Calendar Effects

Do quarterly allocation updates for the Best Value and Weighted versions of the “Simple Asset Class ETF Value Strategy” (SACEVS) work as well as monthly updates? These strategies allocate funds to the following asset class exchange-traded funds (ETF) according to valuations of term, credit and equity risk premiums, or to cash if no premiums are undervalued:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

Changing from monthly to quarterly allocation updates does not sacrifice information about lagged quarterly S&P 500 Index earnings, but it does sacrifice currency of term and credit premiums. To assess alternatives, we compare cumulative performances and the following key metrics for quarterly and monthly allocation updates: gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD), annual gross returns and volatilities and annual gross Sharpe ratios. Using monthly dividend-adjusted closes for the above ETFs during September 2002 (earliest alignment of months and quarters) through September 2021, we find that:

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Does the technically adjusted Seasonal Timing Strategy popularized some years ago in Sy Harding’s Street Smart Report Online (now unavailable due to Mr. Harding’s death) generate attractive performance? This strategy combines “the market’s best average calendar entry [October 16] and exit [April 20] days with a technical indicator, the Moving Average Convergence Divergence (MACD).” According to Street Smart Report Online, applying this strategy to a Dow Jones Industrial Average (DJIA) index fund generated a cumulative return of 213% during 1999 through 2012, compared to 93% for the DJIA itself. To check over a longer sample period with an alternative market proxy, we apply the strategy to SPDR S&P 500 (SPY) since its inception and consider several alternatives, as follows:

1. SPY – buy and hold SPY.
2. Seasonal-MACD – seasonal timing per specified dates with MACD refinement, holding cash when not in SPY.
3. Seasonal Only – seasonal timing per the same dates without MACD refinement, again holding cash when not in SPY.
4. SMA200 – hold SPY (cash) when the S&P 500 Index is above (below) its 200-day simple moving average at the prior daily close. 

For all strategies, we use the yield on short-term U.S. Treasury bills (T-bills) as the return on cash. Using daily closes for the S&P 500 Index, dividend-adjusted closes for SPY and T-bill yield during 1/29/93 (SPY inception) through 10/1/21, we find that: Keep Reading

Unlike publicly traded assets generally, investors/speculators can buy and sell bitcoin any day of the week. Do bitcoin returns exhibit anomalies by day of the week, perhaps especially because of weekend trading? To investigate, we calculate (1) average returns and return variabilities for each day of the week; and, (2) gross cumulative returns for holding bitcoin only one specific day of the week. Using daily bitcoin prices from Coindesk during 11/3/2014 (the earliest offered) through September 6, 2021, we find that: Keep Reading

A subscriber requested review of a swing trade strategy that buys and sells Invesco QQQ Trust (QQQ) according to the following rules:

• Buy at the close when it is either Monday or Tuesday and QQQ (Close-Low)/(High-Low) is 0.15 or less.
• Subsequently sell at the close when it is higher than the prior-day high.

To investigate, to simplify portfolio cash management, we assume that there are no overlapping trades (if a position opens on Monday, another position does not open on Tuesday). We further assume that cash earns the 3-month U.S. Treasury bills (T-bill) yield when not in QQQ and that frictions for switching between T-bills and QQQ are 0.10% of trade value. Using daily high, low, close and dividend-adjusted close (to calculate returns) for QQQ and daily T-bill close during March 10, 1999 (QQQ inception) through August 5, 2021, we find that:

A subscriber requested testing of a strategy that buys SPDR S&P 500 (SPY) at the open on the day before each scheduled Federal Open Market Committee (FOMC) meeting and sells at the close. Using daily dividend-adjusted SPY open and close prices and dates of FOMC meetings during January 2016 through June 2021 (43 meetings), we find that: Keep Reading

In view of research indicating that overnight (close-to-open) returns are on average significantly higher than open-to-close returns, a subscriber proposed an enhancement to the Simple Asset Class ETF Momentum Strategy (SACEMS), as follows:

• Instead of ranking SACEMS assets at the market close on the last trading day of each month, rank them at the open.
• Sell any assets leaving SACEMS portfolios at the open.
• Buy any assets entering SACEMS portfolios at the close.

