Strategic Allocation

How should investors evaluate the effectiveness of a safe haven asset? In their July 2020 paper entitled “A Safe Haven Index”, Dirk Baur and Thomas Dimpfl devise and apply a safe haven index (SHI) to evaluate over 20 individual potential safe haven assets. SHI consists of seven equal-weighted assets: gold, Swiss franc, Japanese yen, 2-year, 10-year and 30-year U.S. Treasuries and 10-year German government bonds. For evaluations, they focus on four safe haven events: the October 1987 stock market crash, the September 2001 terrorist attacks, the September 2008 Lehman collapse and the March 2020 COVID-19 pandemic. Using daily data for index components and other potential safe haven assets as available during January 1985 through May 2020, they find that:

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How should investors think about the interactions between working years (retirement account contributions) and retirement years (retirement account withdrawals)? In his June 2020 paper entitled “Retirement Planning: From Z to A”, Javier Estrada integrates working and retirement periods to estimate how much an individual should save and how they should invest to achieve a desired retirement income and ultimate bequest to heirs. He illustrates his analytical solution empirically for U.S. stocks and bonds, first using a base case plus sensitivity analysis and then using Monte Carlo simulations. His base case assumes:

• Work will last 40 years with a 60%/40% stocks/bonds retirement portfolio.
• Retirement will last 30 years with beginning-of-year real (inflation-adjusted) withdrawals of $60,000 from a 40%/60% stocks/bonds retirement portfolio and ultimate bequest $300,000.

Using annual data for U.S. stocks (the S&P 500 Index total return), bonds (10-year U.S. Treasury notes) and U.S. inflation during 1928 through 2019, he finds that: Keep Reading

Does multi-class investing boost performance for sophisticated investors such as educational endowments? In his June 2020 paper entitled “Endowment Performance and the Demise of the Multi-Asset-Class Model”, Richard Ennis examines recent performance of educational endowment funds, with focus on allocations to alternative assets. Using performance data from a report on 774 university endowments and from hand-collected annual reports for some large individual endowments during June 2008 through June 2019, he finds that: Keep Reading

In response to “Review of Dual Momentum with Just Three Assets”, a subscriber suggested adding gold in competition with long-term U.S. Treasury bonds as a safe haven from equities. To test this potential enhancement of Accelerating Dual Momentum (ADM), we each month:

1. Calculate for each of SPDR S&P 500 (SPY), iShares MSCI EAFE Small-Cap ETF (SCZ), iShares 20+ Year Treasury Bond (TLT) and SPDR Gold Shares (GLD) the sum of its 1-month, 3-month and 6-month past returns.
2. If sums for both SPY and SCZ are negative, buy the one of TLT and GLD with the higher sum.
3. If both sums for SPY and SCZ are not negative, buy the one with the higher sum.

Using end-of-month dividend-adjusted prices of these ETFs during December 2007 (limited by SCZ) through April 2020, we find that: Keep Reading

A subscriber asked for an update on whether weekly or biweekly (every two weeks) measurement of asset class momentum works better than monthly measurement as used in “Simple Asset Class ETF Momentum Strategy (SACEMS)” (SACEMS). Do higher measurement frequencies respond more efficiently to market turns? To investigate, we compare performances of strategies based on monthly, weekly and biweekly frequencies with comparable lookback intervals. For this comparison, we align weekly and biweekly results with monthly results, though they differ somewhat due to mismatches between ends of weeks and ends of months. We consider portfolios of past ETF winners based on Top 1 and on equally weighted (EW) Top 2 and Top 3. Using weekly dividend-adjusted closing prices for the asset class proxies per baseline SACEMS and the yield for Cash during February 2006  through April 2020, we find that: Keep Reading

How should investors think about combining strategies into a broader portfolio that reliably exploits their interactions over time? In the March 2020 version of his paper entitled “Preferred Portfolios: An Improved Blueprint to Construct Multi Strategy Portfolios”, Lars Kestner discusses how to combine individual strategies into a portfolio that performs robustly out-of-sample base on five principles. His objective is to sift data with a systematic process, find small edges and fit them together into a reliable combination of return streams that in aggregate perform well under almost all market conditions. His process employs two sets of building blocks: (1) diverse quantitative strategies clustered into four categories; and, (2) nine asset markets/classes. Based on theoretical considerations and his experience as an investment manager, he concludes that:

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A subscriber requested testing Martin Zweig’s Combined Super Model, which each month specifies an equity allocation based on a system that assigns up to eight points from his Monetary Model and 0 or 2 points from his Four Percent Model. We consider two versions of the Combined Super Model:

1. Zweig-Cash – Allocate to Fidelity Fund (FFIDX) as equities, with the balance in cash earning the 3-month U.S. Treasury bill (T-bill) yield.
2. Zweig-FGOVX – Allocate to FFIDX as equities, with the balance in Fidelity Government Income Fund (FGOVX)

The benchmark is buying and holding FFIDX. We focus on compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio, with average monthly T-bill yield during a year as the risk-free rate for that year. We ignore impediments to mutual fund trading and any issues regarding timeliness of allocation changes for end-of-month rebalancing. Using monthly Combined Super Model allocations and monthly fund returns/T-bill yield during December 1986 through March 2020, we find that: Keep Reading

Are equity factors used in leading models of stock returns reliable performers in practice? In his March 2020 paper entitled “Factor Performance 2010-2019: A Lost Decade?”, David Blitz measures performances of factors tracked in the Kenneth French data library and the q-factor model library during 2010-2019 and compares results to their performances in prior decades. Using data from these libraries for 32 U.S. equity factors and six global non-U.S. factors over available sample periods through 2019, he finds that:

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Subscribers are posing questions about the 2019 coronavirus (COVID-19) as a driver of current market conditions that are difficult to address with evidence-based analyses. Here are some questions and thoughts: Keep Reading

“Verification Tests of the Smart Money Indicator” reports performance results for a specific version of the Smart Money Indicator (SMI) stocks-bonds timing strategy, which exploits differences in futures and options positions in the S&P 500 Index, U.S. Treasury bonds and 10-year U.S. Treasury notes between institutional investors (smart money) and retail investors (dumb money). Do these sentiment-based results diversify those for the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS)? To investigate, we look at correlations of annual returns between variations of SMI (no lag between signal and execution, 1-week lag and 2-week lag) and each of SACEMS equal-weighted (EW) Top 3 and SACEVS Best Value. We then look at average gross annual returns, standard deviations of annual returns and gross annual Sharpe ratios for the individual strategies and for equal-weighted, monthly rebalanced portfolios of the three strategies. Using gross annual returns for the strategies during 2008 through 2019, we find that: Keep Reading