Asset Allocation and Bad Habits
Andrew Ang, et al.
September 17, 2014
This article documents the “bad habits” of investors in asset allocation practices. Whereas financial markets exhibit momentum over multi-month horizons but more reversion to the mean over multi-year horizons, many investors act like momentum investors even at these longer horizons. Both these patterns are well known anecdotally but have not been well documented statistically, especially together. This article therefore addresses two empirical questions. First, How do funds reallocate based on past returns? The authors provide direct evidence using the CEM Benchmarking data on pension fund target allocations over a 22-year period. Second, What are momentum/reversal patterns in financial markets returns? Evidence is provided using more than a century of data. Merging the findings from the two data sets provides evidence consistent with the premise that investors chase returns over multi-year horizons, which is likely to hurt their long-run performance. However, the statistical evidence on pro-cyclical multi-year asset allocations and multi-year mean reversion patterns in asset-class returns is on the borderline of statistical significance.
Betting on ‘Dumb Volatility’ with ‘Smart Beta’
Claude B. Erb
July 30, 2014
It is possible that some investors make the “dumb mistake” of “buying high and selling low”. This may create “dumb volatility” which might allow some smart beta strategies to exploit the “behavior gap” by “buying low and selling high”. “Live data” suggests 1) the value-added from exploiting dumb volatility has been about 2-4% per year, 2) dumb volatility strategy risk-adjusted-returns have been similar, 3) there could be a “dumb volatility return frontier” offering more “return from dumb volatility” in exchange for more “dumb volatility” and 4) some dumb volatility strategies have achieved Warren Buffett-like “value-added”. A six factor model shows no evidence that traditional factors, such as “size” and “value”, drove the dumb volatility return. Going forward, the ability of a strategy to absorb capital will be an important economic moat.
Tracking Error and Portfolio Diversification Using Mutual Funds and ETFs
August 7, 2014
Current practice and research in portfolio asset allocation employs necessary assumptions about which securities and asset classes are held in particular proportions in order to arrive at an “optimal” mix of investments. These studies typically employ the average performance in the input process, yet it is unlikely that investors hold average funds, and that the specific funds they own exhibit tracking error. We find that when we examine a representative investor year, 2013, mutual funds outperformed ETFs and ETFs exhibited a greater degree of risk. ETFs had lower expenses but a greater tracking error. In addition, simulations revealed that it took twice as many ETFs to reduce tracking error compared to mutual funds. Finally, key variables that drive tracking error for both traditional mutual funds and ETFs are a manager’s experience and whether the fund employs leverage.
Retirement Risk, Rising Equity Glidepaths, and Valuation-Based Asset Allocation
Michael Kitces and Wade Pfau
September 16, 2014
This research investigates two types of dynamic asset allocation strategies (predetermined equity glidepaths and valuation-based asset allocation) for retirees using U.S. historical data. We analyze fixed asset allocations, traditional declining equity glidepaths, rising equity glidepaths, accelerated traditional and rising glidepaths, valuation-based allocations tethered around a fixed allocation, and glidepaths with valuation-based overlays. With U.S. historical data, it is difficult to beat a strategy which maintains a consistently high allocation to stocks (especially as measured by terminal median wealth), to the extent that a retiree’s risk tolerance allows for this, and subject to the caveat that high stock allocations cannot always be expected to do as well in the future. However, when we consider retirements beginning in varying valuation environments (as defined by the level of Robert Shiller’s cyclically-adjusted price-earnings ratio relative to its then-current historical median), we find the potential for different dynamic allocation strategies to help retirees sustain higher spending levels with lower average stock allocations in certain situations. When retirements begin in overvalued market environments (which reflects the situation for new retirees today), an accelerated rising equity glidepath has shown much potential to provide downside risk protection for retirees by minimizing equity exposure when an adverse market event would have the greatest impact. In other valuation environments, historical worst-case scenario sustainable withdrawal rates were highest with valuation-based asset allocation strategies, which maintain a midrange average stock allocation but adjust higher or lower when markets are deemed undervalued or overvalued, respectively.
