Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for March 2024 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for March 2024 (Final)
1st ETF 2nd ETF 3rd ETF

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.

A Few Notes on The Missing Billionaires

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:

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Review of the Quantified Market Psychology Strategy

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:

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QQQ vs. Simplest Asset Class ETF Momentum Strategy?

“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:

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Volatility-adjusted Retirement Income Streams

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

SACEVS-SACEMS Leverage Sensitivity Tests

“SACEMS with Margin” investigates the use of target 2X leverage via margin to boost the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS). “SACEVS with Margin” investigates the use of target 2X leverage via margin to boost the performance of the “Simple Asset Class ETF Value Strategy” (SACEVS). In response, a subscriber requested a sensitivity test of 1.25X, 1.50X and 1.75X leverage targets. To investigate effects of these leverage targets, we separately augment SACEVS Best Value, SACEMS EW Top 2 and the equally weighted combination of these two strategies by: (1) initially applying target leverage via margin; (2) for each month with a positive portfolio return, adding margin at the end of the month to restore target leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore target leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratiocompound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate. Using monthly total (dividend-adjusted) returns for the specified assets since July 2002 for SACEVS and since July 2006 for SACEMS, both through October 2023, we find that:

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SACEVS with Margin

Is leveraging with margin a good way to boost the performance of the “Simple Asset Class ETF Value Strategy” (SACEVS)? To investigate effects of margin, we augment SACEVS by: (1) initially applying 2X leverage via margin (limited by Federal Reserve Regulation T); (2) for each month with a positive portfolio return, adding margin at the end of the month to restore 2X leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore 2X leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratiocompound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics for Best Value (which picks the most undervalued premium) and Weighted (which weights all undervalued premiums according to degree of undervaluation) variations of SACEVS. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate and consider a range of margin interest rates as increments to this yield. Using monthly total returns for SACEVS and monthly T-bill yields during July 2002 through October 2023, we find that:

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SACEMS with Margin

Is leveraging with margin a good way to boost the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS)? To investigate effects of margin, we augment SACEMS by: (1) initially applying 2X leverage via margin (limited by Federal Reserve Regulation T); (2) for each month with a positive portfolio return, adding margin at the end of the month to restore 2X leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore 2X leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratiocompound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate and consider a range of margin interest rates as increments to this yield. Using monthly gross total returns for SACEMS and monthly T-bill yields during July 2006 through October 2023, we find that:

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Simplest Asset Class ETF Momentum Strategy Update

A subscriber asked about an update of “Simplest Asset Class ETF Momentum Strategy?”, which 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. We begin the test at the end of June 2006, limited by SACEMS inputs. We ignore monthly switching frictions for both strategies. 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 starting July 2006, all through October 2023, we find that: Keep Reading

Seasonal SACEVS-SACEMS Strategy?

A subscriber requested testing of a strategy that holds a combination of 50% Simple Asset Class ETF Value Strategy (SACEVS) Best Value and 50% Simple Asset Class ETF Momentum Strategy (SACEMS) equal-weighted (EW) Top 2 strategies during November through April and idle cash during May through October. We consider three strategies:

  1. Best Value – EW Top 2 – hold Best Value-EW Top 2 during all months.
  2. Best Value – EW Top 2 Seasonal (Idle Cash) – hold Best Value-EW Top 2 during November through April and idle cash during May through October, as requested.
  3. Best Value – EW Top 2 Seasonal (6-month T-bill) – hold Best Value-EW Top 2 during November through April and 6-month U.S. Treasury bills (T-bill) bought at the beginning May each year during May through October.

We run annual statistics for each variation as in “Combined Value-Momentum Strategy (SACEVS-SACEMS)”. Annualized returns are compound annual growth rates. Maximum drawdown is the deepest peak-to-trough drawdown for these strategies based on monthly measurements over the sample period. For Sharpe ratio, to calculate excess annual return, we use average monthly yield on 3-month Treasury bills during a year as the risk-free rate for that year. Using monthly returns for SACEVS Best Value and SACEMS EW Top 2 and the specified T-bill yield during July 2006 through October 2023, we find that: Keep Reading

All Stocks All the Time?

Is the the conventional retirement portfolio glidepath as recommended by many financial advisors, away from stocks and toward bonds over time, really optimal? In their October 2023 paper entitled “Beyond the Status Quo: A Critical Assessment of Lifecycle Investment Advice”, Aizhan Anarkulova, Scott Cederburg and Michael O’Doherty present a lifecycle income/wealth model using stationary block bootstrap simulations (average block length 120 months to preserve long-term behaviors) with labor income uncertainty, Social Security income, longevity uncertainty and historical monthly returns for stock indexes, government bonds and government bills across developed countries. They apply this model to estimate outcomes for several age-dependent, monthly rebalanced portfolios of stocks and bonds, including a representative target-date fund (TDF), as well as some fixed-percentage allocation strategies. They focus on a U.S. couple (a female and a male) who save during working years starting at age 25 and consume Social Security income and savings starting at age 65 with constant real 4% annual withdrawals. They evaluate four outcomes: (1) wealth at retirement; (2) retirement income; (3) conservation of savings; and, (4) bequest at death. Using monthly (local) real returns for domestic stock indexes, international stock indexes, government bonds and government bills as available for 38 developed countries during 1890 through 2019, they find that:

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