Ohlson o-score

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The Ohlson O-Score for predicting bankruptcy is a multi-factor financial formula postulated in 1980 by Dr. James Ohlson of the New York University Stern Accounting Department as an alternative to the Altman Z-score for predicting financial distress. [1]

New York University private research university in New York, NY, United States

New York University (NYU) is a private research university based in New York City. Founded in 1831, NYU's historical campus is in Greenwich Village, Lower Manhattan. NYU also has degree-granting campuses in Abu Dhabi and Shanghai, and academic centers in Accra, Berlin, Buenos Aires, Florence, London, Los Angeles, Madrid, Paris, Prague, Sydney, Tel Aviv, and Washington, D.C.

Altman Z-score

The Z-score formula for predicting bankruptcy was published in 1968 by Edward I. Altman, who was, at the time, an Assistant Professor of Finance at New York University. The formula may be used to predict the probability that a firm will go into bankruptcy within two years. Z-scores are used to predict corporate defaults and an easy-to-calculate control measure for the financial distress status of companies in academic studies. The Z-score uses multiple corporate income and balance sheet values to measure the financial health of a company.

Contents

Calculation of the O-Score

The Ohlson O-Score is the result of a 9-factor linear combination of coefficient-weighted business ratios which are readily obtained or derived from the standard periodic financial disclosure statements provided by publicly traded corporations. Two of the factors utilized are widely considered to be dummies as their value and thus their impact upon the formula typically is 0. [2] When using an O-Score to evaluate the probability of company’s failure, then exp(O-Score) is divided by 1 + exp(O-score). [3]

In mathematics, a coefficient is a multiplicative factor in some term of a polynomial, a series, or any expression; it is usually a number, but may be any expression. In the latter case, the variables appearing in the coefficients are often called parameters, and must be clearly distinguished from the other variables.

The calculation for Ohlson’s O-Score appears below: [4]

where

Liability (financial accounting) future sacrifices of economic benefits that an entity is obliged to make to other entities as a result of past transactions or other past events

In financial accounting, a liability is defined as the future sacrifices of economic benefits that the entity is obliged to make to other entities as a result of past transactions or other past events, the settlement of which may result in the transfer or use of assets, provision of services or other yielding of economic benefits in the future.

Working capital is a financial metric which represents operating liquidity available to a business, organisation or other entity, including governmental entities. Along with fixed assets such as plant and equipment, working capital is considered a part of operating capital. Gross working capital is equal to current assets. Working capital is calculated as current assets minus current liabilities. If current assets are less than current liabilities, an entity has a working capital deficiency, also called a working capital deficit.

Net income Measure of the profitability of a business venture

In business and accounting, net income is a measure of the profitability of a venture. It is an entity's income minus cost of goods sold, expenses, depreciation and amortization, interest, and taxes for an accounting period. It is computed as the residual of all revenues and gains over all expenses and losses for the period, and has also been defined as the net increase in shareholders' equity that results from a company's operations. It is different from the gross income, which only deducts the cost of goods sold.

Interpretation

The original model for the O-Score was derived from the study of a pool of just over 2000 companies, whereas by comparison its predecessor the Altman Z-Score considered just 66 companies. As a result, the O-Score is significantly more accurate a predictor of bankruptcy within a 2-year period. The original Z-Score was estimated to be over 70% accurate with its later variants reaching as high as 90% accuracy. The O-Score is more accurate than this.

However, no mathematical model is 100% accurate, so while the O-Score may forecast bankruptcy or solvency, factors both inside and outside of the formula can impact its accuracy. Furthermore, later bankruptcy prediction models such as the hazard based model proposed by Campbell, Hilscher, and Szilagyi in 2011 [5] have proven more accurate still. For the O-Score, any results larger than 0.5 suggest that the firm will default within two years.

Bankruptcy prediction is the art of predicting bankruptcy and various measures of financial distress of public firms. It is a vast area of finance and accounting research. The importance of the area is due in part to the relevance for creditors and investors in evaluating the likelihood that a firm may go bankrupt.

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Edward I. Altman is a Professor of Finance, Emeritus, at New York University's Stern School of Business. He is best known for the development of the Altman Z-score for predicting bankruptcy which he published in 1968. Professor Altman is a leading academic on the High-Yield and Distressed Debt markets and is the pioneer in the building of models for credit risk management and bankruptcy prediction. He is the brother of Stuart Altman, a noted health care economist.

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References

  1. ↑ "Ohlson's O-Score Definition". ycharts.com. Retrieved 2014-06-12.
  2. ↑ Stokes, Jonathan (13 February 2013). "Improving On The Altman Z-Score, Part 2: The Ohlson O-Score". stockopedia.com. Retrieved 2014-06-12.
  3. ↑ Mitchell, Karlyn; Walker, Mark D. (7 January 2008). "Bankers on Boards, Financial Constraints, and Financial Distress (Preliminary and incomplete. Please do not quote.)" (PDF). Retrieved 2014-06-12.
  4. ↑ James A. Ohlson. "Financial Ratios and the Probabilistic Prediction of Bankruptcy" (PDF). Retrieved 2014-08-13.
  5. ↑ Campbell, John Y.; Hilscher, Jens; Szilagyi, Jan. "Predicting financial distress and the performance of distressed stocks". CiteSeerX   10.1.1.233.2093 .Cite journal requires |journal= (help)
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