20 Quant Value Books @ Liquidation Prices

/20 Quant Value Books @ Liquidation Prices

20 Quant Value Books @ Liquidation Prices

By | 2017-08-18T17:11:31+00:00 August 25th, 2014|Research Insights, Book Reviews|11 Comments

We’ve got a box of books (20 copies) in the office that we want to share with our readers at a discount.

We posted them to Amazon.com for $29.99, which is a nice discount off the normal price of $50+. The picture below shows how you can find the price in Amazon.



First come, first serve.




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About the Author:

Wes Gray
After serving as a Captain in the United States Marine Corps, Dr. Gray earned a PhD, and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management that delivers affordable active exposures for tax-sensitive investors. Dr. Gray has published four books and a number of academic articles. Wes is a regular contributor to multiple industry outlets, to include the following: Wall Street Journal, Forbes, ETF.com, and the CFA Institute. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.


  1. Michael Milburn August 25, 2014 at 7:20 pm

    I ordered mine a couple weeks ago and am reading through it now. Is there a thread where specific ideas/parts of the book are discussed? In particular the ideas of avoiding losers, and the longer term price ratio calculations.

    • Wesley Gray, PhD
      Wesley Gray, PhD August 25, 2014 at 7:37 pm

      sure, here is the TOC from Wiley’s website.

      avoid losers: chap 3/4
      long term price ratio (and a lot of others): chap 7/8

      PART ONE The Foundation of Quantitative Value 1

      CHAPTER 1 The Paradox of Dumb Money 3

      Value Strategies Beat the Market 9

      How Quantitative Investing Protects against Behavioral Errors 23

      The Power of Quantitative Value Investing 30

      Notes 32

      CHAPTER 2 A Blueprint to a Better Quantitative Value Strategy 35

      Greenblatt’s Magic Formula 36

      It’s All Academic: Improving Quality and Price 45

      Strategy Implementation: Investors Behaving Badly 54

      Notes 59

      PART TWO Margin of Safety—How to Avoid a Permanent Loss of Capital 61

      CHAPTER 3 Hornswoggled! Eliminating Earnings Manipulators and Outright Frauds 63

      Accruals and the Art of Earnings Manipulation 64

      Predicting PROBMs 72

      Notes 79

      CHAPTER 4 Measuring the Risk of Financial Distress: How to Avoid the Sick Men of the Stock Market 81

      A Brief History of Bankruptcy Prediction 83

      Improving Bankruptcy Prediction 85

      How We Calculate the Risk of Financial Distress 86

      Scrubbing the Universe 89

      Notes 91

      PART THREE Quality—How to Find a Wonderful Business 93

      CHAPTER 5 Franchises—The Archetype of High Quality 95

      The Chairman’s Secret Recipe 96

      How to Find a Franchise 99

      Notes 112

      CHAPTER 6 Financial Strength: Foundations Built on Rock 113

      The Piotroski Fundamentals Score (F_SCORE) 114

      Our Financial Strength Score (FS_SCORE) 119

      Comparing the Performance of Piotroski’s F_SCORE and Our

      FS_SCORE 122

      Case Study: Lubrizol Corporation 123

      Notes 126

      PART FOUR The Secret to Finding Bargain Prices 127

      CHAPTER 7 Price Ratios: A Horse Race 129

      The Horses in the Race 130

      Rules of the Race 133

      The Race Call 134

      A Price Ratio for All Seasons 141

      The Offi cial Winner 142

      Notes 143

      CHAPTER 8 Alternative Price Measures—Normalized Earning Power and Composite Ratios 145

      Normalized Earning Power 147

      Compound Price Ratios: Is the Whole Greater than

      the Sum of Its Parts? 150

      Notes 163

      PART FIVE Corroborative Signals 165

      CHAPTER 9 Blue Horseshoe Loves Anacott Steel: Follow the Signals from the Smart Money 167

