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Quantitative Value: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors
A must-read book on the quantitative value investment strategy Legendary investment gurus Warren Buffett and Ed Thorp represent different ends of the investing 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 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
1) How to avoid stocks that can cause a permanent loss of Learn how to uncover financial statement manipulation, fraud, and financial distress.
2) How to find stocks with the highest 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 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 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 .
1) How to avoid stocks that can cause a permanent loss of Learn how to uncover financial statement manipulation, fraud, and financial distress.
2) How to find stocks with the highest 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 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 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 .
288 pages, Hardcover
First published November 29, 2012
115 people are currently reading
1709 people want to read
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Displaying 1 - 30 of 36 reviews
February 1, 2015
This is one of the best value investing books I have ever read. It bridges the gap between the colloquial rhetoric found in value investing and translates them into quantifiable metrics.
I would recommend this book to anyone who has already read any value investing books (i.e. Intelligent Investor, The Little Book That Beats The Market.), and once to go even in more depth of in field.
I would recommend this book to anyone who has already read any value investing books (i.e. Intelligent Investor, The Little Book That Beats The Market.), and once to go even in more depth of in field.
January 13, 2016
This is an outstanding book for anyone considering value -based investing. And for anyone who isn't, to help them decide that perhaps they should. It is quite expensive yet worth the price.
(It could, however, have benefited from another round of editing, especially by someone who thinks in terms of usability.)
Here's the central notion. Value investing, introduced at Columbia Business School by Graham and Dodd, and captured in their book Security Analysis, means taking advantage of occasional market inefficiency to buy good quality stocks that are momentarily under-valued. You buy them when they are cheap, and ride them up to profit.
A big problem though is that our emotional, subjective views of what makes for a good value buy just do not work well. The authors explain why (and Dr. Gray goes on to explain this in more and even more interesting detail in his later book, DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth ).
The authors motivate the idea that just as a pilot runs through a checklist before takeoff, so must investors take their decision making to a model and set of procedures. This makes it less likely that we sabotage a good strategy by trying to out-thing the algorithm.
The book then takes us through the steps of building a winning algorithm. It starts with Joel Greenblatt's Magic Formula The Little Book That Still Beats the Market approach and addresses its obscurity problems.
The authors build off of the Magic Formula to consider and objectively analyze a variety of metrics. For example, what is the best means to determine price as a value indicator? EBIT/TEV wins, and the authors show every step of how they evaluate it against other options.
In fact, they test everything, which is part of the fun of the book. And at the end provide a detailed checklist that takes you through every screen required to identify which stocks to buy. Full transparency here: you walk away with the ability to implement a model.
There are some pet peeves of mine that weren't addressed (or not sufficiently). I wasn't always clear on when the comparisons included dividend reinvestment (e.g., when comparing to S&P 500 Total Return which does). I believe dividends completely skew the apparent value of any methodology. Plus, a reinvestment model isn't realistic for individual investors who must pay taxes even on reinvested gains.
Another is the lack of short term charts. Yes, there's value in a very long back test. But once the 1974-2011 view is complete, it would be so very useful to see the visualization of rolling five-year results. Unlike an endowment, I have an end date. I'd like to understand the efficacy of the model in time periods that I can relate to my own life events. Wouldn't most folks?
Finally, I'd like to see the authors address selling. It isn't completely clear if the model completely turns over the holdings annually or if it is buy and hold (an example of where some more editing could clarify things). It would be great to have a discussion on sell algorithms. (For example: if a value stock over time achieves its efficient valuation it is no longer a value stock. If there's annual rebalancing that liquidates, is that appropriate? Might a sell algorithm indicate to hold an asset longer? How about assets that remain on the value list for more than one rebalancing; should it stay on without limit?
All in all though, I highly recommend this book.
(It could, however, have benefited from another round of editing, especially by someone who thinks in terms of usability.)
Here's the central notion. Value investing, introduced at Columbia Business School by Graham and Dodd, and captured in their book Security Analysis, means taking advantage of occasional market inefficiency to buy good quality stocks that are momentarily under-valued. You buy them when they are cheap, and ride them up to profit.
