued. The MMLP Shares are valued using the FCFE method, which yields an intrinsic value of IDR 4,857, indicating that the stock is undervalued as the market price is IDR 298. The PER method yields an intrinsic value of IDR 686, indicating that the stock is undervalued. The intrinsic value of the stock is IDR 893, indicating that it is undervalued. The PBV method yields an intrinsic value of IDR 686, also indicating undervaluation. The POLL stock is undervalued according to the FCFE method, with an intrinsic value of IDR 251. This is due to the market price of IDR 147. Finally, the PER method yields an intrinsic value of IDR 109, indicating that the stock is undervalued. The intrinsic value of IDR 109 indicates that the stock is undervalued. The PBV method yields an intrinsic value of IDR 214, which also indicates undervaluation. In conclusion, the FCFE and Relative Valuation methods can be used to determine the fairness of Islamic stock prices in the property sector. The data was obtained from property companies listed on the Indonesia Sharia Stock Index (ISSI) for the period 2019-2022. The results indicated that the intrinsic value of MKPI stock, as determined by the FCFE method, was Rp. 24,134. This value suggests that the stock is overvalued, given that the market price was Rp. 27,200. The PER method yielded the following results. [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]

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]

Copyright of Revista de Estudios Empresariales Segunda Época is the property of Editorial de la Universidad de Jaen 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]

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.)

This research was conducted to analyze the performance of governance in implementing BLUD and provide recommendations for financial governance at Al Ihsan Hospital, West Java Province. This research uses a postpositivism paradigm using a balanced scorecard (BSC) approach using primary data and secondary data obtained from interviews and documentation studies. The analytical tools used are for financial performance including financial ratios and operational cost ratios to operational income (BOPO) as well as compliance aspects of financial management. Meanwhile, service performance includes customer aspects, internal business processes and learning and growth aspects. So, this research will depict the transformation conditions of BLUD management in the field in terms of financial and service performance aspects. The research results show that the growth and learning perspective has the best value compared to the other four perspectives. Therefore, in order for RSUD's to be able to implement better BLUD governance so that they can improve performance, this can be done through adjustments to regional regulations to accommodate the flexibility needs of BLUD's and implementation conditions that continue to develop in the field. [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]

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 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.

Abstract Although there has been a growing interest by researchers worldwide over the past decades to identify the factors pertaining to corporate financial distress and to develop financial distress prediction models that serve as early warning signs to the various firm stakeholders, notably to date, studies that were conducted were context specific and cannot be objectively generalized to other countries and rendered mixed inconclusive results. Therefore, the main objective of this study is to thoroughly investigate the factors that affect corporate financial distress in Egypt and to develop a multidimensional financial distress prediction model. Using comprehensive data of EGX100 listed firms, the researcher examines the role played by financial ratios, market-based indicators, macroeconomic factors, and corporate governance mechanisms in modeling corporate financial distress. Empirical results indicate that after controlling for the COVID-19 effects, the most significant financial ratios in predicting corporate financial distress are the working capital to total assets ratio, earnings before interest and taxes to total assets ratio, and the sales to total assets ratio. Such ratios are negatively related to the likelihood of corporate financial distress. However, the market value of equity to total liabilities ratio, and GDP growth rate have a positive impact on the likelihood of financial distress. However, the retained earnings to total assets ratio, the corporate governance mechanisms, the firm market capitalization, the interest rate, and the consumer price index are insignificant in predicting corporate financial distress in the Egyptian context. The resulting model demonstrates outstanding classification accuracy at around 96%.

