The Low Volatility Anomaly: Overconfidence Bias
This series offers an expansive look at the Low Volatility Anomaly, or why lower risk stocks have historically produced stronger risk-adjusted returns than higher risk stocks or the broader market. This article hypothesizes that cognitive biases like the overconfidence bias contribute to the Low Volatility Anomaly. Like previous pieces in this series, this article covers a deviation between model and market that may contribute to the outperformance of low volatility strategies. A cognitive bias is a deviation in judgment where people draw inferences in an illogical fashion. As discussed in the last piece in this ongoing series on lottery preferences , cognitive biases can impact investment decision-making and likely contribute to the Low Volatility Anomaly. This article will discuss an additional cognitive biases that could contribute to this phenomenon – the overconfidence bias. The overconfidence bias suggests that a person’s subjective confidence in their own judgment is reliability greater than the objective accuracy of those judgments. The most oft cited example of the overconfidence bias is the 1980 finding by Ola Svensson that ninety-three percent of American drivers rate themselves better than the median. Traveling back from the highway to the Capital Asset Pricing Model (CAPM) assumptions, the model calls for homogeneous investor expectations, including expected values, standard deviations, and correlation coefficients. Valuing investments necessarily involves forecasting as a means to assessing tradeoffs between risk and return. Empirical evidence suggests that most people form confidence intervals that are too narrow. Borrowing from studies by Fischoff, Slovic, and Lichtenstein (1977) and Alpert and Raiffa (1982) , I conducted a similar study during a lecture to a fixed income class at my undergraduate alma mater in the fall of 2014. The students were asked a set of ten questions with numeric answers and asked to bound their answer by a confidence interval such that there was a ninety percent chance that their numeric answer would fall within the range. The ten questions were as follows: What is the population of the state? What is the seating capacity of the football stadium? What is number of different undergraduate majors at the University? What were the revenues of the athletic program in the previous year? What was the number of degrees conferred in the most recent academic year? What is the current yield level of the 30-year Treasury? What is the size of the U.S. Gross Domestic Product? What was the total number of jobs created in the U.S. in 2014? What was the total number of automobiles sold in the U.S? What is the U.S. Median Household Income? At a ninety percent confidence interval, half the class should have had nine or more results inside their confidence interval. Of the roughly thirty-five students, none had nine. Or eight. Or seven. Or six. Two students had five of their answers inside their bounds, but most of the students had between two and four. The class was overconfident. The first five questions were on the world around them at college, and the second five questions were on basic economic statistics. These topics should have yielded far better forecasts than the multi-year prognostications of market or security variables inherent in investment selection. The students did poorly – as poorly as the author when he first completed a similar exercise. The point of the exercise (aside from breaking up the monotony of my lecture) was to illustrate the overconfidence bias to the class. Overconfidence can drastically damage investment returns. Given the geographic proximity of the university to some of the nation’s leading onshore oil and gas resources, the rapid and unexpected drawdown in oil prices at the time of the lecture and the related implications on energy-related assets proved salient to the audience. Additional examples of the overconfidence bias given in the lecture included persistent overestimates of economic growth from the International Monetary Fund and Federal Reserve post-crisis, and the poor job of private and public sector economists at forecasting long-term interest rates, which were at the time rallying sharply in the face of consensus estimates for rising rates. Like the students surveyed, professional investors have proved similarly overconfident. Active managers implicitly assume that they are capable of beating their benchmark despite long-run evidence demonstrating that the average active manager fails to accomplish this feat on average over time ( Fama, French 2009 ). The collective overconfidence by the cadre of active managers violates that CAPM assumption of rationality and could be a factor that contributes to the Low Volatility Anomaly. If a manager is truly as skilled as they believe, then participation in higher volatility segments of the market offer the largest return proposition to capitalize on their perceived skill. If that same manager believed that the market was likely to fall, then they would not choose to invest in low volatility assets, which would outperform on a relative basis, but choose to exit the market entirely to outperform on an absolute basis. This overconfidence bias then likely contributes to the outperformance of low volatility stocks (referenced by SPLV ) relative to high beta stocks (referenced by SPHB ) depicted in the introductory article to this series . Further connecting the overconfidence bias to investment returns, we see more activity from market optimists than pessimists. Perhaps married to the market frictions inherent in the Leverage Aversion Hypothesis , the market in general is far less likely to short high volatility assets than it is to buy them. With skeptics more often sidelined than short, high beta assets with a more diffuse set of opinions on forward returns will then have more optimists among their holders, potentially pushing prices higher and future returns lower. In coming articles, I will highlight additional empirical evidence on the Low Volatility Anomaly, including utilization by a great investment mind, examples in fixed income, and examples crossing over between the equity and fixed income markets. I will then feature some ways in which Seeking Alpha readers can look to exploit the Low Volatility Anomaly in their portfolios. Disclaimer My articles may contain statements and projections that are forward-looking in nature, and therefore, inherently subject to numerous risks, uncertainties and assumptions. While my articles focus on generating long-term risk-adjusted returns, investment decisions necessarily involve the risk of loss of principal. Individual investor circumstances vary significantly, and information gleaned from my articles should be applied to your own unique investment situation, objectives, risk tolerance, and investment horizon. Disclosure: I am/we are long SPLV. (More…) 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.