Yesterday I had an excerpt up from the recently published book: Quantitative Value: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors* by two veteran investment bloggers Wesley Gray (Turnkey Analyst) and Tobias Carlisle (Greenbackd). In addition I posed a series of questions to Wesley Gray the book’s co-author. The answers are illuminating and can be seen in their entirely below.
AR: You write in the book that there are two arguments for value investing: “logical and empirical.” It seems like the value investing community heavily emphasizes the former as opposed to the latter. Why do you think that is?
WG: Human beings tend to favor good stories over evidence, but this can lead to problems. As Mark Twain says, “All you need is ignorance and confidence and the success is sure.”
This tendency to embrace stories might help explain why being “logical” is more heavily relied upon by investors, – good logic makes as good story. Relying on the evidence, or being “empirical,” is under appreciated because it is sometimes counterintuitive. I’m actually a big fan of a logical story backed by empirical data. This is the essence of our book Quantitative Value. We present a compelling story on the value investment philosophy, but at each step along our journey we pepper our analysis with empirical analysis and academic rigor.
AR: You note in the book the importance of Ben Graham and how a continued application of his “simple value strategy” would still generate profits today. Have you seen the recent video about him? He seems to have been as interesting a guy as he was investor/teacher.
WG: As Toby and I conducted background research for the book, we became more and more convinced that Ben Graham was the original systematic value investor. In Quantitative Value we backtest a strategy Graham suggested in the 1976 Medical Economics Journal titled “The Simplest Way to Select Bargain Stocks.” We show that Graham’s strategy performed just as well over the past 40 years as it did in the 50 years prior to 1976. This is a remarkable “out-of-sample” test and highlights the robustness of a systematic value investment approach.
With respect to your question on the video: the recent video circulating the web reinforces our belief that Graham was an empiricist by nature and relied heavily on the scientific method to make his decisions. I also find it interesting that his discussions are so focused on the fallibility of human decision-making ability. Many of the ideas and concepts Graham mentioned regarding human behavior have been backed by behavioral finance studies written the past 20 years. He was well ahead of his time.
AR: The value community loves to continue to claim Warren Buffett as a disciple. However today he would be best described as a “quality and price” investor more than anything. What is the relevance of how Warren Buffett’s approach to investing has changed over time?
WG: The irony here is that, on average, Warren Buffett’s “new” approach to value investing is inferior to the approach originally described by Ben Graham. Buffett describes an approach that is broader in perspective and allows an investor to move along the cheapness axis to capture high quality firms. Graham, who studied the actual data, was much more focused on absolute cheapness. This concept is highlighted in many of his recommended investment approaches, where the foundation of the strategy prescribed is to simply purchase stocks under a specific price point (e.g., P/E <10).
After studying data from the post-Graham era, we have come to the same conclusion as Graham: cheapness is everything; quality is a nice-to-have. For example, the risk-adjusted returns on the higher-priced, but very high quality firms (i.e., Buffett firms) are much worse on a risk-adjusted basis than the returns on a basket of the cheapest firms that are of extreme low quality (i.e., Graham cigar butts). In the end, if you aren’t exclusively digging in the bargain bin, you’re missing out on potential outperformance.
AR: I write in my own book that one of the big reasons why systematic value strategies work is that they are “psychologically difficult” to implement. This makes sense in light of the findings from Joel Greenblatt’s work that shows investors “reliably and systematically” avoid the best performers stocks according to his Magic Formula. How does a quantitative approach to value investing help offset these biases?
WG: Love your book by the way.
I think there are 2 big issues going against the complete arbitrage of the “value anomaly.” First, there is the issue of tracking error. Second, there is the issue that you mention: “psychological difficulty.” I’ll describe each in brief detail in what follows:
The big issue with value strategies is the huge tracking error. One can look like a fool many times over before finally being “right.” A classic example of this is the Internet bubble time period when value stocks were getting pummeled by their sexier Dot.Com brethren. Fortunately, the agency problems built into the institutional money management (a system designed to “separate brains from the underlying capital”) has stacked the cards against overwhelming amounts of capital flowing into value stocks.
I see this discussion going in two directions. One psychological difficulty with buying value stocks is that the names make you want to hang yourself from the rafters. It is not fun owning Blackberry when Android and the iPhone are taking over the world. Anyone want to join me in buying a bunch of defense-related stocks in the face of a budget crisis and potential sequester? Oh boy, let’s all get on that bandwagon of pain. Yuck…but as I always tell my investment team, “Yucks turn into bucks.”
