Strategic Allocation

Does combining different portfolio performance enhancement concepts actually improve outcome? In their December 2012 paper entitled “Generalized Momentum and Flexible Asset Allocation (FAA): An Heuristic Approach”, Wouter Keller and Hugo van Putten investigate the effects of combining momentum, volatility and correlation selection criteria to form an equally weighted portfolio of the three best funds from a set of mutual fund proxies for seven asset classes, as follows:

1. To follow trend, rank funds from highest to lowest lagged total return (relative momentum).
2. To suppress volatility, rank funds from lowest to highest volatility (standard deviation of daily returns).
3. To enhance diversification, rank funds from lowest to highest average pairwise correlation of daily returns.
4. To avoid drawdown, replace with cash any selected fund that has a negative lagged return (intrinsic or absolute momentum). 

Their seven asset class proxies are index mutual funds for U.S. stocks (VTSMX), developed market stocks outside the U.S. and Canada (FDIVX), emerging market stocks (VEIEX), mid-term U.S. Treasuries (VBMFX), short-term U.S. Treasuries (VFISX), commodities (QRAAX) and real estate (VGSIX). They use a default lagged measurement interval of four months for all four selection criteria. Their method of combining rankings for relative momentum, volatility and correlation is simple weighted average (with default weightings of 1, 0.5 and 0.5, respectively). They assume momentum calculations occur at the end of each month, with portfolio changes at the beginning of the next month. Using daily closing prices in U.S. dollars for the seven mutual funds from mid-1997 through mid-December 2012, they find that: Keep Reading

Does mean-variance optimization enhance the performance of currency carry trades (long currencies with high interest rates and short currencies with low interest rates)? In their November 2012 paper entitled “On the Risk and Return of the Carry Trade”, Fabian Ackermann, Walt Pohl and Karl Schmedders compare a dynamic mean-variance optimal carry trade strategy to naive ones. Specifically, they consider a series of monthly investments that are long (short) those of the following currencies with the highest (lowest) associated interest rates: U.S. dollar (base currency), Swiss franc, Euro, Japanese yen, British pound, Australian dollar, Canadian dollar, Norwegian krone, Swedish krona, Singapore dollar and New Zealand dollar. For monthly mean-variance optimization, they estimate currency correlations based on the last 250 days (one year) of daily data and set an annual excess return target of 5% (relative to the risk-free rate), the approximate excess return on the S&P 500 Index over the same period. For naive portfolios, they consider 1 long/1 short currencies, 3 long/3 short equally weighted currencies and the S&P 500 Index total return. Using daily currency values and monthly S&P 500 Index data during January 1989 through June 2012 (using the first year for initial optimization), they find that: Keep Reading

Is equity market liquidity useful as an asset allocation signal? In their November 2012 paper entitled “Liquidity-Driven Dynamic Asset Allocation”, James Xiong, Rodney Sullivan and Peng Wang examine the performance of a dynamic stocks-bonds allocation strategy with weightings based on equity market liquidity. For liquidity measurement, they focus on monthly changes in Amihud illiquidity (aggregating individual responses of stock prices to trading volume), calculated daily for a broad sample of U.S. stocks, averaged over the past six months and detrended. They also consider a similarly calculated monthly change in aggregate stock market turnover as an alternative liquidity measurement. For each measurement, they define high and low expected liquidity premium conditions based on a fixed liquidity threshold. They then use this threshold to define a dynamic asset allocation (DAA) strategy for a simple portfolio consisting of stocks (four U.S. style indexes, a REIT index and MSCI EAFE as separate proxies) and bonds (proxied by the Barclays Capital 1-3 Year Government/Credit Index). The benchmark strategic asset allocation (SAA) is 50% stocks and 50% bonds, rebalanced monthly. The DAA strategy each month allocates 60% (30%) to stocks and 40% (70%) to bonds when the expected equity liquidity premium is high (low), with monthly trading friction 0.1% of the value of the portfolio turned over. Using liquidity calculation inputs since January 1963 and portfolio asset total monthly returns since December 1980, both through September 2010, they find that: Keep Reading

What is the aggregate posture of all investors or, said differently, the asset class allocation of the average investor? In their November 2012 paper entitled “Strategic Asset Allocation: The Global Multi-Asset Market Portfolio 1959-2011”, Ronald Doeswijk, Trevin Lam and Laurens Swinkels estimate the crowd-sourced relative market valuations of investments in ten asset classes: equities, private equity, listed and unlisted (commercial) real estate, high-yield bonds, emerging market debt, non-government bonds (mostly corporate bonds and mortgage-backed securities), government bonds, inflation-linked bonds, commodities and hedge funds since the beginning of 1990. They also estimate the relative valuations of four core asset classes (equities, commercial real estate, non-government bonds and government bonds) since the beginning of 1959. Using annual market valuation estimates from a variety of sources during 1990 through 2011 across all ten asset classes, and during 1959 through 2011 for the core subset, they find that: Keep Reading

How can a risk parity allocation strategy, equally weighting portfolio components by expected risk contribution, not really spread risk? In their October 2012 paper entitled “The Risk in Risk Parity: A Factor Based Analysis of Asset Based Risk Parity”, Vineer Bhansali, Josh Davis, Graham Rennison, Jason Hsu and Feifei Li examine the essential return and risk drivers embedded in a risk parity diversification strategy applied to multiple asset classes. They apply principal component analysis to extract independent (essential) risk factors from return streams for nine conventional asset classes: U.S. equities; international developed market equities; emerging market equities; real estate investment trusts (REIT); commodities; global bonds; long-maturity U.S. Treasuries; investment-grade corporate bonds; and, high-yield bonds. They then test four commercially available risk parity strategy implementations for dependence on the top two essential risk factors, interpreted as risks to global economic growth (proxied by S&P 500 Index returns) and global inflation (proxied by 10-year Treasury note returns). Using returns for the risk factor proxies and the four commercial risk parity portfolios from portfolio inceptions (ranging from January 1990 to July 2009) through May 2012, they find that: Keep Reading

How can positions in emerging equity markets benefit investment portfolios? In their October 2012 paper entitled “How Large are the Benefits of Emerging Market Equities?”, Mitchell Conover, Gerald Jensen and Robert Johnson examine the returns of emerging equity markets with focus on: (1) performance measures that account for return distribution risk and abnormalities; (2) performance by region; and, (3) effects of global economic/monetary environment on returns and diversification power. Using monthly local-currency and dollar-denominated stock index returns and annual GDP estimates for 20 emerging markets as available, along with monthly returns for MSCI developed market MSCI indexes (including MSCI World and MSCI USA) for comparison, during January 1976 through December 2010, they find that: Keep Reading

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