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Four Decades Of Data Show EBITDA Is The Best Valuation Metric

By Rupert Hargreaves. Originally published at ValueWalk.

Mirror, mirror on the wall, which is the best valuation metric of them all?

I think that, every time you saw the word EBITDA [earnings], you should substitute the word “bullshit” earnings. – Charlie Munger

Charlie Munger famously isn’t a fan of EBITDA earnings, but if a new research paper from Columbia Business School is to be believed, investors should not be so quick in disregarding this financial metric.

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Published last month, the research paper evaluates and compares the informativeness of EBITDA, EBITA, and EBIT in the context of equity valuation using multiples of enterprise value. What the author, Doron Nissim finds is that over the sample period of 1987 to 2016, EBITDA performed better than EBITA in predicting stock returns and EBITA, in turn, performed better than EBIT. However, while EBITDA has shown a predictive power, its power of predicting returns has deteriorated especially relative to EBITA, which has seen an improvement in accuracy since the financial crisis and for companies from high amortization intensity industries.

 EBITDA Is The Best Valuation Metric Says New Study

This isn’t the first study to reach such a conclusion. In 2011 Wesley Grey and Jack Vogel presented evidence showing that the EBITDA/TEV (total enterprise value) is the best valuation metric to use as an investment strategy relative to other valuation metrics. Using data from 1971 through 2010 the duo found that the returns of an “annually rebalanced equal-weight portfolio of high EBITDA/TEV stocks, earn 17.66% a year, with a 2.91% annual 3-factor alpha.” The study also finds that when analyzing the spread in returns between the cheapest and most expensive stocks, given a specific valuation measure, EBITDA/TEV once again turns out to be the most effective measure. Using data from the four decade study period the “lowest quintile returns based on EBITDA/TEV return 7.97% a year versus the 17.66% for the cheapest stocks—a spread of 9.69%. This compares very favorably to the spread created by E/M, which is only 5.82% (9.41%for the expensive quintile and 15.23% for the cheap quintile).”

What’s really fascinating about both of these studies is that they both use many decades of data and are likely to be more accurate as a result. Considering the level of skepticism surrounding EBITDA, it’s difficult to pin down exactly why this anomaly exists. But it’s difficult to argue against four decades of data from two independent studies.

Nissim, Doron, EBITDA, EBITA, or EBIT? (June 30, 2017). Available at SSRN: https://ssrn.com/abstract=2999675

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