This study analyzes the efficiency levels of Indonesian banking companies listed on the Indonesian Stock Exchange (IDX) during the period of 2019-2022, encompassing the impact of the Covid-19 pandemic. Efficiency in banking is crucial for maintaining competitive advantage and ensuring financial stability, especially in the face of global challenges such as economic globalization and digitalization. The research evaluates the efficiency of banks using indicators like the CAMEL method, BOPO, NIM, NPL, LDR, and CAR ratios. The study finds that the pandemic significantly affected banks' efficiency, with notable declines in NIM and ROA ratios due to economic uncertainty and operational cost challenges. Postpandemic, these ratios have shown signs of stabilization. The study uses descriptive quantitative methods, analyzing secondary data from annual financial reports of eight banking companies listed on IDX. The results indicate that strategic measures in risk management and cost efficiency are imperative for banks to navigate future uncertainties. [ABSTRACT FROM AUTHOR]

The prediction of bankruptcy risk poses a formidable challenge in the fields of economics and finance, particularly within the healthcare industry, where it carries significant economic implications. The burgeoning field of healthcare electronic commerce, continuously evolving through technological advancements and changing regulations, introduces additional layers of complexity. We collected financial data from 1265 U.S. healthcare industries to predict bankruptcy based on 40 financial ratios using multi-class classification machine learning models across various industry subsectors and market capitalizations. The exceptionally high post-tuning accuracy rates, exceeding 90%, along with high-performance metrics solidified the robustness and exceptional predictive capability of the gradient boosting model in bankruptcy prediction. The results also demonstrate the power and sensitivity of financial ratios in predicting bankruptcy based on financial ratios. The Altman models highlight the return on investment (ROI) as the most important parameter for predicting bankruptcy risk in healthcare industries. The Ohlson model identifies return on assets (ROA) as an important ratio specifically for predicting bankruptcy risk within industry subsectors. Furthermore, it underscores the significance of both ROA and the enterprise value to earnings before interest and taxes (EV/EBIT) ratios as important parameters for predicting bankruptcy based on market capitalization. Recognizing these ratios enables proactive decision making that enhances resilience. Our findings contribute to informed risk management strategies, allowing for better management of healthcare industries in crises like those experienced in 2022 and even on a global scale. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Akunida is the property of Universitas Djuanda 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 paper investigates the impact of debt financing on dividend payments when they face the agency costs of free cash flow. It focuses on a sample of 120 firms listed on the Saudi Stock Exchange during the period of 2011–2021. The findings from the Generalized Least Squares regression model revealed that the presence of agency costs of free cash flows may limit the funds available for dividend payments. Regarding the moderating effect of debt structure, the research highlights the significant role of long-term debt in making more prudent use of free cash flow. The use of long-term debt becomes more effective and can enhance shareholder wealth when a firm is facing agency costs of free cash flow. More specifically, bondholders primarily focus on affirmative covenants which require the firm to undertake specified actions such as maintaining assets and financial ratios, or paying taxes, but they do not restrict financing activities such as dividend payments. Since interest and debt repayments are fixed obligations, using free cash flow for dividend disbursement is considered a more profitable and beneficial approach for shareholders in the context of Saudi Arabia. This study contributes to our understanding of financial management under different debt structures and improves our scientific knowledge of the culture of Saudi firms regarding the dividend distribution policy. [ABSTRACT FROM AUTHOR]

This study investigates the correlation between corporate financial ratios and cultural dimensions across countries in order to guide technology firms in adapting to the current financial landscape characterized by limited borrowing options and higher interest rates. Using Hofstede's subregional continental classification, it examines the relationship between capital structure and value among 4,237 publicly listed technology firms in 46 countries. The study analyzes financial ratios and connects them with Hofstede's subdimensions of individualism and uncertainty avoidance culture. The findings reveal a significant relationship between cultural dimensions and financial ratios, with individualism and uncertainty avoidance being influential factors. Countries emphasizing individuality tend to have higher borrowing rates, whereas those with a strong preference for uncertainty avoidance exhibit higher gross margins and total debt-to-equity ratios. The borrowing rate variable does not directly affect the dimensions of femininity versus masculinity and uncertainty avoidance; however, it impacts the dimension of individualism versus collectivism. Overall, this study provides valuable insights to enable technology firms to make informed decisions, manage financial structures, and effectively navigate the current financial landscape. [ABSTRACT FROM AUTHOR]

