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Value Investing Strategy (Strategy Overview)

Allocations for June 2024 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

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

Calendar Effects

The time of year affects human activities and moods, both through natural variations in the environment and through artificial customs and laws. Do such calendar effects systematically and significantly influence investor/trader attention and mood, and thereby equity prices? These blog entries relate to calendar effects in the stock market.

Style Performance by Calendar Month

Trading Calendar presents full-year and monthly cumulative performance profiles for the overall stock market (S&P 500 Index) based on its average daily behavior. How much do the corresponding monthly behaviors of the various size and value/growth styles deviate from an overall equity market profile? To investigate, we consider the the following six exchange-traded funds (ETF) that cut across capitalization (large, medium and small) and value versus growth:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

Using monthly dividend-adjusted closing prices for the style ETFs and SPDR S&P 500 (SPY) during August 2001 through October 2021 (limited by data for IWS/IWP), we find that: Keep Reading

SACEVS with Quarterly Allocation Updates

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:

Keep Reading

Simple Tests of Sy Harding’s Seasonal Timing Strategy

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

Bitcoin Day-of-the-Week Effects?

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

Testing a QQQ Swing Trade Strategy

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:

U.S. Stock Market Returns Around Scheduled FOMC Meetings

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

SACEMS with Overnight Return Capture

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

“Sell in May” Over the Long Run

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

Seasonal Timing of Monthly Investment Increments

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

Hope for Stocks Around Inauguration Day?

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

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