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Strategic Allocation

Is there a best way to select and weight asset classes for long-term diversification benefits? These blog entries address this strategic allocation question.

Simple Asset Class ETF Value Strategy

Does a simple relative value strategy applied to tradable asset class proxies produce attractive results? To investigate, we test a simple strategy on the following three asset class exchange-traded funds (ETF), plus cash:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

This set of ETFs relates to three factor risk premiums: (1) the difference in yields between Treasury notes/bonds and Treasury bills indicates the term risk premium; (2) the difference in yields between corporate bonds and Treasury notes/bonds indicates the credit (default) risk premium; and, (3) the difference in yields between equities and Treasury notes/bonds indicates the equity risk premium. We consider two alternative strategies for exploiting premium undervaluation: Best Value, which picks the most undervalued premium; and, Weighted, which weights all undervalued premiums according to degree of undervaluation. Based on the assets considered, the principal benchmark is a monthly rebalanced portfolio of 60% stocks and 40% U.S. Treasury notes (60-40 SPY-TLT). Using lagged quarterly S&P 500 earnings, end-of-month S&P 500 Index levels, end-of-month yields for the 3-month Constant Maturity U.S. Treasury bill (T-bill) and the 10-year Constant Maturity U.S. Treasury note (T-note) and day-before-end-of-month yield for Moody’s Seasoned Baa Corporate Bonds during March 1989 through May 2017 (limited by availability of earnings data), and end-of-month dividend-adjusted closing prices for the above three asset class ETFs during July 2002 through May 2017 (179 months, limited by availability of TLT and LQD), we find that: Keep Reading

Zeta Risk and Future Stock Returns

Can investors predict the return of a stock from its relationship with the dispersion of returns across all stocks? In their May 2017 paper entitled “Building Efficient Portfolios Sensitive to Market Volatility”, Wei Liu, James Kolari and Jianhua Huang examine a 2-factor model which predicts the return on a stock based on its sensitivity to (1) the value-weighted stock market return (beta risk) and (2) the standard deviation of value-weighted returns for all stocks (zeta risk). They first each month estimate zeta for each stock via regressions of daily data over the past year. They then rank stocks by zeta into quantile portfolios and calculate next-month equal-weighted returns across these portfolios and various long-short combinations of these portfolios (hedge portfolios) to measure dependence of future returns on zeta. Finally, they generate performance data for aggregate zeta risk portfolios by adding value-weighted market index returns to returns for each of the long-short zeta-sorted portfolios. Using daily and monthly returns for a broad sample of U.S. stocks in the top 90% of market capitalizations for that year, monthly equity market returns and monthly U.S. Treasury bill yields as the risk-free rate during January 1965 through December 2015, they find that: Keep Reading

Carry Trade Across Futures Asset Classes

Does a carry trade derived from roll yields of futures/forward contracts work within asset classes (undiversified) and across asset classes (iversified)? In his May 2017 paper entitled “Optimising Cross-Asset Carry”, Nick Baltas explores the profitability of cross-sectional (relative) and time-series (absolute) carry strategies within and across futures/forward markets for currencies, stock indexes, commodities and government bonds. He posits that contracts in backwardation (contango) present a positive (negative) roll yield and should generally be overweighted (underweighted) in a carry portfolio. He considers three types of carry portfolios, each reformed monthly:

  1. Cross-sectional (XS) or Relative – Rank all assets within a class by strength of carry, demean the rankings such that half are positive and half are negative and then assign weights proportional to demeaned ranks to create a balanced long-short portfolio. Combine asset classes by applying inverse volatility weights (based on 100-day rolling windows of returns) to each class portfolio.
  2. Times-series (TS) or Absolute – Go long (short) each asset within a class that is in backwardation (contango), such that the class may be net long or short. Combine asset classes in the same way as XS.
  3. Optimized (OPT) – Apply both relative strength and sign of carry to determine gross magnitude and direction (long or short) of positions for all assets, and further apply asset volatilities and correlations (based on 100-day rolling windows of returns) to optimize return/risk allocations.

Using daily data for 52 futures series (20 commodities, eight 10-year government bonds, nine currency exchange rates versus the U.S. dollar and 15 country stock indexes) during January 1990 through January 2016, he finds that: Keep Reading

Making Minimum Variance Stock Portfolios Work

What modifications must investors make to minimum variance portfolios to make them more attractive than equal weighting? In their April 2017 paper entitled “Asset Allocation with Correlation: A Composite Trade-Off”, Rachael Carroll, Thomas Conlon, John Cotter and Enrique Salvador assess conditions under which a minimum variance portfolio (requiring only estimates of asset covariances) beats an equally weighted portfolio. In particular, they test minimum variance portfolios that:

  • Employ one of three ways (one constant and two dynamic) to estimate future asset return correlations.
  • Consider a range of correlation forecasting horizons.
  • Do and do not have shorting restrictions.
  • Limit turnover by rebalancing only when: (1) any weight drifts outside a fixed percentage band; or, (2) any asset drifts outside a no-trade range based on its volatility, such that each asset has the same probability of triggering (allowing riskier assets more latitude).
  • Have rebalancing frictions of either 0.2% or 0.5% of traded value.

These variations enable analyses of trade-offs among parameter estimation error, correlation forecasting horizon, turnover and rebalancing frictions. Their key portfolio performance metrics are volatility, Sharpe ratio and turnover. They consider seven asset universes for forming minimum variance portfolios: 10, 30 or 48 U.S. industry portfolios during January 1970 through December 2013; 20 portfolios of U.S. stocks sorted by size and book-to-market ratio during January 1970 through December 2013; stock indexes for nine developed countries during January 1980 through December 2013; the 30 stocks in the Dow Jones Industrial Average during January 2003 through December 2012; and, the 197 stocks continuously listed in the S&P 500 Index during January 1996 through December 2012. Using daily returns in excess of the risk-free rate for the assets in these universes, they find that: Keep Reading

Smart Life Cycle Investing?

