Low-volatility anomaly

Last updated

In investing and finance, the low-volatility anomaly is the observation that low-volatility securities have higher returns than high-volatility securities in most markets studied. This is an example of a stock market anomaly since it contradicts the central prediction of many financial theories that higher returns can only be achieved by taking more risk.

Contents

The Capital Asset Pricing Model (CAPM) predicts a positive and linear relation between the systematic risk exposure of a security (its beta) and its expected future return. However, the low-volatility anomaly falsifies this prediction of the CAPM by showing that higher beta stocks have historically underperformed lower beta stocks. [1] Additionally, stocks with higher idiosyncratic risk often yield lower returns compared to those with lower idiosyncratic risk. [2] The anomaly is also document within corporate bond markets. [3]

The low-volatility anomaly has also been referred to as the low-beta, minimum-variance, minimum volatility anomaly.

Portfolios sorted on volatility: US stock market 1929-2023. Volatility sorted portfolios 1929 2023.png
Portfolios sorted on volatility: US stock market 1929-2023.

History

The CAPM was developed in the late 1960s and predicts that expected returns should be a positive and linear function of beta, and nothing else. First, the return of a stock with average beta should be the average return of stocks. Second, the intercept should be equal to the risk-free rate. Then the slope can be computed from these two points. Almost immediately these predictions were empirically challenged. Studies find that the correct slope is either less than predicted, not significantly different from zero, or even negative. [4] [1] Economist Fischer Black (1972) proposed a theory where there is a zero-beta return which is different from the risk-free return. [5] This fits the data better. It still presumes, on principle, that there is higher return for higher beta. Research challenging CAPM's underlying assumptions about risk has been mounting for decades. [6] One challenge was in 1972, when Michael C. Jensen, Fischer Black and Myron Scholes published a study showing what CAPM would look like if one could not borrow at a risk-free rate. [7] Their results indicated that the relationship between beta and realized return was flatter than predicted by CAPM. [8] Shortly after, Robert Haugen and James Heins produced a working paper titled “On the Evidence Supporting the Existence of Risk Premiums in the Capital Market”. Studying the period from 1926 to 1971, they concluded that "over the long run stock portfolios with lesser variance in monthly returns have experienced greater average returns than their ‘riskier’ counterparts". [9] [10]

Evidence

The low-volatility anomaly has been documented in the United States over an extended 90-year period. Volatility-sorted portfolios containing deep historical evidence since 1929 are available in an online data library [11] The picture contains portfolio data for US stocks sorted on past volatility and grouped into ten portfolios. The portfolio of stocks with the lowest volatility has a higher return compared to the portfolio of stocks with the highest volatility. A visual illustration of the anomaly, since the relation between risk and return should be positive. Data for the related low-beta anomaly is also online available. The evidence of the anomaly has been mounting due to numerous studies by both academics and practitioners which confirm the presence of the anomaly throughout the forty years since its initial discovery in the early 1970s. The low-volatility anomaly is found across sectors, but also within every sector. [12] Examples include Baker and Haugen ( 1991), [13] Chan, Karceski and Lakonishok (1999), [14] Jagannathan and Ma (2003), [15] Ang, Hodrick, Xing, and Zhang (2006), [16] Clarke De Silva and Thorley (2006), [17] and Baker, Bradley and Wurgler (2011). [18] Besides evidence for the US stock market, there is also evidence for international stock markets. For global equity markets, Blitz and van Vliet (2007), [19] Nielsen and Subramanian (2008), [20] Carvalho, Xiao, Moulin (2011), [21] Blitz, Pang, van Vliet (2012), [22] Baker and Haugen (2012), [23] all find similar results. [24] [25] [26]

Explanations

Several explanations have been put forward to explain the low-volatility anomaly. They explain why low risk securities are more in demand creating the low-volatility anomaly.

For an overview of all explanations put forward in the academic literature also see the survey article on this topic by Blitz, Falkenstein, and Van Vliet (2014) and Blitz, Van Vliet, and Baltussen (2019). [29] [30]

Also see

Related Research Articles

<span class="mw-page-title-main">Capital asset pricing model</span> Model used in finance

In finance, the capital asset pricing model (CAPM) is a model used to determine a theoretically appropriate required rate of return of an asset, to make decisions about adding assets to a well-diversified portfolio.

<span class="mw-page-title-main">Efficient-market hypothesis</span> Economic theory that asset prices fully reflect all available information

The efficient-market hypothesis (EMH) is a hypothesis in financial economics that states that asset prices reflect all available information. A direct implication is that it is impossible to "beat the market" consistently on a risk-adjusted basis since market prices should only react to new information.

<span class="mw-page-title-main">Risk premium</span> Measure of excess

A risk premium is a measure of excess return that is required by an individual to compensate being subjected to an increased level of risk. It is used widely in finance and economics, the general definition being the expected risky return less the risk-free return, as demonstrated by the formula below.

In finance, arbitrage pricing theory (APT) is a multi-factor model for asset pricing which relates various macro-economic (systematic) risk variables to the pricing of financial assets. Proposed by economist Stephen Ross in 1976, it is widely believed to be an improved alternative to its predecessor, the capital asset pricing model (CAPM). APT is founded upon the law of one price, which suggests that within an equilibrium market, rational investors will implement arbitrage such that the equilibrium price is eventually realised. As such, APT argues that when opportunities for arbitrage are exhausted in a given period, then the expected return of an asset is a linear function of various factors or theoretical market indices, where sensitivities of each factor is represented by a factor-specific beta coefficient or factor loading. Consequently, it provides traders with an indication of ‘true’ asset value and enables exploitation of market discrepancies via arbitrage. The linear factor model structure of the APT is used as the basis for evaluating asset allocation, the performance of managed funds as well as the calculation of cost of capital. Furthermore, the newer APT model is more dynamic being utilised in more theoretical application than the preceding CAPM model. A 1986 article written by Gregory Connor and Robert Korajczyk, utilised the APT framework and applied it to portfolio performance measurement suggesting that the Jensen coefficient is an acceptable measurement of portfolio performance.

In finance, the beta is a statistic that measures the expected increase or decrease of an individual stock price in proportion to movements of the stock market as a whole. Beta can be used to indicate the contribution of an individual asset to the market risk of a portfolio when it is added in small quantity. It refers to an asset's non-diversifiable risk, systematic risk, or market risk. Beta is not a measure of idiosyncratic risk.

The equity premium puzzle refers to the inability of an important class of economic models to explain the average equity risk premium (ERP) provided by a diversified portfolio of equities over that of government bonds, which has been observed for more than 100 years. There is a significant disparity between returns produced by stocks compared to returns produced by government treasury bills. The equity premium puzzle addresses the difficulty in understanding and explaining this disparity. This disparity is calculated using the equity risk premium:

A market anomaly in a financial market is predictability that seems to be inconsistent with theories of asset prices. Standard theories include the capital asset pricing model and the Fama-French Three Factor Model, but a lack of agreement among academics about the proper theory leads many to refer to anomalies without a reference to a benchmark theory. Indeed, many academics simply refer to anomalies as "return predictors", avoiding the problem of defining a benchmark theory.

Momentum investing is a system of buying stocks or other securities that have had high returns over the past three to twelve months, and selling those that have had poor returns over the same period.

Robert (Bob) Arthur Haugen was a financial economist and a pioneer in the field of quantitative investing and low-volatility investing. He was President of Haugen Custom Financial Systems and also consulted and spoke globally.

Fundamentally based indexes or fundamental indexes, also called fundamentally weighted indexes, are indexes in which stocks are weighted according to factors related to their fundamentals such as earnings, dividends and assets, commonly used when performing corporate valuations. Indexes that use a composite of several fundamental factors attempt to average out sector biases that may arise from relying on a single fundamental factor. A key belief behind the fundamental index methodology is that underlying corporate accounting/valuation figures are more accurate estimators of a company's intrinsic value, rather than the listed market value of the company, i.e. that one should buy and sell companies in line with their accounting figures rather than according to their current market prices. In this sense fundamental indexing is linked to so-called fundamental analysis.

