Quantitative Value with Momentum

Quantitative Value with Momentum was borne out of years of research. While we have documented every step of the process in detail for investors we present below only the philosophy and a schema detailing how our algorithm is constructed, along with a disclosure of the majority of research utilized to build and validate the strategy. 

The Quantitative Value with Momentum Philosophy

“Fundamentals tell you what to buy, technicals tell you when to buy it.”


Benjamin Graham, who made widely known the idea of purchasing stocks at a discount to their “intrinsic value” back in the 1930’s, is known today as the father of value investing. Since Graham’s time, academic research has shown that stocks trading at low price relative to their fundamentals (e.g. earnings, cash flows etc.) have historically outperformed the market [i]. In the investing world, Graham’s most famous student, Warren Buffett, has inspired legions of investors to adopt the value philosophy.


Momentum investing is simply buying those stocks that have appreciated most relative to other stocks in a given universe. Moreover, when it comes to the “momentum” (formerly called “relative strength price momentum”) value investors tend to scoff at the idea that only the price of a stock could be used as a basis for investment. However, while we consider ourselves value investors, we also consider ourselves evidence-based investors first and foremost.

Eugene Fama, the 2014 co-recipient of the Nobel Prize in Economics and father of the efficient market hypothesis, and his equally well-credentialed co-author, Ken French, have summarized the academic research on momentum as follows [ii]:

“The premier anomaly is momentum.”

When the greatest empirical finance researchers suggest momentum is the leading academic anomaly, we take note. Fama and French make this statement because the empirical research on the momentum effect is compelling. For example, academic researchers have examined stock data going back over 200 years and identified a significant and robust historical performance record [iii]. Momentum is well grounded, historically [iv]. Of course, for a strategy to provide persistent above average returns we need it to be steeped in a behavioural bias, and not simply possess a compelling backtest. We believe, the above average returns to momentum will persist because they are driven by innate human bias [v].

Value with Momentum

We know value investing works; we know momentum investing works; so, what is “value with momentum” and why do we combine two strategies that are known to work independently?

Here we turn to the wisdom of Warren Buffet, “Know your circle of competence, and stick within it. The size of that circle is not very important; knowing its boundaries, however, is vital.”

On deeper reflection, in our view, the “circle of competence” consists of two independent circles, technical and psychological. Where these circles intersect is where you are within both your technical and psychological circle of competence - “Total Competence” - you want to be there. Please refer to the diagram below for a graphical representation of this concept:



As evidence-based investors we are well versed in the ways of both value and momentum. However, momentum, while backed with centuries of evidence does not possess any underlying business fundamentals (e.g. sales, cash flows etc.) supporting its case, unlike value. This fact left us wondering for just how many years could value investors endure underperformance from a momentum strategy [vi] without wilting. After all, the best investing strategy is the one you can stick with! Our reflection resulted in the research and development of a strategy that is “founded on fundamentals and guided by technicals” - Quantitative Value with Momentum.

Human Behaviour & Quantitative Tools

Despite widespread knowledge that value and momentum generate higher returns over the long-haul, both strategies have continued to beat the market. How is this possible? The answer relates to a fundamental truth: human beings behave irrationally. We follow an evolutionary mindset that focuses on surviving in the jungle, not optimizing our portfolio. While we will never eliminate our survival instincts, we can minimize their impact by employing quantitative tools.

“Quantitative,” is often considered to be an opaque mathematical black art, only practiced by Ivory Tower academics and supercomputers. However, quantitative, or systematic, processes are merely tools that value investors can use to minimize their “survival” instincts when investing. Quantitative tools serve two purposes: 1) protect us from our own behavioural errors, and 2) exploit the behavioural errors of others. These tools do not need to be complex, but they do need to be systematic. The research demonstrates that simple, systematic processes outperform human “experts” [vii]. The inability of human beings to robustly outperform simple systematic processes also holds true for investing, just as it holds true for most other fields [viii].

Much of the analysis conducted by investors - reading financial statements, interpreting past trends, and assessing relative valuations - can be done faster, more effectively, and across a wider swath of securities via a systematic process. Investors argue that instinct and experience add value in the stock-selection process, but the evidence doesn’t support this interpretation. Why? When investors respond to non-quantitative signals (e.g., the latest headlines, their expert friend’s opinion, their interpretation of a price pattern etc.), they unconsciously introduce cognitive biases into their investment process. These biases lead to predictable underperformance. The Quantitative Value with Momentum Investing Philosophy is best suited for value investors who can acknowledge their own fallibility. Granted, the approach is not infallible, and should always be questioned; however, the approach seeks to provide the following: a systematic, evidence-based, value/momentum focused investment strategy that is built to beat behavioural bias [ix].

The Quantitative Value with Momentum Algorithm


The complete Quantitative Value with Momentum algorithm is as follows:

References​ (related to above text):


[i] Wesley Gray and Jack Vogel, “Analyzing Valuation Measures: A Performance Horse-Race over the Past 40 Years.” 2012.


[ii] Fama, E. and K. French, 2008, Dissecting Anomalies, The Journal of Finance, 63, pg. 1653-1678.


