Market Seasonality Effect in World Equity Indexes

Fundamental reason

Simple trading strategy

Out-of-sample strategy's implementation/validation in QuantConnect's framework(chart, statistics & code)

Source paper

Doeswijk: The Optimism Cycle: Sell in May

Abstract: The market maxim "Sell in May and go away" is a simple but profitable one. On average, stocks deliver close to zero returns in the six month period from May through October, only giving a risk premium from November through April. This effect, however, has not been widely covered in academic literature. We examine the hypothesis that the seasonal pattern is caused by an optimism cycle. Towards year end, investors start to look towards the new year, often with overly optimistic expectations. This results in attractive returns for stocks. Several months into the year, this initial optimism becomes hard to maintain and the stock market experiences a summer lull. A zero-investment global sector-rotation strategy based on this theory appears to be highly profitable. Global earnings growth revisions also follow a seasonal pattern parallel to that of the stock market. Finally, in a separate analysis for the US stock market, investors' optimism as measured by the initial returns on IPOs almost completely capture the results of the sector-rotation strategy. All these findings support the optimism-cycle hypothesis.

Other papers

• Kamstra, Kramer, Levi: Winter Blues: A SAD Stock Market Cycle

  Abstract: This paper investigates the role of seasonal affective disorder (SAD) in the seasonal time-variation of stock market returns. SAD is an extensively documented medical condition whereby the shortness of the days in fall and winter leads to depression for many people. Experimental research in psychology and economics indicates that depression, in turn, causes heightened risk aversion. Building on these links between the length of day, depression, and risk aversion, we provide international evidence that stock market returns vary seasonally with the length of the day, a result we call the SAD effect. Using data from numerous stock exchanges and controlling for well-known market seasonals as well as other environmental factors, stock returns are shown to be significantly related to the amount of daylight through the fall and winter. Patterns at different latitudes and in both hemispheres provide compelling evidence of a link between seasonal depression and seasonal variation in stock returns: Higher latitude markets show more pronounced SAD effects and results in the Southern Hemisphere are six months out of phase, as are the seasons. Overall, the economic magnitude of the SAD effect is large.

• Jacobsen, Bouman: The Halloween Indicator, 'Sell in May and Go Away': Another Puzzle

  Abstract: We document the existence of a strong seasonal effect in stock returns based on the popular market saying 'Sell in May and go away', also known as the 'Halloween indicator'. According to these words of market wisdom, stock market returns should be higher in the November-April period than those in the May-October period. Surprisingly, we find this inherited wisdom to be true in 36 of the 37 developed and emerging markets studied in our sample. The 'Sell in May' effect tends to be particularly strong in European countries and is robust over time. Sample evidence, for instance, shows that in the UK the effect has been noticeable since 1694. While we have examined a number of possible explanations, none of these appears to convincingly explain the puzzle.

• Jacobsen, Mamun, Visaltanachoti: Seasonal, Size and Value Anomalies

  Abstract: Recent international evidence shows that in many stock markets, general index returns are significantly higher during winter months than during summer months. We study the interaction between this anomaly - known as the Halloween effect - and the January effect and other well-known anomalous findings on portfolios formed on Size, Dividend Yield, Book to Market ratios, Earnings Price ratios and Cash Flow Price ratios in equally but also value weighted portfolios for the US market. Our main findings are that contrary to the January effect, the Halloween effect seems a market wide phenomenon unrelated to these well-known anomalies. All portfolios in our study show higher average winter returns than summer returns. In most portfolios this difference is statistically and economically significant. We confirm recent results which suggest that the January effect plays an important role not only in explaining the small firm effect but also - together with size - in explaining the Book to Market ratio anomaly. In addition, we find in a similar fashion that controlling for the January effect and using value weighted portfolio returns substantially reduces the Earnings to Price, Cash Flow to Price and Dividend Yield effects.

• Jacobsen, Zhang: Are Monthly Seasonals Real? A Three Century Perspective

  Abstract: Over 300 years of UK stock returns reveal that well-known monthly seasonals are sample specific. For instance, the January effect only emerges around 1830, which coincides with Christmas becoming a public holiday. Most months have had their 50 years of fame, showing the importance of long time series to safeguard against sample selection bias, noise, and data snooping. Only - yet undocumented - monthly July and October effects do persist over three centuries, as does the half yearly Halloween, or Sell-in-May effect. Winter returns - November through April - are consistently higher than (negative) summer returns, indicating predictably negative risk premia. A Sell-in-May trading strategy beats the market more than 80% of the time over 5 year horizons.

