Research Review | 12 February 2021 | Equity Factor Risk

Why Are High Exposures to Factor Betas Unlikely to Deliver Anticipated Returns?
Chris Brightman (Research Affiliates) et al.
January 11, 2021
By choosing investment strategies that intentionally create exposure to factor betas, investors may be obtaining uncompensated risks. We show across a wide variety of factors and geographical markets that factors constructed from fundamental characteristics have earned high returns, whereas those constructed from statistical betas have earned returns close to zero. When designing factor-based investment strategies, investors should seek exposure to the fundamental characteristics that define a factor and use statistical measures of factor betas to manage factor risks. Conversely, seeking to gain exposure to factor betas is a misguided means of obtaining the returns available from factor investing.

Do factors carry information about the economic cycle?
Marlies van Boven (FTSE Russell)
January 2021
Institutional investors often pose the question of how factors perform across economic cycles. The concept of a normalized cycle has come under pressure in the post-Global Financial Crisis (GFC) era that has seen sustained quantitative easing, financial repression and lower trend growth. Consequently, this has necessitated a reappraisal of the traditional investment clock. In this paper, we focus on the new thinking, rebooting the investment clock to link factor behavior to secular regime shifts in the US market.

What’s up with Momentum?
Guido Baltussen, et al. (Robeco)
January 2021
Momentum had its ‘shot of momentum’ in 2020. But the ride was a bumpy one, while we also saw large return dispersions between different Momentum approaches. In this article, we examine the drivers behind the strong performance delivered by Momentum, how the factor can at times be exposed to significant style and concentration risks, and why we believe a well-diversified portfolio approach across Momentum signals is prudent to mitigate these risks.

Factor Investing with Black–Litterman–Bayes: Incorporating Factor Views and Priors in Portfolio Construction
Petter N. Kolm and Gordon Ritter (New York University)
December 15, 2020
The authors propose a general framework referred to as Black–Litterman–Bayes (BLB) for constructing optimal portfolios for factor-based investing. In the spirit of the classical Black–Litterman model, the framework allows for the incorporation of investor views and priors on factor risk premiums, including data-driven and benchmark priors. Computationally efficient closed-form formulas are provided for the (posterior) expected returns and return covariance matrix that result from integrating factor views into an arbitrage pricing theory multifactor model. In a step-by-step procedure, the authors show how to build the prior and incorporate the factor views, demonstrating in a realistic empirical example and using a number of well-known cross-sectional US equity factors, that the BLB approach can add value to mean–variance-optimal multifactor risk premium portfolios.

Equity Duration
Gary Mullins (University of Oxford Mathematical Institute)
July 6, 2020
The concept of bond duration was originally introduced by Macaulay (1938) and nowadays is well- established in the fixed-income literature. In this paper, I lift the same concepts from the fixed-income asset class and apply them to equities. I derive three candidate models for estimating the duration of a stock. The models are vastly different in their theoretical underpinnings, yet there is strong empirical evidence of positive co-movements between all three models in my sample. Furthermore, I investigate the relationship between the equity duration factor and various common equity factors. Empirical evidence suggests that low-duration stocks are also high-value, high-profitability, low-investment and low-risk stocks. In particular, there is a strong link between duration and the classical value factor – both theoretically and empirically. Importantly, however, the correspondence between the two factors is not one-to-one in my sample. I perform numerous empirical tests suggesting that a duration strategy out-performed a value-strategy in the period following the Great Financial Crisis (2007–08).

Equity Factor Investing: Historical Perspective of Recent Performance
Benoit Bellone (BNP Paribas Asset Management)
October 23, 2020
We investigate the possible sources of the recent underperformance of multi-factor equity strategies reported by many equity quant managers. We considered the value, quality, low risk and momentum factor styles in mid to large-capitalisation World, USA and European stock universes. When looking at the historical performance of the factors and multi-factor combinations, we find that this is not the first time factor strategies have experienced a period of poor performance. The tech bubble of the late 90s and the great financial crisis of 2008 were other difficult periods for some of the factors and multi-factor combinations. What is different this time around is that poor performance can be mainly attributed to the underperformance of value factors. We also find that long-only portfolios, which tend to be exposed to smaller-capitalisation stocks in their construction, have suffered additionally from that exposure. Not only did the size factor fail to generate a premium in mid to large-capitalisation universes in the long term, but also the recent underperformance of smaller-capitalisation stocks and the consequent increase in the concentration of benchmarks was an additional source of difficulty in long-only benchmarked portfolios. Finally, we discuss the impact of a number of choices available to managers of factor strategies and show that the neutralisation of sectors, neutralisation of beta, control of tracking error and diversification of factors in styles play an important role in improving the performance of equity factor strategies.

Impacts of Sector and Company Size on Effective Factor Investing:Evidence from U.S. Equity Markets
Salil K. Sarkar (University of Texas at Arlington), et al.
March 2019
Identifying the stable long term relationship between factors and asset returns is the main challenge of factor investing. By adopting a two-step regression methodology and controlling the impact of the business cycle, this study investigates industry sector, company size and their interactions with common factors in U.S. equity market from January 1990 to December 2016. The two-step regression recorded a significant sector effect and the influence of company size on common equity market factors. When industry sectors are included in the second-step regression, influences of three common factors: market beta, company size, and value factor are largely explained by the risk-free rate, yield curve, and industry sectors. In the large-cap portfolio, the momentum effect on long term equity returns is robust, but the size and value effects disappear. In the small-cap portfolio the momentum, however, is not significant, but the size premium is recorded in long term returns. This study documents that small-cap portfolio’s long term return is mainly determined by changes of the yield curve, credit curve, and industry sectors. These findings are robust when a business cycle dummy variable is introduced to the two-step regression. This study extended factor investing research further by examining influences of sectors and the company size on stock returns in long run: 1) market beta is mostly explained by the change of risk free rate, yield curve, and industry sectors; 2) the momentum is a determinant factor in large-cap portfolio, but not small-cap portfolio; 3) The return of small-cap portfolio is predominantly attributed to sector exposures, changes of the credit curve and yield curve. We conclude that for effective factor investing in large-cap portfolio, right sector rotation and market momentum strategies must be implemented, while in small-cap portfolio changes of yield curve and credit curve are determining factors.


Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return

By James Picerno


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