Strategic Allocation

Can investors beat a traditional 60%-40% stocks-bonds portfolio by adjusting allocations based on the earnings yield of stocks and the current yield of government bonds? In his March 2024 paper entitled “A Yield-based Asset Ratio to Boost Minimum Investment Returns”, Arthur Eschenlauer tests a strategy that allocates to the S&P 500 Index or 10-year U.S. Treasury notes (T-note) via a Yield-based Asset Ratio strategy (YBAR), specified as follows:

• Compute minimum and maximum stock allocations that vary with S&P 500 long-term past earnings yield and current nominal T-note yield. The earnings yield is average earnings over the past 10 years divided by stock index level.
• Buy the stock index incrementally to rise to the minimum allocation whenever the stock allocation falls below the minimum minus 6% (a margin of safety).
• Sell the stock index incrementally to fall to the maximum allocation whenever the stock allocation rises above the maximum allocation plus 6% (a margin of folly).
• Whenever the stock index is very high, apply a cap to the stock allocation (a margin of reversion). The margin of reversion reflects how the earnings yield stands relative to historical values.

YBAR testing assumes 6% minimum acceptable stock allocation, 85% maximum acceptable stock allocation, 25% maximum reversion hazard and monthly portfolio assessment. Using Robert Shiller’s data as proxies for S&P 500 Index levels and earnings and for T-note yields during 1911 through 2022, he finds that: Keep Reading

A subscriber asked for assessment of a strategy that holds 70% SPDR S&P 500 ETF Trust (SPY) and 30% SPDR Bloomberg 1-3 Month T-Bill ETF (BIL) (SPY-BIL 70-30), rebalanced every eight weeks, with explicit comparison to the 50% Simple Asset Class ETF Value Strategy Best Value-50% Simple Asset Class ETF Momentum Strategy Equal-Weighted Top 2 combined strategy (Best Value-EW Top 2). We measure performance of SPY-BIL 70-30 at 8-week intervals to match the specified rebalancing schedule. We measure performance of Best Value-EW Top 2 bimonthly for approximate comparability. We focus on 8-week or bimonthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). We also look at buy-and-hold SPY (SPY) as a simple alternative. Using weekly SPY and BIL dividend-adjusted prices and monthly Best Value -EW Top 2 returns from late May 2007 (BIL inception) through early February 2024, we find that: Keep Reading

A subscriber observed that backtesting of momentum-based trading systems typically assumes perfect rebalancing each month whether or not they select new assets. Would delaying rebalancing until new assets are selected improve strategy performance? To investigate, we compare the following two versions of the Simple Asset Class ETF Momentum Strategy (SACEMS) equal-weighted (EW) Top 2 portfolio:

1. Rinse-and-Repeat – each month rebalance the two positions to equal weights. This is the assumption for tracked SACEMS.
2. Let-It-Ride – rebalance the two positions to equal weights only when the strategy selects two new assets. In other words, as long as at least one of the two selections is a holdover from the prior month, let the two positions drift away from equal weights.

Using monthly returns for the top two SACEMS selections during July 2006 through February 2024, we find that: Keep Reading

Referring to “Substitute QQQ for SPY in SACEVS and SACEMS?” and “Conditionally Substitute SSO for SPY in SACEVS and SACEMS?”, a subscriber requested a horse race for boosting the performance of the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS), and thereby the Combined Value-Momentum Strategy (SACEVS-SACEMS), based on substituting:

1. ProShares Ultra S&P500 (SSO) for SPDR S&P 500 ETF Trust (SPY) in portfolio holdings, but not in SACEMS asset ranking calculations.
2. Invesco QQQ Trust (QQQ) for SPY in both portfolio holdings and SACEMS asset ranking calculations.

In conducting the horse race, we focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and gross annual Sharpe ratio as key performance metrics. In Sharpe ratio calculations, we employ the average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year. Using monthly total (dividend-adjusted) returns for SACEVS assets, SACEMS assets, SSO and QQQ as available through February 2024, we find that: Keep Reading

A subscriber suggested that the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS) may each exhibit return momentum at the strategy level, such that an investor holding both as in Combined Value-Momentum Strategy (SACEVS-SACEMS) may want to tilt each month toward the one with stronger recent returns. To investigate, we test a SACEVS Best Value-SACEMS Equal-Weighted (EW) Top 2 combination strategy that each month assigns 60% weight to the strategy with the higher return over a specified lookback interval and 40% to the one with the lower return (60-40). We consider lookback intervals of 1 to 12 months. We also look at a “full tilt” version for a selected lookback interval. We use standalone SACEVS Best Value, standalone SACEMS EW Top 2 and monthly rebalanced 50% SACEVS Best Value-50% SACEMS EW Top 2 (50-50) as benchmarks. We look at average gross monthly return, standard deviation of monthly returns, monthly gross reward/risk (average monthly return divided by standard deviation), gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and gross annual Sharpe ratio as key performance metrics. In Sharpe ratio calculations, we employ the average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year. Using SACEVS Best Value and SACEMS EW Top 2 gross monthly returns during July 2006 (limited by SACEMS) through January 2024, we find that:

Keep Reading

A subscriber asked whether more granularity in international equity choices for the “Simple Asset Class ETF Momentum Strategy” (SACEMS), such as considered by Decision Moose, would improve performance. To investigate, we augment/replace international developed and emerging equity market exchange-traded funds (ETF) such that the universe of assets becomes:

