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Quantitative Value, Web Site: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors Hardcover – December 26, 2012
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"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
From the Inside Flap
Legendary investment gurus Warren Buffett and Ed Thorp represent different ends of the investing spectrum: one a value investor, the other a quant. While Buffett and Thorp have conflicting philosophical approaches, they agree that the market is beatable. In Quantitative Value, Wesley Gray and Tobias Carlisle take the best aspects from the disciplines of value investing and quantitative investing and apply them to a completely unique and winning approach to stock selection. As the authors explain, the quantitative value strategy offers a superior way to invest: capture the benefits of a value investing philosophy without the behavioral errors associated with "stock picking." To demystify their innovative approach, Gray and Carlisle outline the framework for quantitative value investing, including the four key elements the investment process:
1) How to avoid stocks that can cause a permanent loss of capital: Learn how to uncover financial statement manipulation, fraud, and financial distress.
2) How to find stocks with the highest quality: Learn how to find strong economic franchises, and robust financial strength. Gray and Carlisle look at long term returns on capital and assets, free cash flow, and a variety of metrics related to margins and general financial strength.
3) The secret to finding deeply undervalued stocks: Does the price-to-earnings ratio find undervalued stocks better than free cash flow? Gray and Carlisle examine the historical data on over 50 valuation ratios, including some unusual metrics, rare multi-year averages, and uncommon combinations.
4) The five signals sent by smart money: The book uncovers the signals sent by insiders, short sellers, shareholder activists and institutional investment managers.
After detailing the quantitative value investment process, Gray and Carlisle conduct a historical test of the resulting quantitative value model. Their conclusions are surprising and counter-intuitive.
The book includes a companion website that offers a monthly-updated screening tool to find stocks using the model outlined in the book, an updated back-testing tool, and a blog about recent developments in quantitative value investing. For any investor who wants to make the most of their time in today's complex marketplace, they should look no further than Quantitative Value.
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- 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.
- 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.
MBA students in Acc 725 (Ross School of Business, Prof. Richard Sloan taught this) did most of the formulas and a lot more. Prof Sloan is now at U C Berkley. Right now there are many universities that provide similar value stock list for free based on value strategies. Also, AAII website gives it with its basic membership. I doubt if the additional complexity in QV produces additional alpha even with all the data mining. Checkout AAII website. See 2013 AAII Stock screen review PDF.
# 1 screen is Piotroski High F Score since inception is 31.7%
# 2 screen is Estimate revision up is 28.6%
I would still read this book many times and use it as a reference. Couple of techniques he uses to determine fraud are good. I will have to see how to use it in future.
Most recent customer reviews
One would assume a book titled, “Quantitative Value” would be about like reading a manual for a DVD player, but it...Read more