Alternative beta is the concept of managing volatile "alternative investments", often through the use of hedge funds. Alternative beta is often also referred to as "alternative risk premia".

In asset pricing and portfolio management the Fama–French three-factor model is a statistical model designed in 1992 by Eugene Fama and Kenneth French to describe stock returns. Fama and French were colleagues at the University of Chicago Booth School of Business, where Fama still works. In 2013, Fama shared the Nobel Memorial Prize in Economic Sciences for his empirical analysis of asset prices. The three factors are (1) market excess return, (2) the outperformance of small versus big companies, and (3) the outperformance of high book/market versus low book/market companies. There is academic debate about the last two factors.

In finance, active return refers to the returns produced by an investment portfolio due to active management decisions made by the portfolio manager that cannot be explained by the portfolio's exposure to returns or to risks in the portfolio's investment benchmark; active return is usually the objective of active management and subject of performance attribution. In contrast, passive returns refers to returns produced by an investment portfolio due to its exposure to returns of its benchmark. Passive returns can be obtained deliberately through passive tracking of the portfolio benchmark or obtained inadvertently through an investment process unrelated to tracking the index.

Factor investing is an investment approach that involves targeting quantifiable firm characteristics or “factors” that can explain differences in stock returns. Security characteristics that may be included in a factor-based approach include size, low-volatility, value, momentum, asset growth, profitability, leverage, term and carry.

Low-volatility investing is an investment style that buys stocks or securities with low volatility and avoids those with high volatility. This investment style exploits the low-volatility anomaly. According to financial theory risk and return should be positively related, however in practice this is not true. Low-volatility investors aim to achieve market-like returns, but with lower risk. This investment style is also referred to as minimum volatility, minimum variance, managed volatility, smart beta, defensive and conservative investing.

Style drift occurs when a mutual fund's actual and declared investment style differs. A mutual fund’s declared investment style can be found in the fund prospectus which investors commonly rely upon to aid their investment decisions. For most investors, they assumed that mutual fund managers will invest according to the advertised guidelines, this is however, not the case for a fund with style drift. Style drift is commonplace in today’s mutual fund industry, making no distinction between developed and developing markets according to studies in the United States by Brown and Goetzmann (1997) and in China as reported in Sina Finance.

David C. Blitz is a Dutch econometrician and quantitative researcher on financial markets. He is a founding researcher of Robeco Quantitative Investments.

Conservative formula investing is an investment technique that uses the principles of low-volatility investing and is enhanced with momentum and net payout yield signals.

Pim van Vliet is a Dutch fund manager specializing in quantitative investment strategies, with a focus on low-volatility equities. As the head of conservative equities at Robeco Quantitative Investments, van Vliet has contributed to the field through both academic research and practical investment management.

Guido Baltussen is a Dutch economist who is professor in Behavioral Finance at Erasmus University Rotterdam and Head of Factor Investing and co-head of Quant Fixed Income at Robeco Asset Management.

