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Strategic Allocation

Is there a best way to select and weight asset classes for long-term diversification benefits? These blog entries address this strategic allocation question.

SACEMS Portfolio-Asset Addition Testing

Does adding an exchange-traded fund (ETF) or note (ETN) to the Simple Asset Class ETF Momentum Strategy (SACEMS) boost performance via consideration of more trending/diversifying options? To investigate, we add the following 23 ETF/ETN asset class proxies one at a time to the base set and measure effects on the Top 1, equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios:

AlphaClone Alternative Alpha (ALFA)
JPMorgan Alerian MLP Index (AMJ)
UBS ETRACS Wells Fargo Business Development Companies (BDCS)
Vanguard Total Bond Market (BND)
SPDR Barclays International Treasury Bond (BWX)
PowerShares DB G10 Currency Harvest (DBV)
iShares JPMorgan Emerging Market Bond Fund (EMB)
First Trust US IPO Index (FPX)
Guggenheim Frontier Markets (FRN)
iShares iBoxx High-Yield Corporate Bond (HYG)
iShares 7-10 Year Treasury Bond (IEF)
iShares Latin America 40 (ILF)
iShares National Muni Bond ETF (MUB)
PowerShares Closed-End Fund Income Composite (PCEF)
PowerShares Global Listed Private Equity (PSP)
IQ Hedge Multi-Strategy Tracker (QAI)
SPDR Dow Jones International Real Estate (RWX)
ProShares UltraShort S&P 500 (SDS)
iShares Short Treasury Bond (SHV)
iShares TIPS Bond (TIP)
United States Oil (USO)
iPath S&P 500 VIX Short-Term Futures (VXX)
iPath S&P 500 VIX Medium-Term Futures (VXZ)

The base set consists of:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

Each month, we rank the base set plus one of the additional ETFs/ETNs based on past return and reform the SACEMS portfolios. The sample starts with the first month all base set ETFs are available (February 2006), but inceptions for most of the additional ETFs/ETNs are after this month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics, ignoring monthly reformation costs. Using end-of-month total (dividend-adjusted) returns for the specified 32 assets as available during February 2006 through May 2019, we find that: Keep Reading

SACEMS Portfolio-Asset Exclusion Testing

Are all of the potentially trending/diversifying asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) necessary? Might one or more of them actually be harmful to performance? To investigate, we each month rank the following assets based on past return with one excluded (nine separate test series) and reform the Top 1, equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

The test starts with the first month all ETFs are available (February 2006). We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics, ignoring monthly portfolio reformation costs. Using end-of-month total (dividend-adjusted) returns for the specified nine assets during February 2006 through May 2019, we find that: Keep Reading

Best U.S. Equity Market Hedge Strategy?

What steps should investors consider to mitigate impact of inevitable large U.S. stock market corrections? In their May 2019 paper entitled “The Best of Strategies for the Worst of Times: Can Portfolios be Crisis Proofed?”, Campbell Harvey, Edward Hoyle, Sandy Rattray, Matthew Sargaison, Dan Taylor and Otto Van Hemert compare performances of an array of defensive strategies with focus on the eight worst drawdowns (deeper than -15%) and three NBER recessions during 1985 through 2018, including:

  1. Rolling near S&P 500 Index put options, measured via the CBOE S&P 500 PutWrite Index.
  2. Credit protection portfolio that is each day long (short) beta-adjusted returns of duration-matched U.S. Treasury futures (BofAML US Corp Master Total Return Index), scaled retrospectively to 10% full-sample volatility.
  3. 10-year U.S. Treasury notes (T-notes).
  4. Gold futures.
  5. Multi-class time-series (intrinsic or absolute) momentum portfolios applied to 50 futures contract series and reformed monthly, with:
    • Momentum measured for 1-month, 3-month and 12-month lookback intervals.
    • Risk adjustment by dividing momentum score by the standard deviation of security returns.
    • Risk allocations of 25% to currencies, 25% to equity indexes, 25% to bonds and 8.3% to each of agricultural products, energies and metals. Within each group, markets have equal risk allocations.
    • Overall scaling retrospectively to 10% full-sample volatility.
    • With or without long equity positions.
  6. Beta-neutral factor portfolios that are each day long (short) stocks of the highest (lowest) quality large-capitalization and mid-capitalization U.S. firms, based on profitability, growth, balance sheet safety and/or payout ratios.

