Contrarian Factor Timing is Deceptively Difficult
Clifford S. Asness (AQR Capital Management), et al.
March 7, 2017
The increasing popularity of factor investing has led to valuation concerns among some contrarian-minded investors, and fears of imminent mean-reversion and underperformance. In this paper, the authors find that despite their recent popularity the most common factors or styles, namely the value, momentum and defensive styles, are not, in general, markedly over-valued as measured by their value spreads.
More broadly, tactical timing, whether of markets or factors, always seems to hold appeal for many. The authors look at the general efficacy of value spreads in predicting future returns to styles. At first glance, valuation-based timing of styles appears promising. This is not surprising as it is a simple consequence of the efficacy of the value strategy itself. Yet when the authors implement value timing in a multi-style framework that already includes the value style, they find somewhat disappointing results. As value timing of factors is correlated to the standard value factor, it adds further value exposure, but as compared to an explicit risk-targeted strategic allocation to value, value timing provides an intermittent and sub-optimal amount of value exposure. Thus, according to the authors, tactical value timing can reduce diversification and detract from the performance of a multi-style strategy that already includes value. Finally, the authors explore whether value timing works better at longer holding periods or at extremes, still finding fairly weak results.
Contrarian value timing of factors is, generally, a weak addition for long-term investors holding well-diversified factors including value and, specifically, not sending a strong signal on stretched valuations today.
A Smoother Path to Outperformance with Multi-Factor Smart Beta Investing
Chris Brightman (Research Affiliates), et al.
● Researchers have identified hundreds of factors that purport to predict equity returns; we find a half dozen that provide an opportunity to outperform the market.
● To maximize risk-adjusted returns, diversify across smart beta strategies that access the value, low beta, profitability, investment, momentum, and size factors.
● Systematic rebalancing to fixed weights—reducing exposure to popular factors that have outperformed over recent years, while increasing exposure to the out-of-favor factors that have underperformed—in a portfolio of smart betas will likely improve performance relative to a buy-and-hold weighting.
● Dynamically rebalancing factor exposures using short-term momentum and long-term reversal signals further improves the return.
Forecasting Factor and Smart Beta Returns (Hint: History Is Worse than Useless)
Rob Arnott (Research Affiliates), et al.
● Using past performance to forecast future performance is likely to disappoint. We find that a factor’s most recent five-year performance is negatively correlated with its subsequent five-year performance.
● By significantly extending the period of past performance used to forecast future performance, we can improve predictive ability, but the forecasts are still negatively correlated with subsequent performance: the forecast is still essentially useless!
● Using relative valuations, we forecast the five-year expected alphas for a broad universe of smart beta strategies as a tool for managing expectations about current portfolios and constructing new portfolios positioned for future outperformance. These forecasts will be updated regularly and available on our website.
Show Me the Money: The Monetary Policy Risk Premium
Ali K. Ozdagli (Boston Fed) and Mihail Velikov (Richmond Fed)
We study how monetary policy affects the cross-section of expected stock returns. For this purpose, we create a parsimonious monetary policy exposure (MPE) index based on observable firm characteristics that are theoretically linked to how firms react to monetary policy. We find that stocks whose prices react more positively to expansionary monetary policy surprises earn lower average returns. This finding is consistent with the intuition that monetary policy is expansionary in bad economic times when the marginal value of wealth is high, and thus high MPE stocks serve as a hedge against bad times. A long-short trading strategy designed to exploit this effect achieves an annualized value-weighted return of 9.96 percent with an associated Sharpe Ratio of 0.93 between 1975 and 2015. This return premium cannot be explained by standard factor models and survives a battery of robustness tests.
Factor Investing and Asset Allocation: A Business Cycle Perspective
Vasant Naik (PIMCO), et al.
December 29, 2016
This monograph draws heavily on the vast body of knowledge that has been built by financial economists over the last 50 years. Its goal is to show how to solve real‐life portfolio allocation problems. We have found that using a broad range of models works best. Also, we prefer simple over complex models. We believe that simplicity and modularity lend substantial robustness to investment analysis. Importantly, the framework presented provides several of the “missing links” in asset allocation—for example, the links between asset classes and risk factors, between macroeconomic views and expected returns, and ultimately between quantitative and fundamental investing.
The Risk-Return Tradeoff Among Equity Factors
Pedro Barroso (UNSW Business School) and P. Maio (Hanken School of Economics)
January 31, 2017
We examine the risk-return trade-off among equity factors. We obtain a positive in-sample risk-return trade-off for the profitability (RMW) and investment (CMA) factors of Fama and French (2015, 2016), while for the market and momentum factors there is a negative relation. The out-of-sample forecasting power (of factor volatility for factor returns) is economically significant for both RMW and CMA: By constructing a trading strategy that relies on such predictability, we obtain annual Sharpe ratios above one and utility gains above 5% per year. We also find weak evidence that the factor variances are negatively correlated with the aggregate equity premium.
Frontier Stock Markets: Local vs Global Factors
Douglas W. Blackburn and Nusret Cakici (Fordham University)
March 1, 2017
We study the returns of stocks from twenty-one frontier markets divided into the four regions of Europe, Africa, Middle East and Asia from January 2006 to June 2016. Factor mimicking portfolios based on market capitalization (SMB), book-to-market equity (HML), and momentum (WML) are constructed and reveal large and significant returns associated with value and momentum in frontier markets. Different from the developed markets, value and momentum effects are observed in both large and small market cap stocks. Empirical asset pricing models are not able to explain the observed value and momentum return patterns. Local asset pricing models, which use factors constructed from frontier market returns, and global asset pricing models, which use factors derived from developed market returns, are rejected in nearly all cases; however, the local four-factor model strongly outperforms the local single-factor capital asset pricing model (CAPM) for all regions, and the local four-factor model is found to be vastly superior to all global models. Surprisingly, there is no difference in performance between the global one-factor CAPM and the global four-factor model in explaining frontier stock market returns. This evidence strongly suggests that frontier and developed markets are segmented.
Asset Pricing Anomalies: Two Hedge Factors with Negative Risk Premia Embedded in Portfolios!
Arun Muralidhar (AlphaEngine Global Investment Solutions), et al.
January 14, 2017
The primary purpose of this research is to empirically test a new asset pricing model, the Relative Asset Pricing Model (RAPM), and to confirm whether hedge portfolios on two new risk factors highlighted in that model, and embedded in all portfolios, have negative and significant risk premia. In a number of specifications, this first test of RAPM appears to validate the importance of these two ubiquitous risk factors: (i) a liability proxy or Strategic Asset Allocation (SAA); and (ii) an index of traditional rebalancing back to the SAA (REBAL). A secondary goal of the research is to then examine what RAPM and the inclusion of these two risk factors mean for other commonly used factors (like Value Premium, Capitalization Premium, and Momentum Premium) that are increasingly being added to portfolios in a new trend called “factor-based investing”. In many regressions, adding these two new factors either individually or together causes traditional factors like Capitalization and – in fewer cases – Momentum to lose their significance. Valuation continues to be significant even in tests where the SAA is heavily biased to include Value indices. These findings have interesting implications for improving portfolio performance especially in a low-yielding environment. Investors would do well to consider an intelligent approach to managing portfolio drift. With respect to factor-based investing, two results stand out: (i) Valuation seems to be a very robust strategy; and (ii) early movers probably benefit before these strategies get incorporated into the industry-wide SAA and then lose significance – a seemingly obvious result but validated in the context of a robust asset pricing model, thereby also serving as a first test of (a special case of) the Adaptive Markets Hypothesis.