Resurrecting the Value Premium
David Blitz (Robeco) and Matthias X. Hanauer (Technische Universität München)
October 15, 2020
The prolonged poor performance of the value factor has led to doubts about whether the value premium still exists. Some have noted that the observed returns still fall within statistical confidence intervals, but such arguments do not restore full confidence in the value premium. This paper adds to the literature by showing that the academic value factor, HML, has not only suffered setbacks in recent years but has, in fact, been weak for decades already. However, we show that the value premium can be resurrected using insights that are well documented in the literature or common knowledge among practitioners. In particular, we include more powerful value metrics, apply some basic risk management, and make more effective use of the breadth of the liquid universe of stocks. Our enhanced value strategy also suffers in recent years, but this is largely explained by an extreme widening of valuation multiples similar to the late nineties. We conclude that a solid value premium is still clearly present in the cross-section of stock returns.
Is Factor Momentum More than Stock Momentum?
Antoine Falck (Capital Fund Management International), et al.
March 27, 2020
Yes, but only at short lags. In this paper we investigate the relationship between factor momentum and stock momentum. Using a sample of 72 factors documented in the literature, we first replicate earlier findings that factor momentum exists and works both directionally and cross-sectionally. We then ask if factor momentum is spanned by stock momentum. A simple spanning test reveals that after controlling for stock momentum and factor exposure, statistically significant Sharpe ratios only belong to implementations which include the last month of returns. We conclude this study with a simple theoretical model that captures these forces: (1) there is stock-level mean reversion at short lags and momentum at longer lags, (2) there is stock and factor momentum at all lags and (3) there is natural comovement between the PNLs of stock and factor momentums at all horizons.
Factors with Style
Keiko Kimura (BlackRock), et al.
June 1, 2020
We document significant spreads in style factors — value, size, quality, momentum, and low volatility — in each of the style box categories. This is also true even for the value and small size factors, which are reflected in the original definition of the style box framework. Some single factors stay within a given style box, like quality in Core, while other factors drift across style boxes, like momentum and even the size factor! We build multifactor portfolios within each style box, giving access to five style factors that can stay within a style category which have exhibited information ratios over 1.0 over June 2003 to March 2019.
The Low Volatility Anomaly in Equity Sectors – 10 Years Later!
Benoit Bellone (independent) and Raul Leote de Carvalho (BNP Paribas Asset Mgt)
August 11, 2020
Ten years after showing that the low volatility anomaly in the performance of stocks is a phenomenon that should be considered in each sector as opposed to on an absolute basis ignoring sectors, we present evidence that this observation has held up well, and that if anything, has become even more valid.
Out-Performing Corporate Bonds Indices With Factor Investing
Thomas Heckel (BNP Paribas Asset Management), et al.
August 25, 2020
We considered a large number of factors from value, quality, low risk and momentum styles and show that these factors can be used to select the corporate bonds with the highest risk-adjusted returns. Our results were confirmed for the three largest corporate bond universes, namely those defined by U.S. Investment Grade, Euro Investment Grade and U.S. High Yield benchmark indices. The factors we investigated can be used to create investment strategies designed to out-perform these benchmark indices by over-weighting the cheapest bonds with the strongest performance trends from the most profitable, better managed and less risky companies.
The World of Anomalies: Smaller Than We Think?
Fabian Hollstein (Leibniz University Hannover)
22 September 2020
I examine a large set of return anomalies in international equity markets. While previous studies find that anomalies are strong in international markets, I show that only few replicate when mitigating the impact of tiny stocks, accounting for multiple testing, and using factor models to adjust for expected returns. Accounting for the former two, only 19 of 132 anomalies yield significant long–short returns in the ex-U.S. world cross-section. Most of these are value anomalies. Factor models hardly seem necessary for Japan and the Middle East. In other international markets, the best U.S. factor models help shrink the cross-sections further.
Factor Performance 2010-2019: A Lost Decade?
David Blitz (Robeco Quantitative Investments)
March 27, 2020
The factors in the widely used Fama-French model experienced a negative average return over the 2010-2019 period. Perhaps surprisingly, such a lost decade is not unprecedented in history, as factor performance in the 2010s is, in fact, remarkably similar to factor performance in the 1990s. By contrast, many other factors did deliver a positive premium over the past decade. These factors include low risk, price momentum, earnings momentum, analyst revisions, seasonals, and short-term reversal. Thus, there appears to be a clear dichotomy in recent factor performance: while generally accepted factors struggled, various factors that are considered to be inferior or redundant remained effective.
A Forecast Combination Approach to Equity Factor Timing
Michael Fraikin (Invesco), et al.
February 5, 2020
We investigate the benefits of forecast combination for timing equity factors based on predictive regressions using macro predictors. Relative to standard predictive regression models, forecast combination reduces the noise of forecasts and hence improves their out-of-sample predictive accuracy. Given the nature of macro predictors, the ensuing dynamic model reacts when major macro events happen. Before transaction costs, portfolio simulation results show considerable outperformance of the factor timing model over a static factor allocation. But much of this performance wedge is eroded when transaction costs are taken into account, rendering this article a cautionary tale about the benefits of factor timing.
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