      Stock Buybacks, Issuance, and Announcements 169

      Insider Traders Beat the Market 173

      Activism and Cloning 176

      Short Money Is Smart Money 179

      Notes 182

      PART SIX Building and Testing the Model 185

      CHAPTER 10 Bangladeshi Butter Production Predicts the S&P 500 Close 187

      Sustainable Alpha: A Framework for Assessing Past Results 189

      What’s the Big Idea? 191

      Rigorously Test the Big Idea 196

      The Parameters of the Universe 206

      Notes 208

      CHAPTER 11 Problems with the Magic Formula 211

      Glamour Is Always a Bad Bet 216

      Improving the Structure of a Quantitative Value Strategy 218

      Our Final Quantitative Value Checklist 222

      Notes 228

      CHAPTER 12 Quantitative Value Beats the Market 229

      Risk and Return 231

      Robustness 239

      A Peek Inside the Black Box 249

      Man versus Machine 257

      Beating the Market with Quantitative Value 262

      Notes 264

      Appendix: Analysis Legend 265

      About the Authors 267

      About the Companion Website 269

      • Michael Milburn August 26, 2014 at 12:13 am

        Hi Wesley, I’m sorry I failed ot communicate properly. I have the book and am reading through it – I think I bought it from you a few weeks ago as your name was the seller. I was wondering if there were discussion threads on this blog about the book. My apologies.

        (Or are you saying the book speaks for itself and needs no discussion? ;-))

          • Michael Milburn August 26, 2014 at 5:15 pm

            Wesley, Thanks for the links. If I’m out of line here let me know. I enjoy thinking about the kind of thing you’re doing here, but know I can be a pain in the a** sometimes and it’s difficult to pickup on social cues on the internet if I ask unwanted questions. If these (uninvited) questions are out of line/scope for the blog/thread just say so and I’ll stop.

            Here goes: I guess I had a question around the manipulator/fraud detection, and was wondering if you had thoughts on it. Again, feel free to disregard if this if it’s something you don’t want to or don’t have time to respond to. I totally understand – most people are busier than me 🙂 I checked the other threads and didn’t see discussion, so am asking here.

            Here goes: I love the idea of filtering the manipulators and distressed companies (the concepts presented are new to me), but was surprised at how small an impact that had. Additionally, it seems like the manipulators/distressed companies in aggregate had positive returns in the study, and I guess that surprised me.

            On page 80 of the book, the universe of stocks returned 10.80% and the 95% of companies in cleaned universe returned 11.04%. Doing a weighted average I think it means the 5% manipulators/fraudsters in aggregate had positive returns – maybe about 6% annually (not sure about the math). I was scratching my head at this result and wondered if you had thoughts on it or if you were surprised that this would be so?

            Do you feel that maybe the value universe has more manipulators overall so it may be more important in the value space? One of the difficulties I’ve seen in working w/ a Greenblatt/Magic Formula universe is there seems to be a lot of oddballs that work their way into the sort (your book points out problem of peak earnings companies being over-represented). Do you think maybe the manipulators/fraudsters might also be over-represented also?

            Anyhow, my mind keeps coming back to this. I would appreciate your thoughts on it if you were inclined.

          • Wesley Gray, PhD
            Wesley Gray, PhD August 27, 2014 at 8:44 am


            Our firm mission is to empower investors through education–no worries.

            You can focus solely on the fraud manipulator screens and find a whole bunch of absolutely terrible firms–but then what? The only way to make money on them is to short them or simply avoid them. Most of the poor returns are driven by small/micro turds.

            In our context, where we are talking about deep liquid firms that have been around 8+ years, the fraud/manipulator type screens are great–at the margin–but they can produce false positives in our universe. We chose to use the screens to eliminate the nasty left tail before we applied our QV approach, but didn’t want to be to aggressive on those screens for fear they would boot out a lot of viable value stocks and shrink our universe down to a size where the portfolios would be too concentrated and thus, hard to assess on a backtested basis.

            Now, if you are examining a micro/small cap type index, you might want to be more aggressive on the fraud/manipulator screens because a lot of stocks in those universes need to be culled from the herd and the threat of false positive identification is smaller.

          • Michael Milburn August 27, 2014 at 3:49 pm

            Thanks Wesley, appreciated. I’m drawn to the exclusion/avoidance ideas. Avoidance, not shorting, is my preference – hence why I really like the idea – especially since I haven’t figured out how to make shorting work for me – and shorting takes up alot of mental space.

