This document summarizes a study examining the efficacy of Altman's Z-score model for predicting bankruptcy among specialty retail firms. The authors apply Altman's Z-score to 17 pairs of retail firms from 2007-2008, with one firm in each pair declaring bankruptcy. They find the model accurately predicted bankruptcy for all but two firms, demonstrating 94% accuracy. The document provides background on Altman's Z-score model and discusses literature questioning its predictive power in more modern contexts.
This document discusses measuring the financial health of Lotte India Corporation Ltd using the Z-score and Springate models. It provides details on the Z-score model developed by Altman, including the formulas and guidelines for interpretation. Financial ratios used in the Z-score and Springate models are explained. The document also includes information on the confectionery industry and company profile. Objectives, methodology, findings and conclusions of analyzing Lotte's financial health over 5 years are presented.
The Altman Z-score is a statistical model that uses multiple corporate financial ratios to predict the probability that a company will go bankrupt within 2 years. The Z-score was developed in 1968 by Edward Altman and uses ratios related to profitability, leverage, liquidity, and other factors. A Z-score above 2.99 indicates low risk of bankruptcy, between 1.81-2.99 is in the gray area, and below 1.81 indicates high risk. The model requires data from a company's balance sheet and income statement to calculate ratios and determine the overall Z-score.
The document presents a study analyzing the relationship between company delistings and bankruptcy as measured by the Altman Z-Score bankruptcy prediction model. The study calculates the Z-Score for 10 companies that were delisted between 2016-2017 using their financial data from 2011-2015. While some companies had Z-Scores in the "safe" or "gray" zones indicating low bankruptcy risk, others had scores in the "distress" zone or very low scores overall. The conclusion discusses some limitations of the Altman Z-Score model and recommends considering additional external factors to improve bankruptcy prediction.
The document discusses the Altman Z-score formula, which was published in 1968 and can be used to predict bankruptcy. The formula uses five financial ratios to calculate a score. A score below 1.8 indicates likely bankruptcy, while a score between 1.8-3 means bankruptcy is possible and above 3 means the company is financially stable. The formula is: 1.2A+1.4B+3.3C+0.6D+1.0E. It requires data from a company's balance sheet and income statement. While the Z-score can help determine bankruptcy risk, it does not work for new companies and does not directly consider cash flow.
Altman Z-Score+ mobile, wearable, web, PC-based Apps, web service and Bloomberg Terminal app provides the client with timely assessments of the credit risk and probability of default of companies on a global basis based on the famed and well tested Altman Z-Score family of models. Business Compass LLC has teamed up with the creator of the Z-Score model, the international global expert on credit risk, Dr. Edward I. Altman, Max L. Heine Professor of Finance at the NYU Stern School of Business and Director of the NYU Salomon Center’s Credit and Debt Markets Program, to provide this important tool for corporate credit analysis. This product makes analysis of 70,000+ companies trading worldwide over 130+ exchanges. Our web site address is altmanzscoreplus.com.
This document discusses five models for predicting business failure: Beaver's model, Altman's Z score model, Wilcox model, Blum Marc's failing company model, and L.C. Gupta's model. It focuses on explaining Blum Marc's failing company model and L.C. Gupta's model. The failing company model was developed by Marc Blum to assess failure probability using financial data from 115 failed and 115 non-failed companies from 1954-1968. It achieved 94% accuracy for failures within one year and 80% accuracy for failures within two years. L.C. Gupta's study on Indian companies found that earnings before interest, taxes, depreciation and amortization/sales and operating cash
This document discusses key concepts around a company's financing decision of whether to issue debt or equity. It covers factors to consider like taxes, financial distress, signaling effects, and how financial leverage can increase expected returns but also amplify risk for shareholders. Examples and equations are provided to illustrate how different financing options like debt versus equity impact returns and financial ratios.
Discounted cash flow techniques are used to evaluate investment projects by estimating relevant cash flows over time and discounting them to calculate net present value (NPV). NPV is the primary criterion for accepting or rejecting projects and considers the timing of all cash flows. When capital is limited, mutually exclusive projects must be ranked according to NPV to allocate funds to the most valuable opportunities. Accurately determining relevant cash flows requires understanding how items like depreciation, working capital, taxes, and capacity utilization affect cash flows over time.
This document discusses measuring the financial health of Lotte India Corporation Ltd using the Z-score and Springate models. It provides details on the Z-score model developed by Altman, including the formulas and guidelines for interpretation. Financial ratios used in the Z-score and Springate models are explained. The document also includes information on the confectionery industry and company profile. Objectives, methodology, findings and conclusions of analyzing Lotte's financial health over 5 years are presented.
The Altman Z-score is a statistical model that uses multiple corporate financial ratios to predict the probability that a company will go bankrupt within 2 years. The Z-score was developed in 1968 by Edward Altman and uses ratios related to profitability, leverage, liquidity, and other factors. A Z-score above 2.99 indicates low risk of bankruptcy, between 1.81-2.99 is in the gray area, and below 1.81 indicates high risk. The model requires data from a company's balance sheet and income statement to calculate ratios and determine the overall Z-score.
The document presents a study analyzing the relationship between company delistings and bankruptcy as measured by the Altman Z-Score bankruptcy prediction model. The study calculates the Z-Score for 10 companies that were delisted between 2016-2017 using their financial data from 2011-2015. While some companies had Z-Scores in the "safe" or "gray" zones indicating low bankruptcy risk, others had scores in the "distress" zone or very low scores overall. The conclusion discusses some limitations of the Altman Z-Score model and recommends considering additional external factors to improve bankruptcy prediction.
The document discusses the Altman Z-score formula, which was published in 1968 and can be used to predict bankruptcy. The formula uses five financial ratios to calculate a score. A score below 1.8 indicates likely bankruptcy, while a score between 1.8-3 means bankruptcy is possible and above 3 means the company is financially stable. The formula is: 1.2A+1.4B+3.3C+0.6D+1.0E. It requires data from a company's balance sheet and income statement. While the Z-score can help determine bankruptcy risk, it does not work for new companies and does not directly consider cash flow.
Altman Z-Score+ mobile, wearable, web, PC-based Apps, web service and Bloomberg Terminal app provides the client with timely assessments of the credit risk and probability of default of companies on a global basis based on the famed and well tested Altman Z-Score family of models. Business Compass LLC has teamed up with the creator of the Z-Score model, the international global expert on credit risk, Dr. Edward I. Altman, Max L. Heine Professor of Finance at the NYU Stern School of Business and Director of the NYU Salomon Center’s Credit and Debt Markets Program, to provide this important tool for corporate credit analysis. This product makes analysis of 70,000+ companies trading worldwide over 130+ exchanges. Our web site address is altmanzscoreplus.com.
This document discusses five models for predicting business failure: Beaver's model, Altman's Z score model, Wilcox model, Blum Marc's failing company model, and L.C. Gupta's model. It focuses on explaining Blum Marc's failing company model and L.C. Gupta's model. The failing company model was developed by Marc Blum to assess failure probability using financial data from 115 failed and 115 non-failed companies from 1954-1968. It achieved 94% accuracy for failures within one year and 80% accuracy for failures within two years. L.C. Gupta's study on Indian companies found that earnings before interest, taxes, depreciation and amortization/sales and operating cash
This document discusses key concepts around a company's financing decision of whether to issue debt or equity. It covers factors to consider like taxes, financial distress, signaling effects, and how financial leverage can increase expected returns but also amplify risk for shareholders. Examples and equations are provided to illustrate how different financing options like debt versus equity impact returns and financial ratios.
Discounted cash flow techniques are used to evaluate investment projects by estimating relevant cash flows over time and discounting them to calculate net present value (NPV). NPV is the primary criterion for accepting or rejecting projects and considers the timing of all cash flows. When capital is limited, mutually exclusive projects must be ranked according to NPV to allocate funds to the most valuable opportunities. Accurately determining relevant cash flows requires understanding how items like depreciation, working capital, taxes, and capacity utilization affect cash flows over time.
This document provides an overview of key financial performance metrics such as return on equity (ROE), return on assets (ROA), profit margin, asset turnover, and financial leverage. It discusses how these metrics can be used to evaluate and compare the financial performance of different companies. Tables of example data on multiple companies are presented to illustrate the concepts.