A big problem though is that our emotional, subjective views of what makes for a good value buy just do not work well. The authors explain why (and Dr. Gray goes on to explain this in more and even more interesting detail in his later book, DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth ).
The authors motivate the idea that just as a pilot runs through a checklist before takeoff, so must investors take their decision making to a model and set of procedures. This makes it less likely that we sabotage a good strategy by trying to out-thing the algorithm.
The book then takes us through the steps of building a winning algorithm. It starts with Joel Greenblatt's Magic Formula The Little Book That Still Beats the Market approach and addresses its obscurity problems.
The authors build off of the Magic Formula to consider and objectively analyze a variety of metrics. For example, what is the best means to determine price as a value indicator? EBIT/TEV wins, and the authors show every step of how they evaluate it against other options.
In fact, they test everything, which is part of the fun of the book. And at the end provide a detailed checklist that takes you through every screen required to identify which stocks to buy. Full transparency here: you walk away with the ability to implement a model.
There are some pet peeves of mine that weren't addressed (or not sufficiently). I wasn't always clear on when the comparisons included dividend reinvestment (e.g., when comparing to S&P 500 Total Return which does). I believe dividends completely skew the apparent value of any methodology. Plus, a reinvestment model isn't realistic for individual investors who must pay taxes even on reinvested gains.
Another is the lack of short term charts. Yes, there's value in a very long back test. But once the 1974-2011 view is complete, it would be so very useful to see the visualization of rolling five-year results. Unlike an endowment, I have an end date. I'd like to understand the efficacy of the model in time periods that I can relate to my own life events. Wouldn't most folks?
Finally, I'd like to see the authors address selling. It isn't completely clear if the model completely turns over the holdings annually or if it is buy and hold (an example of where some more editing could clarify things). It would be great to have a discussion on sell algorithms. (For example: if a value stock over time achieves its efficient valuation it is no longer a value stock. If there's annual rebalancing that liquidates, is that appropriate? Might a sell algorithm indicate to hold an asset longer? How about assets that remain on the value list for more than one rebalancing; should it stay on without limit?
All in all though, I highly recommend this book.
December 7, 2021
Great book, provides the theoretical and empirical underpinning behind the popular value-investing techniques. Where it failed, though, is not going into details about its backtests for its strategies -- how often is the strategy rebalanced, what is the capital allocation, etc. Still, provides a comprehensive 'fundamental' checklist for stock picking and/or analysis.
June 12, 2014
Excellent book on value investing and adapting quantitative strategies based on value investing principles. Dr. Gray took the ideas of Buffett, Graham, Dodd, Klarman and Greenblatt and came up with a comprehensive strategy that incorporates all the best elements of value investing on both the long and short side (Beneish, Altman Z-Score, etc.). Looking forward to following Dr. Wesley Gray's career, and also investing in his new exchange traded quantitative value fund!
August 6, 2015
Good book on screening stocks and understanding how to look for value instead of chasing expensive stocks. If your into modeling the book is filled with charts and figures of many comparisons to give you ideas.
I wanted the site to have a very strong companion site to build a community and good area for Q&A but not for now at least.
I wanted the site to have a very strong companion site to build a community and good area for Q&A but not for now at least.
August 21, 2018
In these times of tight budgets, personnel intensive strategies like value investing has had to make way for indexing but also quantative investing. In the introduction to this book Gray and Carlisle give a short but comprehensive description of the field of value investing, including a wonderful true story with Warren Buffett and Ed Thorp playing bridge. The conclusion from the introduction is that successful value investing more than anything relies of the investor having the temperament to follow through and not succumb to psychological pitfalls. The meeting of Buffett and Thorp thus is an introduction to the theme of the book – value investing merging with quantative investing. This fusion is further explored when in the next section Joel Greenblatt’s Magic Formula is described. The task that the authors give themselves is to come up with a quantative value strategy that improves on the successful recipe of Greenblatt.
