⚡ Quick AI Summary
- What it is: The Ohlson O-Score is a 9-factor logistic regression model created by James Ohlson (1980) to predict the statistical probability of a company going bankrupt within 2 years.
- How it works: It takes core balance sheet inputs (Assets, Liabilities, Net Income, Working Capital, etc.) and multiplies them by specific statistical coefficients to output an exact percentage risk (0% to 100%).
- O-Score Range: A probability below 38% is considered safe. A probability above 50% classifies the company as highly distressed and at significant risk of default.
The Complete Guide to Predicting Corporate Bankruptcy
- What is the Ohlson O-Score Calculator?
- What the Ohlson O-Score is NOT (Guardrails)
- The Ohlson Model Variables (Glossary)
- Bankruptcy Risk by Industry (Predictive Scenarios)
- 2026 Average Ohlson O-Score Benchmarks
- Ohlson O-Score vs Altman Z-Score (Comparison)
- Why Use Our Web Calculator vs Excel Templates?
- Add This Financial Tool to Your Website
- Things People Usually Ask
What is the Ohlson O-Score Calculator?
The Ohlson O-Score is a 9-factor logistic regression model developed by Dr. James Ohlson in 1980 to predict the exact probability of corporate bankruptcy. It analyzes a company's leverage, liquidity, profitability, and size to generate a default percentage ranging from 0% to 100%, with scores over 50% indicating severe financial distress.
The free Ohlson O-Score calculator is a highly sophisticated financial modeling tool used by analysts, investors, and creditors to assess whether a corporation will default on its debt obligations within a two-year timeframe. Ohlson evaluated a massive dataset of distressed firms and published his findings in a highly cited research paper, realizing that existing models (like Altman's) were statistically flawed.
Instead of relying on a static index number, his model uses conditional logit modeling. The fundamental advantage of a bankruptcy prediction probability tool like this is that it outputs a direct percentage—making it vastly easier to interpret the absolute level of financial distress a company is facing without relying on arbitrary "zones."
What the Ohlson O-Score is NOT
To use the Ohlson formula accurately, financial analysts must understand its strict mathematical boundaries. The Ohlson O-Score is:
- NOT a Stock Price Predictor: It measures the likelihood of insolvency, not whether a stock is undervalued or overvalued for investment purposes.
- NOT for Banks or Financial Institutions: You cannot calculate an accurate score for commercial banks because they inherently operate with massive regulatory leverage (holding customer deposits as liabilities).
- NOT Dependent on Market Equity: Unlike the Altman Z-Score, the Ohlson O-Score does NOT use the market capitalization of a company, making it the perfect bankruptcy risk calculator for retail startups and private enterprises.
The Ohlson Model Variables (Glossary)
To truly understand what this predictive model is doing, you must understand the math. The Ohlson O-Score formula coefficients and variables were meticulously derived from thousands of bankruptcies. Our calculator automatically applies these 9 factors to generate your score:
- SIZE (Coefficient: -0.407)
- The natural logarithm of Total Assets divided by the GNP price level index. Larger firms historically have lower default rates.
- TLTA (Coefficient: 6.03)
- Total Liabilities to Total Assets. The go-to measure of leverage. Higher ratios heavily penalize the score, increasing bankruptcy risk.
- WCTA (Coefficient: -1.43)
- Working Capital to Total Assets. Positive working capital decreases bankruptcy risk.
- CLCA (Coefficient: 0.0757)
- Current Liabilities to Current Assets. A reciprocal look at short-term liquidity stress.
- OENEG (Coefficient: -1.72)
- A binary technical insolvency flag. If total liabilities exceed total assets, it is assigned a 1, acting as a massive penalty multiplier.
- NITA (Coefficient: -2.37)
- Net Income to Total Assets (ROA). High profitability relative to asset size strongly defends against insolvency.
- FUTL (Coefficient: -1.83)
- Funds from Operations to Total Liabilities. High cash flow protects against default.
- INTWO (Coefficient: 0.285)
- A binary consecutive loss flag. Triggered if the firm reported negative net income for the past two years sequentially.
- CHIN (Coefficient: -0.521)
- Change in Net Income. A sharp drop in income increases risk, while stabilizing income reduces it.
Bankruptcy Risk by Industry (Predictive Scenarios)
Select any of the financial profiles below to instantly load industry-specific capital structures and visualize how leverage, liquidity, and asset size interact to produce varying default probabilities.
Startups & Tech
Retail Startup Insolvency 🛍️ → Software Firm Default Risk 💻 → Pre-Revenue Tech Risk ⚠️ → E-commerce Cash Burn 🔥 → Venture-Backed O-Score 🚀 → Fintech Default Model 💳 →2026 Average Ohlson O-Score Benchmarks
Interpreting the Ohlson O-score range requires understanding specific statistical thresholds and industry context. Because the result is a logistic probability, it is strictly bound between 0% and 100%.
| Industry / Sector | Average O-Score Prob. | Typical Risk Classification |
|---|---|---|
| Software / SaaS | 5% - 15% | Low Risk (Safe) |
| Healthcare & Pharma | 12% - 25% | Low Risk (Safe) |
| Retail & Consumer Goods | 25% - 40% | Moderate (Grey Zone) |
| Manufacturing & Industrials | 30% - 45% | Moderate (Grey Zone) |
| Real Estate (Highly Leveraged) | 45% - 60%+ | High Risk (Distressed) |
| Oil & Gas (Cyclical) | 40% - 70%+ | High Risk (Distressed) |
The Threshold Rules: Dr. Ohlson's original dataset suggested that a probability below 38% represents a financially sound company. If the probability falls between 38% to 50%, the firm exhibits structural warning signs. An score exceeding 50% means the logistical model predicts the company is more likely than not to face bankruptcy proceedings within two years.