Sound financial management will affect regional progress. Local governments must explore the potential to increase regional financial resources so that financial performance increases and encourages economic growth, improves the human development index, and reduces poverty. This study examines the effect of financial performance on capital expenditure, economic growth, human development index, and poverty. The study used a quantitative approach with path analysis, using secondary data on city/district government finances in Banten Province in 2018-2022. The study's financial performance results significantly affect economic growth as measured by the independence, effectiveness, fiscal decentralization, and dependency ratios. Financial performance, as measured by the independence ratio, effectiveness ratio, fiscal decentralization ratio, and dependency ratio, has an influence and is not significant on poverty. Financial performance, as measured by the independence ratio, effectiveness ratio, fiscal decentralization ratio, and dependency ratio, has an impact and is not significant on the human development index. Capital expenditure significantly influences financial performance measured by the independence ratio, effectiveness ratio, fiscal decentralization ratio, and dependency ratio on economic growth. It has a significant impact on mediating the human development index. Capital expenditure does not significantly judge financial performance as measured by the independence ratio, effectiveness ratio, fiscal decentralization ratio, and dependency ratio to poverty. Economic growth has no significant effect on mediating finance as measured by the independence, effectiveness, fiscal decentralization, and poverty dependency ratios. The human development index does not significantly mediate finance measured by autonomy, energy, fiscal decentralization, and dependency on poverty ratios. [ABSTRACT FROM AUTHOR]

This paper investigates ESG from the perspective of changes in input elasticities of substitution and complementarity. Rather than compute these elasticities from the cost function, we compute them from the Input Distance Function (IDF). Our data are from Refinitiv Eikon Datastream database. We focus on the US economy due to her global role in the world economy and hence spillover effects of uncertainties on the rest of the world. The data consist of 5,798 companies comprising 38 US industries that span for 12 years from 2009 to 2020 and include: (i) financial data on sales, capital and employees; (ii) two financial ratios and (iii) three main ESG indicators. We compute Antonelli Elasticity of Complementarity (AEC) and Allen-Uzawa Elasticity of Substitution (AES) from the translog of IDF function. We find that the standard inputs have positive AEC elasticities; however, ESG cross-elasticities exhibit negative signs, classifying them as q-substitutes. Therefore, an increase in one of the ESG values leads to a decrease in the marginal value of the other. On the other hand, AES elasticities have a negative sign only for the Governance-Environment "doublet'; the rest of the pairs are positive implying that they are p-complements. [ABSTRACT FROM AUTHOR]

Financial distress is a condition where the company cannot or has difficulty fulfilling its obligations to creditors. This study aims to analyze the effect of profitability, sales growth, leverage, cash flow ratio and managerial ownership on financial distress in nonfinancial companies on the Indonesia Stock Exchange for the 2020-2022 period. The sampling technique used in this study was purposive sampling. A total of 411 companies have met the criteria as observation units. The analysis method used is multiple linear regression analysis. The results provide empirical evidence that profitability, leverage, and cash flow ratios affect financial distress. Meanwhile, sales growth and managerial ownership have no effect on financial distress. [ABSTRACT FROM AUTHOR]

This study seeks to investigate the potential effects of the recent pandemic (COVID-19) on capital structure dynamics. The Gulf Cooperation Council (GCC) is a fascinating topic for this study because of its distinct economic characteristics. The analysis draws upon a cross-country dataset covering 208 non-financial listed firms across five GCC countries, with data spanning the years 2010 to 2022. Capital structure is a dependent variable and is measured by total debt to equity, equity multiplier, and short-term debt ratios, while the COVID-19 pandemic, firm size growth, return on assets, tangibility, and growth were applied as independent variables. Using the generalized least squares (GLS) method, findings demonstrated that COVID-19 has a significant and positive influence on debt-to-equity and equity multiplier ratios but a negative one on short-term debt ratio. Thus, non-financial firms increased their debt financing and transferred debt from short-term to long-term funding. In addition, firm-specific factors, such as firm size, tangibility, and macroeconomic factors, such as GDP growth, positively and significantly impact capital financing. Conversely, profitability has a negative relationship with financial leverage. There is a lack of empirical research on how COVID-19 affects the financial structure of non-financial listed companies in GCC nations. Consequently, by filling the previously specified gaps, this study provides proof to support the idea of using debt financing to raise capital for economic recovery. GCC policymakers need to give priority to ensuring that firms have convenient access to inexpensive finance in light of the financial consequences caused by COVID-19. This will guarantee that companies have the resources necessary to bounce back and support economic growth. [ABSTRACT FROM AUTHOR]