The Usefulness of Financial Ratios in Discriminating Between Healthy and Dist...Nerma Saracevic
This document summarizes research analyzing the usefulness of various financial ratios in distinguishing between healthy and distressed companies that are clients of an Islamic bank in Bosnia and Herzegovina. The research analyzed ratios in five categories: liquidity, leverage, activity, efficiency, and profitability. The results found that profitability ratios and activity ratios were most important, with healthy companies exhibiting higher values. Liquidity and leverage ratios were generally not statistically significant. The researchers conclude key ratios include profit margins, returns on assets and equity, and activity turnover ratios. Future work is needed to develop a multi-factor distress prediction model for Islamic banks.
Effect of operating, financial and total leverage on expected stock return an...Shoaib Lalani
Regardless of the size and nature, largely all businesses are dependent on leverage. This research called for analyzing the impact of leverage on equity elements like the earnings to price ratio, Market value of equity and book to market ratio. Later on we also tested to identify the relationship between leverage and on expected stock returns. Financial data for different companies ranging in their own sectors was collected and then financial data from 2002 to 2012 was used to run pooled regression in order to find out any existing relationship
The results were pretty astonishing as it was proved that leverage had no impact on either of the equity elements. Nevertheless, a relationship could be identified between leverage and expected stock returns. Hence it will be safe to conclude that Pakistan’s economy is shortsighted and consumption oriented and that profits and earnings of companies in Pakistan are highly financed by their respective revenues. But nevertheless judgments about a particular sector couldn’t be made as this report has various business sectors of Pakistan.
Discuss the methods of estimating beta.
Explain the market model for calculating beta.
Examine the difference between betas of individual firms and the industry beta.
Highlight the beta instability.
Explain the determinants of beta.
Show the use of beta in determining the cost of equity.
IRJET- Financial Strength Analysis of Unitech Company Using Altman’s Z score ...IRJET Journal
This document analyzes the financial strength of Unitech Company over a 10-year period using Altman's Z-score model. Altman's Z-score model uses multiple financial ratios to predict the likelihood of bankruptcy. The document provides background on Altman's Z-score model and the specific formulas used for different types of companies. It then analyzes Unitech's financial statements over 10 years to calculate financial ratios and the Z-score. The results will help assess Unitech's financial situation and bankruptcy risk during that period.
This document discusses financial and operating leverage. It defines key terms like capital structure, financial leverage, and operating leverage. It explains how financial leverage can increase return on equity but also increases financial risk for shareholders. The document also discusses how operating leverage and financial leverage work together to impact earnings per share, and shows the tradeoff between risk and return that companies face when determining their optimal capital structure.
The document discusses the topics of leverage, including operating leverage and financial leverage. It provides definitions and formulas for calculating degrees of operating leverage (DOL) and financial leverage (DFL). Operating leverage results from fixed costs and increases volatility of earnings. Financial leverage magnifies profits but also losses through the use of debt financing. Both types of leverage can increase risk but also increase potential returns through profit magnification.
This chapter discusses return on invested capital (ROIC) and growth as fundamental drivers of company value. It provides the following key points:
1. ROIC measures a company's ability to generate returns from its capital investments and should be compared to its cost of capital and returns on alternative investments.
2. The value creation formula shows that higher long-term ROIC and growth rates lead to greater company value.
3. Sustainable competitive advantages allow some companies to maintain high ROIC for extended periods, while others see ROIC decline over time as advantages erode.
4. Empirical analysis shows ROIC and growth tend to decrease as companies mature, with 50% of high-ROIC companies
The document discusses the Du Pont analysis method of financial analysis. It was pioneered by the Du Pont company in the United States. The Du Pont analysis breaks down return on equity into three components: profit margin, asset turnover, and financial leverage. It is used to understand how net return on investment is influenced by net profit margin and asset turnover ratio.
The Indian pharmaceutical sector is highly fragmented, with over 20,000 registered companies. The top 250 companies control 70% of the market. These companies currently meet about 70% of the country's drug demands, mainly through Maharashtra and Gujarat, which account for 45% of pharmaceutical manufacturing units.
The industry has grown from $0.3 billion in 1980 to $12 billion in 2012. Branded generics dominate the market, making up 70-80% of it. Drug prices are very low due to intense competition. While India is 10th globally by value of drugs, it is 3rd by volume produced.
Effect of Leverage on Expected Stock Returns and Size of the FirmAakash Kumar
This document presents a study on the effect of leverage on expected stock returns and firm size for companies listed on the KSE100 index in Pakistan. It reviews previous literature that has found mixed results on the relationship between leverage and various performance measures. The study uses linear regression to analyze the impact of leverage on earnings-to-price ratio as a proxy for expected stock returns and market value as a proxy for firm size. Preliminary results are presented along with conclusions and recommendations for further study.
The document defines leverage as using fixed costs to magnify returns. There are two types of fixed costs: operating costs like rent and salaries, and financial costs like interest from debt. Leverage can increase risk but also returns. There are three types of leverage: operating, financial, and total. Operating leverage is the effect of fixed operating costs on income. Financial leverage is the effect of fixed financing costs like debt and preferred stock on earnings per share. Degree of operating leverage and degree of financial leverage measure the multiplier effect of each type of leverage. Examples using data from a levered company show that a 10% increase in sales would increase operating income by 17.14% due to operating leverage of 1.714, and operating income
This document provides an analysis of various financial ratios for a company over two years. It begins with an introduction to ratio analysis and its importance. It then calculates and interprets various ratios including current ratio, debt to equity ratio, net profit margin, gross profit margin, inventory turnover, debtors turnover, creditors turnover and return on capital employed. The analysis found that the current ratio and return on capital employed increased slightly but debt to equity ratio also increased, indicating less financial stability. Profit margins were low. Inventory turnover and creditors turnover decreased. The conclusion recommends the company improve utilization of assets, pricing strategies and timely payment of creditors.
Mba 2 fm u 4 operating and financial leverage,management of working capitalRai University
This document discusses financial and operating leverage. It defines financial leverage as using debt and preference shares in addition to equity in a company's capital structure. This allows earnings from fixed-cost funds like debt to be leveraged to increase returns to shareholders if the cost of debt is lower than returns on assets. However, it also increases risk. The document also defines operating leverage as how much a company's operating profits (EBIT) change with sales. It discusses how financial and operating leverage combine to impact earnings per share and risk.
Operating leverage affects a company's operating profit and measures changes in earnings before interest and taxes relative to sales changes. It helps understand costs and pricing strategies. Degree of operating leverage quantifies how operating income changes with sales. Higher operating leverage means larger profits from increased sales but more risk, while lower leverage means smaller profits but less risk from fluctuating sales. Overall, higher leverage can be better if sales are stable but most companies prefer lower leverage.
capital structure and profitability of a firmromaanqamar
This document discusses how capital structure impacts profitability. It defines capital structure as the mix of long-term funding sources a firm uses. The objective of capital structure is to minimize cost of capital and maximize share price. Different ratios are used to measure capital structure and profitability, including debt-to-equity, short-term debt, and return on equity. Regression analysis of sectors shows that capital structure explains 73% of profitability variations, with higher short-term debt and lower long-term debt correlating to higher returns. The analysis supports that a firm's profitability is significantly affected by its capital structure choices.
This document discusses financial leverage and its implications for risk and return. It defines financial leverage as the use of fixed-cost funding like debt and preferred shares alongside equity. Measures of financial leverage include debt ratio, debt-to-equity ratio, and interest coverage ratio. While financial leverage can magnify shareholder returns in good times by using low-cost debt, it also magnifies risks through increased volatility of earnings per share. The document also discusses operating leverage and how the combination of financial and operating leverage affects total leverage and risk.
Microsoft is a software company that develops operating systems, productivity applications, and development tools. It generates revenue through licensing its software. The document provides an overview of Microsoft's business segments, products, and financial reporting schedule. Key facts include that Microsoft was incorporated in 1981 and is headquartered in Redmond, Washington. It develops software for servers, PCs, and other devices and operates the MSN online network.
