Strategic Allocation

Does adding a proxy for the least developed (frontier) equity markets to a diversified portfolio improve its performance? To check, we add Guggenheim Frontier Markets ETF (FRN) to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for FRN and the average pairwise correlation of FRN monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without FRN. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for FRN and the above nine asset class proxies from July 2008 (first return available for FRN) through April 2013 (58 monthly returns), we find that: Keep Reading

Market volatility tends to rise as returns fall. Does adding a proxy for intermediate-term U.S. equity market volatility to a diversified portfolio improve its performance? To check, we add iPath S&P 500 VIX Mid-Term Futures (VXZ) to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for VXZ and the average pairwise correlation of VXZ monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without VXZ. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for VXZ and the above nine asset class proxies from March 2009 (first return available for VXZ) through April 2013 (only 50 monthly returns), we find that: Keep Reading

Market volatility tends to rise as returns fall. Does adding a proxy for short-term U.S. equity market volatility to a diversified portfolio improve its performance? To check, we add iPath S&P 500 VIX Short Term Futures (VXX) to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for VXX and the average pairwise correlation of VXX monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without VXX. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for VXX and the above nine asset class proxies from February 2009 (first return available for VXX) through April 2013 (only 51 monthly returns), we find that: Keep Reading

How should investors think about alternative asset allocation strategies for risk management? In his May 2013 paper entitled “Advances in Portfolio Risk Control. Risk! Parity?”, Winfried Hallerbach offers a practitioner’s review of new and revived portfolio allocation strategies, including: Equal Weight, Maximum Diversification, Minimum Variance; Equal Risk Contribution (Risk Parity); Inverse Volatility; Maximum Sharpe Ratio; and, Volatility Targeting. He addresses their pluses and minuses and compares them to each other. He observes that the large contribution of equities to (downside) risk within portfolios that lean only moderately toward stocks provides the impetus for risk management research. Based on key studies of portfolio risk management and examples using monthly data for four U.S. asset classes (risk-free rate, stocks, aggregate Treasuries, corporate investment grade bonds, and corporate high-yield bonds) during June 2002 through May 2012, he finds that: Keep Reading

Can investors use aggregate pairwise return correlations across asset classes to identify and exploit financial market regimes? In the April 2013 draft of their paper entitled “Handling Risk On/Risk Off Dynamics with Correlation Regimes and Correlation Networks”, Jochen Papenbrock and Peter Schwendner describe an approach for discovering market regimes based on pairwise correlations across 25 series of futures contracts spanning four asset classes. Each month, they calculate the set of pairwise correlations for these series based on daily returns and assign the resulting correlation structure to one of five regimes. They then look at the data within each regime to count the number of distinct groups of assets based on correlation clustering, with the potential that investors could view clusters as de facto asset classes. Using daily returns for 25 series of rolling futures contracts (six government bonds, seven equity indexes, six commodities and six currency exchange rates) during July 1998 through January 2013 to generate 175 sets of 25×25 monthly correlation matrices, they find that: Keep Reading

Is there a portfolio diversification approach that beats widely used mean-variance optimization and risk parity approaches? In their July 2011 paper entitled “Properties of the Most Diversified Portfolio”, Yves Choueifaty, Tristan Froidure and Julien Reynier compare the performance metrics of their Most Diversified Portfolio (MDP) to those of portfolios based on market capitalization (MKT), equal weight (EW), equal risk contribution (ERC) and minimum variance (MV). They define MDP for a given set of assets as the long-only portfolio with the maximum diversification ratio (weighted average portfolio component volatility divided by aggregate portfolio volatility). They constrain all competing portfolios to be fully invested, long only and unleveraged. For empirical testing, they reform all portfolios semi-annually from the top half of stocks the MSCI World Index by market capitalization. They use a one-year rolling historical window of daily returns to estimate asset volatilities and pairwise correlations as inputs for the MV, ERC and MDP allocations. Using daily and monthly returns for the specified MSCI World Index stocks and contemporaneous monthly Fama-French risk factors (market, size and book-to-market ratio) during 1999 through 2010, they find that: Keep Reading

