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Altman Z-Score and Ohlson O-Score are quantitative models used to predict corporate bankruptcy risk by analyzing financial statement data. While the Altman Z-Score focuses on profitability, leverage, liquidity, solvency, and activity ratios, the Ohlson O-Score incorporates a logistic regression approach considering size, financial structure, and performance indicators. Explore detailed comparisons and practical applications of these scoring models to enhance your financial risk assessment skills.
Main Difference
The Altman Z-Score primarily measures a company's likelihood of bankruptcy within two years by analyzing profitability, leverage, liquidity, solvency, and activity ratios. The Ohlson O-Score employs logistic regression to predict the probability of bankruptcy within one year, integrating financial ratios such as size, financial structure, performance, and current liquidity. Unlike the Z-Score's linear combination of weighted variables, the O-Score uses a probabilistic model with nine predictive variables, including total liabilities to total assets and working capital to total assets ratios. Altman Z-Score is better suited for manufacturing firms, whereas the Ohlson O-Score is designed for a broader range of industries.
Connection
Altman Z-Score and Ohlson O-Score are both financial models designed to predict corporate bankruptcy risk through quantitative analysis of financial ratios and balance sheet data. The Altman Z-Score utilizes multiple weighted financial ratios such as working capital, retained earnings, and EBIT relative to total assets to assess a firm's bankruptcy likelihood, primarily focusing on manufacturing firms. The Ohlson O-Score incorporates a logistic regression approach with variables like total liabilities to total assets, firm size, and net income, broadening applicability across industries and enhancing predictive accuracy in early distress detection.
Comparison Table
| Aspect | Altman Z-Score | Ohlson O-Score |
|---|---|---|
| Purpose | Predicts the likelihood of corporate bankruptcy within two years. | Estimates the probability of company failure within one year using logistic regression. |
| Model Type | Discriminant analysis model combining multiple financial ratios. | Logistic regression model using financial ratios and accounting data. |
| Key Financial Ratios and Variables |
- Working Capital / Total Assets - Retained Earnings / Total Assets - Earnings Before Interest & Taxes / Total Assets - Market Value of Equity / Book Value of Total Liabilities - Sales / Total Assets |
- Size (Total Assets) - Total Liabilities / Total Assets - Working Capital / Total Assets - Current Liabilities / Current Assets - Net Income (loss) to Total Assets, among others |
| Output | Z-Score numerical value indicating financial distress risk categories (safe, gray zone, distressed). | Probability score (0 to 1) representing likelihood of bankruptcy. |
| Industry Applicability | Initially developed for publicly traded manufacturing firms but adapted for various industries. | Applicable primarily to industrial firms, accounting for broader financial situations. |
| Advantages |
- Simple and straightforward to calculate. - Widely used and understood by practitioners and academics. - Effective for early detection of financial distress. |
- Incorporates additional variables for enhanced predictive power. - Uses a probabilistic approach allowing nuanced interpretation. - Effective in short-term bankruptcy prediction. |
| Limitations |
- Less accurate for non-manufacturing or private firms. - Static model may not capture changing economic conditions. |
- More complex calculation requiring detailed data. - May require industry-specific adjustments for accuracy. |
| Developer and Year | Edward I. Altman, 1968 | James A. Ohlson, 1980 |
Bankruptcy Prediction Models
Bankruptcy prediction models employ financial ratios, cash flow data, and market indicators to assess a company's risk of insolvency with high accuracy. The Altman Z-score, developed by Edward Altman in 1968, remains one of the most widely used models, combining profitability, leverage, liquidity, solvency, and activity ratios. Recent machine learning approaches, including random forests and neural networks, have enhanced predictive performance by analyzing complex patterns in large datasets. Timely identification of financial distress supports creditors, investors, and regulators in mitigating potential losses and improving risk management strategies.
Financial Ratios
Financial ratios are key performance indicators used to evaluate a company's financial health by comparing relevant line items from financial statements. Common ratios include liquidity ratios like the current ratio, profitability ratios such as return on equity (ROE), and leverage ratios including the debt-to-equity ratio. These ratios provide insights into operational efficiency, solvency, and profitability by analyzing income statements, balance sheets, and cash flow statements. Investors and analysts rely on financial ratios to make informed decisions, benchmark industry performance, and assess risk levels.
Default Risk Assessment
Default risk assessment in finance involves evaluating the likelihood that a borrower will fail to meet debt obligations, which is critical for lenders and investors. Techniques include analyzing credit scores, financial ratios such as debt-to-income and interest coverage ratios, and employing statistical models like logistic regression and machine learning algorithms. Agencies like Moody's, Standard & Poor's, and Fitch Ratings provide credit ratings that reflect default probabilities, influencing bond yields and loan interest rates. Accurate default risk assessment helps mitigate potential losses and informs decision-making in credit issuance and portfolio management.
