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Engineering Returns

Traders seek to find the next BIG think ala Apple, Bidu and co. Often times this desire is founded by the underlying assumption that taking higher risk is rewarded with higher returns. Furthermore many traders believe in an efficient market where as one can outperform the market only by taking above average risk. However this hasn’t been the case for the US stock market over the last couple of decades or so. A lot has been written about the low-volatility anomaly in financial markets. With this post I want to share some insight how this works out on index stocks over various time frames.

The Setup

For my research I define risk in terms of historical volatility. I looked into S&P100 and S&P500 stocks (survivorship bias free) from Jan. 1990 until Nov. 2011. Stocks are ranked by historical volatility of various time-frames (20, 50, 100, 252 and 500 days). I tested buying the top or bottom decile (long only). So for the S&P500 index the strategy is always invested in top/bottom 50 stocks where as for the S&P100 the strategy is always invested in top/bottom 10 stocks. The test and all calculations are done using daily bars, re-ranking/rotation happened weekly. No trading cost considered.

The Results

(click on the picture to enlarge it)

Thoughts & Observations

  • Low volatility (low risk) outperforms high volatility (high risk) on an absolute as well as risk adjusted basis.
  • The anomaly is consistent among all volatility time-frames (20,50, 100, 252 as well as 500 days).
  • Buying bottom decile (low volatility) outperforms index buy and hold (without considering trading cost).
  • The same test has been conducted for Nasdaq 100 stocks, with confirming results (and conclusions).

.For me this is eye-opening. It refines my way of thinking about risk (and reward). Tell me what you think and how you gonna apply that to your trading strategies.

# Frank

 


6 Responses to “Does Low Risk Outperform High Risk?”


  1. Hi Frank, I was recently at a conference where Larry Connors presented almost identical research. His point was that these are typically stable companies, often producing dividends, etc. I think it has a great deal to do with the mathematics of compounding volatile versus stable return series. Good work, Mrkt


  2. Hi Frank. It’s a good study but there is one question I have:

     Does the above study only go long on stocks which are in an uptrend and in the top or bottom HV deciles or does it go long all stocks that are in the top or bottom HV deciles?
     If its only the latter and no account is taken of whether the stock is in an uptrend or not then am I right in saying the study is “self fulfilling” or is understandable?
     The reason I say this is because volatility normally is higher on stocks or instruments when they have downside volatility versus when they have upside instrument move volatility
     Therefore the stocks in the High Vol top decile could be in a downtrend and therefore will naturally underperform the more stable “turtles” in the lower vol bottom decile stocks

    What do you think??


  3. Excellent study, Frank, confirming some notional observations that I’ve held. Some questions and comments:

    1) Were dividends included in the results? If not, I would hypothesize that the spread between the two groups might be even wider.

    2) Before using the data in a trading strategy, it would be good to know if these results might vary by market conditions? Are there in fact, some market conditions in which higher HV stocks do outperform, and what might they be?

    Any thoughts? Thanks for all your efforts.


  4. @ DOM

    1.) Yes results include cash dividends

    2.) I did a quick test as I had similar ideas, can not confirm that

    Frank


  5. @ Vimal

    1.) trend isn’t considered at all. It’s a one dimensional ranking only.

    2.) I think this is mainly due to compounding. Low volatility stocks show less severe draw-downs. Hence they have less to recover.

    3.) Furthermore: If a stock is in trouble volatility increases and therefore it drops out of the lowest decile.

    Frank


  6. I echo Jeff’s comment about a similar study by Larry Connors. This reverts back to the old “risk adjusted return” philosphy, but with the twist.
    My current ETF sector rotation model ranks based on momentum, but also adds volitility (ala Sortino) into the final ranking to determine the “best” ETF to hold in the current enviroment. I’ve found that by adding a weighting on volitility into the momentum calculation (for an overall rank) I get more stable and longer lasting trends / moves.
    Much more to be studied, but this is interesting and counter to conventional thinking. Press On & good Trading.