            I do prefer larger caps – they seem to work better (or at least as good as small caps) w/ what I’m working on (momentum based)- but that may be because I initially test on larger caps. The studies point to small cap out-performance, but I have more difficulty w/ the models in that space. They work but apparently not as well. I know I’m missing something.

            I came across the Mohanram G-score paper for high P/B stocks
            due to a Marc Gerstein post on seeking alpha,
            and my eyes about popped out when saw the results from his long/short portfolio. I had a tough time understanding some of the tables in the paper, but that one was pretty clear. I only have SIPro data, so don’t have the advertising data to calculate the full G-score – but can calc 7 of the 8 components, and something along those lines seems potentially a pretty powerful exclusionary filtering mechanism for low G. Most of the delistings in his paper come from G of 3 or lower. Marc’s post shows that G still seems to work (albeit maybe w/ reduced effectiveness), although the recent dataset didn’t have many companies in the low G ranges like Mohanram initially showed. I have yet to get my calcs together for this (industry medians will take some work from me), so I don’t have my own data to tinker with yet, but I’m hoping there’s a good chunk of G 0,1,2 for scoring.

            I also perked up when I was reading the survey in your book about short interest. (along w/ a post on this site about high volatility stocks w/ low short interest outperforming.) I’m looking to include short interest as a component in an exclusionary scoring system, along w/ some of the other calcs mentioned in the book w/ goal of cutting off the left edge of the performance distribution, hopefully without too much hurting overall exposure.

            Anyhow, I appreciate all the ideas I get exposed to here, and thanks for sharing your thoughts.

          • Michael Milburn August 28, 2014 at 7:29 pm


            I think the gurufocus review nails many of my impressions/takeaways from the book. The things it has me thinking about:
            1) how a systematic quantitative approach eliminates the subjective factors in learning about companies. The “just do what the model tells you to do” approach now gets a big share of my investments and I’m moving more in that direction as I build the models. (It’s kindof like working on the tricycle while pedaling right now, so my method is a little awkward presently 🙂 ) I recently read Dan Ariely’s books on irrationality and took his coursera class – so maybe I’m more receptive to this now – but for whatever reason I’m more receptive that my input is not helpful beyond the development phase.
            2) different thinking about valuation ratios. For example – the ratios w/ gross profits – I never really used before and wouldn’t have expected them to be so predictive.
            3) simplification vs. complexity. All the backtests you did in that regard are probably my favorite part of the book to bring simplicity forward. The way it was presented was very effective. I struggle with this – for example right now I’m putting together something w/ piotroski f-score and mohanram g-score to filter stocks and am wondering am I just going muddy everything up? Also, my modified greenblatt approach that I run aggregates rankings of many valuation and quality metrics, along w/ a third leg for growth measures. You show that the process probably is bad. That’s good to know. Similarly, it’s scary how simple momentum models can be and still work – perhaps a pure case of effectiveness of simplification.
            4) overall all the backtesting results are HUGELY appreciated. It’s the meat that sells everything else. I could look through backtest results all day. Backtesting like you’ve done w/ point-in-time fundamental data is something I don’t have access to – so hard results like these are highly valued.
            5) I appreciate that you go into details. When I read Greenblatt’s little book it left a lot to the imagination of how to actually implement. I remember being happy when I could build process to generate maybe 60% of the stocks that would show up on his website. It was kindof fun trying to figure it out, but at the same time, a little concreteness would’ve been helpful. The book presents w/ clarity.
            6) short interest study
            7) Avoiding losers. Emphasis is often on what to buy and positive characteristics of those stocks. Looking at the characteristics of high probability losers and associated negative characteristics as filtering mechanism is interesting.

            Anyhow, the book, along with all the studies I’ve encountered since finding this site have me thinking in many new directions – that’s really the fun part of it for me.

  2. Wesley Gray, PhD
    Wesley Gray, PhD August 29, 2014 at 9:40 am

    All copies sold. Wow, that was fast. If we stumble across an inventory again in the future we’ll try and do the same thing

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