Strategic Allocation

“Cross-asset Class Intrinsic Momentum” summarizes research finding that past country stock index (government bond index) returns relate positively (positively) to future country stock market index returns and negatively (positively) to future country government bond index returns. Is this finding useful for specifying a simple strategy using exchange-traded fund (ETF) proxies for the U.S. stock market and U.S. government bonds? To investigate we test the following five strategies:

1. Buy and hold.
2. TSMOM Long Only – Each month, hold the asset (cash) if its own 12-month past return is positive (negative).
3. TSMOM Long or Short – Each month, hold (short) the asset if its own 12-month past return is positive (negative).
4. XTSMOM Long Only – Each month hold stocks if 12-month past returns for stocks and government bonds are both positive, and otherwise hold cash. Each month hold bonds if 12-month past returns are negative for stocks and positive for government bonds, and otherwise hold cash. 
5. XTSMOM L-S-N (Long, Short or Neutral) – Each month hold (short) stocks if 12-month past returns for both are positive (negative), and otherwise hold cash. Each month hold (short) bonds if 12-month past returns are negative (positive) for stocks and positive (negative) for bonds, and otherwise hold cash.

We use SPDR S&P 500 (SPY) and iShares 7-10 Year Treasury Bond (IEF) as proxies for the U.S. stock market and U.S. government bonds. We use the 3-month U.S. Treasury bill (T-bill) yield as the return on cash. We apply the five strategies separately to SPY and IEF, and to an equally weighted, monthly rebalanced combination of the two for a total of 15 scenarios. Using monthly total returns for SPY and IEF and monthly T-bill yield during July 2002 (inception of IEF) through December 2016, we find that: Keep Reading

Are stock and bond markets mutually reinforcing with respect to time series (intrinsic or absolute return) momentum? In their December 2016 paper entitled “Cross-Asset Signals and Time Series Momentum”, Aleksi Pitkajarvi, Matti Suominen and Lauri Vaittinen examine a strategy that times each of country stock and government bond (constant 5-year maturity) indexes based on past returns for both. Specifically:

• For stocks, they each month take a long (short) position in a country stock index if past returns for both the country stock and government bond indexes are positive (negative). If past stock and bond index returns have different signs, they take no position.
• For bonds, they each month take a long (short) position in a country government bond index if past return for bonds is positive (negative) and past return for stocks is negative (positive). If past stock and bond index returns have the same sign, they take no position.

They call this strategy cross-asset time series momentum (XTSMOM). For initial strategy tests, they consider past return measurement (lookback) and holding intervals of 1, 3, 6, 9, 12, 24, 36 or 48 months. For holding intervals longer than one month, they average monthly returns for overlapping positions. For most analyses, they focus on lookback interval 12 months and holding interval 1 month. For a given lookback and holding interval combination, they form a diversified XTSMOM portfolio by averaging all positions for all countries. They measure excess returns relative to one-month U.S. Treasury bills. They employ the MSCI World Index and the Barclays Capital Aggregate Bond Index as benchmarks. Using monthly stock and government bond total return indexes for 20 developed countries as available during January 1980 through December 2015, they find that: Keep Reading

Do any equity asset allocation strategies convincingly outperform equal weighting (1/N) after accounting for data snooping bias and portfolio maintenance frictions? In their December 2016 paper entitled “Asset Allocation Strategies, the 1/N Rule, and Data Snooping”, Po-Hsuan Hsu, Qiheng Han, Wensheng Wu and Zhiguang Cao apply tests based on White’s Reality Check to compare out-of-sample performances of 23 basic allocation strategies and 5,490 combinations of these strategies to that of equal weighting (1/N) after accounting for snooping bias and portfolio frictions. The 23 basic strategies encompass: conventional mean-variance optimization; mean-optimization with parameter shrinkage (to avoid extreme allocations); the capital asset pricing (1-factor) model (CAPM); the Fama-french 3-factor model (market, size, book-to-market); the related 4-factor model (adding momentum); CAPM augmented with a cross-sectional volatility factor; a missing factor extension of CAPM; minimum variance; maximum diversification; equal risk contribution; volatility timing; and, reward-to-risk timing. Strategy combinations use two or three of the basic strategies with weights varied in increments of 10%. They apply these strategies to each of seven sets of equity assets: (1) 25 size and book-to-market sorted U.S. stock portfolios; (2) 49 industry U.S. stock portfolios; (3) the stocks in the Dow Jones Industrial Average; (4) 22 developed country stock indexes; (5) the combination of (1) and (2); (6) 93 long-lived stocks from the S&P 500 Index; and, (7) 100 size and book-to-market sorted U.S. stock portfolios. Specifically, they each month estimate model parameters and asset weights in each dataset based on the most recent 60 months, and then calculate respective strategy performances the next month. They set one-way trading frictions for all assets at either 0.05% or 0.50% to estimate net returns. They focus on associated Sharpe ratios and certainty equivalent returns (CEQ) as strategy performance metrics. Using the specified monthly data mostly since July 1969 (but since July 1990 for developed country markets and since July 1996 for S&P 500 Index stocks) through December 2014, they find that: Keep Reading

