(A condensed version of the below article was published in BusinessLine on December 7, 2024 and can be accessed from the link below)
How often have we heard the age-old investing motto – if you want to earn higher returns, you have to take higher risk? Probably a lot. In fact, so many times that this has been ingrained in our investing behaviour. After all, the high-risk high-return theory makes intuitive sense. There’s no free lunch in this world – if you want to earn higher returns, you must take higher risk.
However, there is clear evidence that this theory is wrong. You can earn high returns by taking low risk – the exact opposite of what is commonly believed! The proof for this is right in front of our eyes, but it will take a lot of unlearning before we can start to notice it. This post is an attempt to remove the blinkers from our eyes.
The origin of the high-risk high-return idea
To understand why the high-risk high-return theory is wrong, we need to take a small detour to its origin. In 1952, Harry Markowitz, a doctoral student at University of Chicago, submitted his thesis titled “Portfolio Selection”. In the years to come, this thesis, which was later published as a book, revolutionized the world of finance.
Until 1952, there was a sufficiently well-developed theory to calculate expected returns of a security. For instance, the value of a stock was theorized to be the discounted value of the dividends it was expected to pay. There was, however, no theory which paired return with risk. Investors, of course, looked at both return and risk when making investment decisions. Markowitz’s seminal contribution was to introduce, for the first time, an elegant mathematical theory which married returns and risk.
Using Markowitz’s theory, we could build a diversified “efficient portfolio” suitable to our risk profile. So grand and pathbreaking was the theory that it earned the epitaph – Modern Portfolio Theory! But there was a niggling issue. The number of computations that had to be done to build an efficient portfolio was very large. This is because the theory required computing pairwise covariances between every security being considered in the analysis. For the stock market as a whole, this could mean calculating over 1 million pairwise covariances.
The job of simplifying Markowitz’s theory was accomplished by William Sharpe in 1964. Sharpe’s theory, referred to as the Capital Asset Pricing Model (CAPM), led to the birth of the most often used Greek alphabet in finance – beta. As per CAPM, the expected return from a stock is dependent on its beta, where beta measures the stock’s sensitivity to the overall stock market.
The elegance of CAPM is irresistible. It argues that the return from a stock is dependent on one factor alone – its beta. Higher the beta, i.e., higher the sensitivity or volatility of the stock, higher will be its expected return. After all, why will investors hold a volatile stock unless they are compensated in the form of higher return?
The CAPM made perfect intuitive sense and spread like wildfire in the investing community. This model provides the perfect foundation for the high-risk high-return idea. It predicts that high beta stocks, which are risky due to their higher volatility, will earn higher returns. The ascendancy of the CAPM cemented the notion that we need to take higher risk to earn higher returns.
Chinks in CAPM
The brilliance of the CAPM led to an outburst of research confirming its findings. But there were also occasional studies which presented counterviews. One of the earliest studies to doubt the findings of CAPM was published in 1972 by Robert Haugen and James Heins. This study concluded that there was actually very little evidence that “risk premiums manifested themselves in realised rates of return.”. In other words, the study did not find evidence that investing in risky stocks is rewarded with higher return.
Such studies, which contradicted CAPM, were mostly ignored. However, a seminal paper by Eugene Fama and Kenneth French, published in 1992, dealt a fatal blow to CAPM. In this paper, the authors introduced the Fama-French Three Factor Model which dethroned beta as the only factor which explains stock returns and added two more factors – the value factor and the size factor.
The 1992 paper by Fama-French opened the floodgates for research on what other factors influenced stock returns. The Three Factor Model was later expanded by Fama-French to a Five Factor Model. In the quest for uncovering new factors, researchers noticed a surprising paradox – low volatility stocks were exhibiting superior return performance, the exact opposite of what we would expect!
Enough theory, let’s see the evidence
Do low volatility stocks really earn superior returns? Let’s look at the evidence.
The first set of evidence pertains to the United States (US) equity market. Evidence from the US equity market is important due to many reasons – it is the most sophisticated market in the world, there is availability of long time series data which makes findings more robust, and trends observed in the US market usually find their way to other financial markets.
The most comprehensive evidence on the low volatility paradox has been compiled by Pim van Vliet, Head of Conservative Equities at Robeco Quantitative Investments, and a global authority on low volatility investing. The data can be accessed from the website he maintains at www.paradoxinvesting.com
Going all the way back to 1929, Pim compiled two portfolios – a low volatility portfolio consisting of stocks with low volatility and a high volatility portfolio consisting of stocks with high volatility. The difference in returns between the two portfolios is astonishing.
