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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 with SMA Filter

A subscriber asked whether applying a simple moving average (SMA) filter to “Simple Asset Class ETF Momentum Strategy” (SACEMS) winners improves strategy performance. SACEMS each months picks winners from among the following asset class exchange-traded fund (ETF) proxies based on past returns over a specified interval:

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)

Since many technical traders use a 10-month SMA (SMA10), we test effectiveness of requiring that each winner pass an SMA10 filter by comparing performances for three scenarios:

  1. Baseline – SACEMS as presented at “Momentum Strategy”.
  2. With SMA10 Filter – Run Baseline SACEMS and then apply SMA10 filters to dividend-adjusted prices of winners. If a winner is above (below) its SMA10, hold the winner (Cash). This rule is inapplicable to Cash as a winner.
  3. With Half SMA10 Filter – Same as scenario 2, but, if a winner is above (below) its SMA10, hold the winner (half the winner and half cash at the T-bill yield).

We focus on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) of SACEMS Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios. Using monthly dividend-adjusted closing prices for the asset class proxies and the yield for Cash over the period February 2006 (the earliest all ETFs are available) through November 2018, we find that: Keep Reading

U.S. Equity Turn-of-the-Month as a Diversifying Portfolio

Is the U.S. equity turn-of-the-month (TOTM) effect exploitable as a diversifier of other assets? In their October 2018 paper entitled “A Seasonality Factor in Asset Allocation”, Frank McGroarty, Emmanouil Platanakis, Athanasios Sakkas and Andrew Urquhart test U.S. asset allocation strategies that include a TOTM portfolio as an asset. The TOTM portfolio buys each stock at the open on the last trading day of each month and sells at the close on the third trading day of the following month, earning zero return the rest of the time. They consider four asset universes with and without the TOTM portfolio:

  1. A conventional stocks-bonds mix.
  2. The equity market portfolio.
  3. The equity market portfolio, a small size portfolio and a value portfolio.
  4. The equity market portfolio, a small size portfolio, a value portfolio and a momentum winners portfolio.

They consider six sophisticated asset allocation methods:

  1. Mean-variance optimization.
  2. Optimization with higher moments and Constant Relative Risk Aversion.
  3. Bayes-Stein shrinkage of estimated returns.
  4. Bayesian diffuse-prior.
  5. Black-Litterman.
  6. A combination of allocation methods.

They consider three risk aversion settings and either a 60-month or a 120-month lookback interval for input parameter measurement. To assess exploitability, they set trading frictions at 0.50% of traded value for equities and 0.17% for bonds. Using monthly data as specified above during July 1961 through December 2015, they find that:

Keep Reading

Retirement Withdrawal Modeling with Actuarial Longevity and Stock Market Mean Reversion

How does use of actuarial estimates of retiree longevity and empirical mean reversion of stock market returns affect estimated retirement portfolio success rates? In the October 2018 revision of his paper entitled “Joint Effect of Random Years of Longevity and Mean Reversion in Equity Returns on the Safe Withdrawal Rate in Retirement”, Donald Rosenthal presents a model of safe inflation-adjusted retirement portfolio withdrawal rates that addresses: (1) uncertainty about the number of years of retirement (rather than the commonly assumed 30 years); and, (2) mean reversion in annual U.S. stock market returns (rather than a random walk). He estimates retirement longevity as a random input based on the Social Security Administration’s 2015 Actuarial Life Table. He estimates stock market real returns and measures their mean reversion using S&P 500 Index inflation-adjusted total annual returns during 1926 through 2017. He models real bond returns using 10-year U.S. Treasury note (T-note) total annual returns during 1928 through 2017. He applies Monte Carlo simulations (3,000 trials for each scenario) to assess retirement portfolio performance by:

  • Assuming an initial retirement portfolio either 100% invested in stocks or 60%/40% in stocks/T-notes (rebalanced at each year-end).
  • Debiting the portfolio each year-end by a fixed, inflation-adjusted percentage of the initial amount.
  • Calculating percentage of simulation trials for which the portfolio is not exhausted before death (success) and average portfolio terminal balance for successful trials.

He considers two benchmarks: (1) no stock market mean reversion (random walk) and fixed 30-year retirement; and, (2) no stock market mean reversion and actuarial estimate of retirement duration. He also runs sensitivity tests to see how changes in assumptions affect success rate. Using the specified data, he finds that:

Keep Reading

A Few Notes on Muscular Portfolios

Brian Livingston introduces his 2018 book, Muscular Portfolios: The Investing Revolution for Superior Returns with Lower Risk, as follows: “What we laughingly call the financial ‘services’ industry is a cesspool filled with sharks intent on siphoning your money away and making it their own. The good news is that it is absolutely possible to grow your savings with no fear of financial sharks or stock market crashes. In the past few years, we’ve seen an explosion of low-cost index funds, along with serious mathematical breakthroughs in how to combine these funds into low-risk portfolios. …This book shows you how.  …You can start with just a little money and make it grow.” Based on research from multiple sources and extensions of that research, he concludes that: Keep Reading

“Current High” Boost for SACEMS?

