When Equity Factors Drop Their Shorts (2020)
David Blitz, Guido Baltussen, Pim van Vliet
Financial Analysts Journal, 76(4), URL
You may (correctly) gain the feeling that I am exploiting the research on factor investing as much as possible. But there are so many interesting papers and questions to study that I really don’t run out of material. This week’s AGNOSTIC Paper examines the important issue of performance contributions from the long and short-legs of the major factor premiums. In English: can we profitably invest in factors without shorting a large number of stocks?
Everything that follows is only my summary of the original paper. So unless indicated otherwise, all tables and charts belong to the authors of the paper and I am just quoting them. The authors deserve full credit for creating this material, so please always cite the original source.
Setup and Idea
As we all know, the typical (theoretical) factor portfolio is a long-short combination of stocks with favorable and unfavorable characteristics. If the factor works and explains differences in stock returns, such a portfolio generates returns without meaningful correlation to the overall market. Although nice in theory, this is quite difficult to implement in practice. Trading a large diversified long-only portfolio is already difficult for retail investors but usually not a problem for institutions. Systematic short selling, however, is also beyond the scope of most institutional investors. With respect to factor premiums, this is of course a concern. If profiting from the factor requires short selling that is only available to the most sophisticated investors out there, these are strong limits to arbitrage.
Even if you can short a large number of stocks, you may not necessarily want to do that. Shorting comes with lending fees, the potential for unlimited losses, and the risk of short-squeezes or counterparty defaults. Against this background, it is therefore interesting for all investors to see whether the major factors also work without shorting. This is exactly where this week’s paper comes in (although you still need to short the overall market, but more on that below)…
Data and Methodology
The authors get the Momentum (WML), Value (HML), and the two quality factors (RMW and CMA) from Kenneth French’s website. They also add a Low-Volatility factor which follows the same construction as the Fama-French factors. The sample covers the US market from 1963 to 2018, and international markets from 1990 to 2018. To evaluate the performance of the factor legs, the authors use Sharpe ratios. Given that they eliminate all market risk from the strategies (see below), the Sharpe ratio is a sufficient proxy for risk-adjusted performance here.
The heart of the paper is a very straight-forward methodology to separate the performance of the long and short-legs of factors. The authors first follow the standard Fama-French methodology to construct the four major factors (Momentum, Value, Quality, Low-Risk). Instead of continuing with the common long-short notations, however, they just hedge out any market risk from the original long and short-legs to view them separately.1They also control for large and small caps and thus also eliminate exposures to the size effect.
Let’s do an example. For the Momentum factor, you would typically go long the 33% of stocks with the highest 12-month returns (“winners”) and short the 33% of stocks with the lowest 12-month returns (“losers”). To isolate the risk-adjusted performance of the long and short-legs, the authors simply combine the “winner” and “loser” portfolio with a short position in the market and view them separately. For example, the “long-momentum” portfolio would be the 33% of stocks with the highest 12-month returns and an offsetting short position in the overall market. In practice, such a strategy is much easier to implement because you can easily short the overall stock market with liquid derivatives.
Important Results and Takeaways
The long-legs of factors are more important than the short-legs
The following table summarizes the key results of the paper and compares the performance of the factors’ long and short-legs with the “traditional” long-short construction. For almost all factors, the market-neutral long-legs generated higher Sharpe ratios than the short-leg. For some factors, the long-leg performance is even better than for the long-short construction or at least comparable. Overall, this is promising and suggests that factors work at least as good on the (easier to implement) long-side like on the (difficult and costly) short-side.
But there is a catch. Despite better risk-adjusted performance, the market-neutral long and short-legs come with considerably lower raw returns than the long-short portfolios. This means that investors who want to hedge all market risk without sacrificing absolute returns would still need to leverage those portfolios.
The table already shows that the “long-better-than-short” pattern also applies to a combination of factors. In the above chart, the authors show this even more thoroughly. On average, the long-leg generated higher risk-adjusted performance than both the short-leg and the traditional long-short construction. Overall, the results therefore suggest that investors don’t lose much risk-adjusted performance if they just focus on the long-legs of factors and hedge market risks with derivatives that are easy to trade.
The same pattern holds in international markets
As you know, I really like out-of-sample tests so I was very happy that the authors provide one as a robustness check. The following table summarizes Sharpe ratios for the long and short-legs of an equal weighted factor portfolio in North America, Europe, Japan, Asia ex Japan, and globally.
Although the sample period is longer for the US, the international results are surprisingly similar. In all regions except Japan, the long-leg generated considerably better risk-adjusted performance than the short-leg. But even for this outlier, the long-leg is still comparable to the short-leg. I think it is therefore fair to conclude that factors don’t necessarily require the short-side to be profitable. In fact, in many cases you get even better risk-adjusted performance with a simple market hedge than with the more complicated and costly long-short construction.
Conclusions and Further Ideas
Apart from the major results that I have shown here, the paper includes a lot more details and robustness tests. In particular, the authors also look at correlations among factors and find that the long-legs also provide better diversification than the short-legs. Interestingly, a portfolio that maximizes the Sharpe ratio based on the long and short-legs of factors invests almost exclusively in the long-legs. This again indicates that investors can fully capture the risk-adjusted performance of a factor by combining a long-only portfolio with a simple market hedge.
Throughout the paper, the word risk-adjusted is key. The authors’ approach generates higher Sharpe ratios but the absolute returns of the long-legs are roughly just half of those from the traditional long-short factors. To translate those higher risk-adjusted returns into more absolute money, investors must therefore lever the long-legs by a factor of about 2. Running a levered equity strategy might be easier than systematic shorting, but depending on the type of investors, it is probably still difficult. So the authors’ approach is a step in the right direction but still not helpful for everyone.
Despite this limitation, the important takeaway remains that we don’t necessarily need to play the short-side to benefit from the major factor premiums. For investors without access to sophisticated trading infrastructure (like me, unfortunately) those are very good news!2For example, one could combine a multi-factor index fund with a simple market hedge to earn the expected outperformance as absolute return. As usual this is no financial/investment advice. Please read the disclaimer at the end of this post.
- AgPa #72: Machine-Reading of Private Equity Prospectuses
- AgPa #71: Go Where the Earnings (per Share) Are
- AgPa #70: Equal vs. Market Cap Weights
- AgPa #69: Rebalancing Luck
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|1||They also control for large and small caps and thus also eliminate exposures to the size effect.|
|2||For example, one could combine a multi-factor index fund with a simple market hedge to earn the expected outperformance as absolute return. As usual this is no financial/investment advice. Please read the disclaimer at the end of this post.|