An amount of USD 100 invested in 1929 in the low volatility portfolio grew close to USD 10 million by the end of 2023. A similar amount invested in the high volatility portfolio grew to only about USD 33,000. The difference is staggering. Low volatility delivered a CAGR of 10%, while high volatility barely delivered a 6.5% CAGR.
Evidence from the US: Conservative Stocks Beat Speculative Stocks
Source: Graph created with data from www.paradoxinvesting.com Note: “Low Volatility” consists of the top 100 stocks that displayed the lowest monthly volatility over the previous three years. Similarly, “High Volatility” consists of the top 100 stocks with the highest monthly volatility over the previous three years. The universe was the top 1000 listed stocks in the US. The respective portfolios were rebalanced every month. Data is for the period 1929 to 2023.
These results are significant because they span nearly a century. A persistent pattern observed over such a long period cannot be a result of data mining. Researchers have observed the low volatility paradox in different geographies and different asset classes. A prominent study, “Betting Against Beta”, published in 2011, found this effect in “in 18 of 19 international equity markets, in Treasury markets, for corporate bonds sorted by maturity and by rating, and in futures markets.” Too pervasive for something to be a fluke.
The second set of evidence is closer to home. We have evidence of low volatility paradox in India as well. Apart from mainstream indices, Nifty Indices also maintains several factor-based indices which allows us to see how different investment strategies would have performed in the past.
For our purpose, I have selected two indices – Nifty 100 Low Volatility 30 (“Low Vol 30”) and Nifty High Beta 50 (“Beta 50”). The Low Vol 30 index selects 30 stocks from the Nifty 100 universe which are displaying the lowest volatility, while the Beta 50 index selects 50 stocks from the Nifty 100 universe with the highest beta. These indices provide a very good head-to-head competition between low risk and high-risk investing.
Evidence in India: Low Volatility Eats High Beta for Breakfast
Source: Samasthiti Advisors, NSE Indices. Data is for Price Returns and updated up to Oct 1, 2024. Rebased to 1000 on April 1, 2005
The gap between Low Vol 30 and Beta 50 is, again, mind blowing. Low Vol 30 delivered 17% CAGR over close to 20-year period, beating Beta 50’s 6.3% cagr hands-down. Low Vol 30 also comfortably beat Nifty 50. The evidence over the last two decades is emphatic.
Outperformance of volatile small cap stocks
We know that typically, small cap stocks exhibit higher volatility than large cap stocks. We also know that over long periods, small cap stocks outperform large cap stocks. Doesn’t this contradict the low volatility effect? Not really.
The low volatility effect does not mean that a security with the lowest volatility will beat all other securities. If this was the case, bonds would earn higher returns than stocks. The low volatility effect simply means that when we identify a universe of similar securities, and filter based on low volatility within that universe, securities with low volatility will outperform.
Thus, small cap stocks may outperform large cap stocks due to their size premium, but within the small cap universe, stocks with low volatility will outperform stocks with high volatility.
Conclusion
Low volatility investing is an intriguing idea, yet few investors use its principles to build portfolios. It has a special significance for retirement planning where a low volatility portfolio can help in minimizing the sequence of return risk.
There are several reasons why, despite the evidence, low volatility investing remains obscure. The most important reason has to do with investor behaviour. Paul Samelson, who inspired John Bogle to create index funds, had remarked – “Investing should be more like watching paint dry or watching grass grow. If you want excitement, take $800 and go to Las Vegas.”
Low volatility investing is like watching paint dry or watching grass grow – which is not an easy thing to do. Imagine holding on to a bunch of boring stocks that don’t move much on a daily basis, while your friends could be investing in volatile stocks that promises to be the next big thing.
Research suggests that most investors view their investments as a lottery. We invest not to benefit from the slow-grind of long term compounding, but to earn outsized returns in the short term. You must have heard of the hare and tortoise story in your childhood. Unfortunately, as investors, we behave more like the hare rather than the tortoise.
Low volatility investing turns conventional investing wisdom on its head. Its time investors woke up and smelt the coffee.
Ravi Saraogi, CFA, is a SEBI Registered Investment Adviser (RIA) and Co-founder of www.samasthiti.in
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