In a rational investment market, investors demand higher returns to compensate for higher risk. Therefore, one should logically expect lower returns from safer securities and higher returns from riskier securities. This has long been one of investing’s fundamental precepts. However, rational is not always an accurate description of investor behavior. This paper questions the long-held belief – some would say myth – that higher risk, high beta stocks deliver the highest returns. Furthermore, we present studies that show how the “low volatility anomaly” creates opportunities to beat the market and create long-term wealth.
In order for people to believe they (or their professional money manager) can beat the market, they must start with the notion that markets are mostly, but not always efficient. If the stock market were perfectly efficient, it would be futile to pay an active money manager. A low-cost index fund would certainly be the best alternative.
Though the aggregated performance of professional investment managers may at times support a case for index funds, a deeper understanding of market dynamics and investor psychology reinforces the potential value of active management and may help investors determine which managers and strategies are better suited to beat the market.
For active managers to gain an edge, there must be market anomalies or inefficiencies. These anomalies must not only exist in the short term but also persist over the long term. In our view, some of the same human behavioral biases that cause investor error also allow anomalies to recur time and time again. In this paper, we discuss investors’ love affair with high beta stocks, as well as the low beta/low volatility anomaly and the natural human tendencies that sustain it. Most importantly, we show how this anomaly creates opportunities to outperform the market with less risk, casting doubt on the well-entrenched myth that high beta stocks are the surest path to higher returns.
Known for his Journal of Finance article, “Efficient Capital Markets: A Review of Theory and Empirical Work,” Nobel Prize Laureate Eugene Fama theorizes that all information is already reflected in all stock prices, thus making it impossible to outperform the market (Fama, 1970). According to the Efficient-Market Hypothesis (EMH), stocks always reflect all available information, eliminating any possibility of purchasing undervalued stocks or selling stocks at inflated prices. If the hypothesis holds, investors – whether professional or not – should be unable to outperform the overall market through stock selection or market timing. The only way investors could obtain higher returns is by taking on additional risk through high beta stocks.
There are many models that attempt to explain the risk-reward relationship of stocks relative to the market portfolio. The Capital Asset Pricing Model (CAPM), which uses a single factor (beta) to explain pricing and asset returns, was a remarkable breakthrough in finance that won William Sharpe the 1990 Nobel Prize in Economics. Unfortunately, beta alone has not done a very good job of explaining observed market risk and returns.
In order for the EMH to hold, investors must be risk-averse and always make rational investment choices. This assumption doesn’t always prove true, however, and many academics have tried to explain why. This has spawned an area of study known as behavioral finance or behavioral economics. Behavioral finance grew from the idea that individuals aren’t always rational and quite often make irrational decisions. There have been many studies on behavioral finance by psychologists such as Daniel Kahneman, Amos Tversky, and Richard Thaler to name just a few. Behavioral economists attribute imperfections in financial markets to a combination of cognitive biases, some of which we’ll review in this paper.
Stock market bubbles are a notable outcome of irrational decision making. Bubbles occur when market participants drive stock prices above their value relative to some system of stock valuation. In the 20th century, the stock market experienced a number of bubbles, including the bubble that preceded the Great Depression and the dot-com Bubble of the late 1990s. Both were fueled by speculative activity involving new technologies. The 1920s saw amazing innovations including radio, automobiles, and aviation. In the 1990s, the Internet and e-commerce technologies emerged.
Some analysts, affirming crowd-sourced wisdom, theorize that price movements, even bubbles, really do reflect rational expectations of fundamental returns. Behavioral finance theory, on the other hand, attributes stock market bubbles to cognitive biases, including the tendency to overpay for excitement, herd instinct, regret avoidance and other common human tendencies. There’s also a more cynical school of thought that one can make money buying securities, whether overvalued or not, because there will always be someone (a greater fool) who is willing to buy that stock at a higher price. In our view, stock market bubbles provide clear evidence that investors do not always act rationally.
Whether we like to admit it or not, we all probably agree that the latest, greatest smartphone or a new social media company is much more exciting (though not necessarily more profitable) than a company that sells salad dressing or creates software for government agencies. In general, financial news is not the most exciting subject matter, so it’s no surprise that financial newscasters spice up their reports and juice their ratings with news of exciting high growth companies. Investors too are beguiled by what are typically classified as “high beta” stocks and sometimes referred to colloquially as “glamor” or “speculative” stocks.
Stocks are tagged as high beta when they exhibit high growth and high price momentum. These stocks are expected to rise more than the overall market in good times and fall more than the market in bad times. They may involve specific speculative risks, for example, uncertainty about an event that may produce either a profit or loss. However, investors who own these stocks are all too willing to take on added risk in hopes of a commensurate payoff. Let’s look at some of the behavioral biases behind this attraction.
