Quantitative Value, + Web Site: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors 1st Edition

I bought this book because Toby Carlisle, one of the authors, writes one of the most interesting value investing blogs on the web. Most blogs, like most value investors, look for tools to add to one's investment criteria. Carlisle's blog, Greenbackd, does the opposite. Greenbackd goes to the core of value investing and tests its tenets to see which of them actually add value and which do not. The point isn't give you more to worry about. It's to give you less.So much advice, conventional wisdom, whatever is thrown at you as an investor that it can become paralyzing. Even worse much of the advice you get flat-out contradicts the rest. Do you buy what's cheap because after all that is the bedrock of value investing? Or is that simply what the unwashed know-nothings do because they can't evaluate a business? Hat tip: Uncle Warren.It was nice to see that this book--co-written by another excellent blogger Wes Gray--continues on the theme familiar to all Greenbackd readers. That theme is pretty straightforward: how much of value investing can be quantified and stripped down to its most productive elements?On this front, the authors have done a pretty awesome job. They not only look at companies which are cheap, they also apply quantitative methods to assess financial strength, earnings quality, earnings sustainability, balance sheet strength and so on. The idea is to replicate what a human analyst does. Essentially they take all the usual concerns a human analyst would have--or ought to have--and reduce them down to algorithms. I'm not aware of any other backtests as broad and thorough as what the authors have done here, and they really have pitted man vs machine. The results are fascinating.It makes you wonder whether fundamental analysts are actually adding value to the basics of value investing or whether their input just gets in the way. James Montier said it best: do quant models represent a floor (to which analysts add value) or a ceiling (from which analysts destroy value). This book doesn't destroy the case for fundamental analysis, but it does show that fundamental analysis is not the only way. And frankly the author's results make me wonder whether the value decile represents a kind of "beta"--where your returns theoretically increase to the extent you're exposed to it.I really got a lot out of this book and the new direction in value investing that it represents. It's like Tommy Lee Jones said in Under Siege: "Welcome to the revolution."Okay, it's not that kind of revolution. But for us value investors, this is thought-provoking, cutting-edge stuff.

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.