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Value Investing Strategy (Strategy Overview)

Allocations for March 2024 (Final)
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Momentum Investing Strategy (Strategy Overview)

Allocations for March 2024 (Final)
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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.

Combining Overnight and TOTM Effects

With reference to “Turn-of-the-Month Effect Persistence and Robustness” and “Persistence of Overnight/Intraday Equity Market Return Patterns”, a subscriber asked about a strategy that is long SPDR S&P 500 ETF Trust (SPY) only from market close to market open during the turn-of-the-month (TOTM). To investigate, we consider three strategies:

  1. TOTM Close-to-Open: long SPY from close-to-open during TOTM, and otherwise in cash (eight round-trip trades per month).
  2. TOTM Close-to-Close: long SPY continuously during TOTM, and otherwise in cash (one round-trip trade per month).
  3. B&H: buy and hold SPY.

We initially ignore trading frictions, but then look at breakeven frictions for the first strategy. Because of trading frequency, we ignore return on cash. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) based on daily data as key performance statistics. Using daily dividend-adjusted opens and closes for SPY during February 1993 (inception) through February 2022, we find that: Keep Reading

Persistence of Overnight/Intraday Equity Market Return Patterns

What best explains the decades-long pattern of strong overnight and weak intraday returns in most equity markets? In his January 2022 paper entitled “They Still Haven’t Told You”, Bruce Knuteson reviews possible explanations for this pattern and identifies the most likely. His theoretical equity index benchmark is a random walk with slight upward drift (due to general economic expansion and survivorship bias), with intraday return on average larger than overnight return due to higher intraday risk. Using close-to-open and open-to-close levels of 21 major stock market indexes as available during January 1990 through December 2021, he finds that: Keep Reading

Sector Performance by Calendar Month

Trading Calendar presents full-year and monthly cumulative performance profiles for the overall U.S. stock market (proxied by the S&P 500 Index) based on average daily behavior. Do monthly behaviors of U.S. stock market sectors deviate from the overall market profile? To investigate, we consider the nine Select Sector Standard & Poor’s Depository Receipts (SPDR) exchange-traded funds (ETF), all of which originate in December 1998:

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

Using monthly dividend-adjusted closing prices for these ETFs, along with contemporaneous data for SPDR S&P 500 (SPY) as a benchmark, during December 1998 through December 2021, we find that: Keep Reading

Combining Defensive-in-May and Sector Momentum

In response to “Combining Defensive-in-May and Sector Reversion”, a subscriber requested testing of a strategy combining seasonal effects (cyclical sectors during November through April and defensive sectors during May through October) and sector momentum. Cyclical and defensive choices are:

At the end of each October, the strategy buys the one cyclical fund with the highest return over some past interval (betting on momentum). At the end of each April, the strategy sells the cyclic fund and buys the one defensive fund with the highest return over the past interval (again, betting on momentum). For convenience, we use a 6-month lookback interval to rank funds. We use buy-and-hold SPDR S&P 500 (SPY) as a benchmark. We focus on semiannual return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using semiannual dividend-adjusted prices for the selected funds during October 2006 (limited by availability of VIG) through October 2021 (defining the first and last available semiannual intervals), we find that: Keep Reading

Combining Defensive-in-May and Sector Reversion

Inspired by “The iM Seasonal ETF Switching Strategy”, a subscriber requested testing of a strategy combining seasonal effects (cyclical sectors during November through April and defensive sectors during May through October) and sector reversion. Cyclical and defensive choices are:

At the end of each October, the strategy buys the one cyclical fund with the lowest return over some past interval (betting on reversion). At the end of each April, the strategy sells the cyclic fund and buys the one defensive fund with the lowest return over the past interval (again, betting on reversion). For convenience, we use a 6-month lookback interval to rank funds. We use buy-and-hold SPDR S&P 500 (SPY) as a benchmark. We focus on semiannual return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using semiannual dividend-adjusted prices for the selected funds during October 2006 (limited by availability of VIG) through October 2021 (defining the first and last available semiannual intervals), we find that: Keep Reading

Defensive-in-May Sector Rotation

A subscriber asked about a strategy that holds a portfolio of cyclical sectors and small capitalization stocks during November through April and a portfolio of defensive sectors during May through October, as follows:

We use NAESX for small stocks to obtain a history as long as those for the equity sectors. We weight components of the cyclical and defensive portfolios equally. We use buy-and-hold NAESX and an equal-weighted, semiannually rebalanced portfolio of all seven funds (Sector EW) as benchmarks. We focus on semiannual return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using semiannual dividend-adjusted prices for the selected funds during April 1999 through October 2021 (defining the first and last available semiannual intervals), we find that: Keep Reading

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

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