Strategic Allocation

Can reacting to short-term changes in asset return correlations improve efficient portfolio allocation? In their May 2012 paper entitled “The Role of Correlation Dynamics in Sector Allocation”, Elena Kalotychou, Sotiris Staikouras and Zhao Gang investigate the economic value of correlation timing in mean-variance optimal sector allocations. They test the usefulness of several correlation forecast methods by constructing dynamic, one-step-ahead (day, week or month) mean-variance optimal portfolios comprised of ten sectors in the Japanese, UK or U.S. equity markets (energy, basic materials, industrials, consumer goods, health care, consumer services, telecommunication, utilities, financials and technology). They use a static portfolio based on total-sample correlations as a benchmark. They use an initial subperiod (July 1996 through May 2005) to generate initial correlation forecasts and a later subperiod (Jun 2005 through May 2007) to implement recursive forecasts. They estimate sector index trading frictions for daily (monthly) portfolio rebalancing as approximately 0.07% (0.09%) for U.S., 0.30% (0.32%) for Japanese and 0.50% (0.52%) for UK sector indexes. Using daily prices for the ten sector indexes for each of the Nikkei 225, FTSE-All and S&P 500 during July 1996 through May 2007, along with corresponding interbank and U.S. Treasury bill yields as risk-free rates, they find that: Keep Reading

What is the big picture on stock return predictors? In their May 2012 paper entitled “The Supraview of Return Predictive Signals”, Jeremiah Green, John Hand and Frank Zhang examine aggregate characteristics of 333 signals for which formal research indicates power to predict stock returns. They categorize each signal as accounting-based (from firm financial statements, such as accruals), finance-based (directly or indirectly from stock prices, such as return momentum) or other-based (such as stock buybacks). They standardize across studies via annualization by multiplying daily, weekly, monthly and quarterly returns by 250, 52, 12 and 4, respectively. They compile equal-weighted returns and value-weighted returns separately. They focus on Sharpe ratio as a widely used metric for comparing investment performance. Using a database of predictive signals as published in top-tier U.S. accounting, finance and practitioner journals and as disseminated in academic working papers via the Social Science Research Network (SSRN) during 1970 through 2010, they conclude that: Keep Reading

Are there exchange-traded notes (ETN) based on S&P 500 Index implied volatility (VIX) futures, or combinations of such ETNs, that are attractive for absolute return and diversification? In the May 2012 version of their paper entitled “Volatility Exchange-Traded Notes: Curse or Cure?”, Carol Alexander and Dimitris Korovilas examine the behaviors of simple (first generation) and enhanced (second generation) ETNs constructed from VIX futures. They focus on: (1) roll return or yield, the loss (gain) of maintaining a position in VIX futures by continually rolling from a near to a far maturity contract when in contango (backwardation); and, (2) term structure convexity of VIX futures, the generally greater magnitude of roll return when rolling between contracts near to maturity versus between contracts far from maturity. To extend the sample period, they replicate recently available ETNs back to December 2005 using S&P constant-maturity VIX futures indexes and March 2004 using daily closes of VIX futures (debting respective annual ETN fees). Using daily closes for all VIX futures contracts, 30-day (VIX) and 93-day (VXV) S&P 500 implied volatility indexes as calculated by CBOE, S&P constant-maturity VIX futures indexes, one-month (VXX) and five-month (VXZ) constant-maturity VIX futures ETNs and two recently launched second-generation VIX futures ETNs (XVIX and XVZ) as available from late March 2004 through March 2012, they find that: Keep Reading

Is drawdown control a practical investment policy? In their February 2012 paper entitled “Optimal Portfolio Strategy to Control Maximum Drawdown: The Case of Risk-based Active Management with Dynamic Asset Allocation” (the National Association of Active Investment Managers’ 2012 Wagner Award third place winner), George Yang and Liang Zhong examine maximum percentage drawdown target as a criterion for active portfolio asset class allocation. They name the strategy Rolling Economic Drawdown-Controlled Optimal Portfolio Strategy (REDD-COPS), with REDD defined as maximum percentage drawdown of an asset’s value during a one-year rolling historical window. They simplify asset allocation calculations by assuming that the maximum drawdown target represents investor risk aversion. They test the active strategy on three asset class indexes: the S&P 500 Total Return Index, Barclays Capital 20+ Year U.S. Treasury Bond Index and Dow-Jones UBS Commodity Total Return Index, with the 3-month U.S. Treasury bill (T-bill) as the risk-free asset. Using daily, weekly and monthly data for these asset class proxies from as far back as January 1951 (but January 1991 for most tests) through June 2011, they find that: Keep Reading

How attractive can currency trading be after optimizing across several anomalies? In the November 2011 version of their paper entitled “Beyond the Carry Trade: Optimal Currency Portfolios”, Pedro Barroso and Pedro Santa-Clara examine the performance of utility-maximized currency strategies designed to exploit interest rate variables, momentum, long-term reversal, current account and real exchange rate during the floating exchange rate era. They also investigate whether such currency strategies are valuable to investors holding portfolios of equities and bonds. Their benchmark portfolio consists of $1 invested in the U.S. risk-free rate and $1 risked in a hedged carry trade (long all currencies yielding more than the U.S. dollar and short all others, with long and short sides equal and equal weighting across currencies within each side). They assume a power law utility function with constant level of risk aversion to specify optimal currency weightings. They perform out-of-sample testing based on inception-to-date regressions executed annually to specify optimal portfolios for the next year, commencing 240 months into the sample. Using spot and one-month forward exchange rates and data on current accounts and inflation as available for 27 developed economies during November 1960 through September 2010 (a total of 7,197 monthly currency returns involving 13 to 21 currencies per year), they find that: Keep Reading

What is the safest way to exploit asset price momentum? In his April 2012 paper entitled “Risk Premia Harvesting Through Momentum”  (the National Association of Active Investment Managers’ 2012 Wagner Award winner with different title), Gary Antonacci investigates systematic capture of upside volatility at the asset class level via a momentum/diversification/absolute return strategy that:

• Exploits momentum via long positions in winners, based on 12-month lagged total returns with no skip month, re-evaluated monthly.
• Maintains diversification by:
  • Using indexes rather than individual securities; and,
  • Holds the equally weighted winners from each the following pairs of competing indexes: gold versus long-term Treasury bonds; U.S. equities versus foreign equities; high yield bonds versus intermediate credit bonds; and equity real estate investment trusts (REIT) versus mortgage REITs.
• Mitigates risk by substituting Treasury bills (T-bills) for each pairwise winner that has not outperformed T-bills during the 12-month ranking interval.

Using monthly total returns for indexes constructed from targeted classes of equities, bonds, REITs and spot gold, along with contemporaneous 90-day Treasury bill yields, during January 1974 (or the earliest available) through December 2011, he finds that: Keep Reading

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