The two great innovations of the past 50 years of asset management were John Bogle’s index fund and Henry Kravis’ invention of the leveraged buyout. Bogle’s diversified, systematic, low-fee approach rescued retail investors from paying too much for bad active management. Kravis demonstrated that buying small companies cheaply, loading them up with debt, and using cash flow to deleverage, could generate returns in excess of 20% per annum for endowments and foundations willing to take the risk.
Verdad combines these two approaches, fusing the skeptical and systematic approach of Bogle and his followers with the classic value LBO model that delivered the super returns of the 1980’s and 1990’s. Our research suggests that investing in leveraged small value stocks in the public markets would have had an average annual return of 25.1% from 1965-2013 and significantly outperformed private equity.
We believe our approach has three key advantages relative to private equity. First, we invest at significantly lower prices and use debt more prudently: while private equity firms are currently paying more than 10x EBITDA for acquisitions and using 5-6x EBITDA of debt, our average purchase price is less than 6x EBITDA and our average debt levels are between 3-4x EBITDA. We do not have to pay control premiums. Second, the public markets are more transparent and less risky than private markets, thanks to government oversight, GAAP accounting, and the scrutiny of financial analysts and short sellers. We can sell our positions at any point. Third, without the burden of executing complex transactions in the private markets, we can charge investors significantly lower fees. We do not charge transaction or deal monitoring fees.
But our biggest advantage is our analytical rigor. Every element of our process is rigorously tested using research and techniques from academic finance. We start with a screen that identifies the stocks that share the key financial characteristics of the most successful LBOs: small companies trading at substantial discounts to broader indices with potential to benefit from deleveraging. We eliminate stocks that have a high risk of bankruptcy or near-term negative catalysts, using credit ratings and bond prices, short selling data, and our own proprietary models. We run every stock through a Bayesian model developed through machine learning analysis that predicts which companies are most likely to pay down debt. We submit this short list of stocks for fundamental analysis, using Monte Carlo models to understand the distribution of potential outcomes, and weighting our portfolio towards stocks with the greatest asymmetry.
Many leveraged companies are issuers of high-yield bonds, and their performance is therefore closely correlated with changes in the high-yield spread, i.e. the difference between the yield on below investment grade debt and treasuries. The companies most likely to deleverage are typically small and trade at value multiples, meaning the performance of these stocks often has a positive beta with size and value factors.