[iii] Geczy, C. and M. Samonov, Two Centuries of Price Return Momentum, 2016. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2292544


[iv] The Quantitative Momentum Investing Philosophy, Jack Vogel, 2015. https://alphaarchitect.com/2015/12/01/quantitative-momentum-investing-philosophy/


[v] William N. Goetzmanny Simon Huangz, Momentum in Imperial Russia, 2018. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2663482


[vi] Is momentum investing dead? Or is it just painful?, Wesley Gray, 2016. https://alphaarchitect.com/2016/09/07/is-momentum-investing-dead-or-is-it-just-painful/


[vii] Painting by Numbers: An Ode to Quant, https://alphaarchitect.com/wp-content/uploads/2013/01/Painting-by-the-Numbers.pdf


[viii]  Grove, W., Zald, D., Lebow, B., and B. Nelson, 2000, “Clinical Versus Mechanical Prediction: A Meta-Analysis,” Psychological Assessment 12, p. 19-30.


[ix] The Quantitative Momentum Investing Philosophy, Jack Vogel, 2015. https://alphaarchitect.com/2015/12/01/quantitative-momentum-investing-philosophy/

Additional references:

Wesley Gray and Tobias Carlisle, Quantitative Value, Chapter 3 (New Jersey: John Wiley & Sons,2012).


Richard Sloan, “Do Stock Prices Fully Reflect Information in Accruals and Cash Flows about Future Earnings?” Accounting Review 71 (1996): 289–315.

David Hirshleifer, Kewei Hou, Siew Hong Teoh, and Yinglei Zhang, “Do Stock Prices Fully Reflect Information in Accruals and Cash Flows about Future Earnings?” Journal of Accounting and Economics 38 (2004): 297–331.

Wesley Gray and Tobias Carlisle, Quantitative Value, Chapter 3 (New Jersey: John Wiley & Sons,2012).

The Predictable Cost of Earnings Manipulation, Messod Daniel Beneish, Craig Nichols, 2007. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1006840

Price-to-Book’s Growing Blind Spot, Chris Meredith, O’Shaughnessy Asset Management, LLC  2016.

Lehman, B. N., 1990, Fads, Martingales, and Market Efficiency, The Quarterly Journal of Economics, 105, pp. 1-28 and Jegadeesh, N., 1990, Evidence of Predictable Behavior of Security Returns, The Journal of Finance, 45, pp. 881-898.

DeBondt, W. F., and R. Thaler, 1985, Does the Stock Market Overreact?, The Journal of Finance, 40, pp. 793-805.

Jegadeesh, N., and S. Titman, 1993, Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency, The Journal of Finance, 48, pp. 65-91.

Wesley Gray, Jack Vogel, Quantitative Momentum: A Practitioner's Guide to Building a Momentum-Based Stock Selection System (Wiley Finance), 2016.

How Portfolio Construction Affects Momentum Funds, Jack Vogel,2015. https://alphaarchitect.com/2015/11/16/how-portfolio-construction-affects-momentum-funds/

Da, Z., U. G. Gurun, and M. Warachka, 2014, Frog in the Pan: Continuous Information and Momentum, Review of Financial Studies, pp. 1-48. https://www3.nd.edu/~zda/Frog.pdf


V. DeMiguel, L. Garlappi, and R. Uppal, “Optimal Versus Naïve Diversification: How Inefficient is the 1/N Portfolio Strategy?” Review of Financial Studies 22, no. 5 (2009): 1915–1953.


Elton, E. and Martin Gruber, 1977, Risk Reduction and Portfolio Size: An Analytical Solution, The Journal of Business 50, p 415-437.


James O'Shaughnessy, What Works on Wall Street: The Classic Guide to the Best-Performing Investment Strategies of All Time, 4th ed. Cahpeter 4 Section 4 (New York: McGraw-Hill, 2011).


Allen C. Benello, Michael van Biema, and Tobias E. Carlisle, Concentrated Investing: Strategies of the World's Greatest Concentrated Value Investors”, 2016.


How to Combine Value and Momentum Investing Strategies, Jack Vogel, 2015. https://alphaarchitect.com/2015/03/26/the-best-way-to-combine-value-and-momentum-investing-strategies/


Wesley Gray and Jack Vogel, “On the Performance of Cyclically Adjusted Valuation Measures” 2013. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2329948


James O'Shaughnessy, What Works on Wall Street: The Classic Guide to the Best-Performing Investment Strategies of All Time, 4th ed. Table 27.2 (New York: McGraw-Hill, 2011).


James O'Shaughnessy, What Works on Wall Street: The Classic Guide to the Best-Performing Investment Strategies of All Time, 4th ed. Table 4.2 (New York: McGraw-Hill, 2011).


What Has Worked In Investing, Tweedy, Browne Company LLC, 2009, Table 5, pg 7. https://www.tweedy.com/resources/library_docs/papers/WhatHasWorkedFundOct14Web.pdf


Asness, Frazzini, Israel, Moskowitz, Pedersen, “Size Matters, If You Control Your Junk”, 2015. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2553889


Does the Small-cap “size” effect exist? Probably, Wesley Gray, 2014. https://alphaarchitect.com/2014/07/02/does-the-small-cap-size-effect-exist-probably/


Chee Seng Cheong A Fin, Justin Steinert, “The size effect: Australian evidence”, Australasian Journal of Applied Finance, 2013.


We are not registered investment advisors. This material has been provided for informational purposes only and should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed.