The low-volatility factor: A very human origin story

The low-volatility factor is the story of the risk-return paradox in finance. This paradox has shaken up the fundamental theory of higher risk and higher reward.[1]

This factor targets securities or portfolios with low-volatility characteristics. Historically, these characteristics have rewarded investors with higher risk-adjusted returns than a broad market capitalisation strategy over the longer term[2].

The low-volatility ‘anomaly’

Low-volatility portfolios came into the spotlight in the 1970s when academics started to challenge the assumptions of the traditional asset pricing models, notably the Capital Asset Pricing Model (CAPM). The CAPM predicts a positive linear relationship between risk and returns – i.e., higher risk should provide higher expected returns.

Robert Haugen and James Heins first reported the low-volatility anomaly in the early 1970s. They analysed US stock performance from 1926 to 1969 and found that portfolios with lower variance in monthly returns experienced higher average returns than their higher-risk counterparts.[3]

Subsequent studies by Frazzini et al in 2014[4] and others reiterated the existence of low-volatility anomalies in the US, Europe and emerging markets.

Behavioural biases are everywhere

Given that the existence of the low-volatility factor contradicts the fundamental assumption of the CAPM, what could explain the apparent mispricing of more volatile stocks?

We believe there are three main behavioural biases that help explain excess demand for higher-volatility stocks that is not warranted by fundamentals. For these reasons, we believe low-volatility stocks tend to benefit from a persistent premium:

• Preference for lotteries – Investors often show a preference for lottery-like payoffs where there is a small chance of a gain despite the higher likelihood of a loss.
• Representativeness – Tversky and Kahneman demonstrated that people often make decisions based on how closely something matches a stereotype rather than on actual probabilities.[5] In investing, this leads individuals to favour speculative, volatile stocks they associate with success stories like early investments in today’s tech giants, ignoring the high failure rates of such investments.
• Overconfidence – Overconfidence bias leads investors to prefer high-volatility stocks. In stock valuation, overconfident investors disagree more on uncertain, volatile stocks, leading to higher prices set by optimists resulting in lower future returns due to the scarcity of short sales.

Why isn’t it arbitraged away?

The second obvious question posed by the existence of the low-volatility factor is why don’t investors pile into low-volatility strategies with the aim of exploiting, and subsequently nullifying, this premium?

As explained above, the average investor has a psychological demand for high-volatility stocks. So why can’t institutional investors, with knowledge of these biases, arbitrage away the premium with their large scale?

We highlight two possible reasons:[6]

• Limits to borrowing – Many institutional investors do not short the very poor performing top-volatility stocks. This is because the most volatile stocks are often small stocks, which can be costly to trade in large quantities both as long and short positions. The volume of shares available to borrow is limited, and borrowing costs are often high for these stocks. The same frictions are present in large-capitalisation stocks, but on a smaller scale.
• Benchmarking – Even with limits to borrowing, why would institutional investors not at least go overweight low-volatility stocks? The reason could be benchmarking. A typical investment management mandate contains an implicit or explicit objective to maximise the excess return versus the portfolio relative risk to a benchmark (the information ratio). Low-volatility strategies generally exhibit relatively high tracking error (the denominator in the information ratio calculation). Therefore, in our opinion, considerable profit opportunities among low volatility stocks are neglected.

How do we define risk?

The focus of this instalment has been investor behaviour (behavioural biases and arbitrage limitations) and why we believe this plays a significant role in helping to explain the existence of the low-volatility anomaly. However, a key question arises – how do we define ‘low volatility’? Traditionally, volatility or risk has been defined as standard deviation. In the next instalment, we’ll consider how the risk of a stock can be defined and will explain why downside volatility (or semi-deviation) could, in our view, provide a better assessment than traditionally used risk metrics.

[1] Zaher F. Index Fund Management: A practical guide to smart beta, factor investing, and Risk Premia. Basingstoke: Palgrave Macmillan; 2020.

[2] Past performance is not a guide to the future.

[3] Haugen, R. A., & Heins, J. A. (1972). On the evidence supporting the existence of risk premiums in the capital market. Working Paper, unpublished. Available at SSRN.

[4] Frazzini, A., & Pedersen, L. H. (2014). Betting against Beta. Journal of Financial Economics, 111(1), 1–25.

[5] Tversky, Amos, D. Kahneman. “Judgment Under Uncertainty: Heuristics and Biases.” Science 185 (1974), pp. 1124-1131.

[6] Baker, Malcolm P. and Bradley, Brendan and Wurgler, Jeffrey A., Benchmarks as Limits to Arbitrage: Understanding the Low Volatility Anomaly (March 2010). NYU Working Paper No. 2451/29593, Available at SSRN: https://ssrn.com/abstract=1585031