In the first article of this series, I highlighted excerpts from several recent research studies, which showed that low-volatility stocks consistently outperformed high-volatility stocks over the long run. As many dividend growth [DG] stocks have betas less than 1.0, the DG investment strategy may not only be an excellent approach for creating a growing income stream, but also for producing strong long-term total return. I have created several DG model portfolios based on research in an effort to identify opportunities to outperform the overall market. These results run counter to the Efficient Market Hypothesis [EMH] and Modern Portfolio Theory [MPT], which respectively assert that investors cannot consistently achieve excess returns, and to obtain higher returns, investors need to take higher risk, as measured by volatility. As shown in Table #2 below, in general the risk-reward assertion holds true across different asset classes, but within equities, research has identified subgroups that consistently outperform. These groups, such as small-cap value stocks and low-volatility stocks, are considered to be anomalies in the theory.
Table Source: Baker, Bradley, and Wurgler (2011).
Research studies showed the low-volatility anomaly to be persistent and applicable to global markets, so why don't individual and institutional investors take advantage of it to the point where the excess returns disappear? There are some smaller ETFs now that focus on volatility, such as the PowerShares S&P Low Volatility (NYSEARCA:SPLV) and High Beta (NYSEARCA:SPHB) ETFs. There is only about a year's worth of price history, but the SPLV is leading so far.
Since the low-volatility anomaly contradicts EMH, then the market must not be truly efficient. The research papers offer numerous potential explanations discussing both individual, and more importantly, institutional investor behavior and incentives. Li and Sullivan (2010) concluded that the abnormal excess returns of the low-volatility anomaly are due to market mispricing as opposed to being a factor based on the volatility. Let's review some of the potential causes for this mispricing.
Irrational Investor Behavior
Not surprisingly, individual investors are not all rational, despite what many theories would like us to believe. Baker, Bradley, and Wurgler (2011) identified three behaviors that result in a preference for higher volatility stocks. This preference leads to higher demand, which contributes to the overpricing of these stocks and hence lower long-term excess returns on average.
Preference for Lotteries:
Behavioral research shows that people typically exhibit "loss aversion," the tendency to avoid losses, even when the overall expected payoff is positive. For example, suppose if you flip a coin and if it is heads, you win $110, but if it is tails, you lose $100. While the average expected payoff is positive, there's a good chance you will lose a sizable amount of money on any given toss. However, people become more willing to take a chance, even in the face of very low odds, when the amount lost is very small and the payout is very large. For example, spending $2 on a lottery ticket for a miniscule chance at winning $10 million. This is comparable to buying a low-priced, volatile stock, such as Bank of America (NYSE:BAC) or Ford (NYSE:F) during the 2008-2009 crisis when they were under $5. This type of investor preference probably has more to do with the positive skewness of the payoff [little to lose, potentially much to gain] than with volatility, but the two traits are connected.
When a layman investor thinks of "great stocks," he typically thinks of firms such as Apple (NASDAQ:AAPL), Microsoft (NASDAQ:MSFT), and Google (NASDAQ:GOOG). This may lead him to conclude that to score the big gain, he needs to invest early in more speculative firms, which are typically more volatile. Looking at all successful firms, these companies are a subset, but they do not represent the full group, nor is every speculative firm successful by any means. These investors incorrectly base their strategy on the traits of a few successful firms, and this behavior contributes to a preference for higher volatility stocks.
Researchers found that when estimating, most people form confidence intervals around their answer that are too narrow, and the more obscure the question, the more this calibration deteriorated. Investors face this same situation when they attempt to forecast a company's future sales, earnings, or price. Overconfident investors will disagree, but will also stick to their estimates. The range of disagreement is likely to be even higher for more volatile stocks, given the increased uncertainty. For these stocks to be overvalued though, pessimists (shorts) must act less aggressively than optimists (longs). Empirically, there are fewer short sales than purchases, so this appears to be a fair assumption. The research found that the optimists generally set the price, leading to overvaluation.
While this list is not all-inclusive, it provides good food for thought for individual investors. Knowing human tendencies allows us to identify these situations and potentially make different choices, though if everyone changes, the advantages of this anomaly could diminish, so don't tell too many people! I believe the larger factor is institutional investor behavior, as they manage much larger sums. The research identified some interesting incentive issues that again lead to a preference for higher-volatility stocks over lower-volatility stocks.
Institutional Behaviors and Incentives
Focus on Bull Markets:
Investors tend to chase returns, which causes fund managers to care more about outperforming during a bull market than about underperforming during a bear market. This leads to a preference for higher beta stocks, as by definition, they should gain more than the market. This preference raises demand for these stocks and reduces long-term excess returns.
Limits on Arbitrage:
If stocks in the highest-volatility quintile perform so poorly overall, then fund managers should short these equities. However, these firms tend to be smaller-cap stocks with limited volume, making it costly and/or impractical to trade these stocks in large amounts.
