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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.

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

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

Unique U.S Equity ETF Seasonalities?

Do exchange-traded funds (ETF) exhibit unique calendar-based anomalies? In their April 2018 paper entitled “Evidence of Idiosyncratic Seasonality in ETFs Performance”, flagged by a subscriber, Carlos Francisco Alves and Duarte André de Castro Reis investigate calendar-based patterns of risk-adjusted returns and tracking errors for U.S. equity ETFs and compare findings to those of underlying indexes. They aggregate returns of their ETF sample via equal weighting. They consider returns calculated based on either market price or Net Asset Value (NAV). For risk adjustment, they consider alpha from either 1-factor (market) or 4-factor (market, size, book-to-market, momentum) risk models of stock returns. They look for raw return or alpha patterns in calendar months, calendar quarters, months of calendar quarters, calendar half-years, days before holidays (New Year’s Day, Martin Luther King Jr. Day, George Washington’s Birthday, Good Friday, Memorial Day, Independence Day, Labor Day, Thanksgiving and Christmas), days of the week and turn-of-the-month (last trading day of a month through three trading days of the next month). Using daily prices and NAVs for 148 index-tracking U.S. equity ETFs and associated indexes, and contemporaneous equity factor model returns, during December 2004 through December 2015 (11 years), they find that:

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January Barometer Over the Long Run

Does long term data support the belief that “as goes January, so goes the rest of the year” (January is the barometer) for the the U.S. stock market? Robert Shiller’s long run sample, which calculates monthly levels of the S&P Composite Stock Index since 1871 as average daily closes during calendar months, offers data for testing. Because average monthly levels differ from monthly closes, we run all tests also on the S&P 500 Index. Using monthly levels of the S&P Composite Stock Index for 1871-2017 (147 years) and monthly and daily closes of the S&P 500 Index for 1950-2017 (68 years), we find that: Keep Reading

Aggregate Firm Events as a Stock Return Anomaly

Should investors view stock returns around recurring firm events in aggregate as an exploitable anomaly? In their October 2017 paper entitled “Recurring Firm Events and Predictable Returns: The Within-Firm Time-Series”, Samuel Hartzmark and David Solomon review the body of research on relationships between recurring firm events and future stock returns. They classify events as predictable (1) releases of information or (2) corporate distributions, with some overlap. Information releases include earnings announcements, dividend announcements, earnings seasonality and predictable increases in dividends. Corporate distributions cover dividend ex-days, stock splits and stock dividends. They specify a general trading strategy to exploit these events that is long (short) stocks of applicable firms during months with (without) predictable events. They use market capitalization weighting but, since there are often more stocks in the short side, they scale short side weights downward so that overall long and short sides are equal in dollar value. Based on the body of research and updated analyses based on firm event data and associated stock prices from initial availabilities through December 2016, they conclude that:

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Effects of Execution Delay on SACEVS

How does execution delay affect the performance of the Best Value and Weighted versions of the “Simple Asset Class ETF Value Strategy” (SACEVS)? These strategies each month 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)

To investigate, we compare 21 variations of each strategy with execution days ranging from end-of-month (EOM) per the baseline strategy to 20 trading days after EOM (EOM+20). For example, an EOM+5 variation computes allocations baed on EOM but delays execution until the close five trading days after EOM. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics. Using daily dividend-adjusted closes for the above ETFs from late July 2002 through mid-September 2017, we find that:

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