Investing Research Articles

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: More…

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: More…

What factors make asset class diversification work? To investigate empirically, we consider 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)

We calculate the cumulative trajectory for an equally weighted, monthly rebalanced portfolio of all nine asset class proxies. Then we recalculate the trajectory nine times, each time excluding one of them, and relate the resulting terminal values to three individual asset return characteristics: (1) average monthly return; (2) standard deviation of monthly returns; and, (3) average pairwise correlation of returns with the other eight assets. We ignore trading frictions associated with monthly rebalancing, which would be similar for all combinations. Using adjusted monthly returns for the above nine asset class proxies from September 2006 (allowing comparison with the momentum strategy output for the entire set of assets) through April 2013 (80 monthly returns), we find that: More…

Can investors rely on commodities to protect against inflation? In their May 2013 paper entitled “Commodities as Inflation Protection”, Andrew Marks, George Crawford and Jim Kyung-Soo Liew examine the belief that commodities represent an intrinsic store of value that hedges against inflation. They use spot prices for commodities because these prices have long histories. They use the U.S. Consumer Price Index (CPI) as the measure of inflation. However, CPI is an aggregate of the prices of many finished goods and services adjusted to reflect a government model of the actual cost of living, so it may not be possible to create a portfolio that tracks its composition. Commodities (such as crops, crude oil and precious metals), as raw inputs to these goods and services, may rise in price with the CPI, and the proliferation of futures contracts and related exchange-traded funds (ETF) makes investing in commodities accessible. Using monthly spot prices for 45 individual commodities and 14 aggregating indexes during January 1960 through December 2012 (53 years), with focus on five subperiods involving relatively high inflation, they find that: More…

Does the Memorial Day holiday signal any unusual return effects? By its definition, this holiday brings with it any effects from three-day weekends and sometimes the turn of the month. Prior to 1971, the U.S. celebrated Memorial Day on May 30. Effective in 1971, Memorial Day became the last Monday in May. To investigate the possibility of short-term effects on stock market returns around Memorial Day, we analyze the historical behavior of the stock market during the three trading days before and the three trading days after the holiday. Using daily closing levels of the S&P 500 Index for 1950 through 2012 (63 observations), we find that: More…

Does adding fundamental firm quality metrics to refine stock sorts based on traditional value ratios, book-to-market ratio (B/M) and earnings-to-price ratio (E/P), improve portfolio performance? In his 2013 paper entitled “The Quality Dimension of Value Investing”, Robert Novy-Marx tests combination strategies to determine which commonly used quality measures most enhance the performance of value ratios. He considers such quality metrics as Piotroski’s FSCORE, earnings accruals, gross profitability (GP) and return on invested capital (ROIC). His general test approach is to reform capitalization-weighted portfolios annually from stocks sorted at the end of each June according to value ratios and quality metrics for the previous calendar year. He uses the 1000 largest (2000 next largest) stocks by market capitalization to represent large (small) stocks. He considers both long-only (long the top 30%) and long-short (long the top 30% and short the bottom 30%) portfolios. He also considers the incremental benefit of incorporating stock price momentum based on return over the previous 11 months with a skip-month (11-1) into stock selection. He estimates trading frictions based on calculated turnover and effective bid-ask spreads. Using stock prices and associated firm fundamentals during July 1963 through December 2011, he finds that: More…

Should investors regard any of the exchange-traded products (ETP) based on S&P 500 Index option-implied volatility (VIX) futures as long-term holdings? In the May 2013 draft of his paper entitled “Trading Volatility: At What Cost?”, Robert Whaley describes these ETPs and evaluates them as buy-and-hold investments. VIX ETPs are based on VIX futures indexes with daily rebalancing, subject to management fees and expenses including commissions and trading fees, licensing fees and (for some ETPs) foregone interest income. Many of the ETPs are exchange-traded notes (ETN), secured not by underlying assets but rather only by the good faith and collateral of the issuer. Using daily price and trading data for VIX futures (starting March 2004) and options (starting February 2006) and for 30 ETPs based on VIX futures (starting January 2009) through March 2012, he finds that: More…

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: More…

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for April 2013. The actual total (core) inflation rate for April is lower (lower) than forecasted.

The new actual and forecasted inflation rates will flow into Real Earnings Yield Model projections about the end of May.

Are returns on real estate usefully predictable? In the June 2012 version of their book chapter entitled “Forecasting Real Estate Prices”, Eric Ghysels, Alberto Plazzi, Walter Torous and Rossen Valkanov examine the evidence of predictability in U.S. residential and commercial real estate markets. They review methodologies used in constructing widely used real estate price indexes. They then survey the key empirical findings from academic studies of short-run momentum and long-run reversals in real estate returns. Finally, they test the ability of different variables (past stock market return, stock market dividend yield, 3-month Treasury bill (T-bill) yield relative to its 12-month moving average, inflation rate, term spread between 5-year and 3-month maturities, combination of forward interest rates and industrial production growth) to predict real estate returns as calculated from several price indexes and a real estate investment trust (REIT) index. Using monthly and quarterly index levels for the real estate market proxies and values for the predictive variables as available, focusing on 1991 through 2010, they find that: More…