Ohlson O-Score vs Altman Z-Score (Comparison)
When analysts search for bankruptcy tools, they frequently compare the Ohlson O-Score vs Altman Z-Score. While Edward Altman pioneered prediction in 1968, James Ohlson addressed critical statistical flaws in that original model. Here is a clear breakdown of why Ohlson is often considered superior for modern analysis:
| Feature | Altman Z-Score (1968) | Ohlson O-Score (1980) |
|---|---|---|
| Statistical Method | Multiple Discriminant Analysis (MDA) | Logistic Regression (Logit) |
| Final Output Format | Static Index Number (e.g., 2.5) | Exact Probability Percentage (e.g., 18%) |
| Variables Used | 5 Factors | 9 Comprehensive Factors |
| Public vs Private Firms | Requires Market Value of Equity | Uses Book Value (Perfect for Private) |
| Distribution Assumption | Assumes normal distribution | No normal distribution assumption |
Why Use Our Web Calculator vs Excel Templates?
Many finance students and professionals waste hours searching for an Ohlson o-score excel spreadsheet or a clunky downloadable PDF template. Here is why our free web-based calculator is the superior choice for your workflow:
- No Mathematical Errors: Building the logistic regression formula ($e^O / (1 + e^O)$) in Excel often leads to misplaced parenthesis and catastrophic calculation errors. Our backend code handles the math flawlessly.
- Instant Visualizations: Our tool automatically generates Waterfall and Radar charts, allowing you to instantly present risk vectors to clients or management without building graphs from scratch.
- Mobile Accessibility: You can check a company's default risk directly from your smartphone during a meeting, something impossible to do smoothly with an Ohlson o-score pdf or spreadsheet.
Add This Financial Tool to Your Website
Do you run a financial blog, an accounting firm website, or an investment research portal? Provide your readers and clients with elite institutional tools. Add this secure, fast Ohlson O-Score Calculator directly onto your web pages.
Things People Usually Ask
Expert answers to the most common queries regarding corporate insolvency modeling, formula coefficients, and financial risk assessment.
What is the Ohlson O-Score model?
The Ohlson O-Score is a multifactor financial model developed by James Ohlson in 1980. It uses logistic regression based on 9 core financial variables to predict the exact probability of a company going bankrupt within the next two years.
What is a good Ohlson O-Score?
You are evaluating the final percentage probability rather than the raw score. An Ohlson default probability below 38% is statistically considered safe and healthy. If the probability crosses the 50% threshold, the company is officially classified as highly distressed and at significant risk of bankruptcy.
How is the Ohlson O-Score calculated?
It is calculated by multiplying 9 financial ratios (like Net Income to Total Assets, and Total Liabilities to Total Assets) by their respective statistical coefficients, adding a constant (-1.32), and then passing that sum through a logistic function (Euler's number) to produce a probability percentage strictly bounded between 0% and 100%.
What is GNP in the Ohlson O-Score?
The Gross National Product (GNP) price index was originally used by Dr. Ohlson to adjust Total Assets for inflation, standardizing the "Size" variable across different historical economic periods. In modern web calculators, if the exact current index is unknown, leaving the multiplier at 1.0 is standard practice for quick comparative estimations.
What are the 9 variables and coefficients in the Ohlson formula?
The 9 variables include Size (TA/GNP), Leverage (TL/TA), Liquidity (WC/TA and CL/CA), Profitability (NI/TA and FFO/TL), Income Volatility (Change in NI), and two binary flags for Technical Insolvency and Consecutive Losses. These variables are multiplied by specific statistical coefficients ranging from -2.37 to +6.03.
How accurate is the Ohlson O-Score in predicting bankruptcy?
Extensive academic back-testing shows the Ohlson O-Score is highly accurate, often predicting corporate distress with over 80% to 90% accuracy within a 2-year window. It outperforms many older models precisely because of its use of conditional logit modeling rather than rigid discriminant analysis.
Ohlson O-Score vs Altman Z-Score: Which is better?
The Ohlson O-Score is generally considered statistically superior for modern analysis. Unlike the Altman Z-score which outputs an arbitrary index number into "zones", the Ohlson model yields an exact percentage probability of default. Additionally, Ohlson does not require the market value of equity, making it perfectly usable for private companies.
Can this model be used for manufacturing vs banking firms?
The Ohlson model is highly accurate for industrial, manufacturing, and retail corporations. However, it should NOT be used for banking or financial institutions. Banks inherently operate with massive leverage (holding customer deposits as liabilities), which will universally output a falsely high and inaccurate bankruptcy probability.