They further test crash protection of varying allocations to the S&P 500 Index and a daily reformed hedge consisting of equal weights to: (1) a 3-month time series momentum component with no long equity positions and 0.7% annual trading frictions; and, (2) a quality factor component with 1.5% annual trading frictions. For this test, they scale retrospectively to 15% full-sample volatility. Throughout the paper, they assume cost of leverage is the risk-free rate. Using daily returns for the S&P 500 Index and inputs for the specified defensive strategies during 1985 through 2018, they find that:

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SACEMS with Different Alternatives for “Cash”

Do alternative “Cash” (deemed risk-free) instruments materially affect performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS)? Changing the proxy for Cash can affect how often the model selects Cash, as well as the return on Cash when selected. To investigate, we test separately each of the following yield and exchange-traded funds (ETF) as the risk-free asset:

3-month Treasury bills (Cash), a proxy for the money market as in base SACEMS
SPDR Bloomberg Barclays 1-3 Month T-Bill (BIL)
iShares 1-3 Year Treasury Bond (SHY)
iShares 7-10 Year Treasury Bond (IEF)
iShares TIPS Bond (TIP)

In other words, we add one of the five risk-free assets to the following base set of eight ETFs:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)

We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics and consider Top 1, equally weighted (EW) EW Top 2 and EW Top 3 SACEMS portfolios. Using end-of-month total (dividend-adjusted) returns for the specified assets during February 2006 (except May 2007 for BIL) through May 2019, we find that:

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Tax-efficient Retirement Withdrawals

Considering taxes, in what order should U.S. retirees consume different sources of retirement savings/income? In their August 2018 paper entitled “Constructing Tax Efficient Withdrawal Strategies for Retirees with Traditional 401(k)/IRAs, Roth 401(k)/IRAs, and Taxable Accounts”, James DiLellio and Daniel Ostrov describe and illustrate an algorithm that computes individualized tax-efficient consumption for U.S. retirees of:

  • Tax-deferred retirement accounts [Traditional IRA/401(k)].
  • Post-tax retirement accounts [Roth IRA/Roth 401(k)].
  • Other taxable retirement accounts.
  • Other sources of money subject to income tax, including: earned income, some pensions, annuities bought with pre-tax money, earnings from annuities bought with post-tax money and sometimes Social Security benefits.
  • Other sources of money that do not affect tax rates of retirement accounts, such as: tax-free gifts, Health Savings Accounts, some pensions, principal from annuities bought with post-tax money and sometimes Social Security benefits.

Their model adapts to individual retiree circumstances and accommodates typical changes in tax policies (changes in marginal rates and number of brackets). For tractability, they make simplifying assumptions. The principal simplification is that  return on stocks, stock dividend yield, inflation rate, tax brackets and rates, other income sources and consumption rates are known each year (not random variables). When the goal is to optimize a bequest, inputs also include year of retiree death, marginal tax rate of the heir and rate the heir consumes inherited retirement accounts. They do not attempt to determine the optimal mix of  stocks and bonds/cash within retirement accounts (their deterministic model would prefer all stocks). Using illustrations of algorithm outputs based on varying input assumptions, they find that: Keep Reading

Mean-Variance Optimization vs. Equal Weight for Sectors and Individual Stocks

Are mean-variance (MV) strategies preferable for allocations to asset classes and equal-weight (EW) preferable for allocations to much noisier individual assets? In their May 2019 paper entitled “Horses for Courses: Mean-Variance for Asset Allocation and 1/N for Stock Selection”, Emmanouil Platanakis, Charles Sutcliffe and Xiaoxia Ye address this question. They focus on the Bayes-Stein shrinkage MV strategy, with 10 U.S. equity sector indexes as asset classes and the 10 stocks with the largest initial market capitalizations within each sector (except only three for telecommunications) as individual assets. The Bayes–Stein shrinkage approach dampens the typically large effects of return estimation errors on MV allocations. For estimation of MV return and return covariance inputs, they use an expanding (inception-to-date) 12-month historical window. They focus on one-month-ahead performances of portfolios formed in four ways via a 2-stage process:

  1. MV-EW, which uses MV to determine sector allocations and EW to determine stock allocations within sectors.
  2. EW-EW, which uses EW for both deteriminations.
  3. EW-MV, which uses EW to determine sector allocations and MV to determine stock allocations within sectors.
  4. MV-MV, which uses MV for both deteriminations.