Can investors improve retirement glide paths via judicious use of smart beta funds? In their March 2017 paper entitled “Life Cycle Investing and Smart Beta Strategies”, Bill Carson, Sara Shores and Nicholas Nefouse augment a conventional equities-bonds life cycle investing glide path with smart beta strategies. They use a conventional glide path, which gradually decreases the allocation to equities with age to a constant after retirement, to determine target risk levels over the life cycle. When the investor is young, they tilt equities toward the MSCI USA Diversified Multiple-Factor (DMF) Index to boost returns via value, size momentum and quality beta exposures. As the investor approaches retirement, they shift equities to the MSCI USA Minimum Volatility Index, designed to match the market return at lower risk. For bonds, they use the Barclays Constant Weights Index, which has greater diversification and higher Sharpe ratio than a conventional market capitalization-based bond index. They incorporate the specified smart beta indexes into the glide path via a procedure that maximizes Sharpe ratio while matching the risk of the conventional glide path. Specifically, they: (1) deviate no more than 3% from conventional glide path risk; (2) constrain smart beta equities beta relative to the Russell 1000 Index and the MSCI World Index ex U.S. to within 5% of the benchmark equities beta; (3) constrain smart beta bond index duration to within 0.05 years of the benchmark bonds duration; and, (4) require at least 1% allocation to bonds for all target date portfolios. Using monthly data for conventional capitalization-weighted U.S. equity and bond indexes and for the specified smart beta indexes during 2007 through 2016, they find that: Keep Reading

SACEMS and SACEVS Changes for Coordination and Liquidity

We developed the Simple Asset Class ETF Momentum Strategy (SACEMS) about six years ago and the Simple Asset Class ETF Value Strategy (SACEVS) about two years ago independently, focusing on the separate logic of asset choices for each. As tested in “SACEMS-SACEVS Mutual Diversification”, these two strategies are mutually diversifying, so combining them works better in some ways than using one or the other. Beginning May 2017, we are making four changes to these strategies for ease of implementation and combination, with modest compromises in logic. Specifically, we are: Keep Reading

Value-at-Risk Estimation Tutorial

What are the ins and outs of crash risk measurement via Value at Risk (VaR)? In their March 2017 paper entitled “A Gentle Introduction to Value at Risk”, Laura Ballotta and Gianluca Fusai provide an introduction to VaR in financial markets, with examples mainly from commodity markets. They address problems related to VaR estimation and backtesting at single asset and portfolio levels. Based largely on mathematics and empirical considerations, they conclude that: Keep Reading

How Large University Endowments Allocate Investments

How are the asset allocations of the largest university endowments, conventionally accepted as among the best investors, evolving? In their December 2016 paper entitled “The Evolution of Asset Classes: Lessons from University Endowments”, John Mulvey and Margaret Holen summarize recent public reports from large U.S. university endowments, focusing on asset category definitions and allocations. Using public disclosures of 50 large university endowments for 2015, they find that: Keep Reading

Testing Stock Anomalies in Practical Context

How do widely studied anomalies relate to representative stocks-bonds portfolio returns (rather than the risk-free rate)? In his March 2017 paper entitled “Understanding Anomalies”, Filip Bekjarovski proposes an approach to asset pricing wherein a representative portfolio of stocks and bonds is the benchmark and stock anomalies are a set of investment opportunities that may enhance the benchmark. He therefore employs benchmark-adjusted returns, rather than excess returns, to determine anomaly significance. Specifically, his benchmark portfolio captures the equity, term and default premiums. He considers 10 potentially enhancing anomalies: size, value, profitability, investment, momentum, idiosyncratic volatility, quality, betting against beta, accruals and net share issuance. He estimates each anomaly premium as returns to a portfolio that is each month long (short) the value-weighted tenth, or decile, of stocks with the highest (lowest) expected returns for that anomaly. He assesses the potential of each anomaly in three ways: (1) alphas from time series regressions that control for equity, term and default premiums; (2) performances during economic recessions; and, (3) crash proneness. He measures the attractiveness of adding anomaly premiums to the benchmark portfolio by comparing Sharpe ratios, Sortino ratios and performances during recessions of five portfolios: (1) a traditional portfolio (TP) that equally weights equity, term and default premiums; (2) an equal weighting of size, value and momentum premiums (SVM) as a basic anomaly portfolio; (3) a factor portfolio (FP) that equally weights all 10 anomaly premiums; (4) a mixed portfolio (MP) that equally weights all 13 premiums; and, (5) a balanced portfolio (BP) that equally weights TP and FP. Using monthly returns for the 13 premiums specified above from a broad sample of U.S. stocks and NBER recession dates during July 1963 through December 2014, he finds that: Keep Reading

Factor Investing and the Business Cycle

What is “under the hood” at quantitative investment firms? In their December 2016 book-length paper entitled “Factor Investing and Asset Allocation: A Business Cycle Perspective”, Vasant Naik, Mukundan Devarajan, Andrew Nowobilski, Sebastien Page and Niels Pedersen examine the process of translating macroeconomic forecasts into alpha-generating portfolios via mean-variance optimization. They address how to: (1) specify the risk factors driving returns in global financial markets; (2) estimate factor returns and volatilities; and, (3) construct an optimal portfolio of factors. They emphasize the primacy of the business cycle in estimating future returns and volatilities of risk factors across multiple asset classes. They also emphasize the importance of market valuations (to identify when price fluctuations create tactical opportunities) in investment decision making. Based on the body of financial markets research over the last 50 years and their own experiences with the investment process, they conclude that: Keep Reading

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