In order to accomplish this tall order they aim to: 1) find effective accounting red flags to weed out stocks that will cause permanent loss of capital due to fraud and bankruptcies, 2) screen away low quality stocks, 3) find cheep stocks and 4) screen for triggers that hopefully shorten the time between the value investor’s purchase of a stock and when the market realizes the full value of it. Finding fraud and triggers add value but the core of the strategy is the other two factors. Basically the authors, just like Greenblatt, seek good and cheap, rather than bad and expensive.
After testing a number of valuation multiples and combinations of multiples, Gray and Carlisle are actually forced to give up. The most effective multiple in the US market between 1964 and 2011 was EV/EBIT, i.e. the same multiple that Greenblatt used. It produced the highest return and Sharpe ratio - substantially outperforming the market. Furthermore stocks cheap on EV/EBIT had the smallest drawdowns. The opportunities for improvement turns out to be larger when it comes to Greenblatt’s quality measure ROC. Almost all single year return on capital measures performed pretty much in line with the market or slightly lower. The problem is that profit margins are mean reverting and by targeting the highest return a screen often finds cyclical highs. The highest quality stocks are a type of glamour stocks. The problem is then mitigated in several ways; firstly the authors screen for value first and then seek high quality within the cheap stocks (instead of using a parallel process), then the time span is expanded to averages over 8 years to find persistence, further the definition of quality is enlarged from return on capital to margins and financial strength and finally the authors not only look to the level of returns, margins etc. but also to the trend and the stability. This vastly improves the performance. The back tested Quality Value strategy had a return of 17,7 percent between 1974 and 2011 where Greenblatt’s Magic Formula portfolio returned 13,9 percent and S&P 500 10,4 percent. The Sharpe ratios were 0,79, 0,55 and 0.37. Both Quality Value and the Magic Formula had smaller drawdowns then S&P 500.
This is a quality book. It’s not hugely original but it’s full of common sense in the execution and I’m sure to come back to it. My only fear is that the presented model is too complex, especially compared to the Magic Formula that was exceedingly simple. Complex models that back test well always run the risk of being over-engineered and disappoint further on. Thankfully the logic behind the chosen process is clear and this alleviates much of my worries. Complexity complicates matters but it also lessens the risk of ending up in the most populated corners of the market. As screening can be performed by so many there is an obvious risk of crowding. Algorithms might not succumb to psychological pitfalls, but if manual analysis is framed to avoid behavioural traps I’m convinced it can add rather than deduct value. Fundamental analysis is not yet superfluous.
In order to accomplish this tall order they aim to: 1) find effective accounting red flags to weed out stocks that will cause permanent loss of capital due to fraud and bankruptcies, 2) screen away low quality stocks, 3) find cheep stocks and 4) screen for triggers that hopefully shorten the time between the value investor’s purchase of a stock and when the market realizes the full value of it. Finding fraud and triggers add value but the core of the strategy is the other two factors. Basically the authors, just like Greenblatt, seek good and cheap, rather than bad and expensive.
After testing a number of valuation multiples and combinations of multiples, Gray and Carlisle are actually forced to give up. The most effective multiple in the US market between 1964 and 2011 was EV/EBIT, i.e. the same multiple that Greenblatt used. It produced the highest return and Sharpe ratio - substantially outperforming the market. Furthermore stocks cheap on EV/EBIT had the smallest drawdowns. The opportunities for improvement turns out to be larger when it comes to Greenblatt’s quality measure ROC. Almost all single year return on capital measures performed pretty much in line with the market or slightly lower. The problem is that profit margins are mean reverting and by targeting the highest return a screen often finds cyclical highs. The highest quality stocks are a type of glamour stocks. The problem is then mitigated in several ways; firstly the authors screen for value first and then seek high quality within the cheap stocks (instead of using a parallel process), then the time span is expanded to averages over 8 years to find persistence, further the definition of quality is enlarged from return on capital to margins and financial strength and finally the authors not only look to the level of returns, margins etc. but also to the trend and the stability. This vastly improves the performance. The back tested Quality Value strategy had a return of 17,7 percent between 1974 and 2011 where Greenblatt’s Magic Formula portfolio returned 13,9 percent and S&P 500 10,4 percent. The Sharpe ratios were 0,79, 0,55 and 0.37. Both Quality Value and the Magic Formula had smaller drawdowns then S&P 500.
