Tag Archives: strategy

One of the great anomalies of investing: the historical long-term outperformance of certain smart beta or factor-based strategies relative to the broader equity market (think choosing stocks based on their valuations, momentum, low volatility or quality metrics such as profitability). For example, according to data from MSCI, the MSCI USA Minimum Volatility (USD) index’s Sharpe ratio, a common way to measure risk-adjusted returns, was 0.61 for the last ten years, above the benchmark MSCI USA Index’s 0.44 ratio. The persistence of smart beta strategies’ outperformance relative to the broader market is surprising, because it doesn’t line up with the idea of an efficient market, one in which investors shouldn’t be able to simultaneously buy and sell securities for a profit without taking extra risk (the so-called “no arbitrage” principle ). In other words, in an efficient market, equity portfolios exhibiting low volatility, for instance, shouldn’t be able to earn comparable returns to their higher-risk counterparts. It’s no wonder, then, that numerous academic and financial industry research papers have been written on this topic, and there are various explanations for factor strategies’ outperformance. According to BlackRock’s smart beta experts, including my fellow Blog contributor Sara Shores, this outperformance can generally be attributed to a risk premium, structural impediment or behavioral anomaly. In other words, the outperformance is to compensate investors for taking on what’s actually a higher level of risk, a reflection of market supply-and-demand dynamics or the result of common decision-making biases. Personally, no shocker for my regular readers, I think explanations for this return performance anomaly rooted in behavioral finance add valuable insights to the discussion. In today’s highly connected world, where we can follow each other’s every move via social media, where we’re bombarded by data from every angle – including information on other investors’ positioning and trades – and where it can be hard to tune out the noise, human behavior may be a stronger performance driver than ever. Put another way, I believe investor behavior likely has a lot to do with the strategies’ outperformance. Behavioral explanations focus on investors’ cognitive biases, and the human tendency to use simple rules of thumb to make quick intuitive decisions, with individuals’ collective decision-making mistakes translating into security price distortions. Here’s a look at explanations for the outperformance of four commonly used equity factors. Value: Value stocks are ones that appear cheap in light of their sales, earnings and cash flow trends. Their returns, according to proponents of the efficient market hypothesis, have to do with investors rationally requiring extra compensation for investing in value firms, which tend to be procyclical, have high leverage and have uncertain cash flows. From a behavioral finance perspective, the outperformance of the value factor may have to do with a common decision-making mistake: people’s tendency to look at recent data trends and believe those trends will continue . If investors extrapolate past positive sales or earnings growth data into the future, they may overpay for growth stocks and underpay for value stocks. As a result, the prices of growth stocks may become too high relative to their fundamentals, predicting future reversal and the outperformance of value stocks. Alternatively, some researchers believe people’s tendency to strongly prefer avoiding losses over achieving gains (known as loss aversion) can help explain this anomaly . They hypothesize that loss-averse investors may perceive value stocks as riskier than they truly are, given the stocks’ recent underperformance, and may therefore require a higher future return from these investments. Momentum: This factor focuses on stocks that have strong price momentum , i.e., they have performed well over the past 6-12 months, and strong fundamental momentum, i.e. their earnings have recently been revised upward by security analysts. One explanation for this factor’s outperformance: Investors rationally demanding a higher return for investing in momentum stocks, which tend to be highly correlated and are perceived to perform poorly in times of distress. The behavioral finance explanation for this equity factor’s outperformance, on the other hand, has to do with analysts and investors putting too much weight on their prior beliefs at the expense of new information, leading to slow dissemination of firm-specific information , delayed price reactions to news and price continuation. For example, if investors like a stock and believe it has high earnings growth potential, they tend not to immediately adjust their beliefs sufficiently in light of new negative information – an investing mistake arising in behavioral finance from ” the anchoring-and-adjustment heuristic .” In other words, investors frequently drive price trends by projecting past wins onto future investments, creating a ” herding effect .” Quality: Quality generally describes financially healthy firms with high return on equity, with stable earnings growth and low financial leverage. They can effectively be characterized as having less risk based on their fundamentals . Behaviorally, people may ignore these potentially profitable, yet also perhaps more boring, companies, and instead, veer toward potentially more exciting, yet also less stable, growth and lottery-like stocks (for example, because the more exciting stocks tend to be featured in colorful news stories). As a result, they may end up overpaying for the less-stable stocks, which quality strategies seek to avoid. This predicts future reversal and potential outperformance of quality stocks. Low volatility: The low, or minimum volatility, factor loads up on stocks with low volatility. Low volatility stocks’ excess returns may be rationally explained by leverage constraints. In the absence of access to leverage, investors may overpay for high-volatility stocks in an attempt to increase risk in their portfolios, potentially leading lower-volatility stocks to become more attractively valued and outperform in the future. From a behavioral perspective, these stocks’ outperformance may be due people’s tendency to overestimate small, and underestimate, large probabilities . The idea is that this tendency leads to a preference for lottery-like stocks with a small chance of a very high payoff, and this preference, in turn, drives up the prices of high-volatility stocks disproportionately, suggesting future underperformance. Further, overconfident individuals may veer toward riskier securities in expressing their outsized faith in their own investing and stock-picking abilities, exacerbating the anomaly. To be sure, while focusing on factor and smart beta strategies has historically, over longer periods of time, earned higher risk-adjusted returns relative to the broader market, there have been stretches, even long ones, when factor-based approaches underperformed (think value during the 1990s), according to data accessible via Bloomberg . Finally, while in an efficient market, these anomalies should diminish in size and ultimately disappear, a widespread belief in the factors’ outperformance may also become a self-fulfilling prophecy. This post originally appeared on the BlackRock Blog.