Multivariate Discriminant Analysis
Multivariate Discriminant Analysis (MDA) is widely applied in finance for credit risk assessment, bankruptcy prediction, and portfolio selection. It uses multiple financial ratios and market indicators to classify firms or investments into distinct risk categories by maximizing the separation between groups. Key variables often include debt-to-equity ratios, return on assets, and liquidity measures, which improve the predictive accuracy of financial distress models. Empirical studies show that MDA models can achieve classification accuracies exceeding 85% when applied to corporate financial health evaluations.
Logistic Regression Model
Logistic regression models are widely utilized in finance to predict binary outcomes such as credit default, loan approval, and fraud detection. These models estimate the probability that a financial event occurs by analyzing independent variables like customer demographics, transaction history, and economic indicators. Logistic regression's interpretability and efficiency make it ideal for risk assessment and decision-making processes in banking and investment sectors. Financial institutions increasingly rely on these models to enhance portfolio management and optimize credit scoring systems.
Source and External Links
Comparative Analysis: Altman Z score and Ohlson O ... - FasterCapital - The Altman Z-score uses multiple discriminant analysis with financial ratios like working capital and sales, while the Ohlson O-score employs logistic regression including market data like stock prices, with both predicting bankruptcy risk but varying by industry and data focus.
Ohlson O-score Model - What It Is, Applications, How To Calculate? - The Ohlson O-score is a logistic regression model using nine financial ratios to assess a company's probability of financial distress or bankruptcy within two years, widely used by investors and analysts.
Ohlson O-score - Wikipedia - Developed as an alternative to the Altman Z-score, the Ohlson O-score combines nine weighted financial ratios from company disclosures to predict bankruptcy risk, with the final score convertible into a failure probability.
FAQs
What is the Altman Z-Score?
The Altman Z-Score is a financial metric used to predict the likelihood of a company going bankrupt within two years by analyzing five key financial ratios related to profitability, liquidity, leverage, solvency, and activity.
What is the Ohlson O-Score?
The Ohlson O-Score is a financial metric designed to predict the probability of a company's bankruptcy within two years based on a combination of nine financial ratios and variables.
How is the Altman Z-Score calculated?
The Altman Z-Score is calculated using the formula: Z = 1.2*(Working Capital / Total Assets) + 1.4*(Retained Earnings / Total Assets) + 3.3*(EBIT / Total Assets) + 0.6*(Market Value of Equity / Total Liabilities) + 1.0*(Sales / Total Assets).
How is the Ohlson O-Score calculated?
The Ohlson O-Score is calculated using a logistic regression model combining nine financial ratios and indicators: size (log of total assets), total liabilities/total assets, working capital/total assets, current liabilities/current assets, indicators for negative retained earnings and net income, funds provided by operations/total liabilities, a measure of financial leverage, and a dummy variable for whether the firm had net losses in the last two years. The formula is: O-Score = -1.32 - 0.407 * log(total assets) + 6.03 * (total liabilities/total assets) - 1.43 * (working capital/total assets) + 0.0757 * (current liabilities/current assets) - 1.72 * (indicator for negative retained earnings) - 2.37 * (indicator for net income < 0) - 1.83 * (funds from operations/total liabilities) + 0.285 * (measure of financial leverage) - 0.521 * (indicator if net losses in last two years). The resulting score is then transformed via logistic function to estimate the probability of bankruptcy.
What are the main differences between Altman Z-Score and Ohlson O-Score?
The Altman Z-Score uses five financial ratios focusing on profitability, leverage, liquidity, solvency, and activity to predict bankruptcy risk primarily for manufacturing firms, while the Ohlson O-Score incorporates nine variables including size, financial structure, and operational measures to assess bankruptcy probability across various industries.
When should you use Altman Z-Score vs Ohlson O-Score?
Use the Altman Z-Score to assess bankruptcy risk for publicly traded manufacturing firms, and apply the Ohlson O-Score for predicting bankruptcy in both public and private firms across various industries.
What are the limitations of both Altman Z-Score and Ohlson O-Score?
Altman Z-Score is limited by its reliance on historical financial data, reduced accuracy for non-manufacturing firms, and sensitivity to accounting practices, while Ohlson O-Score may suffer from model overfitting, exclusion of non-financial predictors, and less effectiveness for large, diversified companies.