Traditional Financial Ratios Research Articles

Construction of a Model for Evaluating the Operating Efficiency and Performance of State-Owned Enterprises Using Fuzzy Comprehensive Evaluation

State-owned energy, transportation, and telecommunications enterprises power many countries' economies and societies. Considering commercial and social objectives, SOE performance evaluation is complex. Rather than innovation, sustainability, and stakeholder satisfaction, financial factors have determined SOE's success. The present study simulates an SOE Fuzzy Comprehensive Evaluation approach to overcome the constraint. The model encompasses quantitative and qualitative performance. The proposed model prioritizes the whole evaluation aim and follows it with performance criteria and sub-criteria. This paper used the analytical hierarchy process with expert survey input to fine-tune the standard weights. Fuzzy logic addresses the uncertainty in qualitative analysis. This method performs traditional financial ratio analysis, balanced scorecard, and data envelope analysis

Evolution of Financial, Non-Financial, and Macroeconomic Predictors in Corporate Bankruptcy: A Comprehensive Review

This paper analyzes the evolving interplay over the decades between financial, non-financial and macroeconomic indicators as predictors of corporate insolvency. A key goal is identifying the most frequently employed and validated models incorporating financial metrics, non-variables, and economic factors to gauge bankruptcy risk. This study conducts a rigorous review of 204 scholarly articles published from 1960 to 2024 accessed through databases, i.e. Scopus and Web of Science, using the PRISMA framework. Analysis categorizes predictors into financial, non-financial and macroeconomic variables, examining their relevance over time. Traditional financial ratios such as Debt-to-Equity and Return on Total Assets remain influential. However, management quality, governance, and economic aspects, including growth and interest rates, now complement standard finance models, enabling more nuanced predictions. The research underscores the value of internally and externally focused management systems for averting insolvencies and augmenting standard advice. Results suggest that separately weighing a firm's financial position against non-financial health, political environment, and location allows for more robust forecasting. Looking beyond solely numeric data to incorporate qualitative aspects also forms a more holistic view that benefits managers and policymakers seeking to support economic stability.

Enhancing Financial Vulnerability Models for Non-Profit Organizations: Integrating Financial Ratios and Macroeconomic Indicators for Comprehensive Risk Assessment

Purpose: This research advances the financial vulnerability prediction model for Non-Profit Organizations (NPOs) by incorporating a comprehensive set of financial and macroeconomic indicators, aiming to refine risk management and strategic planning within the sector. Theoretical Framework: Building upon Tuckman and Chang (1991) foundational work, this study addresses the evolving financial landscape's complexities. It expands on traditional financial ratio analyses to include broader economic conditions affecting NPOs, recognizing the limitations of previous literature. Design/Methodology/Approach: The study analyzes financial data from two NPOs (2017-2021), using three models to assess the influence of internal financial ratios (e.g., Debt Ratio, Cash Ratio, Net Operating Margin) and external macroeconomic factors (e.g., inflation, GDP growth) on NPO financial vulnerability and performance. Findings: Results highlight a significant positive relationship between the Cash Ratio and Return on Assets, underscoring liquidity management’s importance for NPO sustainability. Other factors showed minimal impact, suggesting a nuanced approach to financial vulnerability. Research Implications: The findings emphasize the necessity of integrating financial ratios with macroeconomic indicators in NPO management strategies, offering a refined model for understanding and mitigating financial risk. Originality/Value: This study enhances the financial management literature for NPOs by incorporating macroeconomic indicators into vulnerability assessment models. It provides a comprehensive framework for robust financial planning, filling a crucial gap in non-profit financial analysis and contributing to the sector’s resilience.

