This blog post is available as a PDF here.

Summary

  • Factors are a way to identify unique alpha sources.
  • Factors often have a “light” and “dark” side.  While the light side is expected to outperform the dark side, often the light side also outperforms the market and the dark side underperforms the market.
  • The outperformance and underperformance are volatile and often negatively correlated.  While the light side may have positive expected alpha, giving up some of that alpha by buying the dark side can significantly reduce relative volatility.
  • We’re still likely better off combining low-correlated, positive expected return factors (e.g. value and momentum), but in a style-box driven industry, pure factor investors may consider how incorporating the bad with the good can help smooth out the investor’s ride.

After our recent commentary on the short-term outperformance of bad investment strategies, an astute reader (hat tip to David C.) hit us with an inbound question.  To paraphrase:

“If each factor has a positive and negative side to it, does it ever make sense to hold the negative side as a diversifier to the positive side since their relative performance to the market appears to be negatively correlated.”

To dive into this question, we first have to explain what we mean by the positive and negative side to each factor, or, as we will call it for the remainder of this commentary, the “light” and “dark” sides.

Factors can sometimes be confusing because they often carry a single name, but mean different things in different contexts.  For example, in an academic context, the value factor is a self-funding long/short portfolio where the portfolio buys cheaply priced companies and short sells expensive companies.

For many practitioners, the value factor means a long-only portfolio that over-weights cheap stocks and under-weights expensive stocks (this is similar to the market portfolio overlaid with the value long/short portfolio).

For the value factor, we expect cheap stocks to outperform expensive ones.  Since the cheap stuff is expected to add value and the expensive stuff is expected to lag, the cheap stuff is thelight side and the expensive stuff is the dark side.

Each of the popular factors has a light and dark side.

FactorLight SideDark Side
ValueCheapExpensive
SizeSmallLarge
MomentumStrongWeak
VolatilityLowHigh
QualityHighLow

Often, we not only expect the long side to outperform the dark side, but we expect the light side to outperform the market and the dark side to underperform the market.

These are long­-term expectations, however, and often in the short-run the dark side can outperform while the light side underperforms.  Look no further than value in the dot-com era for a perfect example.

What is perhaps most interesting about this relationship is that often the relative performance of both sides against the market is negatively correlated.  So when the light side is outperforming, the dark side is underperforming and vice versa.

We can see this in rolling 1-year relative performance of the light and dark sides of the value factor against the market.

2016-04-18 - Figure 1Source: Kenneth French Data Library.  Analysis by Newfound Research.  Performance is hypothetical and does not reflect the results of any strategy offered by Newfound Research.

Even though the dark side might be a long-term net drag, negative correlation often means an opportunity to diversify and manage risk.  Furthermore, if the amount that cheap is expected to outperform is greater than the amount that expensive is expected to underperform, are we willing to sacrifice a little bit of outperformance for a smoother ride?

Put simply: can we trade a little alpha in exchange for more consistent outperformance?

To test the idea, we built a simple portfolio, rebalanced monthly, that holds an equal dollar amount of cheap and expensive stocks.

What we find is that not only do we increase the frequency with which 1-year rolling relative returns are positive, but we also significantly reduce the magnitude of relative underperformance.  The -24% relative performance train wreck for value stocks in the late 1990s turns into only -11% relative underperformance.

Another way to look at these results is to look at self-funded long/short portfolios versus the market.  So, for example, go long cheap stocks and short an equal dollar amount of the market and rebalance monthly.  Such a portfolio would allow us to capture the relative return opportunity.

2016-04-18 - Figure 2Source: Kenneth French Data Library.  Analysis by Newfound Research.  Performance is hypothetical and does not reflect the results of any strategy offered by Newfound Research.

2016-04-18 - Figure 3Source: Kenneth French Data Library.  Analysis by Newfound Research.  Performance is hypothetical and does not reflect the results of any strategy offered by Newfound Research.

PortfolioAnnualized ReturnAnnualized Volatility
Expensive – Market-0.58%4.70%
Cheap – Market3.80%14.38%
Cheap / Expensive Blend – Market1.94%6.14%

Source: Kenneth French Data Library.  Analysis by Newfound Research.  Performance is hypothetical and does not reflect the results of any strategy offered by Newfound Research.

By combining the light and dark sides, we lost approximately 86 basis points (“bps”) of return, but also reduced volatility by 824bps.

Pop the champagne and light the fireworks: diversification saves us again!  Hallelujah!

Of course, that is until we compare these results to the results of diversifying with other factors.

Consider, for example, the results of diversifying cheap stocks with strong momentum stocks.

PortfolioAnnualized ReturnAnnualized Volatility
Cheap / Strong Blend – Market5.70%8.49%

Source: Kenneth French Data Library.  Analysis by Newfound Research.  Performance is hypothetical and does not reflect the results of any strategy offered by Newfound Research.

So while expensive may indeed diversify cheap, we’re probably better off working in a diversifying factor that actually has positive expected returns.

Nevertheless, we think there are some interesting implications here for asset managers, particularly in light of how our industry is constructed and the behavioral realities of investors.

Except for the few multi-factor strategies making their way to market, generally speaking, our industry is very “style box” driven.

Managers are often punished for having multiple styles, or drifting styles.  We expect our value managers to remain value managers.

At the same time, while investors often expect managers to try to maximize alpha, investors also have very little tolerance for underperformance.  So for a value manager, while buying cheap stocks may provide much more long-term expected outperformance versus the market, it has significant volatility.

In the short run, that volatility, manifested as underperformance, can put an asset manager out of business.  Our go-to example is anyone who launched a value fund in 1997.

The reality is that investor behavior is one of the largest dictators of realized investor performance.  It is all well and good that buying cheap can create more alpha, but it requires the investor to have the discipline to stick with the process through thick and thin.

If the investor sells after underperformance and buys after outperformance, their own actions may ultimately erode any alpha available from the process.

By reducing the volatility, even at the cost of alpha, we may be able to keep investors participating in the process.  By keeping them participating, we may actually increase their realized alpha.

So the takeaway here is a strange one.  Ask any professional investor if they want to buy the most expensive stocks on the market and I am sure that none will say yes.  Most recognize that we’re much, much better off mixing two low-correlated factors that have positive expectations.

But in a world where we are constrained by style boxes and investors are often their own worst enemy, a curious result emerges: if we were to package both the cheapest and the most expensive into a single product, investors may find it a much more tolerable ride.

Perhaps the dark side brings balance after all.

Corey is co-founder and Chief Investment Officer of Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Corey is responsible for portfolio management, investment research, strategy development, and communication of the firm's views to clients.

Prior to offering asset management services, Newfound licensed research from the quantitative investment models developed by Corey. At peak, this research helped steer the tactical allocation decisions for upwards of $10bn.

Corey is a frequent speaker on industry panels and contributes to ETF.com, ETF Trends, and Forbes.com’s Great Speculations blog. He was named a 2014 ETF All Star by ETF.com.

Corey holds a Master of Science in Computational Finance from Carnegie Mellon University and a Bachelor of Science in Computer Science, cum laude, from Cornell University.

You can connect with Corey on LinkedIn or Twitter.