Strategic Allocation

Do allocations aimed at managing volatility beat simple equal weighting as applied to the cheapest third of 32 country stock markets based on 10-year cyclically adjusted price-to-earnings ratio (CAPE, or P/E10). In their October 2012 paper entitled “Global CAPE Model Optimization”, Adam Butler, Michael Philbrick, Rodrigo Gordillo and Mebane Faber compare the following six volatility management strategies to EW for a low-P/E10 equity index portfolio:

1. Equal Volatility: each selected index contributes equally to the volatility of a fully invested portfolio.
2. Fixed Volatility Limit: allocates to each selected index up to a daily volatility limit of 1%, with any remaining funds going to cash.
3. Portfolio Volatility Target: allocates equally to all selected indexes with a portfolio-level annualized volatility target of 10%, going to cash when above target (but not applying leverage when below).
4. Risk Parity with Portfolio Volatility Target: a combination of strategies 1 and 3; allocates such that all indexes contribute equally to portfolio volatility with a portfolio-level annualized volatility target of 10%, going to cash when above target (but not applying leverage when below).
5. Minimum Variance: minimum variance via a combination of low volatilities and low correlations per Modern Portfolio Theory, but always fully invested. 
6. Minimum Variance with Portfolio Volatility Target: minimum variance allocations adjusted at the portfolio level to target 10% annualized portfolio volatility, going to cash when above target (but not applying leverage when below).

For all strategies, they estimate prescribed volatilities based on the last 60 days of returns, with monthly portfolio reformation. Using daily and monthly total returns for the 32 country stock indexes during April 1999 through August 2012, they find that: Keep Reading

What is the most important aspect of long-term investing? In the July 2012 version of his paper entitled “Strategic Asset Allocation and Portfolio Performance”, Lujer Santacruz assesses the importance of strategic asset class allocation compared to other sources of returns for a set of Australian managed funds. He specifies three fund return components as: (1) strategic allocation, derived from asset class benchmark returns weighted according to stated fund targets; (2) timing, derived from the difference between actual and target fund asset class weights; and, (3) security selection, derived from the difference between actual and benchmark asset class returns. Using actual quarterly total returns, target asset class allocations and actual asset class allocations for Vanguard’s four diversified Australian funds (Conservative, Balanced, Growth and High Growth), along with quarterly returns for six relevant benchmark indexes, during January 2003 through June 2012, he finds that: Keep Reading

How does the risk parity asset allocation strategy (equalizing the volatility contributions of portfolio components) fare in comparison to other commonly used strategies? In their March 2012 research note entitled “The Risk Parity Approach to Asset Allocation – Climbing the Wall of Worries?”, Fabian Dori, Frank Haeusler, Manuel Krieger, Urs Schubiger and David Stefanovits contrast three popular allocation strategies: (1) traditional balanced (40% equities, 50% bonds and 10% commodities); (2) minimum variance (the mean-variance optimized portfolio with the lowest variance); and, (3) risk parity. Their asset universe includes equity index futures (FTSE 100, DAX, S&P 500, TOPIX and ASX SPI 200), 10-year government bond futures (UK, German, U.S., Japanese and Australian) and commodity index futures (GSCI agriculture, energy, industrial and precious metals and softs). For each asset allocation strategy, they model daily rebalancing of assets to specified weights. Using daily futures return data during August 1992 through June 2012, they find that: Keep Reading

How do specialized stock indexes relate to commonly used equity risk factors? In his February 2012 paper entitled “Evaluating Alternative Beta Strategies”, Xiaowei Kang examines risk exposures (betas), construction methodologies and historical performances of alternative stock indexes such as those based on value, low-volatility and diversification strategies. He considers five risk factors: (1) market, representing excess return of the market capitalization-weighted U.S. stock market; (2) size, representing return from a portfolio that is long small-cap stocks and short large-cap stocks; (3) value, representing return from a portfolio that is long high book-to-market stocks and short low book-to-market stocks; (4) momentum, representing return from a portfolio that is long past winning stocks and short past losing stocks; and, (5) volatility, representing return from a portfolio that is long high-volatility stocks and short low-volatility stocks. Using monthly returns for several specialized indexes and the specified risk factors as available through 2011, he finds that: Keep Reading

Are commodities effective diversifiers for stocks and bonds? In his September 2012 paper entitled “Commodity Investments: The Missing Piece of the Portfolio Puzzle?”, Xiaowei Kang examines the diversification properties of commodity indexes relative to stock and bond indexes. He focuses on the widely used S&P GSCI, composed of 24 commodities with liquid futures markets weighted by world production value. He also considers the S&P GSCI Dynamic Roll, designed to suppress negative roll returns by rolling into longer-dated (nearby) futures contracts when a commodity’s term structure is in contango (backwardation). Using monthly levels of these indexes, MSCI World (to represent stocks) and Barclays Global Aggregate Bond Index (to represent bonds), along with contemporaneous U.S. Treasury bill yields to calculate excess returns, from as early as December 1970 through June 2012, he finds that: Keep Reading