Evaluasi Efisiensi dan Profitabilitas Asuransi Syariah Melalui Analisis Laporan Keuangan

In the literature study related to evaluating the efficiency and profitability of Islamic insurance through financial statement analysis, previous studies highlighted various methods and approaches used to measure the performance of Islamic insurance companies. Financial statement analysis is the main focus in identifying factors that affect the operational efficiency and profitability of Islamic insurance companies. Some studies have used traditional financial ratios such as solvency, liquidity, and profitability ratios to evaluate the performance of Islamic insurance. However, due to the unique characteristics of Islamic insurance, studies have also proposed the development of specific metrics that take into account sharia principles, such as sharia compliance ratios and fairness ratios. In addition, non-financial approaches such as technical efficiency and allocative efficiency analysis have also been used to evaluate the operational efficiency of Islamic insurance. This research shows that factors such as scale of operations, business diversification, and managerial efficiency can contribute to the efficiency performance and profitability of Islamic insurance companies. Although there have been many studies conducted in this domain, there is still a need for more in-depth follow-up research to better understand the factors that influence the efficiency and profitability of Islamic insurance. With a better understanding of the performance of Islamic insurance companies, regulators and practitioners can develop more effective strategies to improve the stability and growth of this industry.

Financial structure: A new aspect of analysis and new results

There is growing evidence that the financial structure depends on the level of economic development of the country. In developed countries, firstly, there is a convergence of financial structures and, secondly, an increase in the share of non-bank financial intermediation sectors in national financial systems. Therefore, we propose to proceed to the analysis of the three-tier structure of the financial sector (banks — non-banking financial institutions — financial markets), for which we have developed new indicators that assess the relationship between a non-bank financial intermediation sector and financial markets. They showed that in the last 7—8 years in Russia there has also been an outstripping growth of this sector. Our regression studies, firstly, have confirmed the conclusion that in Russia the financial structure (in the traditional sense) slows down economic growth and, secondly, allowed us to assume (based on the results of a crosscountry analysis) that the current structure of the Russian financial sector (with a predominant share of banks) contributes to an increase in the volatility of economic growth. The behavior of the new financial structure ratios and regression analysis based on traditional financial structure ratios indicate the need to adjust government policy in the financial sector in order to ensure faster growth of financial markets.

Improving Profitability Measurement: Impact of Intellectual Capital Efficiency on Return on Total Employed Resources in Smart and Knowledge-Intensive Companies

This paper aims to emphasize the quantification of intellectual capital, not disclosed on the assets side of the balance sheet in the smart and knowledge-based enterprise, because it is very important for the more precise quantification of the profitability ratio, such as the return on assets (ROA). For this purpose, the paper suggests the EIC (efficiency of intellectual capital) methodology. It points out the necessity for the new profitability formula, gives methodological solutions for it, and investigates the impacts of intellectual capital (IC) efficiency indicators on traditional and new formulas of profitability in the case of knowledge-intensive and smart companies. The research confirms the importance of improving the profitability measurement in the knowledge economy era, where exists the dominance of intangible assets. It emphasizes the need for the correction of the denominator of the traditional ROA indicator. The comprehensive measurement of the total intellectual capital, especially its non-disclosed component in the balance sheet, provides information for more precise and accurate profitability measurements. The paper points out the issue of improving the traditional financial ratio, such as the ROA. This can be achieved by incorporating the value of intellectual resources, which are undisclosed in the balance sheet, in its denominator. This solution results in creating a new profitability indicator—return on total employed resources (EOR). This EOR indicator is more successful in capturing the enterprise’s intellectual performance compared with traditional profitability ROA indicators. This fact leads to the conclusion that EOR is better profitability indicator especially for smart and knowledge-intensive companies.

A literature survey of corporate failure prediction models

PurposeThis study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.Design/methodology/approachThis study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.FindingsThere are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.Originality/valueThe study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

Revisiting SME default predictors: The Omega Score

ABSTRACT SME default prediction is a long-standing issue in the finance and management literature. Proper estimates of the SME risk of failure can support policymakers in implementing restructuring policies, rating agencies and credit analytics firms in assessing creditworthiness, public and private investors in allocating funds, entrepreneurs in accessing funds, and managers in developing effective strategies. Drawing on the extant management literature, we argue that introducing management- and employee-related variables into SME prediction models can improve their predictive power. To test our hypotheses, we use a unique sample of SMEs and propose a novel and more accurate predictor of SME default, the Omega Score, developed by the Least Absolute Shortage and Shrinkage Operator (LASSO). Results were further confirmed through other machine-learning techniques. Beyond traditional financial ratios and payment behavior variables, our findings show that the incorporation of change in management, employee turnover, and mean employee tenure significantly improve the model’s predictive accuracy.

