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This paper is the first to use the individual level, longitudinal catch-up growth of boys and girls in a historical population to measure their relative deprivation. The data is drawn from two government schools, the Marcella Street Home (MSH) in Boston, MA (1889–1898), and the Ashford School of the West London School District (1908–1917). The paper provides an extensive discussion of the two schools including the characteristics of the children, their representativeness, selection bias and the conditions in each school. It also provides a methodological introduction to measuring children’s longitudinal catch-up growth. After analysing the catch-up growth of boys and girls in the schools, it finds that there were no substantial differences between the catch-up growth by gender. Thus, these data suggest that there were not major health disparities between boys and girls in late-nineteenth-century America and early-twentieth-century Britain.

The study aims to evaluate the financial distress position of selected sample banks in India. The top 10 banks with the highest levels of gross non-performing assets (NPA) under both public and private sector ownerships have been chosen for the study. Application of the Altman Z-score model has been used to compare both ownership banks’ financial distress for five years from 2017 to 2021. Based on the study’s findings, it was found that private sector banks demonstrated better financial stability than their public sector counterparts. Specifically, the average Z-score of the selected sample banks was higher than the safe zone threshold of 2.9 during the study period.

The purpose of this article is to provide commentary on the utility of Altman's Z‐score as a strategic assessment and performance management tool. This possibility is suggested in the recently published book Measuring Organizational Performance – Metrics for Entrepreneurship and Strategic Management Research (Northampton, MA: Edward Elgar, 2006) by Robert B. Carton and Charles W. Hofer.

This paper is a corporate manager's analysis of the utility of Altman's Z‐score as a strategic assessment and performance management tool based on published research, with suggestions for further research.

The analysis supports Carton and Hofer's findings with respect to the utility of the Z‐score as a strategic assessment and performance management tool.

While the Z‐score is both popular and widely used in the fields of credit risk analysis, distressed investing, M&A target analysis, and turnaround management it has received relatively little attention as a strategic assessment and performance management tool. The findings of Carton and Hofer's study, in conjunction with the impressive results achieved by GTI Corporation, suggest that applying the Z‐score in strategy and performance management may also be warranted, especially after more research is undertaken.

This article offers a manager's perspective on new research that indicates the potential of a popular financial distress metric to provide insight in the areas of entrepreneurship and strategic management.

Despite the sophisticated regulatory regime established in Solvency II, analysts should be able to consider other less complex indicators of the soundness of insurers. The Z-score measure, which has traditionally been used as a proxy of individual risk in the banking sector, may be a useful tool when applied in the insurance sector. However, different methods for calculating this indicator have been proposed in the literature. This paper compares six different Z-score approaches to examine which one best fits insurance companies. The authors use a final dataset of 183 firms (1,382 observations) operating in the Spanish insurance sector during the period 2010–2017.

In the first stage, the authors opt for a root mean squared error (RMSE) criterion to evaluate which of the various mean and SD estimates that are used to compute the Z-score best fits the data. In the second stage, the authors estimate and compare the explanatory power of the six Z-score measures that are considered by using an ordinary least squares (OLS) regression model. Finally, the authors report the results of the baseline equation using the system-GMM estimator developed by Arellano and Bover (1995) and Blundell and Bond (1998) for dynamic panel data models.

The authors find that the best formula for calculating the Z-score of insurance firms is the one that combines the current value of the return on assets (ROA) and capitalization with the SD of the returns calculated over the full sample period.

The main limitation of the research is that it addresses only the Spanish insurance sector, and consequently, the implications of the findings must be framed in this institutional context. However, the authors think that the results could be extrapolated to other countries. Future research should consider including different countries and analyzing the usefulness of aggregated insurer-level Z-scores for macroprudential monitoring.

The Z-score may be a useful early warning indicator for microprudential supervision. In addition to being an indicator of the soundness of insurers simpler than those established in the current regulation, the information provided by this accounting-based measure may help analysts and investors obtain a better understanding of insurance firms' risk factors.

To the best of the authors’ knowledge, this study is the first to examine and compare different approaches to calculating Z-scores in the insurance sector. The few available results on the predictive power of the Z-score are mixed and focus on the banking sector.

雖然在償付能力標準II 內已建立了精密的監管制度，但分析人員應可以考慮以不太複雑的指標，來分析保險公司的穩健程度。Z-分數的估量在銀行業一向作為是個體風險的代理而使用，而Z-分數如應用於保險業，或許會成為有用的工具。唯在文獻裏，學者和研究人員提出了不同的方法來計算這個指標。本文比較六個不同的Z-分數估量方法，以研究出最適合保險公司的方法。我們使用一個最終數據集，包括在2010年至2017年期間在西班牙保險業界營運的183間公司（1382 個觀察）。

在首個階段，我們選擇使用一個方均根誤差(RMSE) 標準來衡量用來計算Z-分數的各個平均值和標準差估量中哪個最適合使用於有關的數據。在第二個階段， 我們以普通最小平方 (OLS) 迴歸模型，去估計並比較被考慮的六個Z-分數估量的解釋力。最後，我們以Arellano與Bover (1995), 以及Blundell與Bond (1998) 為動態追蹤資料模型而發展出來的系統-廣義動差估計推定量，來發表我們基線方程式的結果。

