A factor is a characteristic or trait that explains portfolio returns. Implementing a factor investing strategy means targeting securities with characteristics that you’d expect to generate different returns than you’d earn from owning the entire market.
For example, the value factor uses fundamental measures such as the book value to find relatively cheap stocks to buy and relatively expensive stocks to short. Eugene Fama and Kenneth French first identified the value factor in 1992 as a dimension of return in their three-factor model, which also identified a premium for owning small companies versus large companies.
Since the publication of the Fama French three-factor model, there has been an explosion in the number of academics and practitioners seeking additional factors that explain portfolio return. The end result is what John Cochrane calls a zoo of factors, with over 600 different factors published in academic and practitioner literature.
Sorting Through the Factor Zoo
Most factors in the zoo don’t hold up to rigorous standards like those laid out by Andrew Berkin and Larry Swedroe in Your Complete Guide to Factor-Based Investing:
For a factor to be considered, it must meet all of the following tests. To start, it must provide explanatory power to portfolio returns and have delivered a premium (higher returns). Additionally, the factor must be:
- Persistent – It holds across long periods of time and different economic regimes.
- Pervasive – It holds across countries, regions, sectors, and even asset classes.
- Robust – It holds for various definitions (for example, there is a value premium whether it is measured by price-to-book, earnings, cash flow, or sales).
- Investable – It holds up not just on paper, but also after considering actual implementation issues, such as trading costs.
- Intuitive – There are logical risk-based or behavioral-based explanations for its premium and why it should continue to exist.
Although this is a useful framework for identifying the drivers of expected returns that are unlikely to be the result of chance, it doesn’t mean you should include every factor that meets these criteria to your portfolio.
Each additional factor you add to a portfolio comes with a diminishing marginal benefit, which lowers the probability that the benefits will outweigh the costs. As a result, you must carefully weigh the expected net benefits versus the degree of uncertainty.
Minimizing Type I and Type II Error
When the FDA evaluates a new drug, they seek to minimize the chance of approving a drug that is not beneficial to people’s health or causes bad side effects. In doing so, they increase the probability of failing to approve a drug that would improve people’s health. This is a tradeoff between minimizing Type I and Type II error.
The same tradeoff occurs when evaluating which factors to include in your portfolio. You can minimize Type I error by owning a couple broad market index funds and never seeking further enhancements to your portfolio. Minimizing Type II error, on the other hand, means setting a very low bar for implementing a new strategy.
The tricky part? Minimizing one error leads to a higher chance of realizing the other error.
The key is to strike a good balance. The fascinating thing about evidence-based investing is that people can look at the same data and come to different conclusions depending on their preference for minimizing Type I or Type II error.
As a factor investor, I know that each additional strategy has a diminishing marginal benefit. In addition, the costs of implementing additional factor exposures at the portfolio level (not just the fund level) increases the uncertainty regarding the net benefit from inclusion.
For these reasons, I tend to be more concerned with implementing a bad idea than missing out on a good one. In other words, I prefer to minimize Type I error.
When it comes to making your own decisions, keep these considerations in mind to better evaluate which factors to include in your portfolio:
- Every potential change to your portfolio should start with a written hypothesis that can be tested using evidence. This helps protect ourselves from a tendency to seek out confirming information or being swayed by narrative.
- Slow down your process. Most people agree that timing factors is a loser’s game, so there shouldn’t be any hurry to including or excluding a factor. Don’t be afraid to lay out a timeline that spans 12 to 24 months for your research phase and hypothesis testing.
- Understand how product methodology differs from the methodology in the underlying research. It’s one thing to find a factor that you believe exists. It’s another thing to find a product that captures the factor in a way that aligns with methodology in the research. Similarly, you ought to understand the weaknesses of the models and their underlying assumptions.
- Diversification and return benefits must always be considered in light of expenses and taxes. Extra fees can offset the benefits of otherwise attractive investments. Most published research focuses on cost at the fund level, but doesn’t consider the portfolio level or unique end-user experience. Running simulations to evaluate trading costs and tax impact of including a new factor (or any fund) can help you better understand the true impact to your end user.
And although it seems obvious, don’t invest in something you couldn’t live with for multiple decades. Like all strategies, factor investing doesn’t always work. You knowingly accept tracking error, which can be negative for long stretches of time, in return for the opportunity to earn excess returns. Successful factor investing requires a long time horizon in order to give the underlying theory enough time to work.