Examples of Key Ratios for Financial Analysis: Useful Financial Ratios (Expla...The-KPI-Examples-Review
In this report we define the key financial ratios based on web search data in 2015. For some of the key financial ratios were defined their formulas and calculation examples. In all our calculations we used the official financial statements of Siemens AG for the fiscal years ended september 30, 2014 and 2013, namely the consolidated statements of income (D.1), financial position (D.3) and others.
The document discusses z-scores and how they are used to standardize scores in a normal distribution. A z-score represents the number of standard deviations a data point is from the mean. It is calculated by subtracting the mean from the data value and dividing by the standard deviation. Examples are given to demonstrate how to calculate z-scores and use them to determine percentiles and the percentage of cases above or below a given score.
El documento explica cómo las puntuaciones z permiten medir de forma universal la posición de una puntuación en relación a la media de cualquier distribución normal, utilizando la desviación estándar como unidad de medida. Esto permite comparar el valor posicional de una misma puntuación entre diferentes distribuciones. El documento también muestra cómo calcular la puntuación z dada una puntuación x y desviación estándar S, y cómo usar la puntuación z para determinar la proporción de casos por encima o debajo de un valor particular en una distribución normal.
The document discusses z-scores and standardization. A z-score is a statistic that indicates how many standard deviations an observation is above or below the mean. It is calculated by subtracting the population mean from an individual raw score and dividing by the population standard deviation. This process converts raw scores to standardized z-scores that allow comparison across different data sets and distributions. The document provides examples of calculating z-scores and discusses how standardization keeps the distribution unchanged but centers it on a value of 0.
This document provides an overview of key financial performance metrics such as return on equity (ROE), return on assets (ROA), profit margin, asset turnover, and financial leverage. It discusses how these metrics can be used to evaluate and compare the financial performance of different companies. Tables of example data on multiple companies are presented to illustrate the concepts.
The Usefulness of Financial Ratios in Discriminating Between Healthy and Dist...Nerma Saracevic
This document summarizes research analyzing the usefulness of various financial ratios in distinguishing between healthy and distressed companies that are clients of an Islamic bank in Bosnia and Herzegovina. The research analyzed ratios in five categories: liquidity, leverage, activity, efficiency, and profitability. The results found that profitability ratios and activity ratios were most important, with healthy companies exhibiting higher values. Liquidity and leverage ratios were generally not statistically significant. The researchers conclude key ratios include profit margins, returns on assets and equity, and activity turnover ratios. Future work is needed to develop a multi-factor distress prediction model for Islamic banks.
Effect of operating, financial and total leverage on expected stock return an...Shoaib Lalani
Regardless of the size and nature, largely all businesses are dependent on leverage. This research called for analyzing the impact of leverage on equity elements like the earnings to price ratio, Market value of equity and book to market ratio. Later on we also tested to identify the relationship between leverage and on expected stock returns. Financial data for different companies ranging in their own sectors was collected and then financial data from 2002 to 2012 was used to run pooled regression in order to find out any existing relationship
The results were pretty astonishing as it was proved that leverage had no impact on either of the equity elements. Nevertheless, a relationship could be identified between leverage and expected stock returns. Hence it will be safe to conclude that Pakistan’s economy is shortsighted and consumption oriented and that profits and earnings of companies in Pakistan are highly financed by their respective revenues. But nevertheless judgments about a particular sector couldn’t be made as this report has various business sectors of Pakistan.
Discuss the methods of estimating beta.
Explain the market model for calculating beta.
Examine the difference between betas of individual firms and the industry beta.
Highlight the beta instability.
Explain the determinants of beta.
Show the use of beta in determining the cost of equity.
IRJET- Financial Strength Analysis of Unitech Company Using Altman’s Z score ...IRJET Journal
This document analyzes the financial strength of Unitech Company over a 10-year period using Altman's Z-score model. Altman's Z-score model uses multiple financial ratios to predict the likelihood of bankruptcy. The document provides background on Altman's Z-score model and the specific formulas used for different types of companies. It then analyzes Unitech's financial statements over 10 years to calculate financial ratios and the Z-score. The results will help assess Unitech's financial situation and bankruptcy risk during that period.
This document discusses financial and operating leverage. It defines key terms like capital structure, financial leverage, and operating leverage. It explains how financial leverage can increase return on equity but also increases financial risk for shareholders. The document also discusses how operating leverage and financial leverage work together to impact earnings per share, and shows the tradeoff between risk and return that companies face when determining their optimal capital structure.
The document discusses the topics of leverage, including operating leverage and financial leverage. It provides definitions and formulas for calculating degrees of operating leverage (DOL) and financial leverage (DFL). Operating leverage results from fixed costs and increases volatility of earnings. Financial leverage magnifies profits but also losses through the use of debt financing. Both types of leverage can increase risk but also increase potential returns through profit magnification.
This chapter discusses return on invested capital (ROIC) and growth as fundamental drivers of company value. It provides the following key points:
1. ROIC measures a company's ability to generate returns from its capital investments and should be compared to its cost of capital and returns on alternative investments.
2. The value creation formula shows that higher long-term ROIC and growth rates lead to greater company value.
3. Sustainable competitive advantages allow some companies to maintain high ROIC for extended periods, while others see ROIC decline over time as advantages erode.
4. Empirical analysis shows ROIC and growth tend to decrease as companies mature, with 50% of high-ROIC companies
The document discusses the Du Pont analysis method of financial analysis. It was pioneered by the Du Pont company in the United States. The Du Pont analysis breaks down return on equity into three components: profit margin, asset turnover, and financial leverage. It is used to understand how net return on investment is influenced by net profit margin and asset turnover ratio.
The Indian pharmaceutical sector is highly fragmented, with over 20,000 registered companies. The top 250 companies control 70% of the market. These companies currently meet about 70% of the country's drug demands, mainly through Maharashtra and Gujarat, which account for 45% of pharmaceutical manufacturing units.
The industry has grown from $0.3 billion in 1980 to $12 billion in 2012. Branded generics dominate the market, making up 70-80% of it. Drug prices are very low due to intense competition. While India is 10th globally by value of drugs, it is 3rd by volume produced.
Effect of Leverage on Expected Stock Returns and Size of the FirmAakash Kumar
This document presents a study on the effect of leverage on expected stock returns and firm size for companies listed on the KSE100 index in Pakistan. It reviews previous literature that has found mixed results on the relationship between leverage and various performance measures. The study uses linear regression to analyze the impact of leverage on earnings-to-price ratio as a proxy for expected stock returns and market value as a proxy for firm size. Preliminary results are presented along with conclusions and recommendations for further study.
The document defines leverage as using fixed costs to magnify returns. There are two types of fixed costs: operating costs like rent and salaries, and financial costs like interest from debt. Leverage can increase risk but also returns. There are three types of leverage: operating, financial, and total. Operating leverage is the effect of fixed operating costs on income. Financial leverage is the effect of fixed financing costs like debt and preferred stock on earnings per share. Degree of operating leverage and degree of financial leverage measure the multiplier effect of each type of leverage. Examples using data from a levered company show that a 10% increase in sales would increase operating income by 17.14% due to operating leverage of 1.714, and operating income
This document provides an analysis of various financial ratios for a company over two years. It begins with an introduction to ratio analysis and its importance. It then calculates and interprets various ratios including current ratio, debt to equity ratio, net profit margin, gross profit margin, inventory turnover, debtors turnover, creditors turnover and return on capital employed. The analysis found that the current ratio and return on capital employed increased slightly but debt to equity ratio also increased, indicating less financial stability. Profit margins were low. Inventory turnover and creditors turnover decreased. The conclusion recommends the company improve utilization of assets, pricing strategies and timely payment of creditors.
Mba 2 fm u 4 operating and financial leverage,management of working capitalRai University
This document discusses financial and operating leverage. It defines financial leverage as using debt and preference shares in addition to equity in a company's capital structure. This allows earnings from fixed-cost funds like debt to be leveraged to increase returns to shareholders if the cost of debt is lower than returns on assets. However, it also increases risk. The document also defines operating leverage as how much a company's operating profits (EBIT) change with sales. It discusses how financial and operating leverage combine to impact earnings per share and risk.