Are economic globalization and market financialization extinguishing diversification? In their October 2016 paper entitled “Nowhere to Run, Nowhere to Hide: Asset Diversification in a Flat World”, John Cotter, Stuart Gabriel and Richard Roll examine diversification within and across equity, government debt and real estate investment trust (REIT) indexes worldwide (a total of 40 indexes spanning 23 countries). They first use principal component analysis on returns available before 1986 to identify a set of 16 global asset pricing factors. They apply that factor model via linear regression to measure the degree to which daily returns behave differently across assets during 1986. From 1986 onward, they each year update the linear factor model based on daily index returns during that year and apply it to measure the degree to which daily returns behave differently across assets the following year. More specifically, they measure integration as the fraction of asset returns explicable by a common linear factor model, quantified as average R-squared statistic by market type (developed or emerging), country and by asset. They translate these integration metrics into diversification indexes with values ranging from 0 (no diversification potential) to 100. They then identify factors associated with diversification potential and assess the relationship between diversification indexes and investment risks. Using daily returns for 40 equity, bond, and real estate indexes in U.S. dollars spanning 23 countries as available through 2012 and focused on 1986 through 2012, they find that: Keep Reading

A subscriber proposed adding an equity style momentum underlay to the Best Value version of the “Simple Asset Class ETF Value Strategy” (SACEVS). SACEVS each month allocates all capital to the one of the following asset class exchange-traded funds (ETF) corresponding to the most undervalued of the term, credit and equity risk premiums at prior month end, or to cash if no premium is undervalued:

3-month Treasury bills (Cash)
iShares 7-10 Year Treasury Bond (IEF)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

The proposed momentum underlay chooses SPY, iShares S&P 500 Value (IVE) or iShares S&P 500 Growth (IVW) based on highest five-month past return whenever the equity risk premium is most undervalued. Based on availability of inputs for month-end risk premium estimates, return calculations are based on closing prices for the first trading day of the next month. Using SACEVS premium estimate inputs since March 1989, first trading day of the month dividend-adjusted closes for SPY, IVE and IVW since IVE-IVW inception in May 2000 and first trading day of the month dividend-adjusted closes for IEF and LQD since their inception in July 2002, all through July 2016, we find that:

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How can investors integrate global asset class diversification, pre-eminent factor premiums and crash protection? In his July 2016 paper entitled “The Trinity Portfolio: A Long-Term Investing Framework Engineered for Simplicity, Safety, and Outperformance”, Mebane Faber summarizes a portfolio combining these three principles, as follows:

1. Global diversification: Include U.S. stocks, non-U.S. developed markets stocks, emerging markets stocks, corporate bonds, 30-year U.S. Treasury bonds, 10-year foreign government bonds, U.S. Treasury Inflation-Protected Securities (TIPS), commodities, gold and Real Estate Investment Trusts (REIT) .
2. Value/momentum screens: For U.S. stocks, each month first rank stocks by value and momentum metrics and then pick those with the highest average ranks. For non-U.S. stocks, each month pick the cheapest overall markets. For bonds, each month pick those with the highest yields.
3. Trend following for crash avoidance: For each asset each month, hold the asset (cash) if its price is above (below) its 10-month SMA at the end of the prior month.