References

  1. 1 2 van der Grient, Bart; Blitz, David; van Vliet, Pim (July 1, 2011). "Is the Relation between Volatility and Expected Stock Returns Positive, Flat or Negative?". SSRN. Rochester, NY. doi:10.2139/ssrn.1881503. S2CID   153743404. SSRN   1881503.
  2. ↑ Ang, Andrew; Hodrick, Robert J.; Xing, Yuhang; Zhang, Xiaoyan (2006). "The Cross-Section of Volatility and Expected Returns" (PDF). The Journal of Finance. 61 (1): 259–299. doi: 10.1111/j.1540-6261.2006.00836.x . ISSN   1540-6261. S2CID   1092843.
  3. ↑ Houweling, Patrick; Muskens, Frederik (September 2023). "The Past, Present, and Future of Low-Risk Corporate Bonds". SSRN. Abstract 4574834.
  4. ↑ Fama, Eugen (1973). "Risk, return, and equilibrium: Empirical tests". Journal of Political Economy. 81 (3): 607–636. doi:10.1086/260061. S2CID   13725978.
  5. ↑ Black, Fischer (1972). "Capital market equilibrium with restricted borrowing". The Journal of Business. 45 (3): 444–455. doi:10.1086/295472.
  6. ↑ Arnott, Robert, (1983) “What Hath MPT Wrought: Which Risks Reap Rewards?,” The Journal of Portfolio Management, Fall 1983, pp. 5–11; Fama, Eugene, Kenneth French (1992), “The Cross-Section of Expected Stock Returns”, Journal of Finance, Vol. 47, No. 2, June 1992, pp. 427- 465; see Roll, Richard, S.A. Ross, (1994), “On the Cross-Sectional Relation Between Expected Returns and Betas”, Journal of Finance, March 1994, pp. 101–121; see Ang, Andrew, Robert J. Hodrick, Yuhang Xing & Xiaoyan Zhang (2006), “The cross section of volatility and expected returns”, Journal of Finance, Vol. LXI, No. 1, February 2006, pp. 259–299; see also Best, Michael J., Robert R. Grauer (1992), “Positively Weighted Minimum-Variance Portfolios and the Structure of Asset Expected Returns”, The Journal of Financial and Quantitative Analysis, Vol. 27, No. 4 (Dec., 1992), pp. 513–537; see Frazzini, Andrea and Lasse H. Pedersen (2010) “Betting Against Beta” NBER working paper series.
  7. ↑ Black, Fischer; Jensen, Michael (1972). "The capital asset pricing model: Some empirical tests". Studies in the Theory of Capital Markets. 81 (3): 79–121.
  8. ↑ Jensen, Michael C., Black, Fischer and Scholes, Myron S.(1972), “The Capital Asset Pricing Model: Some Empirical Tests”, Studies in the theory of Capital Markets, Praeger Publishers Inc., 1972; see also Fama, Eugene F., James D. MacBeth, “Risk, Return, and Equilibrium: Empirical Tests”, The Journal of Political Economy, Vol. 81, No. 3. (May – Jun., 1973), pp. 607–636.
  9. ↑ Haugen, Robert A., and A. James Heins (1975), “Risk and the Rate of Return on Financial Assets: Some Old Wine in New Bottles.” Journal of Financial and Quantitative Analysis, Vol. 10, No. 5 (December): pp.775–784, see also Haugen, Robert A., and A. James Heins, (1972) “On the Evidence Supporting the Existence of Risk Premiums in the Capital Markets”, Wisconsin Working Paper, December 1972.
  10. ↑ Haugen, Robert A., and A. James Heins, (1972) “On the Evidence Supporting the Existence of Risk Premiums in the Capital Markets”, Wisconsin Working Paper, December 1972.
  11. ↑ Van Vliet, Pim; de Koning, Jan (2017). High returns from low risk: a remarkable stock market paradox. Wiley. doi:10.1002/9781119357186. ISBN   9781119351054.
  12. ↑ De Carvalho, Raul Leote; Zakaria, Majdouline; Lu, Xiao; Moulin, Pierre (January 1, 2015), Jurczenko, Emmanuel (ed.), "11 - Low-Risk Anomaly Everywhere: Evidence from Equity Sectors", Risk-Based and Factor Investing, Elsevier, pp. 265–289, ISBN   978-1-78548-008-9 , retrieved August 18, 2023