• Kamstra, Kramer, Levi, Wermers: Seasonal Asset Allocation: Evidence from Mutual Fund Flows

  Abstract: This paper explores U.S. mutual fund flows, finding strong evidence of seasonal reallocation across funds based on fund exposure to risk. We show that substantial money moves from U.S. equity to U.S. money market and government bond mutual funds in the fall, then back to equity funds in the spring, controlling for the influence of past performance, advertising, liquidity needs, capital gains overhang, and year-end influences on fund flows. We find a strong correlation between mutual fund net flows (and within-fund-family exchanges) and the onset of and recovery from seasonal depression, consistent with the hypothesis that investor risk aversion varies with the seasons. Further, we find stronger seasonality in Canadian fund flows (a more northerly location relative to the U.S., where seasonal depression is more severe), and a reverse seasonality in fund flows for Australia (where the seasons are reversed). While prior evidence regarding the influence of seasonal depression on financial markets relies on seasonal patterns in asset returns, we provide the first direct trade-related evidence.

• Hong, Yu: Gone Fishin': Seasonality in Trading Activity and Asset Prices

  Abstract: We use seasonality in stock trading activity associated with summer vacation as a source of exogenous variation to study the relationship between trading volume and expected return. Using data from 51 stock markets, we first confirm a widely held belief that stock turnover is significantly lower during the summer because market participants are on vacation. Interestingly, we find that mean stock return is also lower during the summer for countries with significant declines in trading activity. This relationship is not due to time-varying volatility. Moreover, both large and small investors trade less and the price of trading (bid-ask spread) is higher during the summer. These findings suggest that heterogeneous agent models are essential for a complete understanding of asset prices.

• Dumitriu, Stefanescu, Nistor: The Halloween Effect During Quiet and Turbulent Times

  Abstract: The Halloween Effect is one of the main calendar anomalies used to challenge the Efficient Market Hypothesis. It consists in significant differences between the stock returns from two distinct periods of a year: November - April and October - May. In the last decades empirical researches revealed the decline of some important calendar anomalies from the stock markets around the world. Sometimes, such changes were caused by the passing from quiet to turbulent stages of the financial markets. In this paper we investigate the Halloween Effect presence on the stock markets from a group of 28 countries for a period of time between January 2000 and December 2011. We find that geographical position has a major influence on the Halloween Effect intensity. We also find some differences between the emerging markets and the advanced financial markets. We analyze the Halloween Effect for two periods of time: the first, from January 2000 to December 2006, corresponding to a relative quiet evolution and the second, from January 2007 to December 2011, corresponding to a turbulent evolution. The results reveal, for many stock markets, major changes between the first period of time and the second one.

• Jacobsen, Zhang: The Halloween Indicator: Everywhere and All the Time

  Abstract: We use all available stock market indices for all 108 stock markets and for all time periods to study the ‘Halloween indicator’ or ‘Sell-in-May’effect. In total 55,425 monthly observations over 319 years show winter returns - November through April - are 4.52% (t-value 9.69) higher than summer returns. The effect is increasing in strength: The average difference between November-April and May-October returns is 6.25% over the past 50 years. A Sell-in-May trading strategy beats the market more than 80% of the time over 5 year horizons. The data allows us to address a number of (methodological) issues that have been raised with respect to the effect.

• Sum: Stock Market Performance: High and Low Months

  Abstract: This study analyzes stock market performance in 70 countries to determine which months generate higher returns and which months exhibit lower returns. Results from numerical analyses and t-tests show that returns are significantly higher in January, February, April, July and December relative to the other months of the year. Return in the month of September is the lowest compared to the rest of the months followed by returns in August, October, June, November, May and March, respectively. The findings seem to offer evidence of the other monthly anomalies (April and December anomalies, in this case) in addition to the January anomaly reported in the literature based on the analyses of market level data.

• Okada, Yamasaki: Investor Sentiment in News and the Calendar Anomaly -- New Evidence from a Large Textual Data

  Abstract: The well-known stock market adage "sell in May and go away" arose from long-term stock market seasonality in major financial markets around the globe. Kamastra, Kramer and Levy (2003) present evidence that Seasonal Affective Disorder causes this seasonality, as this condition has a profound effect on people’s mood and makes people increasingly risk averse as daylight diminishes with the onset of winter. In this paper, we present evidence that change in market mood is reflected in the prospect statement in the news text. We employ a text-mining technique to analyze a large quantity of newspaper articles for the period 1986-2010 and created our market mood proxy. We find investor psychology is skewed to optimism in the first half of the calendar year and pessimism in the latter. We also find that semi-annual mood fluctuation is synchronous with market seasonality.