The Mandate to Maximize the Information Ratio:
Fund managers are often evaluated based on their ability to maximize the Information Ratio, which is defined as the active return [Portfolio return - Benchmark return] divided by the standard deviation of this active return, also called the tracking error. Note that the fund manager's return is being compared to a benchmark, let's say the S&P 500 (NYSEARCA:SPY), both in the numerator [return difference] and in the denominator [standard deviation of the return difference] of this ratio.
Mathematically, the information ratio incentivizes the manager to avoid low-beta, high-alpha stocks. By definition, low-beta stocks compared to the market index [beta = 1.0] will result in higher tracking error [the denominator], thus the manager will need even higher excess returns to counteract this effect. Higher returns, at least in the short run, would not be expected from low-beta stocks, particularly during bull markets. While high-beta stocks create the same issue of increasing the standard deviation of returns, they at least offer managers the potential to achieve higher excess returns because of their higher volatility. Therefore a manager may be more likely to purchase CSX (NYSE:CSX) or Coach (COH) over Kimberly-Clark (NYSE:KMB) or Sempra Energy (NYSE:SRE). Ideally, managers should pursue undervalued stocks with a beta close to the benchmark [1.0 for the market]. This minimizes the standard deviation of the tracking error, while potentially offering strong excess gains.
The focus on the information ratio seems to slant demand in favor of stocks with betas near 1.0 and those with higher betas. It begs the question whether this is right metric for measuring institutional fund management as it can impact investment decisions. Other measures, such as the Sharpe ratio or M2 Measure, provide alternative ways to compare the fund's performance with the benchmark without involving the tracking error.
In MPT, risk and reward levels can be scaled up through the use of leverage. In other words, we could borrow funds to purchase more of the low-beta stock, effectively raising its volatility and return levels proportionally. This would solve the information ratio incentive problem, as managers could borrow to scale the risk to match the benchmark. The problem, of course, is that most fund managers are not allowed to use leverage. Therefore, to achieve higher returns, they would be more likely to pursue high-beta stocks.
Option-like Manager Compensation
This explanation from Baker and Haugen (2012) made a lot of sense to me and is easier to follow than the information ratio reasoning. As observed in Figure #5 above, managers typically receive a set salary level until a certain return is achieved, then they get a bonus. The bell curve shows the expected ranges of returns from a low-volatility and high-volatility portfolio. The manager has a higher probability of achieving the bonus with the high-volatility portfolio. While the chance of losing more money is also greater, the manager faces no compensation loss in this case. Surely the manager could lose business with a consistently poor record, but he is potentially driven by the same lottery preference as individual investors.
Analysts Pursue Compelling Stocks
Baker and Haugen (2012) also noted that investment analysts are attracted to stocks for which they can make a strong, compelling case. These stocks tend to be "noteworthy", often receiving lots of media attention. This flow of information can lead to more volatility in the stock. The focus on these stocks leads to more demand, which overvalues the stock price and lowers future expected returns. In addition, going back to EMH, with more information available, the opportunity for excess gains is diminished.
Research has shown that the low-volatility anomaly is persistent across markets worldwide. Despite this data, investors in aggregate appear to continue to prefer high-volatility stocks. The research identified several behaviors and incentive issues, for both individual and institutional investors, which contribute to this preference for high-beta stocks. These tendencies lead to increased demand and to the overpricing of high-beta stocks, which diminished future excess returns.
Based on the research, we would expect institutions to hold more volatile stocks. The data in Figure #6 above confirms this expectation. Regardless of the stock size decile, the stocks with higher institutional ownership also had higher volatility on average.
While institutional investors appear to be victims of their own process, individual investors have the opportunity to benefit from the low-volatility anomaly and the research presented.
- Be aware of human behavioral tendencies and hopefully make fewer irrational decisions.
- Focus on the long-term versus the short-term. Wait patiently for low-beta holdings to payoff, instead of pursuing the fast gains [or losses] from high-beta stocks.
- Choose metrics carefully. As observed, the information ratio has a bias against low-beta stocks. If you have an investment advisor managing your funds, review the performance metrics being used and how they may impact the decisions made.
- Implement the strategies used by this research. Include low-beta holdings in your portfolio and/or use weighting systems based on inverse beta.
The third article of this series will present a new DG model portfolio based on the low-volatility anomaly research and other research that identified dividend growth as the primary contributor to nominal returns. It will highlight the screening process and portfolio construction, and then I will add this portfolio's results to my monthly portfolio updates.
Baker, Bradley, and Wurgler. (2011). Benchmarks as Limits to Arbitrage: Understanding the Low-Volatility Anomaly. CFA Institute, republished from Financial Analysts Journal, 67:1.
Baker, N. and Haugen, R. (April 2012). Low Risk Stocks Outperform within All Observable Markets of the World.
Li, X. and Sullivan, R. (Dec 2010). Why Low-Volatility Stock Outperform: Market Evidence on Systematic Risk Versus Mispricing.
Soe, A. (August 2012). The Low-Volatility Effect: A Comprehensive Look. S&P Dow Jones Indices, McGraw-Hill.
Disclosure: I am long AAPL, COH, MSFT, SRE, KMB. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.