This is a quality book. It’s not hugely original but it’s full of common sense in the execution and I’m sure to come back to it. My only fear is that the presented model is too complex, especially compared to the Magic Formula that was exceedingly simple. Complex models that back test well always run the risk of being over-engineered and disappoint further on. Thankfully the logic behind the chosen process is clear and this alleviates much of my worries. Complexity complicates matters but it also lessens the risk of ending up in the most populated corners of the market. As screening can be performed by so many there is an obvious risk of crowding. Algorithms might not succumb to psychological pitfalls, but if manual analysis is framed to avoid behavioural traps I’m convinced it can add rather than deduct value. Fundamental analysis is not yet superfluous.
April 24, 2020
Firstly, the cover price of this book is significant. The reviews of it were generally favourable and so I bought it.
The information within is probably technically/academically sound. However, it seems to suffer the problem of being largely non-transferable to the real world that I suspect most people inhabit. There are a significant number of processes involved in the strategy that at times require information that simply isn't accessible to most people unless they have access to stock screening memberships that will provide the raw data (bearing in mind this strategy is applied to all stocks in a market universe, and not 'spot applied' to stocks of interest.). The companion website that is mentioned on the cover is simply the authors Alpha Architect website which anyone can navigate to free from Google, which doesn't supply (as far as I can find) a stock screener using all the processes in the book - furthermore it only includes US stocks so is utterly useless for someone who has bought the book in the UK.
The book content itself could use clearer editing - it reads more of an academic research paper than something than can actually be followed.
Most charts in the book were totally unreadable, as they are supplied in various subtle variants of grayscale, so it's impossible to differentiate the lines (at least in the e-book version that I bought.). For a book at this price point that is simply unacceptably poor quality and both the author and publisher should be ashamed at endorsing something that clearly and obviously isn't fit for purpose at such a fundamental level.
Overall, I suspect this might be of passing interest to theoretical academics within the industry - but will be of limited use to most people on a practical level. It strongly appeared to be the author taking an opportunity to 'intellectually masturbate' in public, rather than something of practical value for money to most readers.
The information within is probably technically/academically sound. However, it seems to suffer the problem of being largely non-transferable to the real world that I suspect most people inhabit. There are a significant number of processes involved in the strategy that at times require information that simply isn't accessible to most people unless they have access to stock screening memberships that will provide the raw data (bearing in mind this strategy is applied to all stocks in a market universe, and not 'spot applied' to stocks of interest.). The companion website that is mentioned on the cover is simply the authors Alpha Architect website which anyone can navigate to free from Google, which doesn't supply (as far as I can find) a stock screener using all the processes in the book - furthermore it only includes US stocks so is utterly useless for someone who has bought the book in the UK.
The book content itself could use clearer editing - it reads more of an academic research paper than something than can actually be followed.
Most charts in the book were totally unreadable, as they are supplied in various subtle variants of grayscale, so it's impossible to differentiate the lines (at least in the e-book version that I bought.). For a book at this price point that is simply unacceptably poor quality and both the author and publisher should be ashamed at endorsing something that clearly and obviously isn't fit for purpose at such a fundamental level.
Overall, I suspect this might be of passing interest to theoretical academics within the industry - but will be of limited use to most people on a practical level. It strongly appeared to be the author taking an opportunity to 'intellectually masturbate' in public, rather than something of practical value for money to most readers.
May 29, 2017
This book provides a not-too-technical introduction to the world of quantitative investing, primarily focused on equities. It goes through the developments in the quant world over the years and highlights ways to apply the principles of value investing using the quant framework.
The final product of the book is a comprehensive investment strategy that uses signals based on value and quality factors. It also provides a brief overview of other signals that could be used in conjunction with these basic signals to further improve the output of the strategy.
