Factor Investing: Get Your Exposures Right!
François Soupé (BNP Paribas Asset Management), et al.
October 26, 2018
This paper is devoted to the question of optimal portfolio construction for equity factor investing. The first part of the paper focusses on how to make sure that a given equity portfolio has the targeted factor exposures, even before imposing any constraints. We show that such portfolios can be derived from mean-variance optimization using stock expected returns as inputs provided these are built in a robust way from information about the factors. We propose a framework to build those robust stock expected returns and show that the targeted factor exposures are retained by the portfolios both before and after applying realistic constraints, e.g. long-only. Other more simplistic approaches fail. In the second part of the paper we illustrate the application of the framework to a practical case where the objectives are, first, to decide about the risk budget allocation to factors in some pragmatic way; and second, to construct a long-only constrained portfolio that retains the targeted exposures to four factors from well-known asset pricing equity models, namely High-minus-Low (HML), Robust-minus-Weak (RMW), Conservative-minus-Aggressive (CMA) and Momentum (MOM).
Tail Risk and the Cross-Section of Mutual Fund Expected Returns
Nikolaos Karagiannis (KU Leuven) and Konstantinos Tolikas (Aston U.)
October 9, 2018
We test for the presence of a tail risk premium in the cross-section of mutual fund returns and find that the top tail risk quintile of funds outperforms the bottom by 4.4% per annum. This premium is not simply a reward for market risk, nor do commonly used risk factors offer an adequate explanation. Our findings hold across double-sorted portfolios formed on tail risk and a number of fund characteristics. We also find that funds susceptible to tail risk tend to be small, young, have high management fees, and have managers who do not risk their own capital.
Momentum Strategies for the ETF-Based Portfolios
Daniel Nadler and Anatoly B. Schmidt (Kensho Technologies)
October 18, 2018
We compared performance of past ‘winners’ and past ‘losers’ over the look-ahead period of one month for various portfolios that consist of the US ETFs and the holdings of the US equity Select Sector SPDRs in 2007–2017 and 2011-2017. Namely, we verified the conventional pattern described in the literature according to which there is mean reversion (i.e. past losers outperform past winners in near future) for short past periods and persistent momentum (i.e. past winners outperform past losers in near future) for longer past periods. We also compared performance of momentum-based strategies with that of equal-weight benchmark portfolios (EWBP). We found that the specifics of the momentum strategy pattern and its performance depend on portfolio holdings and whether the bear market of 2008 is included in the data sample. The conventional pattern was statistically significant only for a multi-asset ETF portfolio in both 2007-2017 and 2011-2017, and for proxies of the SPDR S&P500 ETF and Industrials Select Sector SPDR ETF in 2011–2017. Regardless of that, past winners and past losers sometimes outperformed EWBPs.
The Limits of Data Mining: A Thought Experiment
Andrew Y. Chen (Federal Reserve Board)
October 25, 2018
Suppose that asset pricing factors are just data mined noise. How much data mining is required to produce the more than 300 factors documented by academics? This short paper shows that, if 10,000 academics generate 1 factor every minute, it takes 15 million years of full-time data mining. This absurd conclusion comes from rigorously pursuing the data mining theory and applying it to data. To fit the fat right tail of published t-stats, a pure data mining model implies that the probability of publishing t-stats < 6.0 is ridiculously small, and thus it takes a ridiculous amount of mining to publish a single t-stat. These results show that the data mining alone cannot explain the zoo of asset pricing factors.
Market Anomalies and Disaster Risk: Evidence from Extreme Weather Events
Matthew Lanfear (EDHEC Business School), et al.