Operating leverage affects a company's operating profit and measures changes in earnings before interest and taxes relative to sales changes. It helps understand costs and pricing strategies. Degree of operating leverage quantifies how operating income changes with sales. Higher operating leverage means larger profits from increased sales but more risk, while lower leverage means smaller profits but less risk from fluctuating sales. Overall, higher leverage can be better if sales are stable but most companies prefer lower leverage.
capital structure and profitability of a firmromaanqamar
This document discusses how capital structure impacts profitability. It defines capital structure as the mix of long-term funding sources a firm uses. The objective of capital structure is to minimize cost of capital and maximize share price. Different ratios are used to measure capital structure and profitability, including debt-to-equity, short-term debt, and return on equity. Regression analysis of sectors shows that capital structure explains 73% of profitability variations, with higher short-term debt and lower long-term debt correlating to higher returns. The analysis supports that a firm's profitability is significantly affected by its capital structure choices.
This document discusses financial leverage and its implications for risk and return. It defines financial leverage as the use of fixed-cost funding like debt and preferred shares alongside equity. Measures of financial leverage include debt ratio, debt-to-equity ratio, and interest coverage ratio. While financial leverage can magnify shareholder returns in good times by using low-cost debt, it also magnifies risks through increased volatility of earnings per share. The document also discusses operating leverage and how the combination of financial and operating leverage affects total leverage and risk.
Microsoft is a software company that develops operating systems, productivity applications, and development tools. It generates revenue through licensing its software. The document provides an overview of Microsoft's business segments, products, and financial reporting schedule. Key facts include that Microsoft was incorporated in 1981 and is headquartered in Redmond, Washington. It develops software for servers, PCs, and other devices and operates the MSN online network.
Examples of Key Ratios for Financial Analysis: Useful Financial Ratios (Expla...The-KPI-Examples-Review
In this report we define the key financial ratios based on web search data in 2015. For some of the key financial ratios were defined their formulas and calculation examples. In all our calculations we used the official financial statements of Siemens AG for the fiscal years ended september 30, 2014 and 2013, namely the consolidated statements of income (D.1), financial position (D.3) and others.
The document discusses z-scores and how they are used to standardize scores in a normal distribution. A z-score represents the number of standard deviations a data point is from the mean. It is calculated by subtracting the mean from the data value and dividing by the standard deviation. Examples are given to demonstrate how to calculate z-scores and use them to determine percentiles and the percentage of cases above or below a given score.
El documento explica cómo las puntuaciones z permiten medir de forma universal la posición de una puntuación en relación a la media de cualquier distribución normal, utilizando la desviación estándar como unidad de medida. Esto permite comparar el valor posicional de una misma puntuación entre diferentes distribuciones. El documento también muestra cómo calcular la puntuación z dada una puntuación x y desviación estándar S, y cómo usar la puntuación z para determinar la proporción de casos por encima o debajo de un valor particular en una distribución normal.
The document discusses z-scores and standardization. A z-score is a statistic that indicates how many standard deviations an observation is above or below the mean. It is calculated by subtracting the population mean from an individual raw score and dividing by the population standard deviation. This process converts raw scores to standardized z-scores that allow comparison across different data sets and distributions. The document provides examples of calculating z-scores and discusses how standardization keeps the distribution unchanged but centers it on a value of 0.
DuPont was founded in 1802 in Wilmington, Delaware and began as a gunpowder mill. It is now a multinational conglomerate that produces a variety of chemicals, polymers, fibers and other products. The company's mission is sustainable growth through reducing environmental impact and its vision is to be a leader in science and technology providing sustainable solutions. Ellen Kullman is the current CEO and chair of the board. DuPont has a diverse portfolio of products across multiple industries and engages in significant R&D spending.
The Du Pont Company pioneered a system of financial analysis that is widely used. Known as Du Pont analysis, it examines the interrelationships between elements in financial statements. Du Pont analysis depicts Return on Total Assets (ROTA) as the product of Net Profit Margin (NPM) and Total Assets Turnover Ratio (TATR). This decomposition helps understand how ROTA is influenced by NPM and TATR. The analysis can also be extended to Return on Equity (ROE) and evaluates how well a company manages assets, expenses, and debt.
Sesa Goa Limited is India's largest producer and exporter of iron ore in the private sector. The DuPont analysis breaks down return on equity into three parts: profit margin, total asset turnover, and financial leverage. It helps identify which parts of the business may be underperforming if return on equity is unsatisfactory. Several financial metrics are presented including return on equity, return on investment, return on assets, internal growth rate, and sustainable growth rate from 2008-2012.
This document is a research proposal examining whether Roots Fashion Pvt. Ltd. should drop the Pepe Jeans brand from its portfolio to increase profits. The proposal outlines the background, research question, methodology, and potential issues. Primary research will include interviews with the managing director and employees, as well as customer surveys. Secondary research will analyze financial reports and information on Pepe Jeans' performance in the Indian market. The analysis will use financial techniques like ratio analysis and contribution analysis, as well as non-financial tools such as SWOT, PEST, and Boston Matrix. The proposal is submitted by a student for their Business and Management Internal Assessment.
This document presents a Du Pont analysis of Nestle and Cadbury for fiscal year 2007-2008. The Du Pont analysis breaks down return on equity (ROE) into operating profit margin, asset turnover, and financial leverage ratios to examine the drivers of profitability. It finds that Nestle's ROE of 0.35 for 2008 was an increase from 0.21 the prior year, attributed mainly to higher margins and turnover. Net profit margins and turnover ratios increased for Nestle in 2008, while debt ratios decreased, showing better utilization of assets and progress paying off debts.
This document discusses a dissertation that evaluates whether company size affects bankruptcy risk in the oil and gas industry. It uses Altman's z-score model to calculate bankruptcy probabilities for 19 public oil companies from 2008-2012. The study collects financial data from annual reports to calculate ratios used in the z-score formula. This allows comparing z-scores between large and small companies over the research period. The goal is to determine if larger companies face lower bankruptcy risk, as found in prior research, or if company size is less influential in the oil industry.
Financial Distress Prediction With Altman Z-Score And Effect On Stock Price: ...inventionjournals
: This study aimed to obtain empirical evidence about the state of financial distress prediction using the Altman Z-score and ratio-ratio test Z-score in influencing the price of shares in the chemical subsectors listed in Indonesia Stock Exchange 2009-2014 period. The samples were determined by purposive sampling, while data processing using Microsoft Excel, and SPSS. Financial distress only occurs in ETWA company in 2014 in the category of bankruptcy. Effect of a Z-score to the stock price is significantly 0.004 and ratio-ratio of the Altman Z score is working capital to total assets have no significant effect amounted to 0,085, retained earnings to total assets have no significant effect amounted to 0,478, EBIT to total assets have a significant influence amounted to 0,016, and the book value of equity to book value of total debt had no significant effect of 0.078. Contribution ratio-ratio Altman Z-score of 48.6% to the stock price. In conclusion, the financial distress that are in reasonably good condition. Z-score can be used to predict stock prices, and ratios of Z-score only ebit to total assets can significantly affect stock prices partially.
Distress risk and stock returns in an emerging marketAlexander Decker
This research study examines the relationship between financial distress and stock returns of listed companies in Pakistan. The study uses Altman's Z-score model to measure distress risk and subsequent realized stock returns as a proxy for market performance. The sample includes 17 distressed and 17 non-distressed firms listed on the Karachi Stock Exchange between 2006-2011. The results found a positive but insignificant relationship between distress risk and stock returns for distressed firms, suggesting distress risk may be a systematic risk for the Pakistani stock market to some extent. For non-distressed firms, distress risk was negatively correlated with stock returns and this relationship was statistically significant. The study is inconclusive on whether distress risk explains stock returns in Pakistan.
