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The purpose of this research is to evaluate the financial performance measures calculated and reported by the Economic Resource Service (ERS) from Agricultural Resource Management Survey (ARMS) data. The evaluation includes the calculation method and the underlying assumptions used in obtaining the reported values. Recommendations for improving the information reported are proposed to ERS.

The financial measures calculated and reported are compared with those recommended by the Farm Financial Standards Council (FFSC). The underlying assumptions are identified by analyzing the software code used in calculating the values reported. The values reported by ERS are duplicated and alternative methods for calculating the financial performance measures are considered. The values obtained from the various calculation methods are compared and contrasted.

Recommendations for ERS include: calculate and report the financial measures recommended by FFSC, note values that are imputed, periodically update and validate assumptions used in calculating imputed values, review its policy for flagging estimates as statistically unreliable, report medians and other select percentiles, and consider reporting the percent of farm businesses that have values within critical zones.

A total of four methods for calculating financial performance measures are compared and contrasted. These are the aggregate mean, sample mean, sample median, and percentage of farm businesses with values in critical zones.

The purpose of this paper is to examine the impact of financial ratios on the financial performance of a chemical company: LyondellBasell Industries (LYB). Some selected ratios: current ratio (CR) and quick ratio (QR) represent the liquidity ratios, debt ratio (DR) and debt equity ratio (DTER) represent the leverage ratios, while operating profit margin (OPM) and net profit margin (NPM) represent the profitability ratios. LYB faced financial problems after its merger and the financial performance of the company shrank to negative due to the world financial crisis. However, this company has bounced back after a year and is now the world's third largest chemical company based on revenue.

The financial ratios were measured from 2004 to 2011, quarterly. A multiple regression model has been used and secondary data has been analyzed.

The results shows that CR, QR, DR and NPM have a positive relationship while DTER and OPM have a negative relationship with the company's financial performance. Among the six ratios, CR, DR and NPM show the highest significant impact on the company's performance.

This research paper contributed the result of the impact of financial ratios on the financial performance of a chemical company as the previous studies with this focus are hard to find and some of the sources are not specifically related to the topic.

The purpose of this paper is to evaluate firm financial success across a broad range of performance measures and identify areas of the performance spectrum for which positive results were most difficult to achieve. Simultaneously, the authors identify the firms that most frequently ranked among the top five in terms of composite financial performance.

The dichotomous Rasch model was applied to 13 financial ratios for two industries for the years 2002‐2011. Of these ratios, the authors identify those that are consistent with the requirements of the Rasch model and suitable for ranking composite firm financial performance in each industry during the sample years. Ratio difficulty rankings are obtained, along with firm rankings reflecting managers' ability to achieve broad‐based financial success.

For the Foods and Aerospace/Defense industries during 2002‐2011, above average performance was most difficult to achieve in the areas of liquidity, financial leverage, and market valuation. Above average profitability and returns on investment seem to have been easier performance targets during this sample period. The authors also list the ticker symbols of firms with managers who consistently achieved top overall financial performance.

The performance data for each industry and time period have to fit the requirements of the Rasch model. In addition, it must be possible to translate continuous metric readings into binary measures without losing relevant information. Future research might explore the use of more sophisticated Rasch models, measures of non‐financial firm performance dimensions, additional industries and time periods.

This research offers managers, investors and regulators a fresh perspective on the evaluation of firm financial performance and managerial ability.

Rasch models are widely used in the human sciences. Application of this methodology to firms offers a more comprehensive view of firm performance and may reveal factors relevant to firm valuation that have previously been ignored, thus possibly impacting the allocation of capital across firms and industries.

To the authors' knowledge, this research represents a first attempt to apply the Rasch approach to an evaluation of managerial ability as reflected in a firm's overall financial performance.

Firms in different countries operate in different business environments and prepare financial statements following, by necessity, their own countries' accounting standards. Benchmarks for assessing financial ratios of firms in different countries are likely to be different. In conducting financial ratio analyses, each country's unique cultural, business, financial, and regulatory characteristics have to be taken into consideration, for these external factors may exert significant effects on measurements of financial data. This study aims to investigate challenges in comparing financial ratios between Japanese firms and Chinese firms.

This study compares ten major financial ratios of 75 Chinese firms with financial ratios of 75 matched sample Japanese firms to determine if a common benchmark for each of the financial ratios can be applied to firms in both countries.

The results show significant differences in liquidity, solvency, and activity ratios between firms from these two countries. Further examination of differences in accounting standards, economic, and institutional environments between these two countries suggests that these external factors have significant effects on financial ratios and may have contributed to the observed differences.

This study is among the first to investigate the comparability of ratios between Japanese firms and Chinese firms to uncover potential challenges and warn investors of such challenges.

Although operating reserves can aid nonprofit organizations in alleviating periods of fiscal stress, they are not widely used. This study examines organizational factors that impact the level of operating reserves in nonprofit organizations. It also explores the relationship of operating reserves with organizational demographics and financial health variables using a six-year (1998-2003) unbalanced panel regression model containing 460,437 observations. Findings demonstrate a positive relationship between operating reserves and administration ratio, profit margin, operating margin, and organization age. Conversely, the size of operating reserves is negatively related to leverage ratio, donations, and organization size. Revenue diversification, however, shows a mixed relationship with operating reserves among different types of nonprofit indicating complexity in risk-reducing strategy. This study contributes to understanding factors relevant to the presence, or absence, of nonprofit operating reserves.

