Do You Need to Change Your Factor Prescriptions?

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Published on December 12, 2019

| 8 min read

Vassilios Papathanakos, PhD, Distinguished Researcher


Only the United States and New Zealand allow direct-to-consumer advertising for prescription drugs. In the U.S. alone, pharmaceutical companies spend nearly $10 billion annually to advertise drugs.1 Advertising is so widespread that Harvard Medical School suggests due diligence questions for consumers.2 We’ve listed a few below.

What does this have to do with investing? Well, we contend that the similar proliferation of factor-based products demands that you start asking similar questions.


Questions About Drugs: What condition does this drug treat? Which of the drug’s possibly side effects should I be worried about? Should I take this drug if I’m taking other drugs? Questions About Factors? What is the factor’s alpha potential? Does the factor itsself create risks? How does it interact with other factors?


The popularity of factor investing – like the overuse of antibiotics – appears to be at a critical stage. While investors’ adoption of factors has never been broader, their concerns about the effectiveness of factor investing are increasing. In our opinion, asking the right questions about factor investing has never been more justified.

A New Investing Paradigm is Born

Crystalized in the labs of academia, factors were an attempt to study investing systematically within a quantitative framework. Researchers steeped in economics employed well-established regression techniques to explore which “factors” can be used to reliably forecast future outperformance of a security, relative to its peers.

Starting with factors used by investors in practice, like size and value, early researchers confirmed a clear statistical basis. And, like good scientists, they next sought to understand the mechanisms that underlie these statistical observations.

In the meantime, the broader investor community put the research results to work in hopes of generating outperformance. Over time, the statistical methodology for understanding factors evolved from an intermediate step in quantitatively analyzing the market to a fully self-contained paradigm for quantitative investing.

We can roughly summarize this quantitative worldview as follows:

Enough investors behave predictably that their biases meaningfully influence securities’ behavior.

Adherents to this paradigm wield statistical analysis to demonstrate the existence of a factor to estimate its alpha potential and to measure its persistency. Quantitative investing tends to become all about factors: improving the calibration of known factors, and discovering new ones.

Factor Investing Challenges

Investment professionals worldwide have turned to factor investing as a prescription for generating alpha. With enough history available, we are beginning to see some of the challenges to this quantitative investing paradigm. Two inevitable issues have emerged from our vantage point. First, investors have overused the factors that were easy to discover. Second, statistical analysis of factors used to improve or discover factors has become increasingly sterile.

Too Much of a Good Thing?

A good example of overuse is in the application of positive momentum in U.S. equities. The idea, in short, is stocks that beat the market in the past tend to continue to beat the market in the future. This approach may seem rational; however, as investor capital began pouring into these stocks, overcrowded trades ensued and interfered with the future outperformance.

Investors reacted to this by being more patient: they looked for stocks that beat the market for longer before buying them, and they held the stocks for longer before selling them for profit.

Still, the resulting overcrowding has progressively worsened so that it not only largely arbitraged the benefits of the highest-momentum stocks, but, more recently, it suppressed their returns below average.

Figure 1 plots the monthly future return of stocks in the S&P 500 ranked from highest to lowest based on momentum. Generally, stocks with the highest momentum ranks have exhibited higher monthly future returns while those with the lowest momentum ranks have exhibited the lowest, even negative, monthly future returns.3 More recently, however, the connection between high momentum and higher monthly future returns appears more fragile.




Limits in the Laboratory

The second constraint that’s emerged is the increased sterility of the statistical analysis used in understanding factors. When quantitative research analysts examine factors, either to “improve” their calibrations or find genuinely new ones, their research choices usually fall into one of two buckets: 1) keep it simple or 2) make it sophisticated. Both choices present challenges.

Simplicity Risks Arbitrage

ABCThis approach focuses on simple definitions of potential factors. The choice seems appealing because it’s easier to understand and to implement, but the market has likely discovered these factors (or is going to in the near future) and asset managers are on the way to mining them to extinction. Consequently, they find no competitive advantage in keeping things simple.