A subscriber asked whether applying a filter that restricts monthly asset selections of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) to those currently at an intermediate-term high improves performance. This strategy each month reforms a portfolio of winners from the following universe based on total return over a specified lookback interval:

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)

To investigate, we focus on the equally weighted (EW) Top 3 SACEMS portfolio and replace any selection not at an intermediate-term high with Cash. We define intermediate-term high based on monthly closes over a specified past interval ranging from one month to six months. We consider all gross performance metrics used for base SACEMS. Using monthly dividend adjusted closing prices for the asset class proxies and the yield for Cash over the period February 2006 (the earliest all ETFs are available) through July 2018 (150 months), we find that: Keep Reading

A Few Notes on Heads I Win, Tails You Lose

Patrick Donohoe introduces his 2018 book, Heads I Win, Tails You Lose: A Financial Strategy to Reignite the American Dream, by stating that the book: “…will teach you many of the principles and strategies to help discover your own path to financial freedom. Most importantly, it will show you the mindset required to carry out a successful plan. …almost everything you will gain from this book conflicts with what the typical financial planner, financial celebrity, and most financial publications tell you to do. …You will…discover how to pivot the foundation of your wealth to…the private mutual insurance company.” Based on his experience, market research and many examples, he concludes that: Keep Reading

Multi-class Momentum Portfolio with “Canary” Crash Protection

Is it suboptimal to employ the same asset class proxy universe both to exploit momentum and to avoid crashes? In their July 2018 paper entitled Breadth Momentum and the Canary Universe: Defensive Asset Allocation (DAA)”, Wouter Keller and Jan Willem Keuning modify their Vigilant Asset Allocation (VAA) by substituting a separate “canary” asset class universe for crash protection based on breadth momentum (percentage of assets advancing). VAA is a dual momentum asset class strategy specifying momentum as the average of annualized total returns over the past 1, 3, 6 and 12 months, implemented as follows:

  1. Each month rank asset class proxies based on momentum.
  2. Each month select a “cash” holding as the one of short-term U.S. Treasury, intermediate-term U.S. Treasury and investment grade corporate bond funds with the highest momentum. 
  3. Set (via backtest) a breadth protection threshold (B). When the number of asset class proxies with negative momentum (b) is equal to or greater than B, the allocation to “cash” is 100%. When b is less than B, the base allocation to “cash” is b/B.
  4. Set (via backtest) the number of top-performing asset class proxies to hold (T) in equal weights. When the base allocation to “cash” is less than 100% (so when b<B), allocate the balance to the top (1-b/B)T asset class proxies with highest momentum (irrespective of sign).
  5. Mitigate portfolio rebalancing intensity (when B and T are different) by rounding fractions b/B to multiples of 1/T.

DAA replaces step 3 with breadth protection calculated the same way but based on a separate, simpler asset universe, selected experimentally from pre-1971 data based on a unique indicator that that combines compound annual growth rate (R) and maximum drawdown (D). The aim of DAA is to lower the average cash allocation fraction compared to VAA while preserving crash protection. They describe assets in terms of existing exchange-traded funds (ETF) but use best available matching indexes prior to ETF inceptions. Using monthly return data for alternative canary assets during 1926-1970, for backtest (in-sample) DAA universe parameter optimization during 1971-1993 and for out-of-sample DAA universe testing during 1994 through March 2018, they find that: Keep Reading

A Few Notes on The Geometry of Wealth

Brian Portnoy introduces his 2018 book, The Geometry of Wealth: How To Shape A Life Of Money And Meaning, by stating that the book is: “…a story told in three parts,…from purpose to priorities to tactics. Each step has a primary action associated with it. The first is adaptation. The second is prioritization. The third is simplification. …The principle that motors us along the entire way is what I call ‘adaptive simplicity,’ a means of both rolling with the punches and and cutting through the noise.” Based on his two decades of experience in the mutual fund and hedge fund industries, including interactions with many investors, along with considerable cited research (much of it behavioral), he concludes that: Keep Reading

Alternative Momentum Metrics for SACEMS?

A subscriber asked whether some different momentum metric might improve performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS), which each month reforms a portfolio of winners from the following universe based on total return over a specified lookback interval:

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)

To investigate, we compare performances of the following alternative monthly momentum metrics to that of the original baseline metric:

  • Average monthly total returns over the lookback interval.
  • Slope of the dividend-adjusted price series over the lookback interval.
  • Sharpe ratio of the monthly total return series over the lookback interval (using Cash return as the risk-free rate, and setting the Sharpe ratio of Cash at zero).

We focus on the equally weighted (EW) Top 3 SACEMS portfolio. We consider all the performance metrics used for the baseline, with emphasis on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly dividend adjusted closing prices for the asset class proxies and the yield for Cash over the period February 2006 (the earliest all ETFs are available) through May 2018 (148 months), we find that: Keep Reading

SACEVS and SACEMS from a European Perspective

A European subscriber asked about the effect of the dollar-euro exchange rate on the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS). To investigate, we each month adjust the gross returns for these strategies for the change in the dollar-euro exchange rate that month. We consider all strategy variations: Best Value and Weighted for SACEVS; and, Top 1, equally weighted (EW) Top 2 and EW Top 3 for SACEVS. We focus on SACEVS Best Value and SACEMS EW Top 3. We consider effects on four gross performance metrics: average monthly return; standard deviation of monthly returns; compound annual growth rate (CAGR); and, maximum drawdown (MaxDD). Using monthly returns for the strategies and monthly changes in the dollar-euro exchange rate since August 2002 for SACEVS and since August 2006 for SACEMS, both through April 2018, we find that: Keep Reading

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