How do “passive” stock indexes constructed from widely researched allocation rules fare against market capitalization weighting? In their March 2013 paper entitled “An Evaluation of Alternative Equity Indices – Part 1: Heuristic and Optimised Weighting Schemes”, Andrew Clare, Nick Motson and Steve Thomas compare the behaviors of eight alternative stock indexes formed from a common universe of relatively liquid U.S. stocks. They consider five heuristic (rules of thumb) and three formally optimized weighting schemes. The heuristic weighting schemes are: (1) equal; (2) diversity (a compromise between market capitalization and equal weights); (3) inverse volatility (based on standard deviations of monthly returns); (4) equal risk contribution (based on past return volatilities and correlations); and, (5) risk clustering (similar to equal risk contribution, but based on ten statistically similar clusters of stocks constructed from 30 industries). The formal optimization weighting schemes are: (6) long-only, constrained minimum variance (lowest expected volatility on the mean-variance efficient frontier); (7) long-only, constrained maximum diversification (designed to maximize portfolio Sharpe ratio); and, (8) constrained risk efficient (designed to maximize the ratio of portfolio downside deviation to standard deviation of returns). They reform indexes at the end of each year, using five preceding years of monthly data to calculate weighting parameters. They also consider a set of ten million randomly selected/weighted portfolios, reformed annually. Their benchmark is market capitalization weighting. Finally, they test the effectiveness of using a 10-month simple moving average (SMA) rule to generate timing signals for each index. Using monthly total returns for the 1,000 largest U.S. stocks re-selected annually during 1963 through 2011, they find that: Keep Reading

Why do optimal portfolios derived from Modern Portfolio Theory (MPT) often lose to simple equal-weight portfolios? In the March 2013 version of their paper entitled “Why Optimal Diversification Cannot Outperform Naive Diversification: Evidence from Tail Risk Exposure”, Stephen Brown, Inchang Hwang and Francis In explore why mean-variance optimal diversification (giving more weight to those assets driving mean-variance efficiency) do not outperform naive diversification (equal-weight). They consider portfolios formed from each of two sets of assets: (1) factor-based sorts of U.S. stocks (effectively equity style indexes); and, (2) individual U.S. stocks. Unlike prior research, they focus on return distribution tail exposures of test portfolios rather than errors in forecasts of mean returns and return covariances for assets used to construct optimal portfolios. They rebalance competing portfolios monthly. For mean-variance optimization, they use a 10-year rolling history to forecast required parameters. They evaluate both long-short and long-only mean-variance optimal portfolios. Using monthly returns for 20 factor-based U.S. stock sorts from Ken French’s library and for a broad sample of individual U.S. stocks during January 1963 through December 2011 (588 months), they find that: Keep Reading

Is intrinsic (time series) momentum effective in managing risk across asset classes? In his April 2013 paper entitled “Absolute Momentum: a Simple Rule-Based Strategy and Universal Trend-Following Overlay”, Gary Antonacci examines an intrinsic (absolute or time-series) momentum strategy that each month holds a risky asset (U.S. Treasury bills) when the return on the risky asset over the preceding 12 months is greater (less) than the contemporaneous yield on U.S. Treasury bills. He applies the strategy separately to eight risky asset classes: two equity indexes (MSCI US and MSCI EAFE); three bond/credit classes constructed from Barclay’s Capital Long U.S. Treasury, Intermediate U.S. Treasury, U.S. Credit, U.S. High Yield Corporate, U.S. Government & Credit and U.S. Aggregate Bond indexes; the FTSE NAREIT U.S. Real Estate Index; the S&P GSCI; and, spot gold based on the London PM fix. He also evaluates intrinsic momentum strategy performance for a 60%-40% MSCI US-Long U.S. Treasury portfolio and a portfolio consisting of five equally weighted assets, both rebalanced monthly. He assumes a friction of 0.2% for switching between a risky asset and U.S. Treasury bills (T-bill). Using monthly total returns for the eight asset classes as available and 90-day T-bills yields during January 1973 through December 2012, he finds that: Keep Reading

Should investors consider a broader framework to encompass trend-following/momentum investing strategies? In his March 2013 paper entitled “Asset Price Trend Theory: Reframing Portfolio Theory from the Ground Up”, Robert Dubois presents a portfolio allocation strategy that explicitly includes an assumption that asset prices trend (exhibit return autocorrelation or intrinsic momentum). His approach augments risk management by including stop-loss protocols to exit allocations to some assets/strategies based on their trends relative to specified thresholds. He defines three strategic allocation segments: (RB1) risk-free assets; (RB2) liquid risky assets/strategies subject to stop-loss exits at the asset and/or portfolio level; and, (RB3) both liquid and illiquid risky assets not subject to stop-loss exits. All three segments are subject to asset/strategy selection, position sizing and entry rules. RB2 arguably allows strong risk management (containment) via trend-related removal of exposure to risky assets with below-trend performance. Elaborating on this framework, he concludes that: Keep Reading