This study examined the relationship between a firm’s revenue as a measure of growth and the firm’s financial ratios and macroeconomic variables. Fifty firms from the New York Stock Exchange were chosen at random for the period from 2009 to 2022. The variables included in the study were: Earnings per share (EPS), free cash flow (FCF), debt-to-equity ratio (DER), return on assets (ROA), current ratio (CR), inflation (CPI), GDP, federal fund rate (FFR), and unemployment rate (Unempl). The results showed that each variable was significantly related to revenue for some firms. Across firms, the percentage of relationships for each variable was low (56% or less). In particular, the percentages over firms were as follows: EPS (56%), FCF (40%), CPI (30%), GDP (30%), DER (24%), FFR (24%), Unempl (20%), ROA (20%), and CR (22%). Financial ratios showed positive effects, as well as negative relationships with growth. These findings indicated that financial ratios are not good growth indicators. [ABSTRACT FROM AUTHOR]

Purpose: The main objective of the article is to analyze and evaluate the financial situation of selected companies operating in the sugar industry and belonging to the WIG Food index for the years 2016-2022. The secondary objective is to verify the relationship (correlation) between financial liquidity and company profitability and to try to indicate the direction of this relationship if it occurs. Design/methodology/approach: The article uses a literature review, analysis of financial data from the financial reporting of companies using statistical methods. Four models of multidimensional discriminant analysis were applied (model D. Hadasik, model J. Gajdka and D. Stos, D. Wierzba and the 'Poznań' model). The analysis was extended to the calculation of three financial liquidity ratios and three profitability ratios of the company. Data from 2016- 2022 were analysed in order to illustrate how the situation of the selected companies developed in the period before and after the COVID-19 pandemic. Findings: All the analysed companies were in a good financial position during the analysed period. The most rigorous multivariate discriminant analysis models were those of J. Gajdka and D. Stos and D. Wierzba, which indicated deteriorating financial performance in selected years. Moreover, the analysed companies were characterised by high liquidity and profitability in 2016-2022. Only ZPC Otmuchów SA achieved negative values of profitability indicators in the analysed period, which was a result of negative net profit. Practical implications: A practical application of the presented research could be to draw the attention of companies operating in the food industry to methods of assessing their financial health using appropriate tools and to verify the relationship between liquidity and profitability in these companies. Originality/value: The article presents an analysis and evaluation of the financial condition of selected companies from the WIG Food index. Correlation analysis was used, and the reasons for the financial performance are indicated. [ABSTRACT FROM AUTHOR]

Existing research separately examines factors like qualification, tenure, and independence in relation to financial performance, overlooking the potential combined or interactive effects. This research intends to determine the role of the board of directors in addressing future challenges encountered by Indonesian state-owned enterprises. The research methodology involves quantitatively correlating independent and dependent variables, with or without variable control, utilizing nine financial ratios to assess firm conditions. The study utilized secondary data from financial statements and annual disclosures of Indonesian state enterprises from 2009 to 2016. The findings indicated that firstly, the degree, whether with or without control variables, is associated with Return on Asset and Fixed Asset Turnover. Secondly, board tenure, with or without control variables, does not exhibit a correlation with financial performance. Thirdly, the size of the independent board of directors, with control variables, does not correlate with financial performance, whereas without control variables, it is correlated with Net Profit Margin and Cash Ratio. This study introduces a new concept of board performance to predict bankruptcy indicators in companies, considering their Return on Assets (ROA) and Fixed Asset Turnover. [ABSTRACT FROM AUTHOR]

