Wesley Gray, manager of the read more ValueShares US Quantitative Value ETF (QVAL), may very well be the most interesting quant you’ll ever meet. Granted, the word “quant” brings to mind an old man in a white lab coat stooped over reams of data, but hear me out.
Before getting his PhD in finance from the University of Chicago, Gray did four years of service as an active-duty U.S. Marine Corps ground intelligence officer in Iraq and other posts throughout Asia. Quantitative Value isn’t even his first book. That distinction goes to Embedded: A Marine Corps Adviser Inside the Iraqi Army.
It’s hard to imagine the average fund manager crawling through the muck and gathering intelligence in Iraqi Arabic. But that is Dr. Gray, and his work is far from average. Quantitative Value, co-written by Gray and Tobias Carlisle, is a solid piece of research that combines the successful value investing framework of Benjamin Graham and Warren Buffett with the analytical rigor seen in Jim O’Shaughnessy’s What Works on Wall Street and Joel Greenblatt’s The Little Book that Beats the Market. In fact, Gray and Carlisle write extensively about Greenblatt’s “Magic Formula” and much of the book is an attempt to build the proverbial better mousetrap.
We’ll take a look at some of Gray and Carlisle’s methods and then see how they perform in the real world by tracking the returns of the Quantitative Value ETF.
The Quantitative Value screening process for stocks resembles a funnel:
cheap cialis online canadian pharmacy Step 1: Avoid Stocks That Can Cause a Permanent Loss of Capital
This is a more elegant version of Warren Buffett’s first rule of investing: Don’t lose money. In first screening for risky stocks, Gray and Carlisle uses some of the same metrics used by short seller John Del Vecchio to identify short candidates, such as days sales outstanding. They also give special attention to accrual accounting in the hopes of weeding out earnings manipulators and run additional screens for probability of financial distress. By removing the riskiest stocks from the pool at the beginning, Gray and Carlisle are a lot less likely to get sucked into a value trap.
https://www.smithersoasis.com/about-us/research-and-technology/smithers-oasis-global-research-capabilities/corporate-polymer-horticulture-research/ trusted tablets online pharmacy Step 2: Find the Cheapest Stocks
Gray and Carlisle do extensive back testing on virtually every valuation metric under the sun, including industry standards such as price/earnings (“P/E”), price/sales (“P/S”) and price/book value (“P/B”). In the end, they opt to use the same valuation metric as Greenblatt in his Magic Formula: the Earnings Yield, defined here as earnings before interest and taxes (“EBIT”) divided by enterprise value. For those unfamiliar with the term, “enterprise value” is defined here as market cap (including preferred stock) + value of net debt, or what you might think of as the acquisition price of the company.
Gray and Carlisle find that of all the assorted valuation metrics, the Earnings Yield yields the best results.
Step 3: Find Highest-Quality Stocks
This is another nod to both Buffett and Greenblatt. Buffett has repeated often that it is better to buy a wonderful business at a fair price than a fair business at a wonderful price, and Greenblatt tried to capture this mathematically by screening for companies that generated high returns on capital (“ROC”). Gray and Carlisle take it a step further by using an 8-year ROC figure.
And they don’t stop there. Gray and Carlisle run additional screens for profitability and combine the metrics into a Franchise Power score. And taking it yet another step, they combine Franchise Power with Financial Strength to form a composite Quality score.
Again, the objective here is to capture mathematically what makes intuitive sense: That companies with wide competitive moats, strong brands and strong balance sheets make superior long-term investments.
So, how does the Quantitative Value model actually perform?
In back-tested returns, it crushed the market. From 1974 to 2011, Quantitative Value generated compounded annual returns of 17.68% to the S&P 500’s 10.46%.
Of course, we should always take back-tested returns with a large grain of salt. For a better comparison, let’s see how the Quantitative Value ETF (QVAL) has performed in the wild.
We don’t have a lot of data to work with, as QVAL only started trading in late October 2014. But over its short life, QVAL is modestly beating the S&P 500’s price returns, 9.96% vs. 9.15%. As recently as April, it was beating the S&P 500 by a cumulative 4%.
Looking at the returns of a substantially-similar managed account program managed by Gray’s firm, the “real world” results look solid. From November 2012 to May 2015, the Quantitative Value strategy generated compoud annual returns of 21.1% vs. the 18.3% return of the S&P 500. The Quantitative Value strategy was modestly more volatile (beta of 1.2) and had slightly larger maxmimum drawdowns (-6.0% vs. -4.4%). But this is exactly what you would expect from a concentrated portfolio.
I look forward to seeing how QVAL performs over time, and I congratulate Gray and Carlisle on a book well written.
Note: When referring to the book, “Quantitative Value” is italicized. When referring to the ETF or to the broader strategy, it is not.
Charles Lewis Sizemore, CFA, is chief investment officer of the investment firm Sizemore Capital Management and the author of the Sizemore Insights blog.