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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.

Diversifying Across Strategic Allocation Strategies?

Different strategic allocation strategies employ different ways of: (1) estimating future values of key asset variables (return, volatility, correlation); and, (2) combining these variables to set future allocations. Each strategy thus produces a distinct return stream. Does it therefore make sense to diversify across strategies? In his February 2012 paper entitled “Diversifying Diversi cation Strategies: Model Averaging in Portfolio Optimization”, Felix Miebs examines three approaches for diversifying across strategic allocation strategies: (1) naive average (equal weighting), (2) expected variance minimization; and (3) preceding measurement interval return weighting (strategy momentum weighting). He illustrates the three strategy diversification approaches with a set of eight individual minimum expected variance allocation strategies applied to U.S. stocks (industries or individual stocks). He benchmarks results against a simple equal weighting of the industries or stocks. Using 45.5 years of simulated monthly returns for sets of assets similar to U.S. stocks and empirical monthly returns for four sets of U.S. industries and a set of the largest 250 U.S. stocks during July 1963 through December 2008, he finds that: Keep Reading

Fading Diversification Value of Commodity Futures?

Can investors rely on the power of commodity futures to diversify equities, or have growth in industrial hedging and general financialization of commodities permanently changed correlations? In the November 2011 version of their paper entitled “Correlation in Commodity Futures and Equity Markets Around the World: Long-Run Trend and Short-Run Fluctuation”, Xiao-Ming Li, Bing Zhang and Zhijie Du explore the question of whether recent increases in commodities-stocks correlations are transitory. Specifically, they decompose these correlations across equity markets worldwide into two components: long-run trend, and short-run deviation-from-trend. They apply a “best practices” dynamic conditional correlation model to estimate time-varying return correlations, with additional tests to detect structural breaks in long-run trends. Using daily levels of the Goldman Sachs Commodity Index (GSCI) to represent commodities and 45 country stock market indexes (24 developed and 21 emerging) during 2000 through 2010, they find that: Keep Reading

Safe Haven Asset Dynamics

How does the effectiveness of safe havens vary over time? In the February 2012 draft of their paper entitled “Safe Haven Assets and Investor Behaviour under Uncertainty”, Dirk Baur and Thomas McDermott examine the roles of gold and U.S. Treasury instruments as safe haven assets during times of financial markets uncertainty. They define a safe haven asset as an asset that is either uncorrelated or negatively correlated with other assets when those other assets are in distress. They focus on the effects of changes in uncertainty (shocks) on asset values and on the pairwise relationships between stocks, bonds and gold. Using daily returns in U.S. dollars for a global stock market index, U.S. Treasuries (2-year, 10-year and 30-year) and gold bullion (spot and futures) from 1980 through 2010 (more than 8,000 daily returns over 31 years), they find that: Keep Reading

Combining Sharpe Ratio and Pairwise Correlation for Diversification

How can an investor decide whether a new strategy or new asset class (more generally, a stream of returns), is better than those currently in a portfolio? In their February 2012 paper entitled “The Sharpe Ratio Indifference Curve”, David Bailey and Marcos Lopez de Prado introduce a process for assessing addition of a new strategy (return stream) to an existing portfolio of strategies (return streams). They weight the return streams in the existing portfolio based on equal risk contribution (each return stream contributing equally to aggregate portfolio volatility). Their goal is to boost the Sharpe ratio of the portfolio by adding a new return stream. Using derivations and examples, they show that: Keep Reading

Alternative Portfolio Efficiency Measures

Some experts use the mean-variance analysis of Modern Portfolio Theory (MPT), which penalizes large upside volatility, to measure portfolio efficiency. Others use Second-order Stochastic Dominance (SSD) analysis, purer mathematically than MPT but open to unrealistic investor behavior. Is there a better way? In the February 2012 version of his paper entitled “The Passive Stock Market Portfolio is Highly Inefficient for Almost All Investors”, Thierry Post describes and tests a portfolio efficiency measure based on an Almost Second-order Stochastic Dominance (ASSD) that aims to exclude unrealistic investor behaviors. He applies the measure to a market portfolio (value-weighted average of NYSE, AMEX and NASDAQ stocks) and three alternative sets of ten equity portfolios formed using NYSE decile breakpoints for: (1) market capitalization (size); (2) book-to-market ratio; and, (3) past 11-month return with skip month (momentum). He considers investment horizons of one, 12 and 120 months over sample periods of 1926-2011 and 1963-2011. Using monthly value-weighted returns and contemporaneous stock/firm characteristics from July 1926 through December 2011 (1,026 months), along with the contemporaneous one-month Treasury bill yield as the risk-free rate, he finds that: Keep Reading