This paper evaluates drivers of personal overindebtedness in the European Union. Overindebtedness gives rise to poverty, social exclusion, and other serious social problems. However, the European Union has not adopted a common definition of overindebtedness. This paper explores whether tax policy, indicators of economic freedom, and macroeconomic traits such as the debt to gross domestic product ratio can explain private indebtedness. Sparsity-inducing regression methods applied to data from 27 European Union countries from 2013 to 2020 isolated two predictors that reduce household debt and two predictors that increase it. The level of social contributions and the overall tax burden are associated with lower private debt. Financial freedom and the ratio of public debt to gross domestic product elevate private debt. These are the only four predictive variables among 17 to attain statistical significance under all four regression methods deployed in this study. Future research should expand the methodological toolkit and examine additional demographic factors such as sex and education. [ABSTRACT FROM AUTHOR]

The bankruptcy of companies is essential in financial literature, and the development of bankruptcy forecasting techniques and models is the priority of financial research goals. Many studies have been conducted on predicting the bankruptcy of companies. This study first used a combination of theoretical and expert analysis to determine the financial ratios and macroeconomic variables affecting bankruptcy. Thus, bankrupt companies were distinguished from non-bankrupt ones referring to Black and Scholes's asset pricing models based on the intrinsic value of liabilities and assets. Therefore, 144 companies were studied in the 12 years of 2010-2021 in the screening process. The analysis of multilayer artificial neural networks for evaluating the reliability of the results in identifying the factors affecting the prediction of bankruptcy and prioritizing these factors showed that the least important factor was the ratio of capital to the net profit of the company and the most critical factor was the ratio of profit before interest and taxes to the total assets of the company. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to evaluate the synergy of corporate governance (CG) with intellectual capital (IC) and to assess the moderating effect of profitability indicator on the aforementioned synergy using agency theory, resource-based view theory and theory of financial ratios as conceptual frameworks. Design/methodology/approach: The sample includes 72 companies with a six-year data set drawn from the KSE 100 Index companies of Pakistan. In addition, the study adopts Pulic's model to compute the efficiency of IC. The research uses fixed-effect panel regression for analysis and two-stage least squares regression (2SLS) to address endogeneity issues in the estimation process. Findings: The results showcased that chief executive officer duality possesses negligible impact on IC efficiency (ICE), while independent directors, audit committees and board size tend to attain a strong association with IC. Moreover, it postulates that the moderation of return on equity strengthens the path between all governance components and ICE significantly. Originality/value: The research uses a 2SLS regression analysis to explore how CG practices take hold on the effectiveness of IC in Pakistan while taking into account the moderating impact of profitability. The findings add to the body of knowledge on the value that strong governance practices have on businesses and society. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to develop a robust predictive model for anticipating financial distress within Pakistani companies, providing a crucial tool for proactive economic turbulence management. Design/methodology/approach: To achieve this objective, the study examines a comprehensive data set comprising nonfinancial firms listed on the Pakistan Stock Exchange from 2005 to 2022. It investigates 23 financial ratios categorized under profitability, liquidity, leverage, asset efficiency, size and growth. Findings: The study reveals that financial ratio indices are more effective in forecasting financial distress compared to individual ratios. These indices achieve impressive accuracy rates, ranging from a robust 93.90% in the first year leading up to bankruptcy to a commendable 73.71% in the fifth year. Furthermore, the research identifies profitability, liquidity, leverage, asset efficiency, size and growth as pivotal indicators for financial distress prediction. Originality/value: This research underscores the utility and practicality of financial ratio indices, offering a comprehensive perspective on risk assessment and management. In conclusion, this pioneering study provides valuable insights into financial distress prediction, highlighting the enhanced information capture made possible by financial ratio indices. It equips stakeholders in the Pakistan Stock Exchange with an effective means to proactively address financial risks. [ABSTRACT FROM AUTHOR]

