Low-risk investment strategies prefer leveraged low-risk assets over high-risk assets. The measure of risk can be based on price statistics, such as volatility and market correlation, or fundamental features. The rationale for low-risk strategies is that leverage is not available for all investors (but required to increase the weight of low-risk longs) and that many investors pay over the odds for assets with lottery-like upwardly skewed return expectations. Popular versions of this strategy principle include “betting against beta”, “betting against correlation”, “stable minus risky” or “quality minus junk”. Empirical research suggests that low-risk strategies have delivered significant risk-adjusted returns for nearly a century and that this performance has not deteriorated over time.

The post is based on Alquist, Ron, Andrea Frazzini, Antti Ilmanen, and Lasse Heje Pedersen (2020), “Fact and Fiction about Low-Risk Investing”.

The below are quotes from the paper. Headings, cursive text and text in brackets have been added.

The post ties in with this sites’ summary on implicit subsidies, particularly the section on equity markets.

### What are low-risk strategies?

“The general idea of low-risk strategies is to buy or __overweight low-risk assets and to sell or underweight high-risk assets__. There are many return-based statistical risk metrics (beta, volatility, correlation, etc.) and many fundamental risk metrics (quality and its subgroups, etc.) that one can use.”

“We focus on long-short strategies to highlight how low-risk assets perform relative to high-risk assets…

- The ‘
**betting against beta**’ (BAB) factor…involves__buying stocks with low [market] beta and selling stocks with high beta__every month, weighting stocks by the strength of their signal (rank-weighting) and targeting market neutrality. That is, the long side of low-beta stocks is levered up and the short side of high-beta stocks is levered down to ensure ex-ante beta of zero for the long-short portfolio… - The ‘
**stable minus risky**’ (SMR) factor also__ranks stocks using betas but weights them in a dollar-neutral rather than a market-neutral way. No leverage is used, resulting in a net negative beta__…We buy a value-weighted portfolio of the 30% of stocks with lowest betas and sell a value-weighted portfolio of the 30% of stocks with highest betas separately for the large- and small-cap universes, and then average the returns of the portfolios… - The ‘
**market-neutral version of stable minus risky**’ (SMRMN)…resembles the ‘betting against beta’ strategy design in the beta estimation method and market-neutrality target, i.e.,__levering up low-beta longs and levering down high-beta shorts, but it resembles ‘stable minus risky in the stock weighting__design within the long and short legs… - The ‘
**betting against correlation**’ (BAC) [strategy] uses the design choices of ‘betting against beta’ except that it__uses correlation rather than beta to guide its tilts__; notably, it targets market-neutrality and rank-weights stocks… - The ‘
**idiosyncratic volatility**’ (IVOL) and ‘**maximum recent daily return**’ (MAX) [strategies]…follow ‘stable minus risky’ in creating__dollar-neutral portfolios__and…stock weighting design… - Turning to
**fundamental risk metrics**, there is a long list of diverse__quality measures in the literature — profitability, earnings quality, credit quality, low leverage, earnings stability__, etc. — but we focus on the broad composite series ‘quality minus junk’ (QMJ), which is based on 16 single metrics, and its subgroups profitability, growth, and safety.”

“The [historically] strong performance of low-risk strategies means that the risk-return relation is not consistent with the capital asset pricing model [CAPM]. Instead, low-risk securities have historically delivered higher risk-adjusted returns than high-risk assets.”

### What is the rationale behind low-risk strategies?

“The low-beta premium contradicts the standard CAPM, which predicts that expected excess returns are proportional to betas, as discussed in the introduction and in more detail in the previous fiction. Nevertheless, low-risk investing is consistent with other economic theories, notably the theory of leverage constraints and the theory of lottery preferences.”

“To earn significantly higher average returns, you need to leverage low-risk securities…__Leverage constraints are both a ‘limit to arbitrage’ for the low-risk effect and can, simultaneously, cause the low-risk effect__. Indeed, an extra demand for the most risky securities within an asset class makes these securities expensive, and, if safer securities are abandoned by leverage-constrained investors, then these securities become cheap, explaining their high returns.”

“The theory of lottery preferences…assumes that __investors have behavioral biases that make them prefer securities that offer even a small chance of a high return, just like a lottery ticket__. Such investors would particularly like securities with a chance of an outsized return such as a biotech stock bouncing on the news of a drug approval. More generally, they may prefer stocks with positive skewness or high volatility. The demand by such investors drives up the price of risky and lottery-like stocks… implying that such stocks have low future returns.”

*For an additional explanation and more references on the leverage and lottery effects view a previous post here.
*

*On sources of institutional bias against low-risk asset also view a previous post here.*

### How successful have these strategies been?

“__Low-risk strategies have delivered large risk-adjusted returns…for nearly a century both in- and out-of-sample__, and the performance is pervasive across countries, industries, country indices, and asset classes – and even sports betting. Furthermore, the performance of low-risk strategies is strong for different statistical and economic measures of risk, is distinct from other common factors, and survives the exclusion of small-cap stocks as well as the inclusion of transaction costs.”

“[The exhibit below] clearly shows the historical success of low-risk strategies. Indeed, __we see positive alphas for all strategies__ and all methods, and these alphas are statistically significant with only a few exceptions…the low-risk return premium is among the highest factor premiums, both for statistical and fundamental risk measures.”

“The best empirical answer is __out-of-sample evidence of continued success after a strategy has become widely known__. For many common factors, subsequent performance has decayed moderately after their past success was highlighted in an academic journal. Low-risk factors are an exception. They have actually performed better during the out-of-sample period than during the in-sample period.”

“We find some bond beta in low-risk strategies (less in beta-neutral and industry-neutral variants), but even where it is strongest, it leaves the long-term alpha nearly unchanged.”

“__The risk-adjusted performance of low-risk strategies is pervasive across countries and has been robust across many asset classes__ and even outside financial markets…The ‘betting-against-beta’ strategy has worked in stock selection within all 24 countries studied…CAPM alphas ranged between 6% to 20% across these countries, and the six-factor alphas were positive in all countries…The low-risk effect was first documented for equities but supportive evidence exists across many asset classes…[including] U.S. Treasury bonds, corporate credit indices, government bonds across countries, and global equity indices.”