Strategic Allocation

Has the free flow of capital since the 1990s weakened geographic (country-based) equity market diversification benefits? In their November 2011 paper entitled “Is World Stock Market Co-Movement Changing?”, Douglas Blackburn and N. K. Chidambaran examine recent trends in co-movement of stock markets worldwide. Their analysis employs principal component analysis to identify country, regional and world equity market return factors, allowing for structural break. They then quantify country commonality with the world factor via correlations. Using weekly return data for 23 developed country equity market indexes as available during 1981 through 2010 (30 years) and ten emerging country equity market indexes as available during 2000 through 2010, they find that: Keep Reading

Should investors diversify U.S. equity holdings with S&P 500 volatility index (VIX) futures or exchange-traded notes (ETN) constructed from these futures? In the March 2012 version of their paper entitled “Diversification of Equity with VIX Futures: Personal Views and Skewness Preference”, Carol Alexander and Dimitris Korovilas examine the performance and equity diversification power of VIX futures. They focus on ETNs with one-month constant maturity, available since January 30, 2009 as VXX (iPath S&P 500 VIX Short Term Futures), and five-month constant maturity, available since February 20, 2009 as VXZ (iPath S&P 500 VIX Mid-Term Futures). They extend these proxies back to December 2005 using matched S&P 500 VIX futures constant maturity index series and further back to April 2004 using futures price data and the Standard & Poor’s methodology. They use SPDR S&P 500 (SPY) to represent equity exposure. For diversity in equity market conditions, they consider three subperiods: April 2004 through September 2006 (tranquil); October 2006 through March 2009 (crisis); and, April 2009 through December 2011 (punctuated volatility). When examining VIX futures contract returns, they roll five days prior to maturity to avoid the effect of maturity on final settlement. Using daily data for SPY, VIX futures, VIX futures indexes, VXX and VXZ as available from March 26, 2004 (the inception of VIX futures) through December 2011, they find that: Keep Reading

It is arguable that many exchange-traded fund (ETF) momentum strategy tests derive more from logical/programming simplicity than common portfolio management practices. Does momentum work for portfolios of ETFs when melded with the latter? In his March 2012 paper entitled “Tactical Asset Allocation Using Relative Strength”, John Lewis tests ETF momentum in the context of real-world portfolio practices. He employs a universe of nearly 100 ETFs encompassing U.S. equity sectors and styles, international/global equities, bonds, commodities, real estate and currencies, including some inverse funds. After initial selection of top ETFs, he replaces weakening funds with strong ones as needed based on daily (or weekly) prices rather than at a fixed interval, depending on four parameters: (1) momentum ranking interval; (2) number of ETFs in the portfolio; (3) buy rank threshold; and, (4) sell rank threshold. To test robustness, he conducts multiple trials based on random selection of ETFs above the buy rank threshold. Specifically, he presents seven examples of 100 iterations of 10-ETF portfolios randomly selected from the top 25% of the ETF universe based on momentum ranking intervals of one month to two years, replacing ETFs when they fall out of the top 25%. Portfolios are apparently equally weighted at initial formation. Examples ignore dividends, management fees and trading frictions. Using daily returns for the ETFs from the end of 1999 through the end of 2011 (12 years), he finds that: Keep Reading

Are there strategic asset allocation methodologies that reliably beat equal weight? In the February 2012 version of their paper entitled “Portfolio Optimization Using Forward-Looking Information”, Alexander Kempf, Olaf Korn and Sven Sassning investigate the performance of a minimum variance portfolio based on returns implied by equity options rather than historical returns. They argue that, since option prices reflect the expectations of market participants, the former approach is inherently forward-looking. The methodology involves calculating option-implied volatilities and option-implied correlations. Using daily prices for the Dow Jones Industrial Average (DJIA) stocks and associated option-implied return statistics during 1998 through 2009 for out-of-sample testing, and DJIA stock prices for 1993 through 1997 for historical data tests, they find that: Keep Reading

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

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

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

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

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

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