Financial ratios with fuzzy logic approach. New estimation perspective

The industrial sector of Cuenca represents an icon of development and progress of this city. The problem of studying this sector is that it calculates traditional financial ratios exposed to volatile contexts with high uncertainty. The aim of this research is to estimate financial ratios with fuzzy logic approach in industrial companies of Cuenca city, Ecuador, due to the great importance of estimating these ratios to determine the financial health of these organizations. The research is descriptive, with a quantitative approach, using the development of expert and counter-expertise techniques, applying a form of calculation through Triangular Fuzzy Numbers (TFNs), and confidence intervals, instruments of fuzzy logic. The results obtained at 80% and 90% of the degree of presumption, are equivalent to the bands [1,488, 1,506] and [1,494, 1,503] respectively, corresponding to the liquid ratio; in a similar way the other working capital ratios are presented in the study. This new estimate will allow directors and managers to make more accurate decisions for the benefit of their companies, overcoming traditional barriers to be framed in the objectivity and reality of the current market.

An Analysis of the Predictors of Financial Distress for Zimbabwe Listed Corporates

This study brings novelty to the area of corporate distress modelling in Zimbabwe by exploring company-specific indicators of corporate distress, unlike most of the previous studies, which used financial performance indicators. Using a binary logistic regression on a time series dataset collated between 2010 and 2017, this study establishes book value, book value per share, average debt to equity and equity per share as very significant determinants of corporate distress on the Zimbabwe Stock Exchange (ZSE). Future studies incorporating artificial intelligence and a combination of both the traditional financial ratios and market-based indicators is recommended to expand the scope of the study.

Management and Economic Sustainability of the Slovak Industrial Companies with Medium Energy Intensity

Industry 4.0 and related automation and digitization have a significant impact on competition between companies. They have to deal with the lack of financial resources to apply digital solutions in their businesses. In Slovakia, Industry 4.0 plays an important role, especially in the mechanical engineering industry (MEI). This paper aims to identify the groups of financial ratios that can be used to measure the financial performance of the companies operating in the Slovak MEI. From the whole MEI, we selected the 236 largest non-financial corporations whose ranking we obtained according to the amount of generated revenues in 2017. Using factor analysis, from eleven traditional financial ratios, we extracted four independent factors that measure liquidity (equity to liabilities ratio, quick ratio, debt ratio, net working capital to assets ratio, current ratio), profitability (return on sales, return on investments), indebtedness (financial leverage, debt to equity ratio), and activity (assets turnover, current assets turnover) of the company. Our analysis is an essential prerequisite for developing a realistic financial plan for companies operating in the MEI, especially when considering investments in new technologies related to Industry 4.0.

Multi-Criteria Assessment of Public Rail Companies

The achievements of ten African public rail companies are compared with each other using the multi-criteria methods ELECTRE I, PROMOTHEE II and JUDGES. The 25 first-level criteria retained are first grouped into 8 families, each capturing a second-level objective. The efficiency of the service is assessed by a technical-economic evaluation function grouping the first 4 families. Its efficiency is measured by an evaluation function involving the other 4 families made up of traditional financial ratios that are likely to capture the management balance and the origin of financial resources. The authors conduct longitudinal and cross-sectional analyses of the data by comparing the results generated by ELECTRE and PROMETHEE. The JUDGES software successively shows the tree of correlations observed between the 8 families of criteria, and the rank distributions of firms according to 8 families. From a methodological point of view, this work is a variant of ELECTRE I and checks the relevance of multi-criteria methods in terms of ranking the performance of public companies.

