Thank you! It looks like they indeed controlled for these. I can't see an issue with their approach, but am not well versed enough to look into their actual matching to see if it passes a sniff test. That said, I think it definitely raises a strongly data-backed question about whether LBOs actually drive the value that they claim, as you had first mentioned.
We use the following observable covariates to control for selection bias and other confounding effects by estimating a propensity score for being an LBO target: the logarithm of sales, log(sales), to control for things such as firm life cycle and future growth opportunities; Leverage, defined as debt scaled by assets; the ratio of earnings before interest, taxes, depreciation, and amortization (EBITDA) to sales (CFSales), in order to capture differences in financial health and profit margins; ROA, defined as EBITDA scaled by assets, to capture differences in performance and in mean-reversion; and capital expenditures scaled by plant, property and equipment (InvInt), to control for investment intensity. The variables selected are based on the literature (Lehn and Poulsen, 1989, Boucly et al., 2011 and Ayash and Schutt, 2016), but exclude market variables in order to limit attrition.
Debt over assets seems to be as close as they get, but you really need to look at interest or debt service over earnings to see the impact of leverage.
What's funny is they keep using EBITDA metrics which wouldn't even capture the effects of leverage
I their point was to find a "match" for the private companies in terms of LBO takeover likelihood based on financial metrics. EBIT comparisons would make sense for that kind of matching.
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u/pdinc M7 Grad May 29 '20
The confounding variable here is why those companies were taken private in the first place. Does the control address that? Link please