This option may be most viable when it involves new data sources previously inaccessible to researchers. Unfortunately, in many cases, the period for which data is available tends to be so short that statistical tests are unable to establish conclusively whether the factor is genuine.

Sophistication Risks Inefficacy

ComplexThis choice considers complicated definitions of potential factors that require multiple computational steps and convoluted statistical techniques. The option is appealing because the brainpower (or computer resources) required could be a competitive advantage. Unfortunately, complex factor definitions are difficult to test reliably, and investors should rigorously question their efficacy.

When there are more steps involved in defining a “winner” there are usually fewer historical examples available for testing. And, the more varied the factor definitions one tests, the more likely a few of them may appear to work well just by accident.

Factor Investing’s Future

Potential Pros and Cons of Common Remedies, Alternatives, Pros: Lower Equity Correlation, Expanded Opportunities, Inflation Hedge, Cons: Higher Fees, Liquidity Risks, Hidden Equity Beta. Factor Exposures, Pros: Targeted Risk Exposures, Lower Correlation Between Factors, Cons: Transient Performance, Overcrowding Risks, Spurious Modeling. Fixed Income, Pros: Lower Equity Correlation, Capital Preservation, Income Generation, Cons: Interest Rate Risk, Credit Risk, Longevity Risk. Tactical Decisions, Pros: Enahcned Returns, Lower Risk, Cons: Market Timing is Difficult, Implementation Costs, Governance Costs.

What do these revelations mean for the future of factor investing? The quantitative investing paradigm we’ve described appears to be outgrowing its usefulness, not just by dramatically diluting the alpha potential it promised investors, but also by materially increasing the uncompensated risks to which they’re exposed.

By decomposing factor performance, we hope to offer a better way to think about factor investing in our paper, “Are You Asking The Right Questions About Factor Investing?

Download the paper today to learn more.

Are You Asking the Right Questions About Factor Investing? Understand factor exposure beyond advertised claims of compensated risks. Download Now


1. Schwartz LM, Woloshin S. Medical Marketing in the United States, 1997-2016. JAMA. 2019; 321(1):80–96. doi:

2. Harvard Health Publishing. (n.d.). Do not get sold on drug advertising. Retrieved November 22, 2019, from do-not-get-sold-on-drug-advertising.

3. FACTOR DEFINITION: Momentum is the relative logarithmic total return of the stock with respect to the reconstructed index, over a period starting 252 days and ending 22 days prior to each date (inclusive in both ends). DATA AND METHODOLOGY: The figures in the text are based on an analysis of the stocks in the S&P 500 index over the period January 1966 through September 2019. Similar results hold for other indices, but this dataset is preferable because it offers the longest historical record of daily returns available to us. For the momentum factor, the score is computed for all the stocks in a reconstructed index on a daily basis using the previous historical data. The reconstructed index consists of the stocks in the S&P 500 index at the beginning of each month, with their weights propagated forward on a daily basis using the corresponding total returns (this assumes an appropriate daily reinvestment of the dividends on the ex-date). Then, the stocks are ranked by their scores from largest to smallest, and their ranks and absolute logarithmic total returns for the following 21 days are collected over the entire period minus the last 21 days. Finally, the aggregate data are separated into 20 quantile groups by rank; the ranks and the returns in each group are averaged, and the average returns are further normalized to have zero mean across the 20 groups.

All historical data is based on back-testing, which is not actual performance, but is hypothetical. Back-tested data is prepared with the benefit of hindsight and is not a guarantee of future results. In addition, factor investing does not provide any assurance of improved performance or risk reduction. Exposure to such factors may detract from performance in some market environments, perhaps for extended periods. In such circumstances, a strategy may maintain exposure to the targeted investment factors and not adjust to target different factors, which could result in losses. Factor-based investing has additional unique complexities that investors should consider when evaluating expected returns.