Bayesian Networks are a well-recognized decision support tool for a wide range of situations that involve uncertainty and probabilistic reasoning. Projects are not an exception to these situations. They are usually one-off experiences where many data are incomplete, suffer from imprecision and accuracy, and estimations are conditionally dependent on assumptions that are a major source of uncertainty. This uncertainty is present during the different stages of a project where complex causal relationships are involved between uncertainty and project performance. In this complex and uncertain environment many project managers are faced with the question, will the project finish successfully? This paper introduces an approach, using Bayesian Networks to know the project manager's confidence on the future of its project. With this aim, a Bayesian Network is modelled using four earned value management indexes, three financial ratios, and the technical and financial capability of the contractor responsible for the execution of the project. [ABSTRACT FROM AUTHOR]

Although there has been a growing interest by researchers worldwide over the past decades to identify the factors pertaining to corporate financial distress and to develop financial distress prediction models that serve as early warning signs to the various firm stakeholders, notably to date, studies that were conducted were context specific and cannot be objectively generalized to other countries and rendered mixed inconclusive results. Therefore, the main objective of this study is to thoroughly investigate the factors that affect corporate financial distress in Egypt and to develop a multidimensional financial distress prediction model. Using comprehensive data of EGX100 listed firms, the researcher examines the role played by financial ratios, market-based indicators, macroeconomic factors, and corporate governance mechanisms in modeling corporate financial distress. Empirical results indicate that after controlling for the COVID-19 effects, the most significant financial ratios in predicting corporate financial distress are the working capital to total assets ratio, earnings before interest and taxes to total assets ratio, and the sales to total assets ratio. Such ratios are negatively related to the likelihood of corporate financial distress. However, the market value of equity to total liabilities ratio, and GDP growth rate have a positive impact on the likelihood of financial distress. However, the retained earnings to total assets ratio, the corporate governance mechanisms, the firm market capitalization, the interest rate, and the consumer price index are insignificant in predicting corporate financial distress in the Egyptian context. The resulting model demonstrates outstanding classification accuracy at around 96%. [ABSTRACT FROM AUTHOR]

This study aims to test the performance of footwear sub-sector companies using financial ratios (liquidity, solvency, activity, profitability), common size and trend analysis by comparing financial ratios, common size and trend analysis for the 2020-2022 period at PT Sepatu Bata Tbk (Bata) and PT Primarindo Asia Infrastructure Tbk (Bima). This research design is a qualitative descriptive research and uses secondary data derived from the Financial Statements of PT Sepatu Bata Tbk (Bata) and PT Primarindo Asia Infrastructure Tbk (Bima) available on the Website of each Issuer and on the IDX website. The results showed that PT Sepatu Bata Tbk has a better financial performance than PT Primarindo Asia Infrastructure Tbk. However, this study only covers some aspects of company performance, so other more comprehensive research is needed, such as analyzing return on investment, management efficiency, or other factors that affect company performance. [ABSTRACT FROM AUTHOR]

Many deep learning works on financial time-series forecasting focus on predicting future prices/returns of individual assets with numerical price-related information for trading, and hence propose models designed for univariate, single-task, and/or unimodal settings. Forecasting for investment and risk management involves multiple tasks in multivariate settings: forecasts of expected returns and risks of assets in portfolios, and correlations between these assets. As different sources/types of time-series influence future returns, risks, and correlations of assets in different ways, it is also important to capture time-series from different modalities. Hence, this article addresses financial time-series forecasting for investment and risk management in a multivariate, multitask, and multimodal setting. Financial time-series forecasting, however, is challenging due to the low signal-to-noise ratios typical in financial time-series, and as intra-series and inter-series relationships of assets evolve across time. To address these challenges, our proposed Temporal Implicit Multimodal Network (TIME) model learns implicit inter-series relationship networks between assets from multimodal financial time-series at multiple time-steps adaptively. TIME then uses dynamic network and temporal encoding modules to jointly capture such evolving relationships, multimodal financial time-series, and temporal representations. Our experiments show that TIME outperforms other state-of-the-art models on multiple forecasting tasks and investment and risk management applications. [ABSTRACT FROM AUTHOR]

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