我們發現，計算保險公司Z-分數的最佳公式是把資產收益率及資本總額的現值，和在整個樣本期間計算出來的囘報的標準差結合起來的公式。

我們研究主要的局限為：研究只涉及西班牙的保險業；因此，研究結果的含意，必須在這個體制的背景框架下來闡釋。唯我們相信研究結果或許可外推至其它國家。未來的研究，應考慮納入不同國家作為研究對象，並分析保險公司層面的集成Z-分數的功用，以求達到宏觀審慎監控的目的。

Z-分數或許就微觀審慎監管而言是一個有用的早期警告器。這些以會計為基礎的估量而提供的資訊，除了較現時規例内已建立顯示保險公司穩健程度的各個指標更簡單外，還會幫助分析人員和投資者更了解保險公司的風險因素。

據我們所知，本研究為首個研究，去探討並比較保險業內的Z-分數的計算方法。以前關於Z-分數預測能力的，為數不多並可供取閱的研究結果均不統一；而且，這些研究都聚焦探討銀行業。

We assess the impact of NZ IAS 32 on the financial reporting of convertible financial instruments by retrospective application of the standard to a sample of New Zealand companies over the period 1988 ‐ 2003. NZ IAS 32 has a broader definition of liabilities than does the corresponding current standard (FRS‐31) and it does not permit convertibles to be reported under headings that are intermediate to debt and equity. The results of the study indicate that in comparison with the reported financial position and performance, the reporting of convertibles in accordance with NZ IAS 32 would result in higher amounts for liabilities and higher interest. Thus, analysts using financial statement information to assess risk of financial distress will need to revise the critical values of commonly used measures of risk and performance when companies report under NZ IAS

The purpose of this study is primarily to implement the emerging market (EM) Z‐score model to predict bankruptcy and to measure the financial performance of major Islamic banks in the UAE. In addition, this study aims to introduce the Z‐score model to this industry as a beneficial diagnostic tool for possible causes standing behind the deterioration of financial performance.

The methodology that has been used in this study is based on Z‐score model for EMs developed by Altman. The related studies have proved that Z‐score has more than 80 percent accuracy and verified it is a robust tool and is useful in assessing the business performance and prediction of potential distress of firms. The approach determined in this study is to examine the financial statements of the UAE Islamic banks by calculating the EM Z‐score for the past three years and comparing it with the current year's score as an effort to measure the overall financial performance as well as the likelihood of bankruptcy of the UAE Islamic banks.

The paper finds that UAE Islamic banks should work on improving the ratios that are dragging their scores down to better understand their past performance and realize their current position in the industry; Z‐score can be adopted by the UAE Islamic banks as effective evaluation approach toward financing the potential long‐term partnership projects including small and medium business enterprises (SMEs); Z‐score model can be adapted by Islamic banks as an independent credit risk analysis approach to measure the competencies and financial strengths of potential projects; Islamic banks in the UAE are by and large financially sound and healthy and that Z‐score is a beneficial analytical tool that can be adapted by Islamic banks in the UAE to complement other financial analysis techniques to establish Islamic banking industry averages. The study also finds that the ratios used in calculating Z‐score can be considered to provide valuable instrumental indicators.

Z‐score model is a valid model to measure the performance of Islamic banks and the ratios used in calculating Z‐score can be considered to provide valuable instrumental indicators. Z‐score can be adopted by the UAE Islamic banks to finance long‐term partnership projects and SMEs. Limitations including the Islamic banking industry are still considered small size, which might has negative effect on the maximum outcomes of the study. Future studies are needed toward updating the coefficient values connected to each ratios in Z‐score model as per the inputs from the Islamic banking industry.

Z‐score model is a valid model to measure Islamic banks performance and the ratios used in calculating Z‐score can be considered to provide valuable instrumental indicators. Z‐score can be adopted by the UAE Islamic banks to finance long‐term partnership projects and SMEs.

The model is believed to widen the industry exposure in order to finance more projects and companies which is believed will reflect positively on the society welfare. By adopted Z‐score SMEs will be provided with all financings needed specially providing the microfinance for small projects.

Introducing Z‐score to the Islamic banking industry as crucial credit risk measuring tool.

The purpose of paper is to provide students with a sorting methodology to select securities and build portfolios.

This paper uses various accounting variables for all firms in the S&P 500, sorted by sector. The fundamental metrics are converted into standardized Z‐scores and then combined into a single score used to rank individual firms within each industry. Equity portfolios are then constructed using the aggregate Z‐scores.

In the authors' experience with student‐managed investment funds (SMIFs), students at the start of the course consistently ask how to begin selecting securities or seek to learn a new model for selecting securities. Discussions on stock selection are helpful to engage students in this area, but an attempt is made to further this by providing a comprehensive stock‐selection exercise to help students better understand how to appropriately pick stocks and create a portfolio.