Tactical Timing of Low Volatility Equity Strategies
Sanne De Boer and James H. Norman
September 5, 2014
Many investors we speak to are interested in making a strategic allocation to low volatility equities to help them better meet their investment objectives. The appeal of this strategy is clear. Low volatility stocks have historically delivered higher returns with lower risk than the capitalization-weighted market. Moreover, the behavioral and market-structural forces that have been suggested as possible explanations are inherently hard to change, which means the anomaly might not readily disappear. However, we often hear two tactical concerns about the timing of an allocation. The first is that relative valuation of low volatility stocks may be expensive compared to the rest of the market so they should wait for more attractive levels. The second is that low volatility stocks, which tend to pay higher dividends, may underperform against the back-drop of potential rate increases. In this research note we examine the validity of these concerns and also incorporate macro-economic and market considerations by researching which factors have been good drivers and predictors of global low volatility equities’ performance relative to the capitalization-weighted index since 1980. We consider valuation levels, the market environment, and the macro-economic backdrop. We find that relative valuation levels have not been a good predictor of low volatility equities’ relative return. In addition, while low volatility equities’ performance was indeed more sensitive to interest rate changes than the capitalization-weighted index, both delivered similar risk-adjusted returns (Sharpe ratios) in rising-rates environments.
Macro-Expectations and Bond Risk Premia
Jonas Nygaard Eriksen
September 16, 2014
This paper studies the source of predictability of bond risk premia by means of expectations to future business conditions using survey data from the Survey of Professional Forecasters. We show that macro-expectations consistently affect expected excess bond returns and that the inclusion of expected business conditions in standard predictive regressions improve forecast performance relative to models using term structure information or current business conditions. Furthermore, an out-of-sample analysis indicates that an investor with mean-variance preferences can obtain sizable economic gains from using the recursively updated forecasts to guide investment decisions in a stylized asset allocation strategy.
Many Risks, One (Optimal) Portfolio
July 28, 2014
This study investigates how to obtain a portfolio which would provide above average returns while remaining robust to most risk exposures. Emphasis is placed on risk management, given our perspective (shared by many other practitioners), that retaining above average portfolio performance in current market environment depends strongly on having an effective risk management process. We rely on a comprehensive survey of the literature to describe stylized facts of market returns and main categories of asset allocation methodologies, including Modern Portfolio Theory, Black-Litterman model, factor-based and risk-based strategies. Furthermore, we present both criticisms and defenses of strategies, together with potential issues identified by practitioners and corresponding solutions (if they do exist). We outline recent enhancements to various types of portfolio strategies, and analyze how to incorporate (in the asset allocation framework) constraints, regularization, personal views, stylized features of empirical market data, and forward information given by financial options market data. More prominence is given to strategies (risk parity, risk factors, factor investing, smart beta, dynamic, etc.) that were shown to deliver better portfolio performance in terms of returns, diversification, risk, etc. We also discuss a wide ranging collection of performance measures proposed in the literature for quantifying portfolio return, risk and diversification, identify which such measures are most popular with practitioners, and which corresponding strategies have best results (as shown in the literature). Since a major topic of this study is managing risks, we provide details on the types of risk that portfolios may be exposed to, on approaches and strategies to handle such exposures, with highlighting of tail risk management. Portfolio insurance is also discussed. We also describe practical aspects needed for a successful portfolio management, including robust estimation of covariances, correlations and model parameters, numerical optimization methods, key questions and issues identified by practitioners, Monte Carlo simulation, comprehensive testing framework, stress testing, available software implementations (usually in R), etc. To summarize, the study analyzes all ingredients that are required, in our opinion, to deliver portfolios with above average performances and resilient to most risks, and concentrates on the strategies which have emerged as frontrunners in the last few years, both in the literature and in the market.
By the Numbers: A Discussion of Risk Management and Quantitative Investing with Robert B. Litterman, PhD
Journal of Investment Consulting
August 1, 2014
A recognized expert in risk management and quantitative investment strategies, Robert B. Litterman, PhD, can point to a career that spans the theoretical to the practical, anchored at one end by his work in academia and at the other by his twenty-three-year tenure with Goldman Sachs & Co. Along the way, he worked with renowned economist Fischer Black, PhD, to develop a key asset allocation tool and published a number of groundbreaking papers on asset allocation and risk management. Today, Dr. Litterman serves as senior partner and chairman of the risk committee at Kepos Capital LP, a global macro investment management firm based in New York. In December 2013, Dr. Litterman spoke with members of the Journal of Investment Consulting Editorial Advisory Board about risk management and some of the lessons of the financial crisis, the development and uses of the Black-Litterman model, and quantitative investing after the quant crisis.