The Low Risk Anomaly: A Decomposition into Micro and Macro Effects

Baker, Malcolm; Bradley, Brendan; Taliaferro, Ryan

doi:10.2139/ssrn.2210003

Cited by 21 publications

(17 citation statements)

References 18 publications

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“…Subsequently, Falkenstein (1994) and Ang, Hodrick, Ying, and Zhang (2006) have emphasized the magnitude and robustness of the anomaly. Blitz and van Vliet (2007), Ang et al (2009), andTaliaferro (2013) confirm its presence within developed markets and Blitz, Pang, and van Vliet (2013) document it in emerging markets.…”

Section: A Backgroundmentioning

confidence: 93%

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The Risk Anomaly Tradeoff of Leverage

Baker

Wurgler

2015

SSRN Journal

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Higher-beta and higher-volatility equities do not earn commensurately higher returns, a pattern known as the risk anomaly. In this paper, we consider the possibility that the risk anomaly represents mispricing and develop its implications for corporate leverage. The risk anomaly generates a simple tradeoff theory: At zero leverage, the overall cost of capital falls as leverage increases equity risk, but as debt becomes riskier the marginal benefit of increasing equity risk declines. We show that there is an interior optimum and that it is reached at lower leverage for firms with high asset risk. Empirically, the risk anomaly tradeoff theory and the traditional tradeoff theory are both consistent with the finding that firms with low-risk assets choose higher leverage. More uniquely, the risk anomaly theory helps to explain why leverage is inversely related to systematic risk, holding constant total risk; why leverage is inversely related to upside risk, not just downside risk; why numerous firms maintain low or zero leverage despite high marginal tax rates; and, why other firms maintain high leverage despite little tax benefit.

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Section: A Backgroundmentioning

confidence: 93%

“…Baker, Bradley, and Taliaferro (2013) Bali, Cakici, and Whitelaw (2011) and Barberis and Huang (2008). Other investors may simply categorize stocks together and neglect to price differences in risk, as in Barberis and Shleifer (2003).…”

Section: A Backgroundmentioning

confidence: 99%

The Risk Anomaly Tradeoff of Leverage

Baker

Wurgler

2015

SSRN Journal

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“…In that spirit, Baker, Bradley, and Taliaferro (2014) studied how much "macro" effects (country and industry) matter versus "micro" effects (stock selection within country and industry) in lowrisk investing. Their findings complement ours.…”

Section: Is a Professor At Copenhagen Business School And New York Unmentioning

confidence: 99%

Low-Risk Investing without Industry Bets

Asness

Frazzini²,

Pedersen

2014

Financial Analysts Journal

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“…Empirical studies of risk and return date back at least to the 1970s, including Black (1972); Black et al (1972);and Haugen and Heins (1975). More recent studies- Fama and French (1992); Ang et al (2006Ang et al ( , 2009Blitz and van Vliet (2007);Baker et al (2011);Baker, Bradley, and Taliaferro (2014); Frazzini and Pedersen (2014)-have used more updated data, global markets, other asset classes beyond equities, and a broader set of risk measures, including idiosyncratic risk. The upshot of all these studies is that risk and return are, at most, weakly related.…”

Section: Implementation: Tilts Vs Trades Thementioning

confidence: 99%

Optimal Tilts: Combining Persistent Characteristic Portfolios

Baker

Taliaferro

Burnham

2017

Financial Analysts Journal

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Systematic equity investing goes by many names: rules-based investing, sorts, style, characteristics-based portfolios, factor investing, smart beta, alternative beta, and even genius beta. Investors use characteristics-based portfolios in two ways. The first way is to evaluate risk. Across multiple equity managers, an investor may monitor and manage intentional or unintentional exposures to one or more characteristics. The second way is to generate returns by combining characteristics in a single portfolio or by assembling multiple single-characteristic portfolios.We can draw another distinction among these investment strategies by using the persistence of the characteristics themselves to divide seven stock characteristics into two groups of strategies. Some strategies are persistent "tilts." For example, small-cap investing requires little annual trading, because small stocks this year are likely to have been small stocks last year, reflecting an annual autocorrelation of 0.97. Other strategies are higher-frequency "trades." Although we can use two labels for simplicity, persistence is a continuum. Growth, momentum, and high-frequency reversal require successively more frequent rebalancing, with annual autocorrelations of 0.30, 0.05, and 0.03, respectively. All the extreme characteristics tend to appear among illiquid stocks, and thus high turnover requires detailed information on implementation costs.When is this categorization of systematic strategies important? It is not crucial for the evaluation of risk. Both tilts and trades can be used to assess contributions to portfolio risk. But the distinction is essential in portfolio construction. Forming mean-variance-efficient portfolios, or assessing the incremental value of adding an additional characteristic portfolio to an existing set, requires that the portfolios under consideration be equally implementable. For example, suppose that the risk and gross return properties of a low-beta portfolio could be roughly matched with a blend of a momentum portfolio and a high-frequency-reversal portfolio. Because the returns net of implementation costs-and thus the capacity-of the low-beta portfolio We examine the optimal weighting of four tilts in US equity markets over 1968-2014. We define a "tilt" as a characteristics-based portfolio strategy that requires relatively low annual turnover. This definition forms a continuum, with small size, a very persistent characteristic, at one end of the spectrum and high-frequency reversal at the other. Unlike with low-turnover tilts, a full history of transaction costs is essential for determining the expected return of, and thus the optimal allocation to, less persistent, more turnover-intensive characteristics. The mean-variance-optimal tilts toward value, size, and profitability are roughly equal to each other and to the optimal low-beta tilt. Notably, the low-beta tilt is not subsumed by the other three.

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The Low Risk Anomaly: A Decomposition into Micro and Macro Effects

Cited by 21 publications

References 18 publications

The Risk Anomaly Tradeoff of Leverage

The Risk Anomaly Tradeoff of Leverage

Low-Risk Investing without Industry Bets

Optimal Tilts: Combining Persistent Characteristic Portfolios

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