Equity factor investors choose stocks based on certain attributes that generate consistently higher returns. Common ones include:
• Value: low market price relative to a stock’s fundamental value.
• Momentum: stocks that outperform in the past tends to exhibit strong returns going forward.
• Size: small-cap stocks produce greater returns than large-cap ones.
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Equity factor investors choose stocks based on certain attributes that generate consistently higher returns. Common ones include:
• Value: low market price relative to a stock’s fundamental value.
• Momentum: stocks that outperform in the past tends to exhibit strong returns going forward.
• Size: small-cap stocks produce greater returns than large-cap ones.
Typically, factor investors hold long positions in stocks with attractive characteristics combined with short positions in stocks with unattractive characteristics. For instance, a long position in value stocks combined with a short position on growth stocks. This captures not only the outperformance of value stocks (its attractive characteristics) but also the underperformance of growth stocks (its unattractive characteristics).
But a new paper, When Equity Factors Drop Their Shorts, authored by David Blitz, Guido Baltussen and Pim Van Vliet, investigates the long and short legs of equity factor premiums separately, arguing that decomposing the factors in this way is crucial to build efficient portfolios. The findings? Returns are determined overwhelmingly by the long leg of the factors. So drop your shorts.
Which Factors Were Used, How Was the Data Sourced, and How Far Back?
Five factors were considered:
(1) Value (HML) average return on the value portfolios minus the average return on the growth portfolios.
(2) Momentum (WML) average return on the high prior return portfolios minus the average return on the low prior return portfolios.
(3) Profitability (RMW) average return on the robust operating profitability portfolios minus the average return on the weak operating profitability portfolio.
(4) Investment (CMA) average return on the conservative (asset growth) investment portfolios minus the average return on the aggressive investment portfolio.
(5) Volatility (VOL) average return on the low volatility portfolio minus the average return on the high volatility portfolio.
Monthly returns for the portfolios were sourced from the online library of Professor Kenneth French, and for VOL Robecco datasets was used. The VOL portfolio was value-weighted by being levered up or down to a market beta of 1 in order to make the VOL factor market neutral (to stop the effect of HML spilling over). The data collected was from the period July 1963 to December 2018 for US Equities.
Portfolio Setup
Portfolios were then broken down in long-short factors in three ways. Taking the value factor only, for instance:
• Long 50% large-cap value and 50% small-cap value, and short 50% large-cap growth and 50% small (the standard portfolio covered in literature).
• Long-minus-market leg, i.e. long 50% large-cap value and 50% small-cap value, and short 50% large caps and 50% small-caps (their unique approach to isolate the long leg).
• For the market-minus-short leg they did long 50% large-caps and 50% small-caps (from the market), and shorted 50% large-cap growth and 50% small-cap growth (their novel approach to isolate the short leg).
Investors are Better off Combining Factors at the Long Legs of the Portfolio
Long legs offered higher Sharpe ratios (Figure 2) and better diversification (Table 1) benefit than the short leg. The combined Sharpe ratio of long legs was 1.10 (equally weighted factor portfolio). The short side combined Sharpe ratio was 0.69 and the standard long-short factors had 0.86. Moreover, when all the factors are combined the Sharpe ratio increases relative to the individual elements which ranged from 0.40 to 0.61 for the long leg. This improvement is prominent in the long leg since the average correlation is -0.4 vs the 0.31 of the short leg (Table 1), implying the diversification benefits of factors are unequal and much stronger on the long side than on the short side.
Should Investors Ignore the Short Leg of the Portfolio Altogether for Optimal Performance?
In short, yes. Short legs have weaker performance than the long legs and provide minimal diversification to the long legs when a portfolio is constructed incorporating both simultaneously.
To investigate this, a maximum Sharpe ratio portfolio was constructed. The optimal portfolio (highest Sharpe Ratio) contained only 2.6% of the portfolio being invested in a single short leg (high volatility) (Panel B). Finally, the researchers regressed its long leg on its short leg, and vice versa. They find that the alpha of the long-leg portfolio (the performance that remains after adjusting for the exposures to the short-leg portfolio) is positive (1.09%), and statistically significant (t-stat 7.44). By contrast, the short-leg portfolio has a statistically significant negative alpha of -1.00% (t-stat -3.89).
Are the Findings Robust Internationally, Across Time, and During Black Swan Events?
Four regions (North America, Europe, Japan, and Asia exc. Japan) were considered using data from July 1990 to December 2018. As before, long leg outperformed.
The US equities results were analyzed again in subsamples (decade by decade). Across the six sub-samples, the long legs still had higher Sharpe ratios.
Finally, the tail events were considered. Factor performance was generally weak during the dotcom bubble, but the losses on the shorts (risky, unprofitable, growth stocks) exceeded the losses on the longs (stable, profitable, value stocks). Also, in the aftermath of the Global Financial Crisis (GFC) most factors underperformed, and momentum in particular. But again, the losses of the shorts (risky, unprofitable, losers) were up to three times larger than the losses of the longs (stable, profitable, winners) during 2009. In other words, when factors fail, the shorts tend to be hit harder.
How Can Investors Take Advantage of This Research?
Practically, investors can capture the premiums offered by common factors by focusing only on the long legs of factors and can use highly liquid market index futures to hedge out market exposure (market beta). Additionally, findings of this paper excluded consideration for transaction cost of shorting and feasibility of shorting (i.e. is it available to short?) and this real-life consideration would further strengthen the argument to focus on the long legs.
Mehdi is a research analyst at Macro Hive. He’s currently pursuing an MSc in Finance & Investment at Nottingham University Business School and he is a CFA level 3 candidate. Mehdi has previously pursued roles as an Equity Research Analyst, Junior Economist & in Proprietary Trading.
(The commentary contained in the above article does not constitute an offer or a solicitation, or a recommendation to implement or liquidate an investment or to carry out any other transaction. It should not be used as a basis for any investment decision or other decision. Any investment decision should be based on appropriate professional advice specific to your needs.)