Strategic Allocation

What is “under the hood” at quantitative investment firms? In their December 2016 book-length paper entitled “Factor Investing and Asset Allocation: A Business Cycle Perspective”, Vasant Naik, Mukundan Devarajan, Andrew Nowobilski, Sebastien Page and Niels Pedersen examine the process of translating macroeconomic forecasts into alpha-generating portfolios via mean-variance optimization. They address how to: (1) specify the risk factors driving returns in global financial markets; (2) estimate factor returns and volatilities; and, (3) construct an optimal portfolio of factors. They emphasize the primacy of the business cycle in estimating future returns and volatilities of risk factors across multiple asset classes. They also emphasize the importance of market valuations (to identify when price fluctuations create tactical opportunities) in investment decision making. Based on the body of financial markets research over the last 50 years and their own experiences with the investment process, they conclude that: Keep Reading

What is a safe portfolio withdrawal rate for early retirees who expect more than 30 years of retirement? In their February 2017 paper entitled “Safe Withdrawal Rates: A Guide for Early Retirees”, ERN tests effects of several variables on retirement portfolio success:

• Retirement horizons of 30, 40, 50 and 60 years.
• Annual inflation-adjusted withdrawal rates of 3% to 5% in increments of 0.25%.
• Terminal values of 0% to 100% of initial portfolio value in increments of 25%.
• Implications of different starting levels of Shiller’s Cyclically Adjusted Price-to-Earnings ratio (CAPE or P/E10).
• Implications of Social Security payments coming into play after retirement.
• Effects of reducing withdrawal rate over time (planning a gradual decline in consumption during retirement).

They assume 6.6% average real annual return for U.S. stocks with zero volatility. For 10-year U.S. Treasury notes (T-note), they assume 0% real return for the first 10 years and 2.6% thereafter (zero volatility except for one jump). They assume monthly withdrawal of one-twelfth the annual rate at the prior-month market close, with monthly portfolio rebalancing to target stocks and T-note allocations. They assume annual portfolio costs of 0.05% for low-cost mutual fund fees. Based on the stated assumptions, they find that: Keep Reading

A subscriber requested a horse race among the following four simple asset class allocation strategies:

1. Seasonal SPY-VFITX – the strategy tested in “Bonds During the Off Season?”, which switches between SPDR S&P 500 (SPY) and Vanguard Intermediate-Term Treasury (VFITX) based on the calendar. This strategy switches between U.S. equity risk and U.S. interest rate risk.
2. SPY:SMA10-VFITX – a strategy that holds SPY (VFITX) when the S&P 500 Index is above (below) its 10-month simple moving average (SMA10). This strategy also switches between U.S. equity risk and U.S. interest rate risk.
3. SACEVS Best Value – the version of the Simple Asset Class ETF Value Strategy (SACEVS), which holds SPY, a corporate bond exchange-traded fund (ETF), a mid-duration U.S. Treasuries ETF or cash according to which offers the best yield. This strategy offers three ways to escape U.S. equity risk and two ways to escape U.S. interest rate risk based on relative yields.
4. SACEMS EW Top 3 – the version of the Simple Asset Class ETF Momentum Strategy (SACEMS) that holds the equally weighted (EW) three of nine ETFs spanning multiple asset classes with the highest past returns. This strategy offers multiple ways to escape both U.S. equity risk and U.S. interest rate risk based on relative price trends.

Because of the different available sample periods, we pit 1 vs. 2 since January 1993 (limited by data for SPY), 1 vs. 2 vs. 3 since July 2002 (limited by availability of bond ETFs) and 1 vs. 2 vs. 3 vs. 4 since July 2006 (limited by availability of all ETF asset class proxies). For these tests, we ignore fund switching frictions. Using monthly data for the specified assets through January 2017, we find that: Keep Reading

Does adjusting stocks-bonds allocations according to trend following rules improve the performance of 30-year retirement portfolios? In their November 2016 paper entitled “Applying a Systematic Investment Process to Distributive Portfolios: A 150 Year Study Demonstrating Enhanced Outcomes Through Trend Following”, Jon Robinson, Brandon Langley, David Childs, Joe Crawford and Ira Ross compare retirement portfolio performances for variations of the following three strategies that may hold a broad stock market index, a 10-year government bond index or cash (3-month government bills) in the U.S., UK or Japan:

1. Buy and Hold – each month rebalance to fixed 60%-40% or 80%-20% stock-bond allocations.
2. T8 – each month set allocations among stocks, bonds and cash according to whether each of stocks and bonds are above (uptrend) or below (downtrend) respective 8-month exponential moving averages (EMA).
3. T12 – same as T8 but using a 12-month EMA.

See the first two tables below for precise T8 and T12 allocation rules. The authors consider annual portfolio distributions of 0%, 4%, 4.5% or 5% over a 30-year holding interval. They employ the S&P 500, FTSE 100 and TOPIX total return indexes for the U.S., UK and Japan, respectively. When 3-month government bill data are unavailable for the UK or Japan, they insert U.S. data. Using monthly total returns for the specified asset class proxies since 1865 for the U.S., 1935 for the UK and 1925 for Japan, all through 2015, they find that: Keep Reading

“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