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.

Exploit U.S. Stock Market Dips with Margin?

A subscriber requested evaluation of a strategy that seeks to exploit U.S stock market reversion after dips by temporarily applying margin. Specifically, the strategy:

  • At all times holds the U.S. stock market.
  • When the stock market closes down more than 7% from its high over the past year, augments stock market holdings by applying 50% margin.
  • Closes each margin position after two months.

To investigate, we assume:

  • The S&P 500 Index represents the U.S. stock market for calculating drawdown over the past year (252 trading days).
  • SPDR S&P 500 (SPY) represents the market from a portfolio perspective.
  • We start a margin augmentation at the same daily close as the drawdown signal by slightly anticipating the drawdown at the close.
  • 50% margin is set at the opening of each augmentation and there is no rebalancing to maintain 50% margin during the two months (42 trading days) it is open.
  • If S&P 500 Index drawdown over the past year is still greater than 7% after ending a margin augmentation, we start a new margin augmentation at the next close.
  • Baseline margin interest is U.S. Treasury bill (T-bill) yield plus 1%, debited daily.
  • Baseline one-way trading frictions for starting and ending margin augmentations are 0.1% of margin account value.
  • There are no tax implications of trading.

We use buying and holding SPY without margin augmentation as a benchmark. Using daily levels of the S&P 500 Index, daily dividend-adjusted SPY prices and daily T-bill yields from the end of January 1993 (limited by SPY) through November 2022, we find that: Keep Reading

SACEMS with SMA Filter

In response to a prior analysis (updated here), a subscriber asked whether adding a simple moving average (SMA) filter to “Simple Asset Class ETF Momentum Strategy” (SACEMS) assets, either before or after ranking them based on past returns, improves strategy performance. SACEMS each month picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. Since many technical traders use a 10-month SMA (SMA10), we test effectiveness of requiring that each asset pass an SMA10 filter as follows:

  1. Baseline – SACEMS as presented at “Momentum Strategy” (no SMA10 filter).
  2. Apply an SMA10 filter after asset ranking (SACEMS R-F) – 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).
  3. Apply an SMA10 filter before asset ranking (SACEMS F-R) – If a SACEMS asset is above (below) its SMA10, apply SACEMS ranking rules to it (exclude it from ranking). If there are not enough ranked assets to populate multi-position SACEMS portfolios, put the positions in Cash.

We focus on compound annual growth rates (CAGR), annual Sharpe ratios and maximum drawdowns (MaxDD) of SACEMS Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios. To calculate Sharpe ratios, we use average monthly 3-month U.S. Treasury bill (T-bill) yield during a year as the risk-free rate for that year. Using monthly dividend-adjusted closing prices for the asset class proxies and the (T-bill) yield for Cash over the period February 2006 through November 2022, we find that: Keep Reading

Ranking SACEMS Assets with Unadjusted Returns

A subscriber, wondering if past returns unadjusted by dividends (capital gains/losses only) more accurately reflect relative momentum than dividend-adjusted returns, asked about performance of the Simple Asset Class ETF Momentum Strategy (SACEMS) with assets ranked by unadjusted returns. To investigate, we compare performance statistics for SACEMS Top 1, equal-weighted (EW) Top 2 and EW Top 3 portfolios with assets ranked by either dividend-adjusted (the tracked version of SACEMS) or unadjusted past returns. For both cases, portfolio performance data include reinvested dividends. Using monthly dividend-adjusted and unadjusted prices for SACEMS assets as available during February 2006 through October 2022, we find that:

Keep Reading

Substituting IYH for SPY in SACEMS

Based on high return correlation with the S&P 500 Index and strong past performance of the health care sector, a subscriber suggested replacing SPDR S&P 500 ETF Trust (SPY) with iShares U.S. Healthcare ETF (IYH) in the Simple Asset Class ETF Momentum Strategy (SACEMS). To investigate, we compare performance statistics for SACEMS Top 1, equal-weighted (EW) Top 2 and EW Top 3 portfolios with either SPY or IYH. Using monthly dividend-adjusted returns for baseline SACEMS assets and IYH as available during February 2006 through October 2022, we find that: Keep Reading

Using One Third 3X Funds for Some SACEMS Assets

A subscriber suggested implementing several Simple Asset Class ETF Momentum Strategy (SACEMS) asset class proxies with allocations consisting of one third triple-leveraged (3X) versions of the proxies and two thirds cash, thereby accruing the targeted risk asset returns plus some return on cash. Specifically:

Other SACEMS assets are unaffected, and the approach still uses past returns for SPY, IWM and TLT to determine winners. To investigate the suggestion, we compare performance statistics for SACEMS Top 1, equal-weighted (EW) Top 2 and EW Top 3 portfolios without (Base) and with (1/3 3X) the alternative strategy. Using monthly dividend-adjusted returns for Base SACEMS assets and for SPXL, TNA and TMF as available during July 2006 through October 2022, we find that: Keep Reading