Are managed futures programs good portfolio diversifiers? In his September 2012 paper entitled “Revisiting Kat’s Managed Futures and Hedge Funds: A Match Made in Heaven”, Thomas Rollinger updates prior research exploring the diversification effects of adding managed futures to traditional portfolios of stocks and bonds and to portfolios including stocks, bonds and hedge funds. His proxies for the four asset classes are: (1) for stocks, the S&P 500 Total Return Index; (2) for bonds, the Barclays U.S. Aggregate Bond Index; (3) for hedge funds, the HFRI Fund Weighted Composite Index; and, (4) for managed futures programs, the Barclay Systematic Traders Index (focused on systematic trend-following strategies). He assumes monthly (frictionless) portfolio rebalancing. Using monthly returns for the four asset class indexes during June 2001 through December 2011, he finds that: Keep Reading

Has increasing use of gold as a portfolio diversifier changed the response of its price to crises? In their August 2012 paper entitled “The Destruction of a Safe Haven Asset?”, Dirk Baur and Kristoffer Glover examine the potential of investor behavior to extinguish the safe haven property of gold. Specifically, they consider how widespread inclusion of gold as a diversifier in investment portfolios affects gold price behavior in times of crisis. Based on theoretical conjecture and price data for gold during major financial market crises, they conclude that: Keep Reading

Risk-focused asset allocation strategies derive from evidence that forecasting asset return volatility is easier than forecasting average return. Is there a best risk-focused strategy? In his September 2012 paper entitled “A Small Survey of Quantitative Models that Discard Estimation of Expected Returns for Portfolio Construction”, Stefano Colucci compares asset allocation strategies that rely on forecasted asset risk metrics but not on forecasted average returns. Specifically, he compares future gross annualized return-risk ratios, Ulcer indexes, one-month maximum drawdowns and average monthly portfolio turnovers for the following asset allocation strategies:

1. Minimum Variance (least volatile, or left-most, efficient portfolio per Modern Portfolio Theory).
2. Minimum Expected Shortfall with weightings estimated by Monte Carlo simulation.
3. Equal Risk Contribution (each asset weighted by the inverse of its forecasted maximum expected shortfall).
4. Maximum Diversification (related to expected shortfall with weightings again estimated by numerical simulation).
5. Risk Parity (each asset weighted by the inverse of its portfolio volatility contribution).
6. Equal Weighting (requiring neither average return nor volatility forecasts) as a benchmark.

He reforms portfolios every 20 trading days (approximately monthly) and estimates future risk metrics based on a rolling historical window of 500 trading days (approximately two years). Using daily returns over recent periods for stock and bond indexes and individual stocks segregated into several asset selection universes, he finds that: Keep Reading

Does mean-variance optimization reliably beat simple equal weighting? In his August 2012 paper entitled “The Efficiency of Mean-Variance Optimization with In-depth Covariance Matrix Estimation and Portfolio Rebalancing”, Joonas Hämäläinen tests how many of 96 different mean-variance optimization implementations based on daily data outperform simple equal weighting after accounting for trading frictions. He considers three methods of determining weights for minimum variance portfolios. For each method, he considers three historical intervals for estimating optimal portfolio weights (20, 60 and 250 trading days). He considers three fixed-interval (5, 20 and 60 trading days) and one threshold-based rebalancing rules. His benchmark strategy is equal weight, rebalanced weekly (EW). He tests strategy combinations on four sets of asset returns in euros constructed from 23 MSCI country indexes: 11 European Monetary Union markets during June 2002 through May 2006 (EMU1) and during June 2006 through May 2010 (EMU2); and, 12 big emerging markets during June 2002 through May 2006 (BEM1) and during June 2006 through May 2010 (BEM2). He assumes constant trading frictions of 0.2% (0.4%) of traded value for EMU (BEM) data sets. He focuses on annualized net Sharpe ratio (with risk-free rate zero) and portfolio turnover as critical evaluation metrics. Using daily country total return index levels during June 2001 through May 2010, with out-of-sample tests commencing June 2002, he finds that: Keep Reading

How and how well does mean-variance investing work? In his August 2012 draft book chapter entitled “Mean‐Variance Investing”, Andrew Ang compares outcomes for complex asset allocation strategies based on forecasted return statistics to those for very simple strategies such as equal weighting. He illustrates with a horse race among allocation strategies applied to four asset classes (U.S. government bonds, U.S. corporate bonds, U.S. stocks and international stocks), with portfolios reformed monthly based on return statistics estimated from five-year lagged rolling intervals and shorting constrained to no more than -100% for each asset. Using mathematical derivations and monthly return data for the example asset classes during 1973 through 2011, and contemporaneous one-month Treasury bill yields as the risk-free rate, he concludes that: Keep Reading