In this exercise, students are reminded of the limitations surrounding the stock‐screening process and are provided with an alternative, more robust method for selecting securities that is commonly utilized by investment professionals. While the exercise described in this paper is done in reference to SMIFs, it is equally applicable to standard investment courses.

This paper provides an exercise which provides students a way to dive deeper into stock selection through stock sorting. Stock selection is typically a hot topic for most students in finance courses. Stock screens may permit a search on multiple variables simultaneously but typically do not allow for applying specific weights to each metric. A sorting method, avoids these issues by permitting the user to create custom variables, affords the opportunity to view all of the variables used in the screening process simultaneously, and includes the option to apply specific weights to each variable.

This study aims to develop a model based on the financial variables for better accuracy of financial distress prediction on the sample of private French, Spanish and Italian firms. Thus, firms in financial difficulties could timely request for troubled debt restructuring (TDR) to continue business.

This study used a sample of 312 distressed and 312 non-distressed firms. It includes 60 French, 21 Spanish and 231 Italian firms in both distressed and non-distressed groups. The data are extracted from the ORBIS database. First, the authors develop a new model by replacing a ratio in the original Z”-Score model specifically for financial distress prediction and estimate its coefficients based on linear discriminant analysis (LDA). Second, using the modified Z”-Score model, the authors develop a firm TDR probability index for distressed and non-distressed firms based on the logistic regression model.

The new model (modified Z”-Score), specifically for financial distress prediction, represents higher prediction accuracy. Moreover, the firm TDR probability index accurately depicts the probabilities trend for both groups of distressed and non-distressed firms.

The findings of this study are conclusive. However, the sample size is small. Therefore, further studies could extend the application of the prediction model developed in this study to all the EU countries.

This study has important practical implications. This study responds to the EU directive call by developing the financial distress prediction model to allow debtors to do timely debt restructuring and thus continue their businesses. Therefore, this study could be useful for practitioners and firm stakeholders, such as banks and other creditors, and investors.

This study significantly contributes to the literature in several ways. First, this study develops a model for predicting financial distress based on the argument that corporate bankruptcy and financial distress are distinct events. However, the original Z”-Score model is intended for failure prediction. Moreover, the recent literature suggests modifying and extending the prediction models. Second, the new model is tested using a sample of firms from three countries that share similarities in their TDR laws.

The literature has revealed auditors' going concern risk disclosures are examined in research as a homogenous risk class. This is despite the various going concern modifications auditors are entitled to give pertaining to this issue. A five‐level risk class is established in this paper derived from Australian Auditing Standard pronouncements to examine the appropriateness of auditors' going concern reporting relating specifically to the likelihood of firm failure.

Time is necessary to reveal the appropriateness of going concern reporting therefore a longitudinal research methodology was adopted. The research focuses on all Australian listed companies within the building industry in 1989 and follows all of the reporting of going concern by auditors and directors through until 2007. The building industry was selected because of its volatility, which increased the possibility of going concern reporting allowing a more in‐depth focus in the research. All auditors' going concern modifications were examined along with all indications of going concern problems identified by directors. To properly investigate the appropriateness of auditors' reporting, all sampled audit reports were examined using Altman's Z‐score model which were matched with a risk class model using the relevant requirements to report in order to determine the appropriateness of the auditors' and directors' opinions.

The level of under reporting of going concern risk by auditors (75 per cent) implies they are more affected by the agency relationship found in literature than directors who are found to have an incidence of underreporting of 57 per cent.

Literature classifies auditors along with directors as part of the agency problem. Altman's Z‐score bankruptcy prediction model is used because of its enduring nature, reliability and ability to be externally calculated to independently compare the going concern reporting performance of auditors and directors as part of the contribution to this research area.

The paper for the first time examines going concern reporting at a multi‐risk level rather than the binomial level used in research previously. The approach is developed in this paper using auditing pronouncements. These risk levels are linked with an independent measure being the Altman Z‐score to determine the appropriateness of auditors' and directors' reporting of going concern issues.

This paper aims to empirically examine the relationship between disclosure and risk-taking of banks in Ghana. The study also aims to gain an insight into the general risk-taking behaviour of banks in Ghana for the period 2007-2011.

The study used panel regression model and relate risk-taking to disclosure, controlling for bank size, profitability, liquidity and treasury bill rate. Disclosure scores from a disclosure index are used as a measure of disclosure, likewise Z-score as a measure of total risk. Also, the ratio of provisions for loan losses to gross loans by each bank for each year was used to examine the general risk-taking behaviour of Ghanaian banks.

The study revealed that the election year and the immediate subsequent year are characterized by an increase in non-performing loans. Greater disclosure is associated with more risk-taking and vice versa. This implies that market discipline is not effective in Ghana. Treasury bill rate, profitability and liquidity were found to be economically meaningful and statistically significant in influencing risk-taking of banks in Ghana.

As there are relatively few studies conducted in this area, specifically among banks in Ghana, this study will broaden the scope of the literature on disclosure and risk-taking by providing empirical evidence.