• Behavioral Finance
  • The Case for Systematic Decision-Making

    by Wesley R. Gray Ph.D.

    “If you do fundamental trading one morning you feel like a genius, the next day you feel like an idiot….by 1998 I decided we would go 100% models…we slavishly follow the model. You do whatever it [the model] says no matter how smart or dumb you think it is. And that turned out to be a wonderful business.”

    This quote, from Jim Simons, founder of the world’s most successful hedge fund, Renaissance Technologies, demonstrated the utility of systematic decision-making in an MIT video.

    The urge to use our judgment throughout the investing process is strong. I argue that, while investors need human experts to design models, they should let computers be in charge of applying those models and fight the urge to use their judgment in the implementation process. “Gut-based,” or discretionary, stock pickers certainly have a compelling story: Invest countless hours in research, identify investment opportunities and profit from the hard work. Stock pickers, however, rely on the false premise that “countless hours of being busy” adds value in the context of investment management. The empirical evidence on the subject of systematic versus discretionary decision-making is abundantly clear: Models beat experts. In fact, the late Paul Meehl, one of the great minds in the field of psychology, describes the body of evidence on the “models versus experts” debate as the only controversy in social science with “such a large body of qualitatively diverse studies coming out so uniformly in the same direction.”

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    Wesley R. Gray Ph.D. is the founder, CEO and CIO of Alpha Architect. He also manages the exchange-traded funds ValueShares U.S. Quantitative Value (QVAL) and ValueShares International Quantitative Value (IVAL), which are based on the FS-Score.


    Discussion

    Paul H from CA posted over 2 years ago:

    Hi-

    How complex are the investment models generated by experts? Are these models accessible to anyone? Do these models change with time? Is the shadow portfolio, for example, considered as one of these models?

    Thank you for the article.


    Ricardo Moran from FL posted over 2 years ago:

    Excellent article. I would also be very interested in the answer to Paul's third question: "Is the shadow portfolio, for example, considered as one of these models?" regarding both the shadow stock and the shadow mutual fund portfolios.
    Thanks for the article,
    R


    Charles Rotblut from IL posted over 2 years ago:

    Paul,

    The model can be created from any set of quantitative data. A basic stock screen that seeks out profitable companies with low valuations counts as a quantitative model. In the simplest of terms, a model is simply a method of identifying stocks that match or violate a set of pre-specified characteristics.

    The key is to use the model as the basis for a disciplined approach to investing. Let the model determine what meets the buy and sell guidelines, which is the approach we follow with the Shadow Stock portfolio and our other portfolios. Then conduct due diligence to ensure there isn't any a negative characteristic not considered by the model that would alter your view.

    -Charles


    Paul Firgens from Wisconsin posted over 2 years ago:

    Dr. Gray wrote an excellent book, "Quantitative Value, A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors", which elaborates on his approach to finding a model. It will give you a sense of the complexity involved. Recommended!


    Dave K from CA posted over 2 years ago:

    Charles,

    Your advice about applying "due diligence" to the Shadow Stock screen/model seems somewhat contrary to the thrust of the article. Isn't due diligence a form of human/clinical thinking that is subject to emotions? If so, the evidence presented by Wesley Gray strongly suggests that the model altered by due diligence will most likely underperform the model itself.

    If due diligence is more like applying additional fixed rules to the model, such "tinkering" is still subject to the danger identified in reason #4 above: Incorrect modifications to the model outnumber correct modifications.

    I thank the experts at AAII for developing the Shadow Stock portfolio model. I'm not at all confident that I can improve upon it.


    Charles Rotblut from IL posted over 2 years ago:

    Dave,

    The only characteristics stocks screens consider is what they are instructed to filter for. Nothing else about a passing company is considered. This is why it is important to look beyond a screen results to ensure there is not a negative trait beyond the screen's parameters that would alter your opinion.

    -Charles


    David Phillips from AL posted over 2 years ago:

    Charles,

    Please address the subject of backtesting a model to see if it produced the desired results. If not, modify the model and keep testing. What are the tools to accomplish this?

    However, when does this become data mining or data fitting? On the other hand, "if a model won't hold up to rigorous backtesting why should one think it will hold up going forward", to quote professor Glover.

    Thanks for such an interesting article.

    David


    Shane Milburn from TN posted over 2 years ago:

    Just wanted to post that I enjoyed this article. Good information and perspective. Much of my portfolio decisions are based on a variation on Joel Greenblatt's methods - but I do find it difficult to just buy highly rated companies at random. I realize I might be hurting performance, but I just can't stop myself from learning further about the companies - even realizing I might be hurting my results.

    I also second the idea that learning more about a company can cause a false confidence, and conviction on a stock can easily become tied up in ego - and it's important to keep the ego out of it if possible.


    Bert Krauss from CT posted over 2 years ago:

    Thank you for a very interesting article.

    However I have two concerns with its conclusions.

    1. Assuming the model is made by humans rather than an intelligent computer which can analyze data according to its own methods, why doesn't system 1 thinking affect the making of the model.

    2. More significantly doesn't the use of the model assume that the environment it is working in is constant? If some future humans no longer have an appendix but still suffer abdominal pain for other reasons wouldn't the model still predict appendicitis?


    Steven Stark from ID posted over 2 years ago:

    I e-mailed Joel Greenblatt's website about a stock they had listed as a recommended buy. I thought it shouldn't be included. They basically said follow the formula, forget due diligence, stay diversified and you'll be fine.
    I also have difficulty following a system but I see the merits of doing so.


    Paul Campbell from UT posted over 2 years ago:

    Terrific article. The only issue I have with value screening models is that the process calls for buying when a stock passes the screen, and selling when it does not. Nearly half of the stocks on a value screen one quarter are off the next quarter.

    Turns an investor into a short term trader.

    Comments and suggestions are welcomed.


    Thomas H from VA posted over 2 years ago:

    The conclusions here seem correct but the data presentation is skewed. Models equal or beating experts 94% should be compared to experts equal or beat models which would be 54%. Or the more dramatic models beat experts 46%, experts beat model 6%. Yes the correct titles were used but were they searching for the readers who skim too fast?


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