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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.

Pervasive Seasonal Relative Weakness Cycles?

Is there a flip side of cyclic relative weakness to the cyclic relative strength described in “Pervasive 12-Month (and 5-Day) Relative Strength Cycles?”? In their October 2018 paper entitled “Seasonal Reversals in Expected Stock Returns”, Matti Keloharju,Juhani Linnainmaa and Peter Nyberg test whether cyclic weakness (seasonal reversal) balances the cyclic strength (seasonality) effect. For example, if a stock is seasonally strong in March, it may be seasonally weak across other months. They test this hypothesis using actual monthly U.S. stock returns and simulated returns calibrated to actual returns. Specifically, they compute correlations between average historical returns for a stock during one month and the sum of its historical average returns during other months. In robustness tests, they repeat this test for 10-year subperiods and for daily U.S. stock returns, monthly non-U.S. stock returns, monthly country stock indexes, monthly country government bond indexes and monthly commodity returns. Finally, they construct the following three factors for U.S. stocks by first each month sorting stocks into two size groups (small and big market capitalizations) and then:

  1. Seasonality factor – Sorting each size group into three average same-calendar-month past return portfolios. The factor return is the difference in value-weighted returns between the two highest-average portfolios and the two lowest-average portfolios.
  2. Seasonal reversal factor – Sorting each size group into three average other-calendar-month past return portfolios within each size group. The factor return is the difference in value-weighted returns between the two lowest-average and the two highest-average portfolios.
  3. Annual-minus-non-annual factor – Sorting each size group into three portfolios based on the difference between the average same-calendar-month and other-calendar-month returns. The factor return is the difference in value-weighted returns between the two largest-difference and the two smallest-difference portfolios.

Using U.S. monthly and daily stock returns since 1963 and monthly returns for country stocks and stock market indexes, country government bond indexes and commodities since the end of 1974, all through 2016, they find that:

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U.S. Equity Turn-of-the-Month as a Diversifying Portfolio

Is the U.S. equity turn-of-the-month (TOTM) effect exploitable as a diversifier of other assets? In their October 2018 paper entitled “A Seasonality Factor in Asset Allocation”, Frank McGroarty, Emmanouil Platanakis, Athanasios Sakkas and Andrew Urquhart test U.S. asset allocation strategies that include a TOTM portfolio as an asset. The TOTM portfolio buys each stock at the open on the last trading day of each month and sells at the close on the third trading day of the following month, earning zero return the rest of the time. They consider four asset universes with and without the TOTM portfolio:

  1. A conventional stocks-bonds mix.
  2. The equity market portfolio.
  3. The equity market portfolio, a small size portfolio and a value portfolio.
  4. The equity market portfolio, a small size portfolio, a value portfolio and a momentum winners portfolio.

They consider six sophisticated asset allocation methods:

  1. Mean-variance optimization.
  2. Optimization with higher moments and Constant Relative Risk Aversion.
  3. Bayes-Stein shrinkage of estimated returns.
  4. Bayesian diffuse-prior.
  5. Black-Litterman.
  6. A combination of allocation methods.

They consider three risk aversion settings and either a 60-month or a 120-month lookback interval for input parameter measurement. To assess exploitability, they set trading frictions at 0.50% of traded value for equities and 0.17% for bonds. Using monthly data as specified above during July 1961 through December 2015, they find that:

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U.S. Stock Market Returns Around Thanksgiving

Does the Thanksgiving holiday, a time of families celebrating plenty, give U.S. stock investors a sense of optimism that translates into stock returns? To investigate, we analyze the historical behavior of the S&P 500 Index 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 1950-2017 (68 events), we find that: Keep Reading

Recent Overnight-Intraday Stock Return Correlations

Do intraday U.S. stock returns still tend to reverse preceding overnight returns as found in prior research? In their August 2018 paper entitled “Overnight Return, the Invisible Hand Behind The Intraday Return? A Retrospective”, Ben Branch and Aixin Ma revisit prior research on the relationship between overnight and intraday returns of U.S. stocks. Specifically, they relate average intraday stock returns to preceding average overnight returns based on: (1) whether average overnight returns are positive or negative; and, (2) by ranked fourths (quartiles) of average overnight returns. They perform a separate regression analysis to isolate correlation effects among overnight, intraday and one-leg lagged overnight and intraday returns. Using daily open-to-close and close-to-open returns for a broad sample of U.S. stocks during January 2011 through December 2017, they find that: Keep Reading

Turn of the Year and Size in U.S. Equities

Is there a reliable and material market capitalization (size) effect among U.S. stocks around the turn-of-the-year (TOTY)? To check, we track cumulative returns from 20 trading days before through 20 trading days after the end of the calendar year for the Russell 2000 Index, the S&P 500 Index and the Dow Jones Industrial Average (DJIA) since the inception of the Russell 2000 Index. We also look at full-month December and January returns for these indexes. Using daily and monthly levels of all three indexes from December 1987 through January 2018 (31 December and 31 January observations), we find that: Keep Reading

U.S. Stock Market End-of-Quarter Effect

Does the U.S. stock market have a predictable pattern of returns around the ends of calendar quarters? Do funds deploy cash to bid stocks up at quarter ends to boost portfolio values in quarterly reports (with subsequent reversals)? Or, do they sell stocks to raise cash for fund redemptions? Is any end-of-quarter effect distinct from the Turn-of-the-Month (TOTM) effect? To investigate, we calculate average daily stock market (S&P 500 Index) returns before and after ends of calendar quarters and compare those returns to TOTM returns. Using daily closes of the S&P 500 Index during January 1950 through September 2018 (275 quarters), we find that: Keep Reading

Distinct and Predictable U.S. and ROW Equity Market Cycles?

A subscriber asked: “Some pundits have noted that U.S. stocks have greatly outperformed foreign stocks in recent years. What does the performance of U.S. stocks vs. foreign stocks over the last N years say about future performance?” To investigate, we use the S&P 500 Index as a proxy for the U.S. stock market and the ACWI ex USA Index as a proxy for the rest-of-world (ROW) equity market. We consider three ways to relate U.S. and ROW equity returns:

  1. Lead-lag analysis between U.S. and ROW annual returns to see whether there is some cycle in the relationship.
  2. Multi-year correlations between U.S. and next-period ROW returns, with periods ranging from one to five years.
  3. Sequences of end-of-year high water marks for U.S. and ROW equity markets.

For the first two analyses, we relate the U.S. stock market to itself as a control (to assess whether ROW market behavior is distinct). Using end-of-year levels of the S&P 500 Index and the ACWI ex USA Index during 1987 (limited by the latter) through 2017, we find that: Keep Reading

Lunar Cycle and Stock Returns

Does the lunar cycle still (since our last look seven years ago) affect the behavior of investors/traders, and thereby influence stock returns? In the August 2001 version of their paper entitled “Lunar Cycle Effects in Stock Returns” Ilia Dichev and Troy Janes conclude that: “returns in the 15 days around new moon dates are about double the returns in the 15 days around full moon dates. This pattern of returns is pervasive; we find it for all major U.S. stock indexes over the last 100 years and for nearly all major stock indexes of 24 other countries over the last 30 years.” To refine this conclusion and test recent data, we examine U.S. stock returns around new and full moons since 1990. When the date of a new or full moon falls on a non-trading day, we assign it to the nearest trading day. Using dates for new and full moons for January 1990 through August 2018 as listed by the U.S. Naval Observatory (355 full and 354 new moons) and contemporaneous daily closing prices for the S&P 500 Index, we find that: Keep Reading

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

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

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