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Over the past several years, financial analysts have been contributing significantly to society in the field of predicting bankruptcy. Corporate managers spend a lot of time understanding and changing policies to avoid bankruptcy, especially after the global meltdown in the year 2008. Post Covid-19 there is a growing interest in the corporate world to understand and assess the implications and fallout in the aftermath of global business disruptions. Serious issues arise when a company goes bankrupt, it hampers the interest of all stakeholders including investors and promoters. It is imperative to check and review the financial performance of a business regularly to take prompt actions before the occurrence of any unforeseen events. Bankruptcy of companies can be predicted based on ratio analysis; proven by studies conducted on several such cases. A case in point is Altman’s Z score. To achieve this study Altman’s Z score model is applied and concluded that the sample companies are Healthy and stable for the coming two to three years. For this purpose, researchers have taken 10 listed unicorn startup companies in India from the year 2019 to 2023.
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A Study of the Efficacy of Altman’s Z To Predict Bankruptcy of Specialty Retail Firms Doing Business in Contemporary Times
1. A Study of the Efficacy of Altman’s Z To
Predict Bankruptcy of Specialty Retail
Firms Doing Business in Contemporary
Times
Suzanne K. Hayes
University of Nebraska at Kearney
Kay A. Hodge
University of Nebraska at Kearney
Larry W. Hughes
Central Washington University
Authors are listed in alphabetical order.
Abstract: Altman’s Z, a multiple discriminant analysis bankruptcy model using
commonly accepted cutoff criteria, may provide a useful decision rule to predict
financial distress in firms operating in a wide variety of industries. In this study,
we outline the construction and interpretation of the Z-Score and apply it to
several pairs of firms (N=17) from a variety of specialty retail industries spanning
two consecutive years. Past research indicates that Altman’s Z predicted future
financial distress in 90 percent of the firms studied. In this study, all but two of
the bankruptcies (94 percent) would have been accurately predicted. Despite
some criticism of the model’s efficacy, two firms were misclassified yet later
revealed potential financial distress.
Keywords: Altman’s Z, financial distress, bankruptcy, performance, strategy
Introduction firms with assets in excess of $100 million
and as much as$400 billion in debt and
Before the financial disaster of 2007 claims filed for bankruptcy during that time.
and the current economic crisis, the period As total bankruptcies have increased
from 1999 to 2002 hosted an steadily over the past thirty years, business
“unprecedented number of [U.S.] corporate filings have shown a slight downward trend
bankruptcies” (Moyer, 2005, p. 1). Over 400 (Figures 1 and 2). Although public firms
Economics & Business Journal:
Inquiries & Perspectives 122 Volume 3 Number 1 October 2010
2. Efficacy of Altman’s Z to Predict Bankruptcy Hayes, Hodge & Hughes
filing for bankruptcy are typically less than firms with total assets exceeding $375
one percent of all business filings, the billion filed. Thirty-four of the 2002
volume of assets and debts involved are bankruptcy filings were by firms with assets
considerable. For example, in 2001, 257 in excess of $1 billion each (Administrative,
companies with $256 billion in assets filed 2009).
for bankruptcy; a year later, 191 public
Total
Business
Non-Business
y = 5.6626x6 - 361.35x5 + 8905.9x4 - 104987x3 + 582276x2 - 1E+06x + 2E+06
Trendline
R² = 0.5939
Figure 2. Bankruptcy Filings, Business-only
Source: Administrative Office of U.S. Courts. (2009). Bankruptcy statistics. Information retrieved July,
2009, from http://www.uscourts.gov/bnkrpctystats/bankruptcystats.htm
Economics & Business Journal:
Inquiries & Perspectives 123 Volume 3 Number 1 October 2010
3. Efficacy of Altman’s Z to Predict Bankruptcy Hayes, Hodge & Hughes
gained acceptance by auditors,
Given the relatively high frequency management accountants, and database
of bankruptcies filed by publicly-traded systems beginning in the mid-1980s.
businesses, and the threat posed to Although, Altman originally developed the
suppliers and other stakeholders that rely Z-Score based on a small sample of
on firms’ solvency for their own success, a manufacturing firms, some research seems
reliable bankruptcy model with consistent to show that it is useful in other areas, such
predictive power is essential in today’s as healthcare, with some modifications (Al-
business environment. Sulaiti & Almwajeh, 2007).
Bankruptcies seem to unfold rapidly Altman’s Z-Score formula is a
and news about them seems unexpected, multivariate formula used to measure the
although the signs may have been in financial health of a company and to
evidence for years before the filing takes diagnose the probability that a company
place. Naturally, many organizational will go bankrupt within a two-year period.
stakeholders are interested in finding a Studies of Altman’s Z have yielded mixed
reliable method to predict bankruptcy and results, and recent literature questions
financial distress. To date, the methods whether or not the formula, tested in the
designed to predict bankruptcy events have mid-twentieth century on manufacturing
had mixed reviews. One common firms, is useful in today’s marketplace.
bankruptcy prediction method is Altman’s The Z-Score uses various accounting
Z-Score formula. The objective of this study ratios and market-derived price data to
is to apply Altman’s Z-Score in a predict financial distress and future
contemporary analysis during a period of bankruptcy. The original formula was
rapidly-changing business conditions. The developed on a sample of 66 manufacturing
authors study the predictive ability of the Z- firms, half of which filed Chapter 7
Score using data from 2007 and 2008 and bankruptcy. Firms with assets of less than
apply it to a group of firms. In addition to $1 million were eliminated from the
using current data, this paper extends sample. The Altman’s Z formula works well
current research by utilizing Altman’s Z- provided the scores fall within the “in the
Score with a new subset of firms: the retail tails,” meaning that low and high scores
industry may more accurately predict financial
distress than scores that fall in the gray
Literature Review area. More moderate scores may be easily
misclassified (Moriarty, 1979). In the early
What is Altman’s Z Score? 2000s, Altman amended the formula to
allow its application to certain situations
Altman’s Z is one of the best known, not originally included in the original
statistically derived predictive models used sample set (Altman, 2006).
to forecast a firm’s impending bankruptcy
(Moyer, 2005). Edward Altman, a financial Constructing Altman’s Z-Score
economist and professor at New York’s
Stern School of Business, developed The Altman Z-Score, based on
Altman’s Z (the Z-Score) in 1968.The Z-Score discriminant analysis, includes basic
Economics & Business Journal:
Inquiries & Perspectives 124 Volume 3 Number 1 October 2010
4. Efficacy of Altman’s Z to Predict Bankruptcy Hayes, Hodge & Hughes
financial ratios as inputs (Calandro, 2007). upcoming problems. Last, X5 is a standard
To determine the formula, Altman utilized turnover measure; however, this varies
five common business ratios and greatly from industry-to-industry and is
systematically weighted them in his omitted when non-manufacturing firms are
calculations. Some research has shown that studied.
the model is 72 to 80 percent accurate in In response to requests for a
predicting bankruptcy one to two years in measure to predict the likelihood of
advance. The accuracy rate depends largely bankruptcy for non-manufacturing firms,
on the industry and other factors relevant Altman developed the Z” Model, (Altman &
to the industry. Hotchkiss 2006). This alternative model
Although Altman’s formula has been was designed for non-manufacturing
described in many of the works cited here, industrials. This is model that was employed
we will provide a description of its in the investigation reported in this study.
construction and an overview of two of the The formula, which differs from the original
three versions of the formula that have formula presented above, is:
been studied.
