20 Quant Value Books @ Liquidation Prices

20 Quant Value Books @ Liquidation Prices

August 25, 2014 Research Insights, Book Reviews
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(Last Updated On: September 23, 2014)

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.

qvlowprice

 

First come, first serve.

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Definitions of common statistics used in our analysis are available here (towards the bottom)




About the Author

Wesley R. Gray, Ph.D.

After serving as a Captain in the United States Marine Corps, Dr. Gray received a PhD, and was a finance professor at Drexel University. Dr. Gray’s interest in entrepreneurship and behavioral finance led him to found Alpha Architect. Dr. Gray has published three books: EMBEDDED: A Marine Corps Adviser Inside the Iraqi Army, QUANTITATIVE VALUE: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors, and DIY FINANCIAL ADVISOR: A Simple Solution to Build and Protect Your Wealth. His numerous published works has been highlighted on CBNC, CNN, NPR, Motley Fool, WSJ Market Watch, CFA Institute, Institutional Investor, and CBS News. 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.


  • Michael Milburn

    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.

  • 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

    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

    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.

  • Michael,

    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

    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
    http://papers.ssrn.com/sol3/papers.cfm?abstract_id=403180
    due to a Marc Gerstein post on seeking alpha,
    http://seekingalpha.com/article/2428835-a-different-take-on-a-john-galt-portfolio
    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

    Wesley,

    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.

  • 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