A second deeper point related to the psychological difficulty of value investing has little to do with value investing and a lot to do about investment discipline. Investors find it very difficult, if not impossible, to let models to the work on their behalf. Greenblatt’s work where he pits man against machine highlights this point: give investors the answer and they’ll screw it up; force investors to not vary from the answer and they do fine. I suffer from the same problem. I want to feel like I add value. I want to know that my years of experience are actually worth something. I’ve attempted a “quantamental” approach in the past–use the quant model to screen the universe and then try to pick stocks using your “skills.” I was a failure at picking stocks. Yet, I’m proud to admit this failure, because I now know I’m past the denial stage, which is the first part in overcoming the disease I call “stock-picking egomania.” But fear not, my stock-picking friends! Researchers have shown that models outperform experts in other fields. One can look at wine prices, presidential elections, and medical diagnosis for further evidence.
You asked how Quantitative Value offsets bias. Good question. Let’s begin with a quote from Einstein: “Two things are infinite: the universe and human stupidity; and I’m not sure about the universe.” I will never disagree with Einstein. Hence, our goal with the Quantitative Value approach is not to cure the ailments of human nature–this is impossible–however, we do hope our investment philosophy helps inform investors in a clear and concise manner. Our dream is that the research we describe will set investors free from the bonds of irrationality.
AR: To that end: would investors looking to exploit the value effect be better off investing in a fund (quantitatively run or index) as opposed to trying to do it on their own?
WG: My coauthor, Toby Carlisle, likes to say that systematic investing is simple, but not easy. At Empiritrage, LLC we manage around $130mm, with $26mm of that dedicated to the Quantitative Value approach. Just push a few buttons and let the computer make money, right? Wrong. Every day there is something new and exciting: corporate actions, special dividends, trade breaks, trade errors, proxy votes, coffee spills on the server, wild bears breaking into the office and trampling our research team, and so forth. That said, if you are super motivated and have the time and attention to dedicate to managing the portfolio–go for it. If you want to do other things in life besides stare at flashing lights all day, find someone who can implement the system on your behalf at a reasonable price. Also, another piece of advice: whenever you see the word “proprietary” in the context of quant and it is coupled with “2/20″ price tag, you should run for the hills.
AR: You spend an entire chapter of the book talking about the challenges of building and analyzing back tests. In a world where ETFs are launched on the back of positive-seeming backtests what are the key lessons investors need to keep in mind when thinking about backtested results?
WG: Markets are extremely efficient–Eugene Fama is 99% right, but it is a lot more fun arguing about the 1% where he is wrong. And not only are markets very efficient, but it is also impossible to outperform the market forever, or in the end, one would own all the capital in the world. And how can you outperform yourself? You can’t! So at the outset, when in the world of backtested results (and even live results in some cases), skepticism is key. Past performance means nothing; process means everything. There are so many ways to game the system and if you have not spent years and thousands of hours coding up and cleaning the underlying data it is hard to appreciate what I’m talking about.
I’ve seen almost all forms of manipulation at this stage in my career. I’ve probably seen a thousand pitchbooks. Some are good, some are bad, and some are borderline fraudulent. The biggest games usually involve small/micro stocks, non-transparent leverage schemes, and over-optimization of some sort. My rule of thumb is easy to follow: In the context of unlevered equity investing with a reasonably diversified portfolio (>25stocks), dismiss any results with compound annual growth rates over 25%, unless it is clear they are using inside information, at which point you should swiftly put your wallet back in your pocket and report the firm to the SEC.
AR: Your approach throughout the book is based on finding high quality stocks at cheap prices. It seems that this approach should work across markets. Have you had a chance to look at how this approach would have worked in global markets. Or said another way: would a global approach provide even better results?
WG: We are able to conduct a very thorough and detailed analysis in the context of US stocks, because of the treasure trove of historical data that is available. With international equity, attaining the data required to test our model is not possible. The data inputs are too great and the data are unreliable. But getting back to your question, yes, our systems, although augmented for data realities, still work. Why? Well, buying the cheapest high-quality junk in the market is a timeless investment strategy. The tracking error is huge, these portfolios require 3-5+ horizons, and the names are vomit-inducing, but you have a great shot at outperformance over the long-haul.
In closing, the research question we address in our book is simple:
What is the most efficient and effective way to identify top performing stocks? Our answer is also clear: Quantitative Value.
Thanks for the chat, Tadas. Keep up the great work at Abnormal Returns. I review your site daily and always look forward to great content.
You can read an excerpt from the book as well.
*I provided a blurb for the book and received a complimentary copy from the publisher.