Meat & Potatoes Investing with Joel Greenblatt
Joel Greenblatt has a long resume. Besides being the founder and managing partner of Gotham Capital, Mr. Greenblatt is the author of The Little Book That Beats the Market and an adjunct Professor at Columbia Business School. Now he is adding “Quant-fund Manager” to his work history. In his recent CNBC interview (below), Greenblatt discusses the real-world portfolio implementation of his “Magic Formula” on www.FormulaInvesting.com, a new venture he has undertaken.
The Magic Formula as it turns out is not all that magical, but rather very simple. The formula is based on two straightforward meat and potato factors gathered from Standard & Poor’s data: 1) the trailing Price/Earnings ratio on a stock (value factor); and 2) the Return on Capital ratio of a stock using historical earnings. The portfolio management strategy is fairly basic as well. Twenty to thirty securities are selected from the model, with the ability of the investor to customize if they so choose, and the portfolios are rebalanced on an annual basis making sure any relevant tax-loss selling occurs before the end of the calendar year.
Based on the back-tests, the model portfolio was up +291% over the last 10 years versus down -2% for the S&P 500 index. For 2008, however, the performance of the Magic Formula was not too enchanting – down about -36% versus -37% for the S&P 500 index, according to Greenblatt.
As with any back-test, or model, I am very skeptical about the output and inferences that can be drawn. Here are a few reasons why:
1) Past ≠Future: Just because this strategy worked in the past doesn’t mean it will work in the future. Greenblatt admits that the strategy can underperform for long periods of time.
2) Limited Data: Ten years is an extremely limited period of time to base a robust strategy on – much more data should be used.
3) Cost Estimates: Following a potentially very illiquid, out of favor value strategy with possibly large sums of money can cause past results to look quite different. Factors such as trading costs and impact costs can be underappreciated in computer based back-tests.
4) Data Mining: With any model, problems can arise when reams of data are sliced and diced for the sole purpose of creating a positive outcome. Often, there are no cause and effect between a variable and future returns, yet practitioners will jump to that conclusion because the factors fit the data.
To learn more about shortcomings in quantitative models, I suggest you learn more about butter production in Bangladesh (read article here). I will eagerly watch how Mr. Greenblatt’s “Magic Formula” works from a distance. In the mean time, I’m hungry. I think I’ll keep it simple…a steak and baked potato.
Wade W. Slome, CFA, CFP®
Plan. Invest. Prosper.
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