I finished reading Quantitative Value a few days ago, a friend read it, liked it and sent me a copy asking my thoughts. The book has been making the rounds on value blogs and the subject tied into something I wrote about recently on quantitative investing. As you'll see in my review the book was extremely thought provoking, yet by the end I had more questions about the approach than answers. I haven't seen anyone pose these questions, and I thought they might be worth posing to readers for consideration. This review is broken into two parts, first is an overview of the book, and second are my specific quibbles, or questions.
The book is written by Wesley Gray and Toby Carlisle both managers of hedge funds. Toby used to write daily on the Greenbackd blog which I read habitually when he was posting about net-nets and deep value stocks. That site along with others motivated me to investigate these strange creatures called "net-nets". I am indebted to Toby for illuminating such a lucrative area of the market for me.
The authors originally set off to understand why they couldn't replicate the results from the Magic Formula investment system. The Magic Formula is a system to high high quality stocks at low prices. Joel Greenblatt the author of the system wrote about it in The Little Book That Beats The Market. The results shown in the book are simply unbelievable, and unfortunately no one has been able to replicate them either.
Carlisle and Gray like the concept of buying the highest quality cheap stocks. They present a lot of evidence that supports the notion that buying the cheapest stocks outperforms glamour stocks. They they consider whether they can improve upon the notion of just buying cheap and eliminating frauds and accounting manipulators (you can). They also look into why certain types of glamour stocks perform the way they do. One observation I found fascinating was that the quality metric that the Magic Formula is based on actually picks the wrong time of quality (high priced glamour vs high quality) which in turn drags down the Magic Formula returns.
As the authors walk through their model they show their research into each metric they decided to use. For me most of the value of the book lies in these chapters. There were lots of fascinating points that prompted me to think about my approach.
1.) Does the quality metric really measure quality? The authors posit that the best metric to measure the quality of a business is gross margin divided by total assets. This formula is supposed to eliminate company financing decisions and focus on companies that earn the highest gross margins on the smallest invested capital.
I spent a long time thinking about this equation and my conclusion was it would bias quality towards franchisers, software companies, and any service company where personnel not fixed assets generate a return. The equation also fails to capture lease expenses, so given two of the same companies one that leases all of their assets would be considered higher quality over the one that owned all of their assets.
I also thought about what I would prefer as a business owner, a company with high gross margins that has significant SG&A expenses or a low margin product that has almost no marginal costs. The low margin product in this case would result in more profits but would be captured by the formula as low quality.
2.) Who is the intended audience? Throughout the book the authors appeared to be writing to investors who wished put their investment theory into practice. They discuss portfolio considerations with regards to small and illiquid stocks. Yet later in the book the authors disregard certain types of stocks because they aren't considered academically pure. As an investor I want the highest returns regardless of if they are respected by academics or not. One of the authors Wesley Gray is a professor at Drexel, and I presume decisions like this are the influence of his academic finance background. There are a few other academic finance-isms scattered throughout the book that detracted from the main story.
3.) Why ignore small caps? There are two specific places in the book where the authors mention strategies that far outperform both the market and Quantitative Value, yet they're both dismissed almost immediately as uninvestable because they deal with smaller stocks. The authors maintain that any stock below a market cap of $1.4 billion is a small cap. They categorize small caps as almost impossible to trade.
The first strategy they discussed was one where an investor would buy all of the stocks trading below book value and divide them into two parts based on F_SCORE (a measure of financial strength). The investor would buy the stocks with high F_SCORES and short the ones with low F_SCORES. This strategy outperformed the market by an astounding 23% during the period tested.
The second discarded strategy is one near and dear to my heart, Graham's net-net strategy. Graham maintained that an investor could earn north of 20% a year investing in a handful of net-nets. The book confirms this and then goes on to say that net-nets are near impossible to find and invest in.
While I understand that pension funds, and large mutual funds can't invest in smaller stocks I would wager that most investors, and professional investors are actually working with sums of money that could easily invest in stocks at the $1.4b level and below.
Paradoxically the book shows over and over that smaller stocks have higher returns over any test they run, yet the book pushes readers towards larger cap stocks. The website associated with the book only lists stocks $10b and larger.
4.) How is performance compared to fundamental indexes? The Quantitative Value strategy appears to be a different type of fundamental index. It would have been nice to see the strategy compared to the Research Affiliates or WisdomTree indexes. The Quantitative Value approach seems like a costly approach (transaction costs/taxes) especially if there is an fundamental index that performs closely.
5.) Is there more? The book went to great lengths to describe the authors strategy and thesis, but unfortunately they didn't touch on how anyone would actually implement it short of investing with them. Both authors run hedge funds that are out of investment reach by everyone outside of institutional and accredited (wealthy) investors. If an investor with $500k wanted to implement Quantitative Value the book doesn't give much detail on where to start.
The biggest issue with implementation is gathering and crunching the data. My sense is that an investor would need a Bloomberg or CapitalIQ to gather the required data to implement this strategy. Maybe the authors plan on unveiling a product similar to Formula Investing and that's why they were light on implementation details.
This review is probably sending a mixed message; parts of the book are great, yet there are some fundamental issues I had as well. While I don't think investing is as easy as programming this formula into a computer and sitting back as the money prints, there is a lot that's worthwhile in this book.
While I'm not quantitative value practitioner I did recognize that I apply some of the patterns discussed in the book. I will find pools of the market that are cheaper than the rest and fish accordingly. I try to watch for fraud and accounting manipulation, and I buy as cheap as possible in quantity. I'd rather own five cheap semiconductor stocks rather than the best cheapest semiconductor stock.
I also realized as I read the book that I've applied a psuedo-quant strategy to parts of my portfolio. I've purchased Japanese net-nets based on a simple formula, less than 2/3 NCAV, profitable, pays a dividend, and is a business that will be around in 10 years. I've invested in other areas like this as well.
I enjoyed reading the book, it was quick, and it made me think. I also had some lively discussions with some friends about concepts in the book. On the whole if one approaches the book as Gray and Carlisle's thesis on their investment method I think a reader will be satisfied. If one approaches the book looking for something that could be applied to their own portfolio I think they'll be left wanting, at least until the QuantitativeValueApplied fund launches.
Talk to Nate
Disclosure: I receive a small commission if you click and buy through the Amazon link. Think of this as a way of supporting the site, so if you enjoy my work go ahead and buy a big screen TV, or a luxury watch! I'd recommend you buy a car or a house through Amazon if I could figure out a way to do it