  13. ↑ R. Haugen, and Nardin Baker (1991), “The Efficient Market Inefficiency of Capitalization-Weighted Stock Portfolios”, Journal of Portfolio Management, vol. 17, No.1, pp. 35–40, see also Baker, N. and R. Haugen (2012) “Low Risk Stocks Outperform within All Observable Markets of the World”.
  14. ↑ Chan, L., J. Karceski, and J. Lakonishok (1999), “On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model”, Review of Financial Studies, 12, pp. 937–974.
  15. ↑ Jagannathan R. and T. Ma (2003). “Risk reduction in large portfolios: Why imposing the wrong constrains helps”, The Journal of Finance, 58(4), pp. 1651–1684.
  16. ↑ Ang, Andrew; Hodrick, Robert J.; Xing, Yuhang; Zhang, Xiaoyan (2006). "The Cross-Section of Volatility and Expected Returns". The Journal of Finance. 61 (1): 259–299. doi:10.1111/j.1540-6261.2006.00836.x. ISSN   0022-1082.
  17. ↑ Clarke, Roger, Harindra de Silva & Steven Thorley (2006), “Minimum-variance portfolios in the US equity market”, Journal of Portfolio Management, Fall 2006, Vol. 33, No. 1, pp.10–24.
  18. ↑ Baker, Malcolm, Brendan Bradley, and Jeffrey Wurgler (2011), “Benchmarks as Limits to Arbitrage: Understanding the Low-Volatility Anomaly”, Financial Analyst Journal, Vol. 67, No. 1, pp. 40–54.
  19. ↑ Blitz, David C., and Pim van Vliet. (2007), The Volatility Effect: Lower Risk without Lower Return, Journal of Portfolio Management, vol. 34, No. 1, Fall 2007, pp. 102–113. SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=980865
  20. ↑ Nielsen, F and R. Aylur Subramanian, (2008), “Far From the Madding Crowd – Volatility Efficient Indexes”, MSCI Research Insight.
  21. ↑ Carvalho, Raul Leote de, Lu Xiao, and Pierre Moulin,(2011) “Demystifying Equity Risk-Based Strategies: A Simple Alpha Plus Beta Description”, The Journal of Portfolio Management”, September 13, 2011.
  22. ↑ Blitz, David, Pang, Juan and Van Vliet, Pim, “The Volatility Effect in Emerging Markets” (April 10, 2012). Available at SSRN: http://ssrn.com/abstract=2050863.
  23. ↑ Baker, Nardin and Haugen, Robert A., “Low Risk Stocks Outperform within All Observable Markets of the World” (April 27, 2012). Available at SSRN: http://ssrn.com/abstract=2055431
  24. ↑ "Low Risk, High Return? - May 2014 - SagePoint Financial" (PDF). SagePoint Financial. Archived from the original (PDF) on May 4, 2015.
  25. ↑ "Why Low Beta Stocks Are Worth a Look". Portfolio Investing Blog: Portfolioist. Archived from the original on June 6, 2014.
  26. ↑ "The Greatest Anomaly in Finance: Low-Beta Stocks Outperform". Investing Daily. Archived from the original on May 31, 2014.
  27. ↑ Frazzini, Andrea and Pedersen, Lasse (2014). Betting against beta. Journal of Financial Economics, 111(1), 1-25.
  28. ↑ Blitz, David; Huisman, Rob; Swinkels, Laurens; Van Vliet, Pim (2019). "Media Attention and the Volatility Effect". Finance Research Letters. forthcomin. doi:10.2139/ssrn.3403466. hdl: 1765/120091 . S2CID   198634762.
  29. ↑ Blitz, David; Falkenstein, Eric; Van Vliet, Pim (2014). "Explanations for the Volatility Effect: An Overview Based on the CAPM Assumptions". The Journal of Portfolio Management. 40 (3): 61–76. doi:10.3905/jpm.2014.40.3.061. S2CID   201375041.
  30. ↑ Blitz, David; Van Vliet, Pim; Baltussen, Guido (2019). "The Volatility Effect Revisited". doi:10.2139/ssrn.3442749. S2CID   202931436.{{cite journal}}: Cite journal requires |journal= (help)
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.