Impairment of assets is something no investor likes to see. Impairments can catch people by surprise and blow up an earnings statement with no warning.
Or what if you could measure that potential risk?
Not the risk of a specific asset being impaired; that takes in-depth analysis over time. We were more curious whether you could measure the potential damage to earnings that an impairment might cause — regardless of the asset getting impaired or the dollar value of the impairment.
Here’s what we did.
Using our Multi-Company search page, we examined the goodwill and right-of-use assets listed by the S&P 500. We also then pulled net income and diluted shares outstanding to calculate earnings per share for third-quarter 2019.
Then we asked: What would happen to EPS if goodwill and ROU assets were impaired by 1 percent?
Mathematically that’s a straightforward exercise. You just subtract 1 percent of goodwill and ROU assets from net income and then recalculate EPS. That’s a measure of how sensitive a firm’s earnings are to impairment.
So we did that, measuring firms’ sensitivity to impairments of 1, 5, and 10 percent.
Below are 10 firms with high impairment sensitivity. That is, even impairments of only 1 percent would lead to relatively large swings in EPS. For example, if Macy’s ($M) declared an impairment of only $65 million, that would have caused a 20-fold drop in its EPS.
To be clear, other firms would suffer larger EPS declines in absolute terms. For example, if Charter Communications ($CHTR) declared a 1 percent impairment on its goodwill and ROU assets, that would be a charge of $306 million and cut net income by $1.38 EPS. But because Charter reported EPS of $2.10 in the third quarter, that actually cuts EPS by only 65 percent.
Nobody would like that, but in relative terms it’s nowhere near the wipeout that any firms in Table 1, above, would experience.
Putting Impairment Sensitivity to Use
OK, so an impairment sensitivity test exists — what do you do with it?
By modeling a firm’s potential exposure to impairment, that can help financial analysts sharpen the questions they want to ask about management strategy.
For example, if a highly impairment sensitive company is carrying lots of goodwill on the books — say, it’s a highly acquisitive firm that’s collected numerous brands over the years — that can inform questions you might ask management about what it plans to do with those brands, or whether integration plans are moving along well enough to justify whatever the company paid in goodwill. (Don’t forget, you can also look up purchase price allocation on Calcbench to determine how much of an acquisition went to goodwill and other assets.)
Or if you’re following a retailer that has high impairment sensitivity thanks to lots of leased ROU assets, you might ask more pointed questions about customer traffic or potential sub-lease value if the retailer wants to shutter its stores.
Our point is only that the data does exist to model impairment sensitivity. All you need is the data, Calcbench has that in spades.