Improving on the Altman Z-Score | My Assignment Tutor

Improving on the Altman Z-Score, part 2: The Ohlson O-Score Feb 13, 2013 by Jonathan Stokes No comments yet •35,970 Reads In this series of posts we’re exploring some of the alternatives to the 45 year old Altman Z-Score for successfully predicting bankruptcies. So far we’ve looked at the 2010 CHS model which is generally regarded as the best all-round replacement. Today we’re going to take a look at the 1980 Ohlson O-score, followed by the 1974 Merton ‘Distance-to-Default’ (DD) method in the third and final instalment. The O-score is still heavily referenced in academic literature and has its place in the arsenal of analysts around the world. All together it sounded like it was worth taking a closer look at. Ohlson O-score Similar to the Z-score, the O-score can be described as a statistical bankruptcy indicator generated from a set of balance sheet ratios. Where it differs from Altman’s original is in its application of a much larger sample of corporate successes and failures to inform the model. The wider pool of just over 2000 companies gives it a more robust sample for basing the scaling factors applied to its nine variables with the aim of increasing its accuracy. The difference in this sample size is especially apparent when compared to Altman’s original whose statistical technique of pair matching limited him to just 66 companies (it’s amazing it’s still as successful as it is!). Subsequent studies have generally found the O-score to be a better forecaster of bankruptcy than the Z-score, however neither has been able to regularly beat Merton’s DD or the CHS model since their discoveries. How It Works To begin with, let’s take a look at each of the variables and think about why they’ve been included. Adjusted Size: Ohlson measures a company’s size as its total assets adjusted for inflation. Smaller companies are deemed to be more at risk of failure.AS = log(Total assets/GNP price-level index)Where GNP price-level index = (Nominal GNP/Real GNP)*100Leverage Measure: Designed to capture the indebtedness of a company, the more leveraged the more at risk the company is to shocks.LM = Total liabilities/Total assetsWorking Capital Measure: Even if a company is endowed with assets and profitability, it must have sufficient liquidity to service short-term debt and upcoming operational expenses to avoid going bust.WCM = Working capital/Total AssetsInverse Current Ratio: This is another measure of a company’s liquidity.ICR = Current liabilities/Current assetsDiscontinuity Correction for Leverage Measure: Dummy variable equalling one if total liabilities exceeds total assets, zero otherwise. Negative book value in a corporation is a very special case and hence Ohlson felt the extreme leverage position needed to be corrected through this additional variable.Return on Assets: An indicator of how profitable a company is, assumed to be negative for a close to default company.ROA = Net income/Total AssetsFunds to Debt Ratio: A measure of a company’s ability to finance its debt using its operational income alone, a conservative ratio because it does not include other sources of cash. If the ratio of funds from operations to short-term debt is less than one the company may have an immediate problem.FTDR = Funds from operations/Total liabilitiesWhere Funds from operations = pretax income + depreciationDiscontinuity Correction for Return on Assets: Dummy variable equalling one if income was negative for the last two years, zero otherwise.Change in Net Income: Designed to take into account any potential progressive losses over the two most recent periods in a company’s history.CINI = (Net income(t) – Net income(t-1)) / (Net income(t) + Net income(t-1)) Why these variables? Ohlson’s true explanation for choosing these variables is actually rather candid and surprising; he says his main driver was simplicity and that there was no attempt to select predictors on the basis of rigorous theory. The first six were chosen just because he perceived them to be the ones most frequently mentioned in previous literature. At least he’s honest about it! Having calculated our variables, we would then need to enter them into the logit model he provides to find the O-score. The model is based on the data of industrial firms between the years 1970-1976 that had been trading on the exchange for at least three years. It gave him a sample of just over 2000 firms of which 135 had failed. Shown below is an example of the formula for ‘Model 1’ of the O-score, the model used to predict failure within 12 months. O-score = -1.32 – 0.407*AS + 6.03*LM – 1.43*WCM + 0.757*ICR – 2.37*ROA – 1.83*FTDR -1.72*DCLM + 0.285*DCRA – 0.521*CINIProbability of Failure = P = exp(O-score)/1+exp(O-score) The O-score is transformed into a probability using a logistic transformation whereby P>0.5 indicates an at risk company and P

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