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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.

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

Two Biggest Mistakes of Long-term Investors

How can long-term investors maximize their edge of strategic patience? In their November 2011 paper entitled “Investing for the Long Run”, Andrew Ang and Knut Kjaer offer advice on successful long-term investing (such as by pension funds).  They define a long-term investor as one having no material short-term liabilities or liquidity demands. Using the California Public Employee’s Retirement System and other large institutions as examples, they conclude that: Keep Reading

Translating Risk Strategies into Common Factors

Do somewhat abstract risk-based portfolio strategies translate to familiar stock/firm characteristic tilts? In their September 2011 paper entitled “Demystifying Equity Risk-Based Strategies: A Simple Alpha plus Beta Description”, Raul Leote de Carvalho, Xiao Lu and Pierre Moulin investigate how the following five risk-based equity allocation strategies relate to four common portfolio factors.

  1. Equal Weight – long only, same portfolio weight for each stock.
  2. Equal Risk Budget – long only, same portfolio weight times volatility for each stock.
  3. Equal Risk Contribution – long only, similar to equal-risk budget, but tilted toward stocks with low market correlations (low beta).
  4. Minimum Variance – long only or long-short, limited to stocks with the extreme volatilities and market correlations.
  5. Maximum Diversification – long only or long-short, limited to stocks with extreme market correlations.

They map these five strategies to market capitalization (size), book-to-market ratio (value), market beta and residual (idiosyncratic) volatility factors based on quarterly rebalancing for empirical tests. Using weekly total returns for the stocks in the MSCI World Index of developed countries and the one-month U.S. Treasury bill yield during January 1997 through December 2010, they find that: Keep Reading

Harvesting Equity Market Premiums

Should investors strategically diversify across widely known equity market anomalies? In the October 2011 version of his paper entitled “Strategic Allocation to Premiums in the Equity Market”, David Blitz investigates whether investors should treat anomaly portfolios (size, value, momentum and low-volatility) as diversifying asset classes and how they can implement such a strategy.  To ensure implementation is practicable, he focuses on long-only, big-cap portfolios. To account for the trading frictions associated with anomaly portfolio maintenance and for time variation of anomaly premiums, he assumes future (expected) market and anomaly premiums lower than historical values, as follows: 3% equity market premium; 0% expected incremental size and low-volatility premiums; and, 1% expected incremental value and momentum premiums. He assumes future volatilities, correlations and market betas as observed in historical data and constrains weights of all anomaly portfolios to a maximum 40%. He considers both equal-weighted and value-weighted individual anomaly portfolios, and both mean-variance optimized and equal-weighted combinations of market and anomaly portfolios. Using portfolios constructed by Kenneth French to quantify equity market/anomaly premiums during July 1963 through December 2009 (consisting of approximately 800 of largest, most liquid U.S. stocks), he finds that: Keep Reading

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