Data envelopment analysis for measuring performance in a competitive market

In today’s increasingly competitive markets, it is essential to be able to determine the position of a company as opposed to its competitors. Today the traditional financial ratios are most widely used to measure corporate performance, but more and more authors begin to criticize their use. It is difficult to use financial ratios as a complex measurement tool. It is crucial to use an appropriate method or tool to measure corporate performance, which can measure the company’s performance in a complex way represented by one indicator. In this study, the Data Envelopment Analysis (DEA) method is used, which is one of the potential tools available. Several researchers have used the DEA method to measure corporate performance. Many authors consider DEA as a useful tool for measuring corporate performance, while others criticize it. The authors analyze the performance of retail food companies in Hungary’s Northern Great Plain region. The companies analyzed were chosen from the region investigated, and they have “food retail grocery store” as their main activity, and they had six cleared annual reports in the period 2012–2017. There was a total of 887 companies in the region examined, and 563 (63.5%) met the conditions. The analysis was made using the time-series data of companies for 2012–2017 based on their financial reports, and the authors dealt with various possibilities for extending DEA, which can support its more accurate use. Based on evaluating the retail food companies’ performance in the Northern Great Plain region, one can state that the efficiency of companies shows a very mixed picture over the years examined. The study suggests solutions to the indicated problem. The findings indicate that the application of extended DEA methods gives better results; that is, one can get better estimates of the efficiency of companies.

Continued discussion on conventional versus Islamic banks: combining financial ratios and efficiency

Two different types of banking systems, Islamic and conventional, dominate the banking structure in Saudi Arabia. The purpose of this paper is to contribute to the ongoing debate as to which of the two is better. Using data for the period 2014–2018, the study compares Islamic and conventional banks. It combines traditional financial ratios, Return on Assets (ROA) and Return on Equity (ROE), with Data Envelopment Analysis (DEA) to perform a comprehensive analysis. In terms of ROA, the performance of conventional banks is better than that of Islamic banks, but in terms of ROE, vice versa. DEA results show that conventional banks are more efficient than Islamic banks. In fact, in terms of ROA and ROE, Al Rajhi Bank, an Islamic bank, is the best performer. But in terms of efficiency scores from DEA, Al Rajhi ranks seventh among all banks, while NCB, a conventional bank, ranks first. Issuing shares and utilizing funds in profitable options, such as loans and advances to increase net income, are the policy recommendations for Islamic banks to further improve. In addition, as the study finds no correlation between the ratio and efficiency scores, it proposes to use a combined measure of ratio analysis and efficiency analysis for a comprehensive assessment of bank performance.

Predicting stock market returns in the US: evidence from an average correlation approach

PurposeThis study aims to draw on a less explored predictor – the average correlation of pairwise returns on industry portfolios – to predict stock market returns (SMRs) in the USA.Design/methodology/approachThis study uses the average correlation approach of Pollet and Wilson (2010) and predicts the SMRs in the USA. The model is estimated using monthly data for a long time horizon, from July 1963 to December 2018, for the portfolios comprising 48 Fama-French industries. The model is extended to examine the effects of a longer lag structure of one-month to four-month lags and to control for the effects of a number of variables – average variance (AV), cyclically adjusted price-to-earnings ratio (CAPE), term spread (TS), default spread (DS), risk-free rate returns (R_f) and lagged excess market returns (R_s).FindingsThe study finds that the two-month lagged average correlation of returns on individual industry portfolios, used individually and collectively with financial predictors and economic factors, predicts excess returns on the stock market in an effective manner.Research limitations/implicationsThe methodology and results are of interest to academics as they could further explore the use of average correlation to improve the predictive powers of their models.Practical implicationsMarket practitioners could include the average correlation in their asset pricing models to improve the predictions for the future trend in stock market returns. Investors could consider including average correlation in their forecasting models, along with the traditional financial ratios and economic indicators. They could adjust their expected returns to a lower level when the average correlation increases during a recession.Social implicationsThe finding that recession periods have effects on the SMRs would be useful for the policymakers. The understanding of the co-movement of returns on industry portfolios during a recession would be useful for the formulation of policies aimed at ensuring the stability of the financial markets.Originality/valueThe study contributes to the literature on three counts. First, the study uses industry portfolio returns – as compared to individual stock returns used in Pollet and Wilson (2010) – in constructing average correlation. When stock market becomes more volatile on returns, the individual stocks are more diverse on their performance; the comovement between individual stock returns might be dominated by the idiosyncratic component, which may not have any implications for future SMRs. Using the industry portfolio returns can potentially reduce such an effect by a large extent, and thus, can provide more reliable estimates. Second, the effects of business cycles could be better identified in a long sample period and through several sub-sample tests. This study uses a data set, which spans the period from July 1963 to December 2018. This long sample period covers multiple phases of business cycles. The daily data are used to compute the monthly and equally-weighted average correlation of returns on 48 Fama-French industry portfolios. Third, previous studies have often ignored the use of investors’ sentiments in their prediction models, while investors’ irrational decisions could have an important impact on expected returns (Huang et al., 2015). This study extends the analysis and incorporates investors’ sentiments in the model.