I believe this should be a good read for someone new to the world of quant investing but may not add value for someone who has been in the business for a while.
The final product of the book is a comprehensive investment strategy that uses signals based on value and quality factors. It also provides a brief overview of other signals that could be used in conjunction with these basic signals to further improve the output of the strategy.
I believe this should be a good read for someone new to the world of quant investing but may not add value for someone who has been in the business for a while.
November 9, 2017
whew this book is thick.... divide this metric by that metric and backtest it against a bunch of other metrics and then we'll see what wins. it is a long research paper that heavily cites dozens, maybe hundreds of other books. good news is i don't think i need to read security analysis after this (and several other books). definitely some great insight on behavioral bias, but for the most part this is a deeply quantitative look at assessing the correlation between various financial metrics and price performance. i mean, that's useful and all but this is a book where you skim for the formulas and the results....use it as a reference for building a strategy. In that respect it is basically a text book. Even so I'll probably read quantitative momentum too.
September 18, 2018
Solid book and comes from a place a thorough research. The qualitative tales of Buffet keep it interesting reading and help to make the points. Given how dry the material could be I think this works well and keeps the interest level high.
Unfortunately the print version leaves a lot to be desired. Grayscale charts in a black and white book? Why not use lines with different weights/patterns? Often times vertical axis labels are missing and in a couple chapters the figure/graph titles were just wrong (although the proper one could be fairly easily deduced). For a book at this price point I would have expected a slightly higher quality.
Also the “+website” aspect of the book is no longer a site with the publisher and instead a firm started by one of the authors.
Unfortunately the print version leaves a lot to be desired. Grayscale charts in a black and white book? Why not use lines with different weights/patterns? Often times vertical axis labels are missing and in a couple chapters the figure/graph titles were just wrong (although the proper one could be fairly easily deduced). For a book at this price point I would have expected a slightly higher quality.
Also the “+website” aspect of the book is no longer a site with the publisher and instead a firm started by one of the authors.
April 22, 2020
Excellent book that provides direct insight and a specific strategy.
The book is quite dense and is not the typical "buy low sell high" book with typical but obvious advices such as "long term beats short term".
It provides a step-by-step investment strategy with a clear and concise checklist.
The only problem I see with the Strategy/Checklist is that it might not be realistically possible to apply it for the typical personal investor, as he/she will not have the tools to analyze such a wide scope of companies.
The book is quite dense and is not the typical "buy low sell high" book with typical but obvious advices such as "long term beats short term".
It provides a step-by-step investment strategy with a clear and concise checklist.
The only problem I see with the Strategy/Checklist is that it might not be realistically possible to apply it for the typical personal investor, as he/she will not have the tools to analyze such a wide scope of companies.
June 7, 2020
an excellent book about value but in a world of cognitive bias, this is must read for all value investor. There is quite a lot of simple math which could lead some reader a bit of confusion, but don't worry, you will understand it all.
This book has a very useful notes which help you to find another great books as well, so if you're serious and want to understand more about value, then it's a great source.
Based on a domain where a lot of number, and fact, this book made a great job to help you understand much easier.
Happy reading!
This book has a very useful notes which help you to find another great books as well, so if you're serious and want to understand more about value, then it's a great source.
Based on a domain where a lot of number, and fact, this book made a great job to help you understand much easier.
Happy reading!
November 18, 2021
Khoảng cách từ lý thuyết tới tài chính định lượng: đó là backtest.
Sách nói về cách ứng dụng định lượng vào đầu tư giá trị, thông qua nghiên cứu phân tích và kiểm thử nhiều mô hình khác nhau, các tác giả đã trích xuất ra một công thức đầu tư giá trị định lượng. Một công thức lựa chọn cổ phiếu, một checklist sàng lọc những ngoại lệ xấu để nhà đầu tư có thể có kết quả tối ưu nhất trong dài hạn.
Lựa chọn của bạn là sử dụng công thức trong sách, kiểm thử lại công thức hay dựa vào phương pháp trong sách để tìm ra chén thánh của riêng bạn?
