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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.

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

Asset Allocation Strategy Horse Race

Do sophisticated asset allocation strategies beat simple ones? In the December 2010 version of their paper entitled “Risk Parity Portfolio vs. Other Asset Allocation Heuristic Portfolios”, Denis Chaves, Jason Hsu Feifei Li and Omid Shakernia conduct a horse race among six asset allocation weighting strategies applied to nine asset classes:

  1. 60/40: 60% S&P 500 Index/40% Barclays Capital U.S. Aggregate Bond Index.
  2. U.S. Pension Model: 55% stocks (80% U.S. and 20% international); 35% bonds (60% U.S. Long Treasury, 20% investment-grade corporate and 20% global); and, 10% alternative investments (2.5% each commodities, REITs, emerging market equities and high-yield bonds).
  3. Equal: simple equal weight for all nine asset classes.
  4. Risk Parity: equal volatility contribution for all nine asset classes (asset classes contribute equally to expected portfolio fluctuations).
  5. Mean-Variancemean-variance optimization based on a five-year rolling return history.
  6. Minimum-Variance: like mean-variance optimization but using only variances and ignoring returns, based on a five-year rolling return history.

The nine asset classes proxies are: Barclay’s Capital U.S Long Treasury Index; Barclay’s Capital U.S. Investment Grade Corporate Bond Index; J.P. Morgan Global Goernment Bond Index; Barclay’s Capital U.S. High Yield Corporate Bond Index; S&P 500 Index; MSCI EAFE Index; MSCI Emerging Market Index; Dow Jones UBS Commodity Index; and, FTSE NAREIT US Real Estate Index. Using annual and monthly data as available for these indexes over the period January 1980 through June 2010, they find that: Keep Reading

Alternative Global Equity Diversification Approaches

What approaches to global diversification work best? In their July 2011 paper entitled “What Matters in International Equity Diversification? “, Chun-hung Chen, Tom Goodwin, and Wenling Lin use mean-variance spanning and optimization tests of indexes to compare benefits of alternative approaches to global diversification of the equity portion of an investor’s portfolio. Specifically, they investigate potential contributions to global diversification from: (1) American Depository Receipts (ADR); (2) multinational firms; (3) global versus country-specific delineation of large capitalization versus small capitalization; and, (4) non-U.S. developed small and micro capitalizations, emerging market small and micro capitalizations and frontier markets. Using monthly returns for the Russell 1000 and Russell 2000 indexes for a benchmark U.S. portfolio, and for various global and country indexes as available through February 2011, they find that: Keep Reading

Diversifying with Equity Volatility Exposure?

Can diversification via allocations to volatility-related securities enhance the absolute and risk-adjusted returns of equity portfolios? In other words, can investors construct useful asset classes from equity volatility? In their early 2010 paper entitled “Volatility Exposure for Strategic Asset Allocation”, Ombretta Signori, Marie Briere and Alexandre Burgues investigate potential benefits to long-term U.S. equity investors of including two volatility-related assets: (1) a rolling dynamic long position in VIX futures that is bigger when VIX is relatively low and smaller when it is relatively high; and, (2) a rolling short position in one-month variance swap contracts to exploit the tendency of option-implied volatility to exceed realized volatility (volatility risk premium). The former lowers the downside risk of holding equities, and the latter offers returns from selling “insurance” against volatility. Because the return distributions of such volatility investments are clearly non-normal, the authors employ a risk-return optimization approach that takes distribution skewness and kurtosis into account. Using S&P 500 Index, VIX, VIX futures and S&P 500 Index variance swap contract data as available over the period February 1990 through August 2008, they find that: Keep Reading

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