Risk-based Allocation to Frontier Equity Markets

What is the best way to include the least developed (frontier) stock markets for portfolio diversification? In his December 2011 paper entitled “Frontier Markets: Punching Below their Weight? A Risk Parity Perspective on Asset Allocation”, Jorge Chan-Lau compares the diversification effects of frontier markets within a world equity portfolio based on risk parity and market capitalization weighting approaches. Risk parity equalizes risk contributions across equity classes by assigning the same risk budget to each asset based on co-movement between the asset’s returns and the portfolio returns. The asset allocation comparison assumes five major equity classes: U.S., European including the UK, East Asia and Far East, emerging markets and frontier markets. Co-movement of asset and portfolio returns derive from weekly return measurements over five-year rolling historical windows. Using weekly returns in U.S. dollars for each equity class based on corresponding Morgan Stanley Capital Indexes during June 2002 through November 2011, he finds that: Keep Reading

Pension Fund Real Estate Allocation, Cost and Performance

How do pension funds, arguably representative of sophisticated and conservative investors, use real estate as an alternative investment? In their January 2012 paper entitled “Value Added From Money Managers in Private Markets? An Examination of Pension Fund Investments in Real Estate”, Aleksandar Andonov, Piet Eichholtz and Nils Kok investigate the allocation, costs and performance of pension funds with respect to real estate investments. Using self-reported investment data for 884 U.S., Canadian, European and Australian/New Zealand pension funds during 1990 through 2009, they find that: Keep Reading

Adaptive Asset Allocation Policy

Are the relatively placid financial markets of the American Century evolving to a high-volatility regime in a more evenly competitive world? In his December 2011 paper entitled “Adaptive Markets and the New World Order”, Andrew Lo examines the implications of the Adaptive Markets Hypothesis (AMH), wherein “markets are not always efficient, but they are usually highly competitive and adaptive, varying in their degree of efficiency as the economic environment and investor population change over time.” He believes that investors can prepare for occasional failures of market efficiency by viewing financial markets and institutions from the perspective of evolutionary biology. Applying this perspective to markets since 1926, he concludes that: Keep Reading

University Endowment Performance: Strategic versus Tactical Allocation

Is strategic asset class allocation or active management paramount for U.S. university endowment investment performance? In the October 2011 draft of their paper entitled “Do (Some) University Endowments Earn Alpha?”, Brad Barber and Guojun Wang explore the investment performance of U.S. university endowments with regard to overall alpha, performance persistence and sources of superior performance. They assess three groups of universities: Ivy League; other elite universities based on high average math SAT entrance scores; and, the balance of universities. They measure alpha as the residual return (from specific asset selection and tactical asset class allocation) after accounting for the combined returns of best-fit constant (strategic) asset class allocations to five indexes representing U.S. stocks (S&P 500 Index), non-U.S. stocks (MSCI non-U.S.), U.S. bonds (Barclays Capital Aggregate Bond Index), hedge funds (Hedge Fund Research Fund‐Weighted Composite Index) and private equity (Cambridge Associates U.S. Private Equity Index). Using annual voluntarily reported university endowment investment returns, benchmark index returns and math SAT score statistics for incoming freshmen during 1991 through 2010 (279 endowments report in all 20 years), they find that: Keep Reading

Stocks versus Bonds as Investment Horizon Lengthens

Should investors believe in the superiority of stocks for the long run and bonds for the short run? In his December 2011 paper entitled “Stocks, Bonds, Risk, and the Holding Period: An International Perspective”, Javier Estrada examines how the absolute and relative risks of stocks and bonds evolve as investment horizon grows (time diversification). Considering both annual and cumulative returns and various measures of variability/risk, he focuses on the question of whether stocks become less risky than bonds for long holding periods. Using annual total returns for stocks and bonds in 19 countries during 1900 through 2009, he finds that: Keep Reading

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