This paper compares the performance of the big four UK banks and four Australian banks between 2004‐2009. The banks are chosen according to the total assets as listed in The Banker magazine 2009. The purpose is to analyse why UK banks were more vulnerable to the financial crisis of 2007‐2009 than Australian banks. The consequence of this study is what improvements can be made in relation to liquidity, leverage, loan to deposit, asset quality and capital ratios.

The author adopts an empirical approach and gathers data from the annual reports of the big four UK banks and Australian banks and the database “Factiva” and the Financial Times. The data contains liquidity, debt, capital, asset quality and profitability ratios during 2004‐2009.

The author's data show UK banks had on average higher cash ratios, higher leverage ratios, higher loan to deposit ratios, higher capital ratios, lower asset quality, lower ROA but higher ROE than the Australian banks.

The results support the findings in the Financial Development Index 2011 of the World Economic Forum. UK banks should ameliorate its ranking on financial stability by improving the quality of loans and capital.

The analysis is of use to regulators who are contemplating the need for reforms aimed at improving financial ratios of banks. Basel III Accord has introduced some recommendations but has its limitations.

This paper's value lies in providing analysis of the top four UK and Australian banks' performances during 2004‐2009. There is room for improvement in providing a more stable financial environment in the UK.

The purpose of this paper is to analyze the financial structure of Italian cooperatives in the period before and during the crisis (2004-2013), in relation to two discriminating factors. At this end, it focuses on two research questions: What financial dynamics the Italian cooperatives have involved before, during and after the 2008 crisis, that is, in the decade 2004/2013? Are there statistically differences between business sectors and geographic area?

Secondary data on AIDA database have been used. The financial structure is assessed using two ratios: the financial leverage ratio and quick ratio. The final sample consists of 1,446 cooperatives. The trend and exploratory analysis, analysis of variance and Tukey-Kramer post-hoc test have been used.

The financial structure of cooperatives has not been substantially affected by the crisis in any geographic area and business sector, by virtue of resilience of their business model. Moreover, these two factors produce statistically significant differences in the financial structure of cooperatives.

The study takes into account only the cooperatives that survived the crisis, so, presumably, the strongest. Moreover, another and more ratios should be considered at the end to have a more complete view on the financial dynamics.

The literature on resilience of cooperatives is still not very rich. Moreover, this work analyses and integrates aspects and approaches that are not usually considered together.

In this study the factors behind the decision‐makers’ erroneous judgements regarding failure prediction (classification of firms as bankrupt and non‐bankrupt) are analysed. The purpose is to find out the factors causing incorrect responses, i.e. the cases in which the decision‐maker is for some reason incapable of using the given information to arrive at the correct classification. The following five possible sources of disturbance in this decision‐making were hypothesized: firm‐specific factors, data, decision‐maker‐specific factors, external factors, and failure process. In further analysis these factors were empirically operationalized and their significance was tested applying logistic (logit) analysis separately for the Type I and Type II classification errors identified in an HIP study. The results indicated that the effect of all of the five hypothesized factors on misclassifications is statistically significant. The inconsistency of the cues (firm‐specific factors) may be the main factor causing errors in evaluation. Moreover, the failure process is another important factor (Type I error). Thus, human bankruptcy prediction can be improved mainly by checking the consistency of financial statements (that they give a true view of the firm’s economic status) and by paying special attention to timely identification of the possible failure process. Future HIP studies on bankruptcy prediction and also other economic events should pay attention to control the kinds of sources of disturbance identified in this study, to maintain validity.

This paper primarily uses statistical methods to establish financial early‐warning models that make it possible to predict, in advance, the probability of a company experiencing financial distress.

In its empirical analysis, this is the first study that attempts to use financial ratios and non‐financial ratios as variables to analyze business groups, and the present study uses the (K‐S tests), and (M‐U tests) and logit regressions model.

Financial ratio variables remain the primary variables for predicting corporate financial distress. Upon examining the predictor variables for corporate financial distress at one, two, and three years prior to distress, it was found that financial ratio variables were the main ones at one and two years prior to distress, while at three years prior to distress there was one financial ratio variable and two ownership structure variables that showed significant differences. Financial structure, solvency, profitability, and cash flow indicators are the principal financial ratio variables. Ratios of director and supervisor ownership stakes after pledging of shares differed significantly between financially distressed and non‐distressed companies. Establishing independent directors and supervisors can lower the likelihood of financial distress.

As the time remaining before occurrence of financial distress grows shorter, test results show that the number of financial ratios with significant differences goes up. But the longer the time that remains before occurrence of financial distress, the more the financial ratios show non‐significant differences. That is why a number of scholars hold that the longer the period under study, the less explanatory power it has.

The mean contribution of this paper is that establishing independent directors and supervisors can lower the likelihood of financial distress. The paper is useful to researchers or practitioners who are focused on financial risk management and corporate governance implementation.

Presents a case study demonstrating financial statement ratio analysis (FSRA). This analysis matches company to industry data and builds sales forecasting models. FSRA imputes forecast standards of sales and costs, and applies them to a budgeted financial statement variance analysis for the EE (electronic and electrical) industry. Develops the concept of industry base standards, integrating them into the more traditional statistical and accounting concepts of quality control standards. Provides an implementation example, and reviews possible improvements to the current methodology and approach. Uses a similar methodology to forecast the stock market value with some exceptions. Models sales and costs of an individual company and an industry based largely on aggregate industry databases. For this purpose, uses a multivariate linear trend regression analysis for the sales forecasting model. Defines and tests related hypotheses and evaluates their significance and confidence levels. For an illustration uses the EE industry and the APM company. Also demonstrates a microcomputer‐based FSRA software that speeds, facilitates, and helps to accomplish the stated objectives. The FSRA software uses industry financial statement databases, computes financial ratios and builds forecasting models.