Low Volatility Investment strategy – A New Mantra for Winning through Volatility Cycles

Before we start with anything on low risk anomaly, it makes sense to quickly look at some of the important theories that have evolved around definite positive relationship between the risk and the expected return.

Finance theory suggests that there is a definite positive relationship that exists between the risk and the return. In order to earn higher returns, one has to assume higher risk. Modern Portfolio theory attempts to offer a solution in terms of optimizing the risk return tradeoff based on your risk budget by allowing you to construct a portfolio that offers you the highest level of expected return at the given level of volatility or alternatively allows you to construct a portfolio that offers the higher expected return as per the given level of volatility. Capital Asset Pricing Model argues that while there is a positive relation between the risk and return, the reward for the additional risk is limited to the systematic risk only and not available on the total risk. The logic is that a rational investor should be able to diversify away the unsystematic risk and hence no risk premium should be associated with such risk. However, when it comes to measuring performance of any managed portfolio, the most popular measure is the Sharpe ratio that measures the excess return (return of a portfolio over risk free return) to volatility, measured by standard deviation and not beta. The reason why an actively managed portfolio may not be a fully diversified is that it intends to outperform the benchmark market portfolio (A proxy for fully diversified portfolio with zero unsystematic risk) on a risk-adjusted basis. Therefore they may retain unsystematic risk for the superior returns. In such cases, investors need reward not only for the systematic risk of the portfolio but for the unsystematic risk retained in the portfolio as well.

In closing, finance theory suggests that there is a strict positive relation existing between systematic risk and expected return. One may also consider positive relation between the unsystematic risk and expected return depending on the assumptions and the context.

Questions started emerging about the positive risk return relationship as early as 1970’s where many of the including famous personalities like Black, Haugen etc. claimed flattish to even negative returns between risk and expected return. More recently, there are several studies that have highlighted that risk and expected return relationship is not only flat but actually negative within the asset class such as equity if not across the asset class. This is known as “low risk anomaly”. The proposition is that “portfolio consisting of low risk stocks not only outperforms its high volatility counterpart but also market cap weighted benchmark portfolio over a period of full market cycle”.

Methodology:

There are two ways to capture and test such anomaly.

  1. Construct minimum variance portfolio using mean-variance optimizer and rebalance it on a dynamic basis.
  2. Construct a portfolio consisting of low volatility stocks using ranking based method and rebalance it on dynamic basis.

We choose the second one at the moment for the ease of understanding. In our first attempt, we decided to explore low risk anomaly in the Indian context in a funded study under the “NSE Student Research project”. In that study, we decided to use all the constituents of CNX 500 stocks as our universe and ranked stocks based on their monthly standard deviation using trailing thirty-six months data. We divided stocks into deciles. The equally weighted portfolio constructed using lowest decile stocks was designated a LV portfolio and similarly the one constructed from the highest volatility decile was designated as HV portfolio. The exercise was repeated every month and the results are interesting. We found that LV portfolio not only outperformed its HV counterpart but also CNX500 index by deliver higher return and that too with lower risk. NSE went on launching Low volatility as well as high beta index in November, 2012 with base date of January 1, 2004.

Waves LV30 Dynamic index

We went on launching our own index “Waves LV30 Dynamic” from the present and past constituents of Nifty 200 index with a base date of January 31, 2007. Table 1 reports the performance of Waves LV30 Dynamic index vs. Nifty 200 index up to November 30, 2014. As you can see that Waves LV30 Dynamic index has outperformed Nifty 200 index both in absolute return terms as well as on a risk adjusted basis. However, if you can see the performance of Nifty 200 has delivered higher absolute returns over past one year and on YTD basis. This is consistent with global results. The real strength of LV portfolio is that it has a much lower drawdown compared to market as well as HV portfolios. Graph (figure 1) reports comparison of composite index for Waves LV30 Dynamic and Nifty 200. As you can see just before the onslaught of global financial crisis of 2008, Nifty 200 outperformed Waves LV30 Dynamic on absolute return basis but when it started falling it never recovered to match the Waves LV30 Dynamic till now (November 28, 2014). This is despite of superior performance delivered by Nifty 200 during past one year.

Table 1: Waves LV30 Dynamic vs. Nifty 200 as on November 30, 2014

  Waves LV 30 Dynamic Nifty 200

CAGR (since inception)

15.76% 9.15%

Annual Standard deviation

19.47%

27.94%

YTD (Since January 1, 2014) 31.34%

38.85%

1 year return 35.45%

42.67%

Beta (With respect to Nifty 200) 0.63 1

Reward/Risk

0.81

0.33

 

Let us explain the reason in layman terms. If a stock falls from 100 to 50 it loses 50% but to regain the level of 100 it needs to gain 100%! The strength of low volatility strategy is that while it may underperform market weighted portfolio when markets are rising, it will face much smaller drawdown when markets are falling. The outperformance of LV portfolio in falling market is much higher than the underperformance in rising markets and the net result can be seen in the difference of CI values. As of November 28, 2014, Waves LV30 Dynamic stands at 343.66 compared to a corresponding level of 204.79. If you are thinking that base date would have led to such result due to the fact that our base date is too close to the end of last Bull Run, we would like to share with you the index levels of Nifty 200, CNX Low volatility and CNX High beta numbers. We are keeping Nifty 200 just to keep the base constant as all the indices have the same base date of January 1, 2004. You are free to compare using any other broad based index. As on November 28, 2014, Nifty 200 stands at 4388.35, CNX Low volatility at 6856.84 and CNX High beta at 1918.06! Needless to say that CNX Low volatility index delivered the highest, performed the best among all and that too with much lower risk – measured by beta or standard deviation. I am sure that 2004 beginning was just the beginning of the last bull run and not otherwise. This shows that low risk anomaly works under all situations.

LV30VsNifty200 on Nov2014

Possible Explanations

While the results favor low risk anomaly, the biggest question is that if there is a strategy that offers higher returns at much lower risk, such phenomenon should not sustain for sure. Ultimately, investors will chase low volatility portfolios and they become expensive and won’t be able to sustain higher returns. Having said that it has been around and sustaining for over a decade, not only in Indian markets, but also, in more developed markets as well. There are enough explanations for why this low risk anomaly should not only exist but sustain as well. Some of them are rational whereas others are behavioral in nature. While we may not discuss explanations in this article due to paucity of space, we just list down some of them below.

  • Borrowing restrictions- Does not allow investors to take higher risk by using leverage on optimum portfolio but force them to move to high risk stocks to use their risk budget.
  • Fund managers’ compensation- Fund managers’ compensation in PMS like business is linked to outperformance in the zone of gain only and therefore forces them to take risky bets.
  • Behavioral biases- There are some of the behavioral biases which force investors to choose high volatility, small and penny stocks over their boring and less volatile low volatility stocks. These biases include preference for lottery, representativeness and overconfidence etc.

Conclusion

To conclude, Low risk anomaly is here to stay as long as irrational investors and traders operate in restricted environment. It offers an opportunity to earn superior returns to market weighted benchmark portfolio at a much lower risk over a period of full market cycle. This strategy is best suited to investors who are using equity as an asset class for building wealth to achieve their long term goals such as building for retirement corpus without suffering extreme turbulence that equity markets are known to go through time and again. To know more on low volatility investment strategy, you can visit us on www.invetsmentwaves.in.

                          

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