How Much Popularity Can Low Volatility Stand?

The low volatility anomaly — i.e., the tendency for low-volatility or low-beta portfolios to outperform market averages — has been the subject of at least 40 years of academic research.  Given its challenge to what “everyone knows” about risk and return, it’s a fertile field for both professors and practitioners, some of whom recently characterized “the long-term outperformance of low-risk portfolios [as] perhaps the greatest anomaly in finance.”

But anomalies, especially ones that suggest higher return and lower risk, attract investor dollars, and enough investor dollars often spell the end of anomalies.  It’s been suggested that “the low-volatility anomaly may [be] eliminated by its popularization.”

So how much popularity can low volatility stand?  Before we can suggest an answer to this question, we have to understand the source of the low volatility anomaly.  Perhaps the simplest and most intuitive explanation comes from behavioral finance, specifically from the cognitive bias that behavioral economists call the “preference for lotteries.”  Their argument is that no rational person would ever buy a lottery ticket, since the expected return of such a purchase is negative.  But billions of lottery tickets are sold all over the world every day.  Why do so many people behave in a way that classical economics can only regard as completely irrational?  The behavioral argument is that some people are willing to risk a known amount of money in exchange for the possibility, however slim, of a gigantic payoff.

If this happens in a game of pure chance, how does it apply to financial markets?  The stock market’s lottery tickets are the stocks of highly volatile, often young and untested, companies.  Ultimately, they may not amount to much, but one of them could be the next Apple or Google.   Some investors are willing to pay up for the chance of that sort of large reward — in effect buying volatility for volatility’s sake.  Low volatility strategies benefit by avoiding other investors’ volatility-seeking behavior.  We can estimate the capacity of low vol if we can estimate the extent of of volatility seeking on the other side of the trade.

Last week witnessed the much-anticipated initial public offering of Twitter, Inc., a young and volatile company if ever there was one.  The IPO price was $26; the stock closed its first day of trading (November 7) at $44.90, which implied a total market value of approximately $25 billion.  For illustrative purposes, let’s assume that $26 is a sober estimate of TWTR’s fair value (after all, the presumably well-informed selling shareholders were willing to sell there).  Then arguably the $18.90 first day’s appreciation represents the action of volatility-seeking investors.  That’s 42% of TWTR’s closing first-day valuation, or better than $10 billion.

Granted, this is a simple example with some perhaps-unrealistic assumptions.  But it gives us at least a rough gauge with which to answer our question about the capacity of low volatility strategies.  One company, in one day, produced $10 billion in volatility-seeking market value.  That’s more than the total market value of the two largest U.S. low volatility ETFs.  Whatever the ultimate capacity of low volatility strategies is, we’ve got a long way to go.

The posts on this blog are opinions, not advice. Please read our disclaimers.

2 thoughts on “How Much Popularity Can Low Volatility Stand?

  1. Jim

    Good article. I agree that low volatility ETFs have a long way to go. I’ll be especially interested to see how they perform in a downward trending market. EELV has held up well as emerging markets bounce around. But with the exception of SPLV and USMV, the others do not seem to be attracting the assets they need to survive.

    Reply
  2. Pingback: Coming Soon to a Dictionary Near You | S&P Dow Jones Indices

Leave a Comment

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>