Stocks-Bonds Return Correlation and Inflation

A subscriber asked whether the correlation between returns on stocks and bonds is elevated when inflation is above 5%, such that equities and fixed income offer little diversification protection. To investigate, we calculate the U.S. overall inflation rate from monthly values of the consumer price index over the prior year to find months with the inflation rate over 5%. We then compute monthly total return correlations for the following two pairs of funds when inflation is above or not above 5%:

  1. Fidelity Fund (FFIDX) and Fidelity Investment Grade Bond Fund (FBNDX).
  2. SPDR S&P 500 ETF Trust (SPY) and iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD).

Using monthly dividend-adjusted prices for FFIDX and FBNDX since January 1980 and for SPY and LQD since July 2002, all through September 2022, we find that:

Keep Reading

SACEMS Equal-weighted 2-3 Portfolio

Referring to “SACEMS Top 1 Mean Reversion?”, a subscriber asked about the performance of the equal-weighted (EW) second and third ranked Simple Asset Class ETF Momentum Strategy (SACEMS) assets both as a standalone strategy and in 50-50 combination with the Simple Asset Class ETF Value Strategy (SACEVS) Best Value portfolio. This alternative portfolio (SACEMS EW 2-3) would avoid any reversion tendency of the top-ranked asset. To investigate, we compare performance statistics for SACEMS EW 2-3 to those for the baseline Top 1, EW Top 2 and EW Top 3 portfolios. We also compare performance statistics for the 50-50 SACEVS Best Value-SACEMS EW 2-3 portfolio to those for 50-50 SACEVS Best Value-SACEMS EW Top 2 and 50-50 SACEVS Best Value-SACEMS EW Top 3 portfolios. Using monthly returns for SACEMS winners and for SACEVS Best Value during July 2006 through September 2022, we find that: Keep Reading

More Aggressive Pursuit of the Credit Premium in SACEVS?

Noting that iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD) and iShares 20+ Year Treasury Bond ETF (TLT) exhibit a moderately positive return correlation, a subscriber asked about substituting Vanguard High-Yield Corporate Fund Investor Shares (VWEHX) for LQD in the Simple Asset Class ETF Value Strategy (SACEVS) to exploit undervaluation of the credit risk premium. SACEVS seeks diversification across a small set of asset class exchanged-traded funds (ETF), plus a monthly tactical edge from potential undervaluation of three risk premiums:

  1. Term – monthly difference between the 10-year Constant Maturity U.S. Treasury note (T-note) yield and the 3-month Constant Maturity U.S. Treasury bill (T-bill) yield.
  2. Credit – monthly difference between the Moody’s Seasoned Baa Corporate Bonds yield and the T-note yield.
  3. Equity – monthly difference between S&P 500 operating earnings yield and the T-note yield.

To investigate, we compare performances of SACEVS Best Value and SACEVS Weighted portfolios with either VWEHX or LQD as the credit risk asset. Using monthly total returns for SACEVS assets during July 2002 through September 2022, we find that:

Keep Reading

Optimal Monthly Cycle for SACEMS?

Is there a best time of the month for measuring momentum within the Simple Asset Class ETF Momentum Strategy (SACEMS)? To investigate, we compare 21 variations of baseline SACEMS by shifting the monthly return calculation cycle from 10 trading days before the end of the month (EOM) to 10 trading days after EOM. For example, an EOM+5 cycle ranks assets based on closing prices five trading days after EOM each month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using daily dividend-adjusted prices for SACEMS assets during mid-February 2006 through mid-October 2022, we find that:

Keep Reading

Global Safe Retirement Withdrawal Rate

Does a constant real annual withdrawal rate of 4% of household savings at retirement, derived from U.S. asset return experience, really protect against financial ruin? In their September 2022 paper entitled “The Safe Withdrawal Rate: Evidence from a Broad Sample of Developed Markets”, Aizhan Anarkulova, Scott Cederburg, Michael O’Doherty and Richard Sias consider data from 38 developed countries to assess safe withdrawal rates. This sample mitigates survivorship/easy data biases of the U.S. experience by including multiple left-tail instances of trading halts, wars, hyperinflation and other extreme events. They use this data to model retirement portfolio performance via stationary block bootstrap simulation, with longevity risk incorporated from U.S. Social Security Administration mortality tables. Their base case examines joint investment-longevity outcomes for a couple retiring in 2022 at age 65 using a 60% domestic stocks-40% bonds (60-40) portfolio strategy. They also look at other fixed stocks-bonds allocations and investment strategies pursued by target-date funds. Using monthly (local) real returns for domestic stocks, international stocks, bonds and bills as available for 38 developed countries during 1890 through 2019, they find that: Keep Reading

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