They consider four net performance metrics: annualized certainty equivalent return (CER) gain for moderately risk-averse investors; annualized Sharpe ratio (reward for risk); Omega ratio (average gain to average loss); and, Dowd ratio (reward for value at risk). They assume constant trading frictions of 0.5% of value traded. They perform robustness tests for U.S. data by using alternative MV strategies, different parameter settings and simulations. They perform a global robustness test using value-weighted equity indexes for UK, U.S., Germany, Switzerland, France, Canada and Brazil as asset classes and the 10 stocks with the largest initial market capitalizations within each index as individual assets (all in U.S. dollars). Using monthly total returns for asset classes and individual assets as specified and 1-month U.S. Treasury bill yield as the risk-free rate during January 1994 through August 2017, they find that: Keep Reading

Best Factor Allocation Strategy?

For investors embracing the concept of portfolios based on factor premiums (rather than asset classes), what is the best factor allocation approach? In their March 2019 paper entitled “Factor-Based Allocation: Is There a Superior Strategy?”, Hubert Dichtl, Wolfgang Drobetz and Viktoria-Sophie Wendt search for the best way of combining factors in a portfolio after accounting for bias introduced from snooping many alternative allocation strategies. They consider the following 10 factors (mostly long-short) suitable for a U.S. institutional investor constrained to global equity and fixed income securities: equity, value, size, momentum, quality, low-volatility, term, real rates, credit and high-yield. They construct factors using associated published indexes denominated in U.S. dollars, with 1-month U.S. Treasury bill (T-bill) yield as the risk-free rate. They consider 17 factor allocation strategies: equal weight, minimum variance, equal risk, maximum diversification, volatility timing, reward-to-risk timing, mean-variance optimization without and with shrinkage, Black-Litterman and eight combinations of these strategies. Their test portfolio holds a 100% position in cash and a fully hedged (long-short, or zero net investment) factor portfolio, subject to 0.5% trading frictions on portfolio turnover. Using monthly data required to construct factors and T-bill yield during January 2001 though December 2018, with the first 60 months set aside to estimate strategy inputs, they find that:

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More International Equity Market Granularity for SACEMS?

A subscriber asked whether more granularity in international equity choices for the “Simple Asset Class ETF Momentum Strategy” (SACEMS), as considered by Decision Moose, would improve performance. To investigate, we replace the iShares MSCI Emerging Markets Index (EEM) and the iShares MSCI EAFE Index (EFA) with four regional international equity exchange-traded funds (ETF). The universe of assets becomes:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Pacific ex Japan (EPP)
iShares MSCI Japan (EWJ)
SPDR Gold Shares (GLD)
iShares Europe (IEV)
iShares Latin America 40 (ILF)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

We compare original (SACEMS Base) and modified (SACEMS Granular), each month picking winners from their respective sets of ETFs based on total returns over a fixed lookback interval. We focus on gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD) and rough gross annual Sharpe ratio (average annual return divided by standard deviation of annual returns) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using daily and monthly total (dividend-adjusted) returns for the specified assets during February 2006 (limited by DBC) through April 2019, we find that: Keep Reading

Home Prices and the Stock Market

Homes typically represent a substantial fraction of investor wealth. Are there reliable relationships between U.S. home prices and the U.S. stock market? For example, does a rising stock market stimulate home prices? Do falling home prices point to offsetting liquidation of equity positions. Do homes effectively diversify equity holdings? Using:

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Creating and Maintaining Antifragile Portfolios

How should investors manage their portfolios to withstand market crashes. In his March 2019 paper entitled “Managing the Downside of Active and Passive Strategies: Convexity and Fragilities”, Raphael Douady discusses how to construct an “antifragile” portfolio given that most equity market risk is not readily observable. He describes ways to monitor the probability of a new crisis. Based on in-depth analysis of market behaviors during past speculative bubbles and other crises, he concludes that:

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