Sách nói về cách ứng dụng định lượng vào đầu tư giá trị, thông qua nghiên cứu phân tích và kiểm thử nhiều mô hình khác nhau, các tác giả đã trích xuất ra một công thức đầu tư giá trị định lượng. Một công thức lựa chọn cổ phiếu, một checklist sàng lọc những ngoại lệ xấu để nhà đầu tư có thể có kết quả tối ưu nhất trong dài hạn.
Lựa chọn của bạn là sử dụng công thức trong sách, kiểm thử lại công thức hay dựa vào phương pháp trong sách để tìm ra chén thánh của riêng bạn?
December 25, 2025
Of every finance book I have picked up on portfolio management, this has proved the most interesting. A lot of writers in the genre of "value investing" use a lot of folksy and self congragulatory language about how "all you have to do is keep reading and be patient and you will be rich." This is a shame, because value investing in itself is very interesting.
Dodd and Graham's writings are, while considered timeless, at times difficult to translate into modern relevance. Quantative value has a number of metrics, backed by data, that I look forward to testing in my own portfolio analysis.
Dodd and Graham's writings are, while considered timeless, at times difficult to translate into modern relevance. Quantative value has a number of metrics, backed by data, that I look forward to testing in my own portfolio analysis.
October 20, 2020
Most of the investment analysis approaches (and books written) pursue either the quantitative analysis approach of stocks or discuss qualitative aspects of stock selection. In this book, the authors have actually tried to merge both approaches and presented their findings tested over past data showing (the potential of) improved results. A valuable asset in any investors' books portfolio.
Practitioners could share actual field results with the suggested approaches.
Practitioners could share actual field results with the suggested approaches.
March 31, 2020
This book is a must read for both people who are more experienced in investing as well as people who are getting started in investing. The concepts are explained in a very clear and narrative way, although some concepts and formulas could have been explained more in-depth. Also the references to the papers that they use makes it easier to find those details missing in the book.
April 30, 2018
A book to keep on the shelf and reference for decades. In a era where index investing is the preferred way to invest, I look forward to implementing the Quantitative Value strategy in the near future to beat the market.
May 11, 2018
I started my career following the magic formula and ended with a lot of value traps. this book pointed out the flaws of MF.
July 12, 2018
If I had to pick one book to shape my entire investing philosophy, it’d be Quantitative Value. Carlisle and Gray, give you a step by step blueprint to millions. Doesn’t get easier then this!
February 17, 2019
Probably the most in-depth manual on how to actually implement a quant-value portfolio.
May 14, 2019
The authors provide a good checklist for quantitative value investing.
October 10, 2020
A good overview of the quantitative equities literature, but a bit long-winded at times. Tldr, EV/EBIT
March 1, 2021
Good for intermediary investors
June 18, 2021
Very effective. Tried and tested.
November 6, 2024
The figures are all in grayscale (without any distinguisher other than colour), are extremely hard to read, and not very informative.
Overall still a good book to read though.
Overall still a good book to read though.
February 25, 2024
Amazone
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.
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.
May 9, 2020
Typically I stay away from value investing books but made an exception for this one and it was worth it. This is the best fundamentals book I have ever read from investing perspective. The author beautifully intertwines fundamental concepts and ratios with their ability to signal value and price. He also uses similar frameworks researched elsewhere to prove/disprove their efficacy. Essentially, Gray has codified Buffets philosophy - buy good quality companies at fair price, hence the name - Quantitative Value.
January 28, 2013
The writing has a clunky research paper feel to it, but I found the ideas presented to be fascinating. The main lesson I took away is that simple models can often outperform humans, and human intervention with those models typically makes the results worse, not better. I'm especially interested to learn more about our behavioral biases.
January 2, 2014
Very good systematic empirical approach to investing in a world where investing is predominantly taught by anecdote or apprenticeship. Good explanation for how investors can craft their own empirical process and metrics for investing.
September 19, 2016
A very interesting book, the authors take it upon themselves to see if they can improve upon the magic formula. Spoiler they manage it. I greatly enjoyed the read, and had some good insights though I won't likely be using their model.
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