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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.

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Bonds During the Off Season?

As implied in “Mirror Image Seasonality for Stocks and Treasuries?”, are bonds better than stocks during the “Sell-in-May” months of May through October? Are behaviors of government, corporate investment grade and corporate high-yield bonds over this interval similar? To investigate, we test seasonal behaviors of:

SPDR S&P 500 (SPY)
Vanguard Intermediate-Term Treasury (VFITX)
Fidelity Investment Grade Bond (FBNDX)
Vanguard High-Yield Corporate Bond (VWEHX)

Using dividend-adjusted monthly prices for these funds during January 1993 (limited by SPY) through July 2018, we find that: Keep Reading

Momentum Strategy, Value Strategy and Trading Calendar Updates

We have updated monthly Simple Asset Class ETF Momentum Strategy (SACEMS) winners and associated performance data at “Momentum Strategy”. We have updated monthly Simple Asset Class ETF Value Strategy (SACEVS) allocations and associated performance data at “Value Strategy”. We have also updated performance data for the “Combined Value-Momentum Strategy”.

We have updated the “Trading Calendar” to incorporate data for July 2018.

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) and annual returns and volatilities. Using monthly dividend-adjusted closes for the above ETFs during September 2002 (earliest alignment of months and quarters) through June 2018, we find that:

Keep Reading

Monthly Returns During Presidential and Congressional Election Years

Do the hopes and fears of elections in the U.S. affect the “normal” seasonal variation in monthly stock market returns? To check, we compare average returns and variabilities (standard deviations of returns) by calendar month for the Dow Jones Industrial Average (DJIA) during years with and without quadrennial U.S. presidential elections and biennial congressional elections. Using monthly closes for the DJIA over the period October 1928 through May 2018 (nearly 90 years), we find that: Keep Reading

Stock Market Behavior Around Mid-year and 4th of July

The middle of the year might be a time for funds to dress their windows and investors to review and revise portfolios. The 4th of July celebration might engender optimism among U.S. investors. Are there any reliable patterns to daily U.S. stock market returns around mid-year and the 4th of July? To check, we analyze the historical behavior of the S&P 500 Index from five trading days before through trading days after both the end of June and the 4th of July. Using daily closing levels of the index for 1950-2017 (68 years), 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 since 1950. 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 S&P Depository Receipts (SPY) over the period August 2001 through May 2018 (202 months, limited by data for IWS/IWP), we find that: Keep Reading

Isolating Desirable Turnover via Separate Alpha and Beta Portfolios

Does separating the active (alpha) and passive (market exposure, or beta) components of an overall equity investment strategy, thereby isolating turnover, reduce overall tax burden? In their May 2018 paper entitled “The Tax Benefits of Separating Alpha from Beta”, Joseph Liberman, Clemens Sialm, Nathan Sosner and Lixin Wang investigate the tax implications of separating alpha from beta for equity investments. Specifically, they compare two quantitative investment strategies:

  1. Conventional long-only – overweights (underweights) stocks with favorable (unfavorable) multi-factor exposures within a single portfolio.
  2. Composite long-short – allocates separately to a passive (index fund) portfolio and to an active long-short portfolio targeting multi-factor exposures but with no exposure to the market.

They design these competing strategies so that aggregate exposures to the market and target factors, and thus pre-tax returns, are similar. They consider three target factors: value (60-month reversion) and momentum (from 12 months ago to one month ago), together and separately; and, short-term (1-month) reversal only separately. Their base simulation model has: 8% average annual market return with 15% volatility; 2% average incremental annual return for each target factor with 4% volatility; and, 180% annual turnover for value, momentum and value-momentum and 1200% annual turnover for short-term reversal. Their test methodology involves 100 iterations of: simulating a multifactor return distribution of 500 stocks; then, simulating portfolios of these stocks with monthly factor rebalancing for 25 years. They assume long-term (short-term) capital gain tax rate 20% (35%) and a highest-in, first-out disposition method for rebalancing. Based on the specified simulations, they find that: Keep Reading

Firm Sales Seasonality as Stock Return Predictor

Do firms with predictable sales seasonality continually “surprise” investors with good high season (bad low season) sales and thereby have predictable stock return patterns? In their May 2018 paper entitled “When Low Beats High: Riding the Sales Seasonality Premium”, Gustavo Grullon, Yamil Kaba and Alexander Nuñez investigate firm sales seasonality as a stock return predictor. Specifically, for each quarter, after excluding negative and zero sales observations, they divide quarterly sales by annual sales for that year. To mitigate impact of outliers, they then average same-quarter ratios over the past two years. They then each month:

  1. Use the most recent average same-quarter, two-year sales ratio to predict the ratio for next quarter for each firm.
  2. Rank firms into tenths (deciles) based on predicted sales ratios.
  3. Form a hedge portfolio that is long (short) the market capitalization-weighted stocks of firms in the decile with the lowest (highest) predicted sales ratios.

Their hypothesis is that investors undervalue (overvalue) stocks experiencing seasonally low (high) sales. They measure portfolio monthly raw average returns and four alphas based on 1-factor (market), 3-factor (market, size, book-to-market), 4-factor (adding momentum to the 3-factor model) and 5-factor (adding profitability and investment to the 3-factor model) models of stock returns. Using data for a broad sample of non-financial U.S common stocks during January 1970 through December 2016, they find that: Keep Reading

Stock Market Continuation and Reversal Months?

Are some calendar months more likely to exhibit stock market continuation or reversal than others, perhaps due to seasonal or fund reporting effects? In other words, is intrinsic (times series or absolute) momentum an artifact of some months or all months? To investigate, we relate U.S. stock index returns for each calendar month to those for the preceding 3, 6 and 12 months. Using monthly closes of the S&P 500 Index since December 1949 (using the January 1950 open) and the Russell 2000 Index since September 1987, both through April 2018, we find that: Keep Reading

Stock Returns Around Memorial Day

Does the Memorial Day holiday signal any unusual U.S. stock market return effects? By its definition, this holiday brings with it any effects from three-day weekends and sometimes the turn of the month. Prior to 1971, the U.S. celebrated Memorial Day on May 30. Effective in 1971, Memorial Day became the last Monday in May. To investigate the possibility of short-term effects on stock market returns around Memorial Day, we analyze the historical behavior of the stock market during the three trading days before and the three trading days after the holiday. Using daily closing levels of the S&P 500 Index for 1950 through 2017 (68 observations), we find that: Keep Reading

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