October 24, 2018
We document strong abnormal effects due to U.S. landfall hurricanes over the period 1990 to 2017 on stock returns and illiquidity across portfolios of stocks sorted by market equity (ME), book-to-market equity ratio (BE/ME), momentum, return-on-equity (ROE), and investment-to-assets (I/A). ROE- and I/A-related long/short factors are insensitive to hurricanes, while size-, BE/ME-, and momentum-related factors are extremely sensitive to these extreme weather events. Long and short legs react differently and high momentum stocks experience a negative impact on their returns an order of magnitude greater than other stocks. Abnormal illiquidity is only able to account for a small fraction of the observed abnormal returns.
Market Risk and the Momentum Mystery
James W. Kolari and Wei Liu (Texas A&M University)
November 20, 2018
This paper employs the ZCAPM asset pricing model of Liu, Kolari, and Huang (2018) to show that momentum returns are highly related to market risk arising from return dispersion (RD). Cross-sectional tests show that momentum risk loadings and RD risk loadings are similarly priced in momentum portfolios. Comparative analyses find that zero-investment momentum portfolios and zero-investment return dispersion portfolios earn high returns relative to other risk factors. Further regression tests indicate that zero-investment momentum returns are very significantly related to zero-investment return dispersion returns. We conclude that the momentum mystery is explained by market risk associated with return dispersion for the most part.
Portfolio Construction Matters
Stanislao Gualdi and Stefano Ciliberti (Capital Fund Mgt.)
October 18, 2018
The role of portfolio construction in the implementation of equity market neutral factors is often underestimated. Taking the classical momentum strategy as an example, we show that one can significantly improve the main strategy’s features by properly taking care of this key step. More precisely, an optimized portfolio construction algorithm allows one to significantly improve the Sharpe Ratio, reduce sector exposures and volatility fluctuations, and mitigate the strategy’s skewness and tail correlation with the market. These results are supported by long-term, world-wide simulations and will be shown to be universal. Our findings are quite general and hold true for a number of other “equity factors”. Finally, we discuss the details of a more realistic set-up where we also deal with transaction costs.
Factor Timing Revisited: Alternative Risk Premia Allocation Based on Nowcasting and Valuation Signals
Olivier Blin (Unigestion SA), et al.
September 10, 2018
Alternative risk premia are encountering growing interest from investors. The vast majority of the academic literature has been focusing on describing the alternative risk premia (typically, momentum, carry and value strategies) individually. In this article, we investigate the question of allocation across a diversified range of cross-asset alternative risk premia over the period 1990-2018. For this, we design an active (macro risk-based) allocation framework that notably aims to exploit alternative risk premia’s varying behavior in different macro regimes and their valuations over time. We perform backtests of the allocation strategy in an out-of-sample setting, shedding light on the significance of both sources of information.
Equity Multi-Factor Approaches: Sum of Factors vs. Multi-Factor Ranking
Farouk Jivraj (Barclays Invest. Bank, Imperial College Business School), et al.
September 16, 2016
There is a plethora of literature on the persistence and rationale of individual equity factor premia, which investors are often advised to extract through forming multi-factor portfolios – however, not so much attention is paid to the nuances of forming such portfolios and their implications. There are generally two approaches for forming equity multi-factor portfolios:
* Sum of Factors (SoF) – where separate respective factor portfolios are first formed which are then subsequently combined.
* Multi-Factor Ranking (MFR) – where individual stocks are selected based on a specified multi-factor ranking model.
In this paper, we empirically demonstrate the implications of the two approaches when allocating across four equity factors: Value, Momentum, Low Volatility and Quality. We do so in the context of two topical implementation considerations:
* Portfolio size: Concentrated versus diversified portfolios.
* Factor exposure: Ex-ante versus ex-post factor exposure.
In summary when comparing the two approaches, we find:
* MFR portfolios have historically outperformed SoF portfolios irrespective of portfolio size.
* However, this has been historically driven by the persistently lower CAPM Beta of the MFR portfolios, which has not been by design and which is accentuated in concentrated portfolios.
* This lower CAPM Beta is from an implicit low volatility factor bias.
* Such a bias comes with performance risk implications that investors should be mindful of.