The featured “Trinity” portfolio allocates 50% to a sub-portfolio based on principles 1 and 2 and 50% to a sub-portfolio based on principles 1, 2 and 3. Using monthly returns for the specified asset classes during 1973 through 2015, he finds that: Keep Reading

As summarized in “Twisting Buffett’s Preferred Stocks-bonds Allocation”: (1) Warren Buffett’s preferred fixed asset allocation of 90% stocks and 10% short‐term government bonds (90-10), rebalanced annually, is sensible for U.S. markets; and, (2) investors may be able to beat this allocation modestly by adding simple annual dynamics. Are findings similar internationally? In his July 2016 paper entitled “Global Asset Allocation in Retirement: Buffett’s Advice and a Simple Twist”, Javier Estrada extends his analysis of U.S. markets to 20 other countries. He assumes a 1,000 (local currency unit) nest egg to start a 30‐year retirement. Annual withdrawals (either 4% or 3% of the initial amount, adjusted annually for inflation) and rebalancing to the target allocation occur at the beginning of each year. The first 30‐year retirement interval is 1900‐1929 and the last 1985‐2014, for a total of 86 rolling intervals. He first compares performances of eight fixed stocks-bonds allocations, rebalanced annually, ranging from 100-0 to 30-70. He then compares a fixed 90-10 allocation to one with a dynamic twist that, at the end of each year, compares the stock market’s annualized total return over the last five years to its annualized total return since the beginning of the sample. If 5-year performance exceeds long-term performance, the annual withdrawal comes from stocks with rebalancing to 90-10. If long-term performance exceeds 5-year performance, the annual withdrawal comes from bonds with no portfolio rebalancing (giving stocks time to recover). He focuses on average portfolio failure rate (running out of money within 30 years) and average terminal wealth across countries as key performance metrics. Using annual stock and short-term government bond real total returns (adjusted by local inflation rate) in local currencies for 21 countries as compiled by Dimson‐Marsh‐Staunton for 1900 through 2014, he finds that: Keep Reading

What is the best way to combine styles (smart betas) in one portfolio? In their June 2016 paper entitled “Long-Only Style Investing: Don’t Just Mix, Integrate”, Shaun Fitzgibbons, Jacques Friedman, Lukasz Pomorski and Laura Serban compare two approaches to long-only combined equity style investing:

1. Mixed portfolio – simply picks stocks from single-style portfolios.
2. Integrated portfolio – first combines single-style rankings into an overall score for each stock and then builds a portfolio based on top overall scores.

They focus on combining momentum stocks (highest return from 12 months ago to one month ago) and value stocks (high book-to-market ratio). They first employ simulated data to illustrate differences in stock selection between the two approaches. They then compare net performances for equally weighted, monthly rebalanced mixed and integrated combinations of liquid global stocks. Using monthly data for large-capitalization stocks from developed markets (roughly the MSCI World Index components) during February 1993 through December 2015, they find that: Keep Reading

Matt Hall, cofounder and president of Hill Investment Group, introduces his 2016 book, Odds On: The Making of an Evidence-Based Investor, by stating that: “…the evidence-based movement has been studying market data and academic research to identify the groups of stocks and other investments that provide better odds of long-term success. …I’m inviting you to learn how evidence-based investing could change your life…” Based on his experience, he concludes that: Keep Reading

How hard is it to beat equal weighting in constructing a portfolio of attractive common stocks? In his May 2016 paper entitled “Naive Diversification Isn’t so Naive after All”, Mike Dickson compares performances of 15 portfolio construction methods applied to eight portfolios of stocks with high expected returns. Construction methods include equal weighting, two versions of minimum volatility, three versions of mean-variance optimization, eight versions of reward-to-risk timing (six of which involve factor models) and a characteristic-based scheme that each year estimates stock weights based on market capitalization, book-to-market ratio, gross profitability, investment, short-term reversal and momentum. The eight portfolios consist of stocks with the top 10% or top 20% of expected returns based on rolling averages of multivariate cross-sectional regression coefficients for these same characteristics, formed with or without momentum and with or without microcaps (capitalizations less than the 20% percentile for NYSE stocks). He estimates trading frictions as 1% of the value traded each month in rebalancing to specified portfolio weights. Using monthly data for a broad sample of U.S. common stocks during July 1963 through December 2013 (with evaluated returns commencing July 1973), he finds that: Keep Reading