The Z-Score was originally Z” = 6.56 X1 + 3.26 X2 + 6.72 X3 +
constructed as: 1.05 X4
Z = 1.2X1 + 1.4X2 + 3.3X3 + .6X4 = 1.0X5, where
where X 1= (current assets – current
liabilities) / total assets
X1 = working capital / total assets X2= retained earnings / total assets
X2 = retained earnings / total assets X3= earnings before interest and taxes
X3 = earnings before earnings and / total assets
taxes / total assets X4= book value of equity / total
X4 = market value of equity / book liabilities
value of debt
X5 = sales / total assets. The variable of X5, found in the
original Z-score for manufacturing firms, is
Altman employed X1 because the omitted in Z”. This variable represented
ratio measures liquid assets in relation to sales/total assets, and Altman removed this
the firm’s size. The most widely used variable when calculating the score for non-
current and acid test ratios do not predict manufacturers because this turnover ratio
as effectively as this measure. X2 measures is likely to be significantly higher for retail
a firm’s earning power; failure rates are and service firms as compared to
closely related to this ratio. The X3 ratio manufacturing firms. In other words, if the
measures operating efficiency separated original model was employed to predict
from leverage effects. This part of the bankruptcy in non-manufacturing firms, the
equation recognizes operating earnings are scores would underpredict bankruptcy for
a key to long-run viability. Altman added these firms because of their lower capital
the market to the equation by including X4; intensity.
security price changes may foreshadow
Economics & Business Journal:
Inquiries & Perspectives 125 Volume 3 Number 1 October 2010
5. Efficacy of Altman’s Z to Predict Bankruptcy Hayes, Hodge & Hughes
Interpreting Altman’s Z-Score lower suggests that bankruptcy is likely. For
all three interpretations, the score’s
Altman’s Z is commonly employed predictive probability is 95 percent within
to assess financial distress. Altman’s Z is a one year and 70 percent within two years of
weighted composite of financial indicators the data used to compute the score. In each
relating to profitability, revenue, slack of these three versions the coefficients
resources, and market return (Altman, differ to represent the nuances of the
1968). When interpreting Altman’s Z-Score, industry situations (see Caouette, Altman,
higher values indicate that firms “carry out Narayanan [1998] for coefficients for each
more actions at a fast pace, while low version of the formula).
scores indicate that firms carry out few In the Z” formula, which is employed
total actions and respond slowly” (Ferrier, in this study, scores of 2.6 and greater
Mac Fhionnlaoich, Smith, & Grimm, 2002). indicate that the firm is in a safe zone.
In this section, we discuss the different Scores ranging from 1.1 to 2.6 represent the
cutoff criteria for interpreting the score. grey zone. The distress zone includes scores
Once a Z-Score has been below 1.1.
determined, the ratio is then compared to Early scholars criticized Altman’s
Altman’s predetermined cutoffs. Altman formula as having a poor record as
postulated that companies with a Z-Score predictor despite Altman’s explanation for a
<1.8 were likely to experience bankruptcy, bankruptcy. Altman claimed that
companies with a Z-score 1.8 to 2.99 were predictions varied due to the instability of
in a zone of ignorance, or a grey zone in relationships among the variables within
which distress may or may not be the equation over time. Statistical models
impending. Last, companies with a Z-score based on financial data may appear to
of >2.99 were likely to be financially describe events, but they are not
sound. However, there is no single formula necessarily good at predicting outcomes
that has the power to predict the future; Z- (Moyer, 1977). For example, in a study with
Score users should look at the trend of the public accounting professionals, Altman’s Z
business over time as they interpret the was found to misclassify over 50 percent of
score rather than just looking at the score the firms used in the study as bankrupt and
itself, which is only a snapshot in time. 29 percent as not bankrupt (Moriarty,
Three commonly used 1979). As a result of these and other similar
interpretations of the Altman’s Z-score findings, the formula was studied for use in
include the following: (1) For public different industry contexts.
manufacturing firms, a Z-score of 3.0 or In a test of Altman’s Z in a more
greater shows solvency, while a score of 1.8 current business climate, Grice and Ingram
or lower indicates likely distress. (2) For (2001) found inconsistent results. In
private manufacturing firms, the gray area response to their research questions: (1)
narrows with a positive score being 2.9 or the formula was not found to be as useful in
greater and the likelihood of bankruptcy at predicting distress in more contemporary
1.23 or lower. (3) For private, non- firms than when first developed (2) nor was
manufacturing firms, a score of 2.6 or it as effective in predicting bankruptcy for
greater indicates that impending non-manufacturing as for manufacturing
bankruptcy is unlikely and a score of 1.1 or firms. The formula was found to be as
Economics & Business Journal:
Inquiries & Perspectives 126 Volume 3 Number 1 October 2010
6. Efficacy of Altman’s Z to Predict Bankruptcy Hayes, Hodge & Hughes
useful in predicting bankruptcy as it was to firm can continue to borrow from its bank)
predict other distress conditions. (Alexeev & Kim, 2008)
Carton and Hofer (2006)
Performance Literature investigated a variety of common
performance metrics. The optimal metric
Although Altman’s Z is typically used for providing “the greatest relative
to predict bankruptcy, it is “also an information about the market-adjusted
important multidimensional measure of return to shareholders” was found to be
strategic performance” (Chakravarthy, Altman’s Z-Score. Altman’s formula
1986) in that it is a “composite measure of appeared to rate higher than other
profitability, cash flow, slack, and stock performance metrics such as the widely
market factors (Altman, 1968). High Z used return ratios (i.e., ROE & ROA),
scores indicate strong financial health while economic profit, growth rate of sales, cash
low scores indicate financial distress (Ferrier flow, and expenses. Carton and Hoffer’s
et al., 2002). primary message is that Altman’s Z-score is
Altman’s Z has also been used to more than a financial distress predictor; it is
explore the potential for bankruptcy in also efficacious as a performance
hospitals. The study using hospitals management tool.
revealed that both discriminant analysis and
logistic regression models are able to Method
predict service organizations’ success or
failure, with the latter being more In this study, we sought to apply
predictive in a sample of 65 hospitals (Al- Altman’s Z-Score to a more contemporary
Sulaiti, & Almwajeh, 2007). Liquidity and analysis in a rapidly changing business
profitability ratios had the highest environment. This section contains a
contribution to the results of the Z-score, description of the company selection
followed by productivity and efficiency. criteria. First, only public retail firms with a
In 2007, Kim studied the robustness declared bankruptcy during 2007 or 2008
of the Altman’s Z-Score model under the were considered. The eligible firms were
assumption that it was no longer significant further constrained to only include those
due to market factors. Kim found that the companies with: (1) no bankruptcy filings
Z-Score seems to be a predictor of financial for at least 10 years prior to the period
distress in firms one year prior to under study; (2) assets greater than
bankruptcy, but that the calculations $1,000,000, and (3) complete financial
needed to be used with caution because of information for the period under
the significance of some of the variables. consideration.
Kim cautions that Z-Score predictions for Next, comparable companies were
periods longer than one year have lost selected for each retail firm that remained
some of their significance. In a study of after applying the selection filters.
South Korean firms, a low Altman’s Z-score Comparable firms were identified from the
was found to be a significant predictor of key competitor information listed on
financial distress for those firms using the Yahoo! Finance and/or directly from
soft budget constraint (SBC), such as in company documents. Each qualifying
bank lending (i.e., a financially distressed comparable company was required to be a
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7. Efficacy of Altman’s Z to Predict Bankruptcy Hayes, Hodge & Hughes
public company with no bankruptcy filings peer firm, Jo-Ann Stores, Inc. show
during the preceding ten-year period, have relatively high values for X1. In fact, the Jo-
complete financial information, and show Ann Stores, Inc. showed a lower X1 value
assets greater than $1,000,000. than did Hancock Fabrics and it did not
Four pairs of firms for 2007 and for declare bankruptcy. Additional study of the
2008 satisfied the aforementioned criteria effectiveness of Altman’s Z” score may be
and are the basis for our study. The Altman warranted for this particular subset of the
Z” model was designed for retail industry.
nonmanufacturing firms and it is the The next pair of firms examined
appropriate model for retail firms. Financial during 2007 fell into the category of home
information was retrieved from Reuters. To entertainment specialty retailers. The
assess the predictive ability of Altman’s Z” Altman Z” Score accurately predicted
model in the retail industry, the Z” is financial distress for the bankrupt company.
calculated and compared for each pair of The financial data of Movie Gallery, Inc.
identified companies. A detailed discussion indicated a Z” Score of -1.04. Clearly this
of each comparison follows in the Findings score is in the distress zone. The firm
section. selected for comparison, Blockbuster, Inc.,
had a Z” Score of -4.17 based on December,
2007 Investigation 2006 financial data. The Blockbuster score is
indicative of financial distress. However, if
The 2007 Altman Z” analyses results one looks at X2 for Blockbuster, one would
are summarized in Table 1. The table see that it is high (relatively) and is driven
provides detailed information on the by the large negative retained earnings
bankrupt firms’ filing dates, fiscal year end which resulted from the losses during 2004
(FYE) dates, together with the calculated Z” and 2005. Blockbuster’s results are
Scores and distress determinations. primarily driven by the accumulated deficit
Additional information regarding input of $4,781,900,000. 2004 was a particularly
ratios was provided in the previous difficult year for Blockbuster where it lost
discussion regarding constructing the Z- approximately $1.2 billion. Although
Score. Blockbuster has not formally declared
The first pair of firms conducted bankruptcy at the time of this paper, the
business as retail fabric companies. Altman scoring method may still be
Hancock Fabrics, Inc. declared bankruptcy predictive of future financial distress for this
in March 2007, yet the calculated Z” Score company. Blockbuster was issued a “going
of 5.27 placed the firm squarely in the safe concern” warning in April, 2009 due to
zone. This result is primarily driven by an ongoing negotiations to restructure and
unusually high value for the X1 variable. substantially write down a $40 million term
The variable, X1, is the relationship between loan (Blockbuster, 2009). In this case, the
net working capital and total assets, and it Altman Z” Score provided more than 2
is heavily weighted in the Z” calculation. years warning of financial distress.
