Calendar Effects

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

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

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

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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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

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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Does the broad U.S. stock market, as represented by SPDR S&P 500 (SPY), exhibit reliable day-of-the-week and/or overnight return anomalies? To check, we consider three returns:

• Close-Open: measured from prior close to open. (For example, the Monday Close-Open return is from the close on the prior trading day, usually Friday, to the open on Monday.)
• Open-Close: measured from open to close.
• Close-Close: measured from prior close to close.

We calculate these returns overall, by day of the week and by the number of calendar days since the prior close (for example, three days for a normal weekend). Using daily opening and closing prices for SPY during end of January 1993 through most of August 2017 (6,188 days), we find that: Keep Reading

Do the returns of iPath S&P 500 VIX Short-term Futures ETN (VXX) and VelocityShares Daily Inverse VIX Short-term ETN (XIV) vary systematically across days of the week? To investigate, we look at daily close-to-open, open-to-close and close-to-close returns for both. Using daily split-adjusted opening and closing prices for VXX during February 2009 through July 2017 and for XIV during December 2010 through July 2017, we find that:

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Is there a preferred frequency and are there preferred months for rebalancing conventional asset class portfolio holdings? To investigate we consider annual, semiannual and quarterly rebalancing of a simple portfolio targeting a 60-40 stocks-bonds mix. We consider all possible combinations of calendar month ends as rebalancing points. We ignore rebalancing (and dividend-reinvestment) frictions and tax implications, thereby giving an advantage to frequent rebalancing. We focus on compound annual growth rate (CAGR) as the critical portfolio performance metric. Using dividend-adjusted monthly closes for SPDR S&P 500 (SPY) to represent stocks and Vanguard Total Bond Market Index (VBMFX) to represent bonds over the period January 1993 (SPY inception) through June 2017 (about 24 years), we find that: Keep Reading

A reader suggested looking at the strategy described in “Kaeppel’s Corner: Sector Seasonality” (from November 2005, link no longer in place) and updated in “Kaeppel’s Corner: Get Me Back, Clarence” (from October 2007, link no longer in place). The steps of this calendar-based sector strategy are:

1. Buy Fidelity Select Technology (FSPTX) at the October close.
2. Switch from FSPTX to Fidelity Select Energy (FSENX) at the January close.
3. Switch from FSENX to cash at the May close.
4. Switch from cash to Fidelity Select Gold (FSAGX) at the August close.
5. Switch from FSAGX to cash at the September close.
6. Repeat by switching from cash to FSPTX at the October close.

Does this strategy materially and persistently outperform? To investigate, we compare results for three alternative strategies: (1) Kaeppel’s Sector Seasonality strategy (Sector Seasonality); (2) buy and hold Vanguard 500 Index Investor (VFINX) as an investable broad index benchmark (VFINX); and, (3) a simplified seasonal strategy using only VFINX from the October close through the May close and cash otherwise (VFINX/Cash). Using monthly dividend-adjusted closing levels for FSPTX, FSENX, FSAGX, the 3-month Treasury bill (T-bill) yield as the return on cash and VFINX over the period December 1985 through May 2017 (about 31.5 years), we find that: Keep Reading