Q&A With Alpha Architect’s Wes Gray: Part I

Wes Gray and his asset management firm, Alpha Architect, recently launched their first ETF, called the ValueShares U.S. Quantitative Value ETF (ticker: QVAL). I had the chance to ask Wes some questions about a wide range of topics, including the fund’s strategy. My feeling is that ETFs are eventually going to take over the investment world, so I think it’s important for investors to pay attention to developments in the space. My questions are in bold.

(1) How would you explain QVAL to the average investor so they can understand your investment strategy with the fund?

The QVAL objective is straightforward: Buy the cheapest, highest quality value stocks. We deploy a five-step process to accomplish our objective (see graphic below):

1. Identify Investable Universe: Our universe generally consists of mid- to large-capitalization U.S. exchange-traded stocks.
2. Forensic Accounting Screens: We conduct financial statement analysis with statistical models to avoid firms at risk for financial distress or financial statement manipulation.
3. Valuation Screens: We screen for stocks with low enterprise values relative to operating earnings.
4. Quality Screens: We rank the cheapest stocks on their long-term business fundamentals and current financial strength.
5. Invest with Conviction: We seek to invest in a concentrated portfolio of the cheapest, highest quality value stocks, which maximizes our expected returns over the long run. This form of investing is by definition contrarian, and requires disciplined commitment, as well as a thorough understanding of its theoretical and intellectual underpinnings in order to stick with it, since it may underperform in the short run.

In the end, we systematically form our portfolio via our screening process. One of our clients was describing us to another investor and said, “These guys are not quants, they are value investors.” And that’s right. We really are value investors just like any of these fundamental managers, but we do so in a rigorous and systematic way, using quantitative tools to eliminate the chance we will be influenced by our faulty human biases. And finally, we do so cheaply and in a tax-efficient wrapper via the ETF.

(2) What are some of the best and worst parts about launching an ETF?

Best: The best part is knowing we can offer our services to all investors. Before launching the ETF, we were only able to deal with high net-worth individuals, as we do not have hundreds of employees to manage smaller accounts. We are also excited about the tax benefits the ETF vehicle can potentially provide to investors.

Worst: The biggest issue with an ETF is the time, money, and effort necessary to get the ETF launched. We decided early on to own the entire process, as opposed to sitting on someone else’s exemptive relief. While we are glad we went this route, we’ve spent countless hours figuring out the process. Another drawback is the cost. To properly set up the infrastructure, one can expect to pay between $300,000 to $500,000 just to get the first ETF up and running.

(3) You wrote a book, Quantitative Value, where you did a lot of research on systematic value strategies. What are some of the biggest issues quants run into when implementing a back-tested strategy?

One of the issues we see is that many “quants” like to use an optimization or factor model using past data. They run regressions, find the optimal loadings, and then form portfolios based on the optimized weights. However, we think there is a major problem in the implementation of such a model: What happens if the weights change? Or what happens if the weight estimates are noisy? Many quants overlook these factors, and in the implementation, they may be overexposed to one company or may be buying firms that fall outside of the strategy’s original objective.

We are wary of such models, and form our portfolios via a screening methodology described in our 5 step process. Implementing a value strategy this way ensures we are always going to be buying the cheapest, highest quality value stocks. In the end, our motto is to keep our models as simple as possible, but no simpler.

(4) Value investing has been shown to outperform historically over longer time frames. How patient do investors need to be to see this type of strategy through?

The heart of your question is the following: How long could a value investor underperform the market? We’ve addressed this question empirically.

Consider the experience of a systematic value investor who simply buys low-priced stocks. Our approach, while not exactly the same as a simple low-price value strategy, shares many of the same characteristics—both good and bad—so this thought experiment serves as a nice case study to contextualize the costs and benefits of contrarian investment programs.

Using data on portfolios sorted by book-to-market ratios, we examine time periods where it was painful to be a value investor. One such period is during the run-up to the internet bubble. We examine the gross total returns (including dividends and cash distributions) from 1/1/1994-12/31/1999 for a Value stock portfolio (i.e., the High book-to-market quintile, market-weighted returns in the Ken French dataset), and a Growth stock portfolio (i.e., the Low book-to-market quintile, market-weighted returns in the Ken French dataset), the S&P 500 total return index, and the Risk-Free return (90-day T-Bills).

Figure 1 highlights the extreme underperformance of the simple value portfolio relative to a simple growth portfolio and the broader market. From 1994 to 1999, value underperformed growth by over 10 percentage points a year. Now that’s pain! When one compounds that spread over 5 years it translates into a serious spread in cumulative performance.

Figure 1: Value and Growth 1994 to 1999
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index.

Figure 2: Annual Performance 1994 to 1999
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index.

Figure 2 makes the point even more clear. The value strategy underperformed the broad market for 6 straight years.

Even the most disciplined and hardened value investor would have a hard time sticking with a philosophy that lost to the market for 6 years in a row. Amazingly, Warren Buffett, arguably the greatest investor of all-time, was criticized in the media for “losing his magic touch” at the tail-end of the late ‘90s bull market.

Of course, looking back (see figure 3), we now realize that in 1999 the internet bubble was about to burst. Value investors got the last laugh. From 2000 to 2013 value stocks earned 8.89 percent a year relative to the market’s paltry 3.82 percent performance.

Figure 3: Value and Growth 2000 to 2013
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index.

Over the full cycle from 1994 to 2013, value revealed its true form: Value earned 10.88 percent a year, while the market earned 9.45 percent a year. An investor compounding at a 1.43 percent spread over the market return over nearly twenty years will generate a substantially different wealth profile over time. Figure 3 shows the performance of the simple low-price value stocks relative to the market from 2000 to 2013 and Figure 4 has performance over the entire cycle (1994 to 2013).

Figure 4: Value and Growth 1994 to 2013
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index.

So in conclusion, I would say that we think in very long-term time periods, such as 5 to 10 years.

Thanks Wes.

Check back in tomorrow for Part II where I ask Wes about some of the early influences on his investment philosophy and also some of the lessons learned serving in the U.S. Marine Corps.

And for a deeper dive into QVAL’s strategy, check out Samuel Lee’s profile on the fund at Morningstar: A Deep-Value Quantitative Hedge Fund Strategy

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