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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Kaeppel’s Sector Seasonality Strategy

A reader suggested looking at the strategy described in “Kaeppel’s Corner: Sector Seasonality” (from November 2005) and updated in “Kaeppel’s Corner: Get Me Back, Clarence” (from October 2007). 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 13-week Treasury bill (T-bill) yield as the return on cash and VFINX over the period December 1985 through Octember 2015 (almost 30 years), we find that: Keep Reading

Stock 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 1950-2014 (65 events), we find that: Keep Reading

Day and Night Stock Returns Worldwide

Do stocks worldwide generate most of their total return while the market is open or closed? In their October 2015 paper entitled “Making Money While You Sleep? Anomalies in International Day and Night Returns”, Kevin Aretz and Sohnke Bartram decompose returns and factor premiums into day and night components. When aggregating returns across countries, they first average within countries and then across countries. They estimate factor premiums in each country by ranking stocks into fifths (quintiles) using the same sorting rules as Fama and French and then calculating differences in value-weighted or equal-weighted average returns between extreme quintiles. Using total returns and accounting variables needed to construct factor returns for 48,413 stocks from 35 countries during 1993 (limited by availability of opening prices) through 2011, they find that: Keep Reading

Momentum Strategy and Trading Calendar Updates

We have updated the the monthly asset class ETF momentum winners and associated performance data at Momentum Strategy.

We have updated the Trading Calendar to incorporate data for October 2015.

Simple Tests of Sy Harding’s Seasonal Timing Strategy

Several readers have inquired over the years about the performance of Sy Harding’s Street Smart Report Online (now unavailable due to Mr. Harding’s death), which included the Seasonal Timing Strategy. 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. For robustness testing, we apply this 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 with MACD refinement.
  3. Seasonal Only – seasonal timing without MACD refinement.
  4. SMA200 – hold SPY (13-week U.S. Treasury bills (T-bills) when the S&P 500 Index is above (below) its 200-day simple moving average at the prior daily close. 

Using daily closes for the S&P 500 Index, daily dividend-adjusted closes for SPY and daily T-bill yields during 1/29/93 (SPY inception) through 9/25/15, 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 volatilities (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 August 2015 (over 86 years and 20 presidential elections), we find that: Keep Reading

Optimal Cycle for Monthly SMA Signals?

A reader commented and asked:

“Some have suggested that the end-of-the-month effect benefits monthly simple moving average strategies that trade on the last day of the month. Is there an optimal day of the month for long-term SMA calculation and does the end-of-the-month effect explain the optimal day?”

To investigate, we compare 21 variations of a 10-month simple moving average (SMA10) timing strategy based on shifting the monthly return calculation cycle relative to trading days from the end of the month (EOM) and applied to SPDR S&P 500 (SPY) as a tradable proxy for the U.S. stock market. Using daily dividend-adjusted and unadjusted closes for SPY from inception (end of January 1993) through August 2015 and contemporaneous three-month Treasury bill (T-bill) yields, we find that: Keep Reading

Stock Returns Around Labor Day

Does the Labor Day holiday, marking the end of summer vacations, signal any unusual return effects by refocusing U.S. stock investors on managing their portfolios? By its definition, this holiday brings with it any effects from the turn of the month. To investigate the possibility of short-term effects on stock market returns around Labor 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 2014 (65 observations), we find that: Keep Reading

Stock Market and the National Election Cycle

Many stock market experts cite the year (1, 2, 3 or 4) of the U.S. presidential term cycle as a useful indicator of U.S. stock market returns. Game theory suggests that presidents deliver bad news immediately after being elected and do everything in their power to create good news just before ensuing biennial elections. Are some presidential term cycle years reliably good or bad? If so, are these abnormal returns concentrated in certain quarters? Finally, what does the stock market do in the period immediately before and after a national election? Using S&P 500 Index data from January 1950 through July 2015 (more than 64 years and 16 presidential terms) and focusing on “political quarters” (Feb-Apr, May-Jul, Aug-Oct and Nov-Jan), we find that: Keep Reading

Overnight Momentum-informed Overnight Trading

Can investors refine and exploit the upward bias of overnight stock returns? In the July 2015 version of her paper entitled “Night Trading: Lower Risk but Higher Returns?”, Marie-Eve Lachance presents a way of sorting stocks by strength of overnight return bias and investigates gross and net profitability of associated overnight-only investment strategies. Specifically, she each month regresses daily overnight returns on total returns over the past year to measure an Overnight Bias Parameter (OBP) for each stock. She then forms portfolios based on monthly OBP sorts, focusing on the portfolio of stocks with significantly positive OBPs. She estimates trading frictions by: (1) assuming market-on-open and market-on-close trades, avoiding bid-ask spreads; and, (2) estimating broker charges from the lowest fees available in the U.S. in 2014. Using daily overnight (close-to-open) and intraday (open-to-close) total returns, trading data and characteristics for a broad sample of reasonably liquid U.S. stocks during 1995 through 2014, she finds that: Keep Reading

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Current Momentum Winners

ETF Momentum Signal
for November 2015 (Final)

Winner ETF

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Top 1 ETF Top 2 ETFs
12.2% 12.5%
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Strategy Overview
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ETF Value Signal
for November 2015 (Final)





The asset with the highest allocation is the holding of the Best Value strategy.
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12.8% 9.9% 8.0%
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