Due to complexity of precisely programming a backtest of this setup, we instead run the following tests:

1. Compare average daily open-to-close and close-to-open returns for each SACEMS non-cash asset over available sample periods since July 2002.
2. Compare SACEMS portfolio performances during July 2006 through May 2021 for: (a) ranking assets at the open on the last trading day of each month and executing all trades at the open; and, (b) ranking assets at the close on the last trading day of each month and executing all trades at the close (baseline SACEMS).
3. Calculate SACEMS portfolio performances during July 2006 through May 2021 for a variation that ranks assets at the open on the last trading day of each month, liquidates SACEMS portfolios at the open and reforms them at the close. This variation is more aggressive in exploiting an overnight return effect than the proposed approach, but is easier to program.

We consider Top 1, equal-weighted (EW) Top 2 and EW Top 3 SACEMS portfolios. We focus on full-sample gross compound annual growth rate, gross annual Sharpe ratio and maximum drawdown based on monthly data for portfolio comparisons. Using dividend-adjusted opening and closing prices for all SACEMS assets during July 2002 through May 2021, we find that: Keep Reading

Does the conventional wisdom to “Sell in May” (and “Buy in November”, hence also the term “Halloween Effect”) work over the long run, perhaps due to biological/psychological effects of seasons (Seasonal Affective Disorder)? To check, we turn to the long run dataset of Robert Shiller. This data set includes monthly levels of the S&P Composite Index, calculated as average of daily closes during the month. We split the investing year into two half-years (seasons): May through October, and November through April. Using S&P Composite Index levels, associated dividend yields and contemporaneous long-term interest rates (comparable to yields on 10-year U.S. Treasury notes) from the Shiller dataset spanning April 1871 through April 2021, we find that: Keep Reading

A subscriber requested evaluation of three retirement investment alternatives, assuming a constant increment invested at the end of each month, as follows:

1. 50-50: allocate each increment via fixed percentages to stocks and bonds (for comparability, we use 50% to each).
2. Seasonal 1: during April through September (October through March), allocate 100% of each increment to stocks (bonds).
3. Seasonal 2: during April through September (October through March), allocate 100% of each increment to bonds (stocks).

The hypothesis is that seasonal variation in asset class allocations could improve overall long-term investment performance. We conduct a short-term test using SPDR S&P 500 ETF Trust (SPY) as a proxy for stocks and iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD) as a proxy for bonds. We then conduct a long-term test using Vanguard 500 Index Fund Investor Shares (VFINX) as a proxy for stocks and Vanguard Long-Term Investment-Grade Fund Investor Shares (VWESX) as a proxy for bonds. Based on the setup, we focus on terminal value as the essential performance metric. Using total (dividend-adjusted) returns for SPY and LQD since July 2002 and for VFINX and VWESX since January 1980, all through December 2020, we find that: Keep Reading

Do investors swing toward optimism around U.S. presidential inauguration days, focusing on future opportunities? Or, does the day remind investors of political uncertainty and conflict? To investigate, we analyze daily returns of the S&P 500 Index around inauguration day. We consider subsamples of no party change and party change. Using inauguration dates since 1928 and daily S&P 500 Index levels during 1928 through 2020, we find that: Keep Reading

Do options of individual stocks exhibit momentum and seasonality patterns? In their November 2020 paper entitled “Momentum, Reversal, and Seasonality in Option Returns”, Christopher Jones, Mehdi Khorram and Haitao Mo investigate momentum and seasonality effects for options on U.S. common stocks. They focus on performance of straddles, combining a put and a call with the same strike price and expiration date. They balance needs for liquidity and sample size by requiring positive open interest during the holding period but not the momentum calculation interval. Specifically, on each monthly option expiration date, they:

1. Form two straddles from near-the-money options expiring next month for each for each stock: (1) the pair with call delta closest to 0.5 for calculating momentum; and, (2) the pair with call delta closest to 0.5 and with positive open interest for both the put and the call when selected for calculating momentum portfolio return.
2. Construct from these pairs zero-delta straddles using bid-ask midpoints as prices and calculate monthly straddle excess returns relative to the 1-month Treasury bill yield. This process generates about 1,600 straddles per month with average monthly excess return -5.6% and very large standard deviations.
3. Calculate momentum as average monthly excess return over a specified lookback interval (rather than cumulative return, to suppress effects of return outliers).
4. Rank straddle returns into equal-weighted fifths (quintiles) based on momentum and calculate average return for each quintile and for a portfolio that is long the top quintile and short the bottom quintile.

Using end-of-day open interest and bid-ask quotes for call and put options on U.S. common stocks from OptionMetric and trading data for underlying stocks during January 1996 through June 2019, they find that: Keep Reading