Altman found the measure of the percent Due to the uncertainty of
of assets supporting operations was highly Blockbuster’s financial position, a second
significant in the prediction of financial peer firm, Netflix, Inc., was selected as a
distress. Both Hancock Fabrics, Inc. and the comparator. The Netflix financial data
Economics & Business Journal:
Inquiries & Perspectives 128 Volume 3 Number 1 October 2010
8. Efficacy of Altman’s Z to Predict Bankruptcy Hayes, Hodge & Hughes
indicated a Z” Score of 5.27 which places 2007 period. The Z” Score correctly
the company firmly in the safe zone. Netflix categorized Harvey Electronics Inc. in a
has not filed bankruptcy. position of financial distress with a score of
The third pair of companies -2.85.
investigated are both consumer electronics The results of our 2007 data
retailers. Tweeter Home Entertainment comparisons strongly support the use of
Group Inc. declared bankruptcy in June, Altman’s Z” score as a predictive measure
2007. The Altman Z” Score, -2.01 accurately of financial distress. The method accurately
predicted financial distress for this firm classified eight of the nine firms under
based on financial data posted nine months investigation. Given attention to both Z”
prior to the filing. The comparable firm, Scores and other indicators, managers and
Best Buy Co., Inc.’s calculated Z” Score of investors of these firms would have
4.54 clearly places it in the safe zone. accurately predicted their condition of
Still another bankrupt consumer financial distress or eustress.
electronics retailer was evaluated for the
Table 1
2007 Selected Retail Firms: Calculated Altman Z” Scores
Z Score
Company Name Status FYE Analysis Date Z” Score Zone
Pair Number One
Hancock Fabrics, Inc. Filed 3/07 1/31 1/06 5.27 Safe
Jo-Ann Stores, Inc. Peer Firm 1/31 1/06 4.19 Safe
Pair Number Two
Movie Gallery, Inc. Filed 10/07 12/31 12/06 -1.04 Distress
Blockbuster, Inc. Peer Firm 12/31 12/06 -4.17 Distress
Netflix, Inc.* Peer Firm 12/31 12/06 5.27 Safe
Pair Number Three
Tweeter Home
Entertainment Group, Inc. Filed 6/07 9/30 9/06 -2.01 Distress
Best Buy Peer Firm 3/31 3/07 4.54 Safe
Pair Number Four
Harvey Electronics, Inc. Filed 12/07 10/31 10/06 -2.85 Distress
Best Buy Co., Inc. Peer Firm 3/31 3/07 4.54 Safe
* Netflix, Inc. was selected as a peer firm due to the distress rating of Blockbuster
Economics & Business Journal:
Inquiries & Perspectives 129 Volume 3 Number 1 October 2010
9. Efficacy of Altman’s Z to Predict Bankruptcy Hayes, Hodge & Hughes
2008 Investigation 2008 operated in the footwear industry.
The calculated Z” Score of .82 for Shoe
The 2008 analyses are summarized Pavilion, Inc. correctly places the firm in the
in Table 2. The table provides detailed distress zone. Shoe Pavilion filed
information on the bankrupt firms’ filing bankruptcy in July of 2008. Conversely, in
dates, fiscal year end (FYE) dates, together the comparator firm, Bakers Footwear
with the calculated Z” Scores and distress Group, Inc., the financial ratios indicated a
determinations. Additional information distress rating (Z” = -2.26). The score
regarding input ratios was provided indicates that Bakers will likely experience
previously in this paper. financial difficulties in the future. Despite
The first pair of companies operates the lack of a bankruptcy filing, the Z” Score
within the retail consumer electronics still has predictive ability for future financial
industry. At the date of filing, the Circuit distress, which is supported by more
City Stores, Inc. bankruptcy was the largest current news reported. In April, 2009,
retail Chapter 11 bankruptcy since Kmart Reuters reported on the performance of
filed for bankruptcy in 2002. The Z” Score of Bakers during their fiscal year 2008 (Bakers,
2.38 places the firm in the grey zone. Given 2009). The company’s independent auditor
the magnitude of the Circuit City issued a “going concern” warning. The
bankruptcy filing and the limited revelation warning was based on a potential inability
of the Z” Score, the Altman Z” failed to to comply with financial covenants which
clearly show that Circuit City was headed led the auditor to express “substantial
for bankruptcy. The comparator firm, Best doubt about whether the company can
Buy Co., Inc., displayed a calculated Z” score continue as a going concern.” Again, while
of 3.01. This score places Best Buy, Co., Inc. Bakers had not filed bankruptcy at that
in the safe zone. point in time, the Z” Score, based on pre-
Sharper Image Corp. and RadioShack 2008 data accurately predicted a distress
Corp. are specialty retailers in consumer condition. The Z” Score can also provide
electronics and gadgetry. The calculated Z” information across time periods, rather
Score of -0.96 for Sharper Image predicted than a snapshot at the fiscal year end.
its subsequent bankruptcy. Similarly, the Z” Further examination of Bakers revealed a Z”
Score of 5.75, which ranks RadioShack in score of .95 in January, 2007. Based on
the safe zone, is also predictive. these results, the financial condition of the
Whitehall Jewelers Holdings, Inc. company is clearly deteriorating further into
declared bankruptcy midway through its distress conditions. These results are
fiscal year 2008. Therefore, Z” Scores were supported by the going concern, reported
calculated based on available financial data above, that was issued approximately two
at both six- (Z” = -2.12) and 18-months (Z” = years after the January, 2007 data used to
-1.27) prior to the filing. The Altman’s Z” calculate our initial Z” Score.
Score was found to be predictive of Due to the financial situation
financial distress on both dates and ranked surrounding Bakers Footwear Group, Inc. an
the firm in the distress zone. The peer additional peer firm was selected for
jewelry retailer, Zales, was found to rest in comparison purposes. An examination of
the safe category with a 6.68 rating. the financial information for Brown Shoe
The final set of firms examined in Co., Inc. revealed a relatively high Z” Score
Economics & Business Journal:
Inquiries & Perspectives 130 Volume 3 Number 1 October 2010
10. Efficacy of Altman’s Z to Predict Bankruptcy Hayes, Hodge & Hughes
of 4.84, placing this firm in the safe strong cue that a strategy does not yield the
category. expected results, Altman’s Z is argued by
In summary, for the set of firms some to be broad enough of an indicator
studied during 2008, the ability of the for managers to notice” (Ferrier et al.,
Altman Z” score to predict financial distress 2002). In other words, Altman’s Z may be
was successful in eight of the nine employed to indicate financial distress.
companies under study. The remaining firm In this study, we have endeavored to
produced ambiguous results, ranking in the show the efficacy of the Altman’s Z” Score
grey classification to only later declare one in predicting financial distress in retail firms.
of the largest Chapter 11 retail bankruptcies In eight comparisons, four each in 2007 and
since 2002. 2008, of bankrupt versus non-bankrupt
firms in retail specialties, the Z” Score
Conclusions accurately predicted bankruptcy filing 94%
of the time and accurately predicted
“Although many performance financial distress over 90% of the time.