The Relationship Between Economic Value Added, Market Value Added And Return On Cost Of Capital In Measuring Corporate Performance

The aim of the paper is to assess the relationship between Economic Value Added (EVA), Market Value Added (MVA) and the traditional financial ratio in measuring investment performance by a holding company. The sample is PT. Astra International, Tbk in Indonesia. Astra is one of the largest conglomerate in Indonesia with diversify business from automotive, financial service, agro, infrastructure and technology. All of the investments are consolidated under single company, which is PT. Astra International, Tbk. The research assessed the financial performance from 2009 – 2016. The research will use Economic Value Added (EVA), Market Value Added (MVA) and Return on Weighted Average Cost of Capital (ROC) as financial measurement tools. The research found that there was a direct relationship between ROC and EVA. Negative EVA and negative ROC did not reflect the MVA on company performance. Negative EVA and ROC, could have positive Market Value Added (MVA). However negative MVA value will also reflect on negative EVA and ROC.

Financial Distress Prediction Through Cash Flow Ratios Analysis

The purpose of this study to examine the relationship of cash flow ratios in predicting financial distress companies, with industrial and consumer product companies in Bursa Malaysia as the sample. The study on financial distress is critical as it can lead to bankruptcy, which may adversely affect the economy of the country. Therefore it is worth exploring any indicators that can identify the possibility of financial distress in the company. The tools enable to address the potential problems that can mitigate from distressed financial position. Most prior studies in Malaysia focus on traditional financial ratios, while this study exploits the strength of cash flow ratios. The liquidity ratio, solvency ratio, efficiency ratio and profitability ratio utilized in this study are derived from the statement of cash flows. The Altman Z-score is used to measure the level of the financial distress. The findings show mixed relationships between solvency ratio and financial distress and a negative significant relationship between profitability ratio and financial distress, whilst efficiency ratio has no relationship with the financial distress. These results suggest that cash flow ratios are reliable tools to predict financial distress for Malaysian context. The study is useful in giving insights to the stakeholders in their decision making.

Financial Measures to Identify Financial Distress Risk of Corporations

The prospect of economic failure is of grave concern for an on-going business. Using empirical evidence, many researchers promote ad hoc approaches to identify potential financial distress. In this study, we evaluate and then advocate financial measures based on sound financial theory of a firm. In addition, we examine a major corporate bankruptcy using these measures and find that these measures in combination with the traditional financial ratios do provide insights into a firm’s deteriorating financial position.

Stock Performance, Corporate Governance, and Efficiency of China and Taiwan Banks: Is there a Relationship?

This article examines whether the efficiency and ownership structure of banks is related to their stock performance in China and Taiwan. The DEA and DFA estimated the efficiency scores to be 0.3229 and 0.5048, respectively. The mean efficiency from the DFA method is more than the cost efficiency derived from the DEA. Is there a relationship? This article finds that banks have a greater efficiency then is directly reflected in enhanced expectations for the performance of the banks in the stock market, and the DFA efficiency estimates have a more valuable function reflected in the stock return when compared with the DEA efficiency estimates. This suggests that an X-efficiency score is a better indicatory index than the DEA model and traditional financial ratios for explaining stock returns.

Value and Price of Russian Business: The Entity, the Relationship and Impact of Financial Indicators

Topicality : In the article the authors analyze the relationship between the book and market value and the price (level of capitalization) of the business, and also analyze the nature of the relationship of these indicators with the traditional financial ratios. The purpose of the article is to determine the state of these relations in the modern economy of Russia. Practical Usefulness : Determination of materiality influence of financial coefficients which used in traditional financial analysis on value and price of business that gives the chance of forecasting of development and decision-making within management of business` value, and also more effective investment of means in securities. Novelty : In modern Russian economic conditions, only part of the traditional financial ratios has an impact on the prices and business value. The analysis presented in the article indicates the feasibility of forming such a system of indicators, which is more informative in terms of their use for the implementation of value-based analysis and approach for decision-making in the organization.