• SPDR S&P 500 (SPY)
• iShares Russell 2000 Index (IWM)
• iShares Europe (IEV)
• iShares MSCI Japan (EWJ)
• iShares MSCI Pacific ex Japan (EPP)
• iShares MSCI Emerging Markets Index (EEM)
• iShares JPMorgan Emerging Markets Bond Fund (EMB)
• iShares Latin America 40 (ILF)
• iShares Barclays 20+ Year Treasury Bond (TLT)
• Vanguard REIT ETF (VNQ)
• SPDR Gold Shares (GLD)
• Invesco DB Commodity Index Tracking (DBC)
• 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 gross annual Sharpe ratio (average annual excess return divided by standard deviation of annual excess returns, using average monthly T-bill yield during a year to calculate excess returns) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns for the specified assets during February 2006 through December 2023, we find that: Keep Reading

In their 2023 book, The Missing Billionaires: A Guide to Better Financial Decisions, authors Victor Haghani and James White seek “to give you a practical framework, consistent with the consensus of university finance textbooks, for making good financial decisions that are right for you. Good decisions will take account of your personal circumstances, financial preferences, and your considered views on the risks and expected returns of available investments. …You will likely get the most out of this book if you have already accumulated a decent amount of financial capital or if you are young with a healthy measure of human capital. …The book is written from the perspective of a US individual or family…” Based on their many years of wealth management experience and portfolio systems development, they conclude that:

Keep Reading

At the suggestion of one of his subscribers, Willi Bambach requested independent review of his 1g QMP [Quantified Market Psychology] strategy, tracked since December 2007 on TimerTrac. To facilitate a review, he provided a brief description of the strategy and a medallion (https://timertrac.com/private/medallion.asp?mlid={CDD4AEE6-2A1D-4917-A571-DF23C884D1D3}) to enable public access to the strategy on TimerTrac (very slow to load and may no longer work). The strategy has asset universe, asset allocation and position leverage components as follows:

• Asset universe:
  1. Cash in a money market fund (with assumed 2% fixed yield).
  2. SPDR S&P 500 ETF Trust (SPY)
  3. iShares 20+ Year Treasury Bond ETF (TLT).
• Allocations as signaled mostly per the following three steps:
  1. Examine differences between FactSet consensus analyst earnings forecasts and actual earnings for S&P 500 stocks.
  2. Relate these differences to earnings release price reactions of respective stocks.
  3. Translate this relationship into a sentiment signal that specifies allocations for Cash, SPY and TLT.
• Leverage (with assumed 0.5% fixed financing cost) for SPY and TLT positions added in 0.5 increments as long as three conditions hold for inception-to-date data (as the sample grew, this approach evolved to constant 2X leverage over the last five years):
  1. Standard deviation of 1g QMP returns is lower than than that for the S&P 500 Index.
  2. Downside standard deviation of 1g QMP returns is lower than that for the S&P 500 Index.
  3. 1g QMP Ulcer Index is lower than that for the S&P 500 Index.

Data available via this medallion include a list of 1g QMP allocation changes by date (see the table at the end). For testing 1g QMP, we do not attempt to replicate allocations. Instead, we apply a set of tractable assumptions to them and test versions of 1g QMP with 1X (no leverage) and 2X leverage. We use SPDR Bloomberg 1-3 Month T-Bill ETF (BIL) for cash to approximate money market yields and avoid estimating settlement delays. We supply 2X leverage by substituting ProShares Ultra S&P500 (SSO) for SPY and ProShares Ultra 20+ Year Treasury (UBT) for TLT. We focus on net average daily return, standard deviation of daily returns, daily return/risk (average divided by standard deviation), compound annual growth rate (CAGR), maximum drawdown and annual Sharpe ratio. We use average end-of-month 3-month U.S. Treasury bill (T-bill) yield during a year as the risk-free rate for that year in Sharpe ratio calculations. We do not include partial years in Sharpe ratio calculations. Using the list of strategy allocation changes and daily dividend-adjusted prices of BIL, SPY, TLT, SSO and UBT during 1/25/2008 through 11/30/2023, we find that:

Keep Reading

“Simplest Asset Class ETF Momentum Strategy Update” updates performance of a strategy that each month holds SPDR S&P 500 ETF Trust (SPY) or iShares 20+ Year Treasury Bond (TLT) depending on which has the higher total return over the last three months, including a direct comparison to a portfolio that each month allocates 50% to Simple Asset Class ETF Value Strategy (SACEVS) Best Value and 50% to Simple Asset Class ETF Momentum Strategy (SACEMS) equal-weighted (EW) Top 2. A subscriber asked for additional comparison to a strategy that buys and holds Invesco QQQ Trust (QQQ). As before, we begin the test at the end of June 2006, limited by SACEMS inputs. We ignore monthly switching frictions, to the disadvantage of QQQ. Using monthly dividend-adjusted prices for SPY and TLT starting March 2006 and monthly gross returns for 50-50 SACEVS Best Value and SACEMS EW Top 2 and dividend-adjusted prices for QQQ starting July 2006, all through October 2023 (17.3 years), we find that:

Keep Reading

Should investors consider portfolio volatility when choosing allocations to stocks and bonds in their retirement accounts? In his October 2023 paper entitled “Retirement Planning: The Volatility-Adjusted Coverage Ratio”, Javier Estrada introduces volatility-adjusted coverage ratio (VAC) as an alternative retirement portfolio metric. He defines this metric as coverage ratio (C, number of years of withdrawals supported relative to retirement period length) divided by annual portfolio volatility during retirement. He compares optimal stocks-bonds allocations for different fixed real annual withdrawal rates across 22 country markets and the world market using either C of VAC. For all markets and withdrawal rates, he uses historical returns for stocks and bonds with annual portfolio rebalancing and 30-year retirement periods. Using annual returns for stocks and bonds and annual inflation rates in the U.S. during 1872 through 2022 (Shiller data) and in 21 other countries during 1900 through 2019 (Dimson-Marsh-Staunton data), he finds that: Keep Reading