indicators cannot be expected to incur a
Table 2
2008 Selected Retail Firms: Calculated Altman Z” Scores
Z Score
Company Name Status FYE Analysis Date Z” Score Zone
Pair Number One
Circuit City Stores, Inc. Filed 11/08 2/28 2/08 2.38 Grey
Best Buy Co., Inc. Peer Firm 3/31 3/08 3.01 Safe
Pair Number Two
Sharper Image Corp. Filed 2/08 1/31 1/07 -0.96 Distress
RadioShack Corp . Peer Firm 12/31 12/06 5.75 Safe
Pair Number Three
Whitehall Jewelers
Holdings, Inc* Filed 6/08 1/31 1/08 -2.12 Distress
1/07 -1.27 Distress
Zale Corp. Peer Firm 7/31 7/07 6.68 Safe
Pair Number Four
Shoe Pavilion, Inc. Filed 7/08 12/31 12/07 0.82 Distress
Bakers Footwear
Group, Inc. Peer Firm 1/31 1/08 -2.26 Distress
Brown Shoe Co., Inc.** Peer Firm 1/31 1/08 4.84 Safe
*Z” scores are calculated for two FYE due to the date of the bankruptcy filing
** Brown Shoe Co., Inc. was selected as a peer firm due to the distress rating of Bakers Footwear Group, Inc.
Economics & Business Journal:
Inquiries & Perspectives 131 Volume 3 Number 1 October 2010
11. Efficacy of Altman’s Z to Predict Bankruptcy Hayes, Hodge & Hughes
Our goal has not been to suggest
that Altman’s Z” is an end-all solution to References
predicting financial distress. However, we
do suggest that it resides in the manager’s Administrative Office of U.S. Courts. (2009).
Bankruptcy statistics. Information retrieved on
and investor’s toolbox for diagnosing the July, 2009, from
possibility of future financial distress in http://www.uscourts.gov/bnkrpctystats/bankru
firms. The Z” Score remains efficacious, and ptcystats.htm
it is also useful in public, non-manufacturing Alexeev, M., & Kim, S. (2008). The Korean financial
firms provided that other important data crisis and the soft budget constraint. Journal of
Economic Behavior & Organization, 68(1), 178-
are considered in parallel. As with any 193.
financial tool Altman’s Z should contribute Al-Sulaiti, K.I., & Almwajeh, O. (2007). Applying
to a decision and not be relied on as the Altman Z-score model of bankruptcy on service
litmus test for investment decisions. organizations and its implications on marketing
Although Altman's Z Score has been concepts and strategies. Journal of International
Marketing & Marketing Research, 32(2), 59-74.
employed as a distress metric in areas such Altman, E.I. (1968). Financial ratios, discriminant
as merger and acquisition analysis, credit analysis and prediction of corporate bankruptcy.
risk analysis, and turnaround management, Journal of Finance, 23(4), 189-209.
it has only recently been employed in Altman, E.I. (2006). Corporate financial distress and
performance management (Calandro, bankruptcy: Predict and avoid bankruptcy,
analyze and invest in distressed debt. Hoboken,
2007). However, the formula is not without NJ: Wiley.
its empirical support (Carton & Hofer, Altman, E.I., & Hotchkiss, E. (Eds.). (2006). Corporate
2006). financial distress and bankruptcy: predict and
Several variations of Altman’s Z avoid bankruptcy, analyze and invest in
rd
exist. Although, the formula has been distressed debt (3 ed.). Hoboken, NJ: Wiley.
Bakers Footwear Group Reports Fourth Quarter and
demonstrated to be fairly predictive in a Fiscal 2008 Results. (2009, April 15). Thomson
variety of contexts and cultural settings, it is Reuters. Information retrieved from
not designed to be used in every situation. http://www.reuters.com/article/pressRelease/
Variations of Altman’s Z encompass publicly idUS99202+15-Apr-2009+BW20090415.
versus privately held firms and Blockbuster gets going concern notice-SEC filing.
(2009, April 6). Thomson Reuters. Information
manufacturing versus privately held non- retrieved from
manufacturing firms. There is also the http://www.reuters.com/article/rbssRetailSpeci
question in extant literature as to whether alty/ idUSN0641432620090406
or not smaller firms require a different Calandro, J. (2007). Considering the utility of
formula. Altman’s Z has typically been used Altman’s Z-score as a strategic assessment and
performance management tool. Strategy &
to analyze firms with assets from $1 million Leadership, 35(5), 37-43.
to $100 million. Caoette, J.B., Altman, E.I., Narayanan, P., & Nimmo,
As a result of our findings, we R. (1998). Managing Credit Risk: The Great
suggest that further exploration of Altman’s Challenge for Global Financial Markets. Wiley.
Z score, and alternative formulas, is Carton, R.B., & Hofer, C.W. (2006). Measuring
organizational performance: Metrics for
necessary to refine this potentially useful entrepreneurship and strategic management
tool in order to develop a predictive research. Northampton, MA: Edward Elgar.
collection of tools useful in predicting not Chakravarthy, B.S. (1986). Measuring strategic
only bankruptcy, but financial distress in a performance. Strategic Management Journal,
variety of firms in a variety of contexts. 7(5), 437-458,
Economics & Business Journal:
Inquiries & Perspectives 132 Volume 3 Number 1 October 2010
12. Efficacy of Altman’s Z to Predict Bankruptcy Hayes, Hodge & Hughes
Ferrier, W.J., Mac Fhionnlaoich, C., Smith, K.G., & Moriarty, S. (1979). Communicating financial
Grimm, C.M. (2002). Impact of performance information through multidimensional graphics.
distress on aggressive competitive behavior: A Journal of Accounting Research, 17(1), 205-224.
reconciliation of conflicting views. Managerial & Moyer, S.G. (2005). Distressed debt analysis:
Decision Economics, 23, 301-316. Strategies for speculative investors. Fort
Grice, J.S., & Ingram, R.W. (2001). Tests of the Lauderdale, FL: Ross Publishing.
generalizability of Altman’s bankruptcy
prediction model. Journal of Business Research,
54(1), 53-61.
Kim, B.J. (2007). Bankruptcy prediction: Book value
or market value? Paper presented at 2007
APRIA Annual Meeting.
Economics & Business Journal:
Inquiries & Perspectives 133 Volume 3 Number 1 October 2010
13. Efficacy of Altman’s Z to Predict Bankruptcy Hayes, Hodge & Hughes
Appendix
Altman Z” Calculated Values
Company Name X1 X2 X3 X4
2007 Analyses
Hancock Fabrics, Inc. 0.4397 0.7223 -0.0731 0.4994
Jo-Ann Stores, Inc. 0.3900 0.2921 -0.0079 0.7296
Movie Gallery, Inc. -0.0250 -0.3846 0.0835 -0.1701
Blockbuster, Inc. 0.0501 -1.5255 0.0235 0.3000
Netflix, Inc. 0.3860 -0.0667 0.1071 2.1285
Tweeter Home Ent. Grp. 0.1520 -0.9296 -0.0530 0.3582
Best Buy Co., Inc. (3/07) 0.2049 0.4058 0.1473 0.8415
Harvey Electronics, Inc. 0.0522 -0.6696 -0.1652 0.0952
2008 Analyses
Circuit City Store, Inc. 0.2226 0.2619 -0.0944 0.6702
Best Buy Co., Inc. (08) 0.0449 0.3083 0.1694 0.5419
Sharper Image Corp. 0.0807 0.0311 -0.3731 0.8750
RadioShack Corp. 0.2973 0.8603 0.0758 0.4617
Whitehall Jewelers Hold., Inc. (07) 0.0013 -0.1223 -0.1253 -0.0184
Whitehall Jewelers Hold., Inc. (08) 0.2614 -0.5563 -0.3279 0.1747
Zale Corp. 0.4943 0.5379 0.0526 1.2688
Shoe Pavilion, Inc. 0.2587 -0.0671 -0.1692 0.4513
Bakers Footwear Grp., Inc. -0.1036 -0.1938 -0.2278 0.5517
Brown Shoe Co., Inc. 0.3030 0.3609 0.0878 1.0320
Economics & Business Journal:
Inquiries & Perspectives 134 Volume 3 Number 1 October 2010