what is the ohlson o-score

The Ohlson O-score is a bankruptcy-prediction model published by James Ohlson in 1980. It combines nine financial measures — company size, total liabilities to total assets, working capital to total assets, current liabilities to current assets, net income to total assets, funds from operations to total liabilities, and a couple of indicator flags for negative equity and recent losses — into a single figure. That figure is then converted through a logistic function into an estimated probability that the company will go bankrupt within a defined period. It leans heavily on leverage and profitability.

how is the ohlson o-score different from the altman z-score

They predict the same thing by different statistics. The Altman Z-score uses multiple discriminant analysis on five ratios and produces a score you read against fixed zones — safe, grey, distress. The Ohlson O-score uses logistic (logit) regression on nine inputs and produces an actual probability of bankruptcy, so its output is a percentage rather than a zone. Ohlson’s model also weights leverage and liability structure more, and includes flags for negative net worth and back-to-back losses that the Z-score has no direct equivalent of. Using both gives you two independent statistical reads.

how do you interpret the ohlson o-score

The raw O-score is fed into a logistic function to give a probability between 0 and 1; a common cut-off treats a resulting probability above 0.5 as flagging likely financial distress, with higher values meaning higher estimated bankruptcy risk. But the exact threshold is a convention, not a law, and the model’s coefficients were fitted to a specific historical dataset — so read the output as a relative warning light that says "look harder here", not as a precise verdict that a company will or will not fail.

is the ohlson o-score reliable for indian stocks

Treat it with caution. Ohlson built the model on 1970s US industrial firms, and its coefficients and one size term were calibrated to that data and economy, so applied unchanged to Indian companies it is indicative at best. It also does not fit banks, NBFCs and insurers, whose balance sheets are structurally different — the same exclusion that applies to the Altman Z-score. Use it as one screen among several, alongside the Z-score, the cash-flow statement and the debt schedule, rather than as a standalone judgement of survival.

Ohlson O-score: how it works and how it differs from Altman Z

By now you have met the Altman Z-score for distress and the Beneish M-score for manipulation. The Ohlson O-score belongs to the same family of statistical screens, and it tackles the same question as the Z-score — will this company go bankrupt? — but arrives at its answer by a different route and reports it in a different currency: a probability rather than a zone.

Why a second model is worth having

No single distress model is definitive — each was fitted to a particular sample and each emphasises different parts of the accounts. The Z-score’s discriminant approach and the O-score’s logit approach can disagree, and that disagreement is itself information: a company one model waves through while the other flags deserves a closer look at whichever weaknesses the flagging model reacted to. The O-score’s heavier weighting of total-liabilities-to-assets and its explicit negative-equity and repeated-loss flags mean it often catches a balance sheet that is quietly hollowing out.

Check yourself

What is the key difference in output between the Ohlson O-score and the Altman Z-score?

Simple bhasha mein

Nau number se bankruptcy ka odds

Altman Z-score ek score aur zone deta hai. Ohlson O-score (1980) thoda alag: nau financial inputs ko ek logit (logistic) model mein daalke bankruptcy ki probability deta hai — yaani zone nahi, percentage. Inputs mein leverage aur liability structure ka bhaari weight, plus negative-equity aur repeated-loss ke flags. Z-score discriminant maths se score deta; O-score logit se probability — same sawaal, alag statistics, achha second opinion. ~0.5 se upar common distress flag, par threshold convention hai, kanoon nahi. Coefficients 1970s US data pe fitted aur banks/NBFC pe nahi chalta (Z-score jaisa hi), toh indicative hai — Z-score, cash flows aur debt schedule ke saath use karo, akela verdict nahi.

What to remember

The Ohlson O-score (1980) estimates a probability of bankruptcy from nine financial inputs.
It uses a logistic model and outputs a percentage, unlike the Z-score’s score-and-zone.
It weights leverage and liability structure heavily and flags negative equity and repeated losses.
A probability above ~0.5 is a common distress flag, but the threshold is a convention, not a law.
Coefficients are 1970s US-calibrated and it excludes financials — use it alongside the Z-score and the cash flows.

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what is the ohlson o-score: The Ohlson O-score is a bankruptcy-prediction model published by James Ohlson in 1980. It combines nine financial measures — company size, total liabilities to total assets, working capital to total assets, current liabilities to current assets, net income to total assets, funds from operations to total liabilities, and a couple of indicator flags for negative equity and recent losses — into a single figure. That figure is then converted through a logistic function into an estimated probability that the company will go bankrupt within a defined period. It leans heavily on leverage and profitability.
how is the ohlson o-score different from the altman z-score: They predict the same thing by different statistics. The Altman Z-score uses multiple discriminant analysis on five ratios and produces a score you read against fixed zones — safe, grey, distress. The Ohlson O-score uses logistic (logit) regression on nine inputs and produces an actual probability of bankruptcy, so its output is a percentage rather than a zone. Ohlson’s model also weights leverage and liability structure more, and includes flags for negative net worth and back-to-back losses that the Z-score has no direct equivalent of. Using both gives you two independent statistical reads.
how do you interpret the ohlson o-score: The raw O-score is fed into a logistic function to give a probability between 0 and 1; a common cut-off treats a resulting probability above 0.5 as flagging likely financial distress, with higher values meaning higher estimated bankruptcy risk. But the exact threshold is a convention, not a law, and the model’s coefficients were fitted to a specific historical dataset — so read the output as a relative warning light that says "look harder here", not as a precise verdict that a company will or will not fail.
is the ohlson o-score reliable for indian stocks: Treat it with caution. Ohlson built the model on 1970s US industrial firms, and its coefficients and one size term were calibrated to that data and economy, so applied unchanged to Indian companies it is indicative at best. It also does not fit banks, NBFCs and insurers, whose balance sheets are structurally different — the same exclusion that applies to the Altman Z-score. Use it as one screen among several, alongside the Z-score, the cash-flow statement and the debt schedule, rather than as a standalone judgement of survival.

The Ohlson O-score: bankruptcy odds from nine numbers

Why a second model is worth having

Common questions