"Quantitative Value is a must read for those with a love of value investing and a desire to make the investment process less ad-hoc. A must read."
--Tony Tang, Ph.D., Global Macro Researcher and Portfolio Manager, AQR Capital Management
"Gray and Carlisle take you behind the curtains to build a black box based on the best value minds in finance. They combine academia's best ideas with the ideas of Buffet, Graham, and Thorp, to develop a quant system that performs in markets both good and bad."
--Mebane Faber, Author of The Ivy Portfolio and Portfolio Manager for Cambria Investment Management
"This book is an excellent primer to quantitative investing. It combines insights from both academic luminaries and successful professional investors, and presents them in a clear, engaging manner. The authors rigorously back-test simple strategies that can be used by the individual as well as institutional investor."
--Alex Edmans Ph.D., Finance Professor at The Wharton School, University of Pennsylvania
"Quantitative Value is the new guide to Graham-and-Doddsville. Gray and Carlisle synthesize the lessons of the great value investors to systematically identify high quality value stocks while avoiding common behavioral pitfalls."
--Tadas Viskanta, Founder and Editor, Abnormal Returns; Author of Abnormal Returns: Winning Strategies from the Frontlines of the Investment Blogosphere.
"We seek to marry Ed Thorp's quantitative approach to Warren Buffett's value investment philosophy." That's the approach we take in our Value Investing class at UC Davis and Quantitative Value will become required reading for our class. The book we wish we would have written!"
--Lonnie J. Rush and Jacob L. Taylor, Managing Partners of Farnam Street Investments and Visiting Professors at UC Davis Graduate School of Management
This informative and exceptionally well-researched book wove together many strands of investing and finance for me. Almost everyone knows who Warren Buffett is, and many are familiar with Ben Graham, the father of value investing. If you've read a number of investing (or gambling) books, you may even be familiar with Ed Thorp, who pioneered the application of statistics to making money on Wall Street. Or perhaps you've heard of Nassim Taleb's "The Black Swan," or Daniel Kahneman, who won a Nobel Prize for his work in behavioral psychology.
It takes a powerful unifying theme to demonstrate how the specialties of these diverse financial thinkers can be integrated into a single approach to investing. Wesley Gray, a finance professor with an MBA/PhD from the University of Chicago, and Tobias Carlisle, an M&A lawyer with a Wall Street background, combine a compelling history with voluminous academic research to demonstrate how these different spheres are in reality closely related and complementary.
The authors describe how our innate behavioral and cognitive biases cause us to make poor investing decisions, and how we can avoid such outcomes by adhering to a systematic value investing process, based on techniques used by fundamental investors, that maximizes the statistical likelihood of investment success. Based on wide-ranging and amply documented academic research, the approach uses computers to search the universe of stocks and identify those that meet its robust stock selection criteria.
This is a comprehensive, soup-to-nuts investment process. First, the authors council us to control risk by eliminating stocks that pose a risk of a permanent loss of capital due to fraud, earnings manipulation or financial distress. Next, they tackle value. What does it mean for a stock to be cheap and how do you measure it? Next they do a deep dive on the analysis required to identify high quality, financially strong firms, including a discussion of Warren Buffett's economic moats.
Each section is supported by numerous cutting edge, empirically-based academic studies. We see the evolution of thinking on a given subject and a snapshot of what academics are researching and saying today about a given topic. For example, you could spend an entire academic career investigating the area of fraud and manipulation (which some people do), or you could read in this book a summary of the field and how to apply the latest tools coming out of academia.
Also, this book takes backtesting to a whole new level. Backtesting is a technique whereby one explores a proposed strategy by applying it to historical data and seeing what would have occurred. Critics claim backtested results can be unobtainable in real-world settings, that what has worked in the past may not work in the future, and cite the risk of data mining, where one discerns data patterns that don't actually exist. The authors go to great lengths to address these concerns, via conservative market capitalization cutoffs to deal with liquidity/cost issues, use of the robust CRSP database, and other methods. Additionally, the underlying ideas are based on widely researched and accepted value investing principles, and make intuitive sense from a financial perspective, so it would seem the power of the approach is borne out in the data, rather than data driving the selection of the process.
There is plenty of material here for sophisticated investors to sink their teeth into, and some of the material is pretty technical, including some math and accounting concepts that may not be readily comprehended by financial neophytes, but the discussion is clear, jargon-free and in mostly non-technical language.
One issue I have with the book is that they only present results for a market-weighted portfolio. While I understand this is standard academic practice, it would have been nice to see equal-weighted results as well. Another gripe is that the chapters outlining the models on avoiding frauds, manipulators, and potentially bankrupt firms were a bit tedious and difficult to understand at times. But maybe this goes with the territory for any detailed discussion of esoteric academic topics. There are other books out there that are more accessible and user friendly.
Many are doubtless familiar with Joel Greenblatt, who popularized a computer-driven stock picking strategy in his "Little Book that Beats the Market," which lays out his "magic formula" strategy in layman's terms that can be understood by children. Well this book is Greenblatt's Little Book on steroids, and if you are ready to take the next step beyond Greenblatt, then this is the book for you. It is a more sophisticated approach to Greenblatt's topic, and is definitely geared for financial adults (incidentally, the authors do some analysis on Greenblatt's magic formula, which many will find interesting). Those who have some accounting and financial knowlege, and an appreciation for indexing and passive investing, will enjoy this book and get the most out of it, but even if you haven't spent a lot of time studying finance and the markets, there is lots to learn here.
The book is fantastic. Well written, extensive research with a clear thought process. This is likely the best value investing process I have seen.....but there is one problem....you can't actually implement it.
- The screens are extremely complex and rigorous (which is why they work so well). But you would need a pretty intense and expensive database to actually be able to implement it.
- They provide a website to do it for you (yay!). However, the website only gives you stocks with a market cap in excess of 10 Billion (large cap only), whereas all of the book backtests are for stocks with a market cap of 1.4 billion or more.
I am sure this will work to outperform the market even for large cap only, maybe even by a lot...but it is really too bad we couldn't implement the same thing they are doing in the book.
As far as I know, the only investing books to mesh quantitative investing and value investing have been "What Works on Wall Street," "The Little Book That Still Beats the Market," and "Ben Graham Was A Quant." "Quantitative Value" shares a lot in common with "What Works on Wall Street," and improves on "The Little Book." In fact, this was probably one of the best investing books I've ever read, combining the tried-and-true approach of value investing, behavioral finance, and quantitative methods to produce one very interesting piece. I really, really, REALLY wanted to give this five stars, as it is exceptional, but there were several major issues with their methodology and logic. But first, the positives.
PROs:
- Explains basic cognitive biases typically affecting investing and how behavioral finance can help improve results by methodically sticking with the Quantitative Value program.
- Completely dissects Greenblatt's "Magic Formula" (From "The Little Book That Still Beats the Market"), demonstrating which of the two formulas has contributed more to the returns, how to possibly improve on the formula, and using it as a benchmark to which the authors compare their Quantitative Value approach.
- Tests a composite price metric of EBIT/EV, EBITDA/EV, E/P, B/P, Gross Profit/EV, and FCF/EV. Interestingly, the composite score doesn't outperform the best performing single metric (EBIT/EV), which is at odds with the composite score findings in "What Works on Wall Street," which consisted of P/S, P/E, P/B, EBITDA/EV, and P/FCF. Can draw your own conclusions, but I suspect the divergence is due to O'Shaughnessy included P/S and P/FCF, rather than FCF/EV (a flawed metric discussed below) and GP/EV.
- Uses Gross Profit to Assets [(Revenue - Cost of Goods Sold)/Total Assets] and Gross Profit to Enterprise Value, which are both metrics I've never seen tested before in the literature. GPA as a performance metric makes more sense than the traditional Return on Assets (more of this in a bit), and their test results show both produce solid returns.
- Compares using 10 year average earnings multiples to the typical last twelve month multiples, which is something I wish had been included in "What Works on Wall Street."
- Goes into sufficient detail to detect earnings manipulation (using accruals) and financial strength and distress (Piotroski F-Score, Altman Z-Score, and Beneish M-Score are all discussed). This is particularly useful in deciding which stocks to exclude from a portfolio, as these are the ones most likely to hamper over-all returns.
- Keeps the discussion regarding CAPM and Beta to three or so pages. Beta has been discredited enough that it would be nice for it to be never mentioned again in the literature, but the authors limit it to a perfectly acceptable blurb.
CONS:
- Some of the metrics the authors use to measure "value" and "quality" are not consistent. While Return on Assets (Net income/Total assets) is a popular performance metric, it actually makes very little sense. The numerator, net income, is what's available to common shareholders after interest payments have been made to bondholders. Yet the denominator, assets, is funded with both equity and debt, so comparing it with an income measure that is available only to one class of capital providers just doesn't fit. A better numerator would've been EBIT (earnings before interest and taxes). I would be willing to overlook this, except the authors make the same mistake with measuring Free Cash Flow (defined as Net Income + Depreciation + Amortization - Changes in Working Capital - Capital expenditures), which is cashflow that is available to equityholders, against both Total Assets and with the Enterprise Value multiple (Market value of debt + Market value of equity - Cash). If the authors wanted to include Free cash flow into the mix, they should've used free cash flow to the firm (cash available to both debt and equity holders, which is Cash From Operations + (Interest expense X (1 - Tax rate)) - CapEx) as measured against Total Assets or Enterprise Value. This is too hard to overlook, as when discussing the Magic Formula, the authors EXPLICITLY explain the logic behind using EBIT to Enterprise Value (as it allows to compare firms with different capital structures equally), but then ignore this when using their own metrics. A terrible gap in consistency.
- The authors spend a considerable amount of time talking about Warren Buffett, and even include his quote about how is favorite performance metric is Return on Equity (Net income/Book Value of Equity), which makes much more sense than using Return on Assets. Yet the authors don't even include ROE in ANY of their backtesting at all! How the omitted ROE as a performance metric, but thoroughly backtested ROA and ROC is beyond me.
- When discussing the Magic Formula results as according to Greenblatt, the authors mention that they were unable to replicate the results with their own backtesting. Yet Greenblatt stated in his book that the minimum market capitalization he used in his screen was $50 million, while the authors make the minimum market cap $1.4 BILLION. No wonder they weren't able to replicate his results!
- In a related matter, the authors also limit their market capitalization to a minimum of $1.4 billion in their own Quantitative Value backtesting. They claim this is done due to the illiquid nature of smaller-sized caps (which is true), thus making their test more applicable to the "real world." While this makes sense for large institutions whose activity can materially affect the market price of a small cap and hamper their ability to buy and sell large blocks of shares, the cut off of $1.4 billion seems rather extreme. Further, for the individual investor, who isn't managing millions and billions of dollars, the illiquid nature of smaller cap stocks shouldn't be much of an issue. This is particularly odd as they even include a quote from Eugene Fama stating that the "Value premium" is most prevalent in small cap securities, as these are ones where mispricing is most likely to be prevalent. On top of this, they include a quote by Buffett, detailing why investing larger sums of money actually hinders performance (and why this is an advantage to the individual investor), yet the authors limit their testing to large caps, assuming individual investors are faced with the same liquidity constraints as institutions. I don't understand the logic behind this small cap exclusion at all, especially when they STATE that small cap value stocks are the ones that beat the market most often.
- They use gross profit margin [(Revenue - Cost of Goods Sold)/Revenue] as a signal to whether a firm has "Franchise value" or not. They mention a study by an author who claims that gross profit margin is a better indicator of "true profitability," but provide no evidence beyond quoting that author. As there are other costs associated with running a firm before bond or equityholders receive any cash or earnings (such as sales, general and administrative expenses), I'm skeptical as to how good of an indicator gross profit margin is. Backtesting Gross profit margin with operating profit margin and net profit margin would've helped their case a lot more.
Over all this book is well worth the purchase price. It's a fantastic complement to "What Works on Wall Street," as both provide the individual investor with great insights on how to construct a winning portfolio. The negatives aren't enough to detract from the wealth of evidence they bring to the table on why value investing is the only way to properly invest.
Product Details :
Hardcover: 288 pages
Publisher: Wiley; 1 edition (December 26, 2012)
Language: English
ISBN-10: 1118328078
ISBN-13: 978-1118328071
Product Dimensions: 5.9 x 1.2 x 9.8 inches
More Details about Quantitative Value, + Web Site: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors, Wiley Finance, 1st edition
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