Looking at irrational gambling behaviors may shed some light on why investors prefer high beta stocks. Think about someone who gambles online or loses significant sums playing blackjack at a casino. They very likely view gambling as a low-risk, high-yield proposition. In reality, it’s the opposite: a high-risk, low-yield proposition. The payouts of every casino game always favor the casino which is called the “house edge” or “house advantage.” For example, for each bet on the roulette wheel, the casino keeps 5.26% of winnings on average. Blackjack, the most popular table game, returns a bit more in winnings than roulette, but it too is subject to what is called the house edge. Yet, according to The Wall Street Journal, the U.S. still spent $104.1 billion on gambling in 2015 (WSJ 2016). The thrill of hitting a casino jackpot is often just too alluring – despite its improbability. Excitement and anticipation create a natural high, an adrenaline rush. Studies show that people get this same feeling when buying exciting high momentum growth stocks.
Three experts in the field of Neuroeconomics, Wolfram Schultz, Read Montague, and Peter Dayan have written studies showing that financial gains trigger the release of dopamine, a brain chemical that stimulates a natural high. The studies have shown that the less likely or predictable a financial gain, the more dopamine is released and the longer it affects the brain. This may explain why gamblers are drawn to low-probability bets with high potential payoffs. When such bets pay off, they produce an actual physiological change; a massive dopamine release floods the brain with a soft euphoria. Through the use of MRI technology, Schultz and Montague also found similarity between the brains of people who have successfully predicted financial gains and the brains of people addicted to morphine or cocaine. After a few successful predictions, financial speculators can literally become addicted to the dopamine release.
A person with high dopamine levels typically becomes over-confident and tends to take undue risk. This may cause gamblers to wrongly attribute success to their own prowess or “system” rather than luck. Addiction to the dopamine kick may keep them playing until they lose all their money. Similarly, some investors become “addicted” to hot high beta speculative stocks. The excitement triggers dopamine. On the other hand, successful investors like Warren Buffet often make money in boring industries like insurance, finance, railroads, food, and beverage. With less excitement, there’s likely less risk of dopamine over-riding one’s more rational decision-making abilities.
Turning to baseball, another popular U.S. pastime, the bestselling book “Moneyball: The Art of Winning an Unfair Game” by Michael Lewis provides a concrete example of overpaying for excitement. While most Major League Baseball teams hired expensive home run hitters that fans loved to watch, Oakland Athletics general manager Billy Beane favored an analytical, evidence-based approach to assembling a competitive team. Oakland hired undervalued players who more consistently got on base with a walk or a single. Even though sabermetrics (the empirical analysis of baseball stats) clearly indicated that on-base percentage was critical to offensive productivity, the baseball labor market seriously undervalued that factor.
Overpaying for home runs and underpaying for walks and singles is quite similar to investors’ preference for “speculative/ high beta” stocks over stocks that deliver singles year after year. Many believe the Moneyball anomaly corrected once it was exposed, which may be true since there are only 30 MLB teams. However, in order to correct a parallel anomaly in the stock market, millions of “players” would have to be persuaded to forego the thrill of buying speculative high beta stocks.
So far we have focused on why investors are so attracted to high beta stocks. Let’s now consider whether they are rewarded for their devotion. The notion that high beta stocks should earn higher average returns than low beta stocks has been a cornerstone of modern finance. In an efficient market, investors should enjoy above-average returns only when they take above-average risks. In reality, this isn’t always the case. Over the last 20 years, there has been a significant academic investigation into the relationship between volatility and performance. Study after study has concluded that investors are not appropriately rewarded for taking either systematic (beta) or idiosyncratic (company specific) risk.
There is a persistent anomaly in the risk/return trade-off between high and low beta stocks. Portfolios of low-volatility stocks have produced higher risk-adjusted returns than portfolios of high-volatility stocks. For investors, this is a sort of “Holy Grail” – higher returns with less risk.
The outperformance of low beta stocks and portfolios is not a new discovery. In addition to academic evidence, which we review below, there’s also a growing interest on the part of investors. Following the dot.com bust and the financial crisis of 2008, baby boomers have been burned in the market too many times and seem to be more emotionally ready to tone down the “excitement” in their portfolios in the interest of preserving their hard earned money for retirement.
As early as 1972, Jensen, Black, and Scholes demonstrated that the expected excess return on a security was not linearly related to its beta (Jensen, et al., 1972). The authors found that high beta stocks have negative alphas (i.e. are overvalued) and low beta stocks have positive alphas (are underpriced). Fama and French also observed a weak positive relationship between average return and beta. Low-beta portfolios had average returns that were similar or higher than those of high-beta portfolios (Fama, et al., 1992).
Source: Baker, Bradley, and Wurgler 2011
Source: Baker and Haugen 2012
Source: David C. Blitz and Pim van Vliet (2007) Sharpe ratio uses standard deviation to measure a fund’s risk-adjusted returns. The higher a fund’s Sharpe ratio, the better a fund’s returns have been relative to its risk.
Source: Frazzini and Pedersen (2014)
Source: Clifford Asness, Andrea Frazzini, and Lasse H. Pedersen (2014)
The belief that high beta stocks are the answer to investors’ long-term investment success is a myth that has been burned into our collective psyche. Yet a plethora of academic research and well-documented historical market results show that portfolios of low volatility stocks are better suited to deliver above-average returns over longer time frames. The fundamental question now facing investors is whether they are willing to forego the short-term thrill of owning so-called glamor stocks in exchange for the ultimate reward of achieving their long-term investment goals.
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Illinois State Budget Fiscal Year 2017 p.335. Available at www.illinois.gov
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