Book Review - Quantitative Value

Back from holiday, refreshed and raring to go, I thought I would share my musings on some light pool-side reading. As it was recently recommended to me, I picked up a copy of ‘Quantitative Value: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors’ by Wesley Gray and Tobias Carlisle. Easy to read and concise, the book succeeds in conveying the authors’ findings to a wide audience without detracting from the quality of the research performed.

Content Summary

Setting the scene with a charming story of a game of bridge between Warren Buffett and Ed Thorp in 1968, the book introduces the reader to a brief history of both quantitative and value investment strategies and outlines the premise for the combination of the two. Building on this foundation, the book wastes no time in setting out the line of thinking and findings of the authors. Briefly, the broad topics are as follows:

• A discussion of why quantitative strategies are most suited to the majority of investors and an analysis of one of the best known – Greenblatt’s Magic Formula.
• Consideration of why performance should be improved by shifting return distributions to the right and an investigation of how best to do this.
• Research into the effects of quality on investment returns and testing the most appropriate way to incorporate it into a quantitative strategy.
• A comparison between different methods of establishing cheapness and a discussion of the authors’ findings.
• Finally, there is an examination of other potentially corroborating signals (such as buybacks and short interest) before presenting the complete Quantitative Value strategy and its performance.

Evaluation

Firstly, I can’t commend the writing style enough. It has been written in a very manageable way, such that I believe anybody who is already comfortable with the maths behind the basic investment ratios should be able to understand all of the logic in the book. Critically, the authors have managed to do this without detracting from the content of their research. This is no ‘The Little Book that Beats the Market’! It will take a bit longer to read but you will come away with a far better understanding of the thought process behind factor investment strategies and (barring the more quantitatively minded among us) you won’t be left questioning the assumptions and performance of the model presented at the end. For anybody seeking further depth, each chapter contains a wealth of references to all manner of well-known studies.

An example of how Gray and Carlisle have achieved this accessibility can be found in the formatting of their graphs and tables. There are just a few simple, but effective, table and graph styles that have been used consistently and with which you will be very familiar by the end. This allows quick assimilation of new data and ideas throughout. I do have one small gripe relating to the charts however, and noted that a reviewer on Amazon had the same comment. All of the lines are in very similar shades of grey and so it is a little hard to distinguish them at first. A small point in my view but an absence of required squinting would make the book just a little bit easier to read.

Another factor which aids in making the quantitative work more easily understood is the constant return to the more qualitative ideas that underlie value investment as a whole. Every chapter, without fail, begins with a pertinent quote from Warren Buffett, Benjamin Graham or similar in order to demonstrate the sound economic thinking behind each factor discussed. Because of this, at least for me, at no point was I left wondering, ‘why on earth are we comparing these particular metrics?’

Personally, I think the greatest achievement of the work is the level of scepticism and rationality displayed throughout. Where Gray and Carlisle have been unable to repeat previous results (such as those achieved by Greenblatt), they are quite clear in stating that and so as many ideas are dismissed as accepted in the book. This is something that I think is so often lacking in investment writing (and perhaps from a lot of thinking in general?) and it is really refreshing to see the authors challenge themselves and preconceptions with hard evidence. You feel as if you are privy to their thought process rather than merely being presented with a mysterious final result.

All of this contributes to the ‘Quantitative Value’ strategy carrying that much more weight when it is finally presented. Not only were the results fantastic, but they don’t have quite the same black box quality that so many other well-known strategies do.

Wider Appeal

While the authors are clearly persuaded of the factor investing approach, their qualitative considerations of certain metrics and other value investment principals are interesting in themselves. To more experienced analysts this will be nothing new, but those who are still seeking a deeper understanding of the logic behind value investment will find there is something for them here as well.

As an example, Gray and Carlisle’s work finds that the king of value metrics is the EV/EBIT ratio. Where some quantitative thinkers would simply stop where the evidence does, the authors venture a little further to ask why. The fact that the ratio takes into account the varying capital structures between different businesses is settled upon as a sound economic reason for the ratio's superiority in ranking them. Again, this is nothing new to some, but there are plenty of similar examples throughout and you may be pleasantly surprised by the breadth of the authors’ thinking.

If you are interested in behavioural finance, the book touches on some of those concepts too. It does not develop them in detail but provides reference to work on the subject and highlights the importance of considering such factors when determining the most suitable approach for you to pursue. In particular, I like the discussion of human performance relative to a quantitative screen and how, despite our beliefs otherwise, most of us simply cannot beat the machine. The screen tends to act as a ceiling rather than as a floor to performance, with most participants in studies systematically eliminating the best performers from model portfolios rather than avoiding the worst.

Last but not least, the performance of the final strategy is of interest to any investor, regardless of style or other preferences. The results are very good indeed and outperform both the market and almost any money manager you can think of over the same time period. Everybody should read these results and honestly ask themselves, ‘can I really do better than this?’

Concluding Thoughts

One thing I thought lacking from the book was a discussion of the momentum effect. I was curious to see how these added considerations may have improved (or worsened) results but sadly they were not examined. I note from the Wesley Gray’s website however (alphaarchitect.com), that a new book called ‘Quantitative Momentum’ is due out in early 2016 so perhaps I will have my fill then. Another small complaint is the price tag, it is over £40 on Amazon due, presumably, to the companion website and the additional info and back testing facilities. Personally, I would have rather had an option to not pay for the website too but I’ve yet to properly read their blog or use their tool so maybe it will prove useful.

For those familiar with Stockopedia’s StockRank system who would like another perspective on the factor approach, the book is a great starting place and, particularly if you would like to design your own system, it should help you to better understand the value and limitations of the rankings and their components. Overall, I would recommend the book to anybody whose aim is primarily to improve returns and to those who, like me, just love to learn as much as possibly about as broad a range of ideas as possible! I’ll conclude with a (very) brief summary of the books main findings:

You can beat the market along with most professional money managers.

• EV/EBIT is the king of price ratios, by quite some margin!
• The Magic Formula is overweight high returns on capital which are mean reverting. Instead, sorting for quality once you have already obtained a top value decile works better.
• It is possible to quantitatively identify poor performers and earnings manipulators in advance, shifting the whole return distribution to the right.

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