• Dichtl, Drobetz: Sell in May and Go Away: Still Good Advice for Investors?

  Abstract: The “Sell in May and Go Away” (or Halloween) strategy continues to enjoy great popularity in practice. We explore whether this simple trading rule still offers an opportunity to earn abnormal returns. In contrast to prior studies, we consider sample periods during which adequate investment instruments were available for an effective implementation of the Halloween strategy. In addition, we account for when the first study confirming the Halloween effect was published in a top academic journal. To use the limited data in the most efficient way, and to avoid possible data-snooping biases, we implement a bootstrap simulation approach. We find that the Halloween effect strongly weakened or even diminished in recent years. Our results are robust across different countries and against various parameter variations. Overall, our findings support the theory of efficient capital markets.

• Dichtl, Drobetz: Sell in May and Go Away: Still Good Advice for Investors?

  Abstract: The “Sell in May and Go Away” (or Halloween) strategy continues to enjoy great popularity in practice. We explore whether this simple trading rule still offers an opportunity to earn abnormal returns. In contrast to prior studies, we consider sample periods during which adequate investment instruments were available for an effective implementation of the Halloween strategy. In addition, we account for when the first study confirming the Halloween effect was published in a top academic journal. To use the limited data in the most efficient way, and to avoid possible data-snooping biases, we implement a bootstrap simulation approach. We find that the Halloween effect strongly weakened or even diminished in recent years. Our results are robust across different countries and against various parameter variations. Overall, our findings support the theory of efficient capital markets.

• Dzahabarov, Ziemba: Sell in May and Go Away in the Equity Index Futures Markets

  Abstract: The period May 1 to the turn of the month of November (last five trading days October) has historically produced negligible returns. The rest of the year (late October to the end of April) has essentially all the year's gains. In this paper we show that there is a statistically significant difference and conclude that the strategy go to cash in the weak period and go long in the strong period has about double the returns of buy and hold for large cap S&P500 index and triple for the small cap Russell2000 index during the period 1993-2015 in the index futures markets.

• Hirschleifer, Jiang, Meng: Mood Beta and Seasonalities in Stock Returns

  Abstract: Existing research has documented cross-sectional seasonality of stock returns - the periodic outperformance of certain stocks relative to others during the same calendar month, weekday, or pre-holiday periods. A model based on the differential sensitivity of stocks to investor mood explains these effects and implies a new set of seasonal patterns. We find that relative performance across stocks during positive mood periods (e.g., January, Friday, the best-return month realized in the year, the best-return day realized in a week, pre-holiday) tends to persist in future periods with congruent mood (e.g., January, Friday, pre-holiday), and to reverse in periods with non-congruent mood (e.g., October, Monday, post-holiday). Stocks with higher mood betas estimated during seasonal windows of strong moods (e.g., January/October, Monday/Friday, or pre-holidays) earn higher expected returns during future positive mood seasons but lower expected returns during future negative mood seasons.

• Hull, Bakosova, Kment: Seasonal Effects and Other Anomalies

  Abstract: We revisit a series of popular anomalies: seasonal, announcement and momentum. We comment on statistical significance and persistence of these effects and propose useful investment strategies to incorporate this information. We investigate the creation of a seasonal anomaly and trend model composed of the Sell in May (SIM), Turn of the Month (TOM), Federal Open Market Committee pre-announcement drift (FOMC) and State Dependent Momentum (SDM). Using the total return S&P 500 dataset starting in 1975, we estimate the parameters of each model on a yearly basis based on an expanding window, and then proceed to form, in a walk forward manner, an optimized combination of the four models using a return to risk optimization procedure. We find that an optimized strategy of the aforementioned four market anomalies produced 9.56% annualized returns with 6.28% volatility and a Sharpe ratio of 0.77. This strategy exceeds that Sharpe ratio of Buy-and-Hold in the same period by almost 100%. Furthermore, the strategy also adds value to the previously published market-timing models of Hull and Qiao (2017) and Hull, Qiao, and Bakosova (2017). A simple strategy which combines all three models more than doubles the Sharpe ratio of Buy-and-Hold between 2003-2017. The combined strategy produces a Sharpe ratio of 1.26, with annualized returns of 18.03% and 13.26% volatility. We publish conclusions from our seasonal trend and anomaly model in our Daily Report.

• Plastun, Sibande, Gupta, Wohar: Halloween Effect in Developed Stock Markets: A US Perspective

  Abstract: In this paper, we conduct a comprehensive investigation of the Halloween effect evolution in the US stock market over its entire history. We employ various statistical techniques (average analysis, Student’s t-test, ANOVA, and the Mann-Whitney test) and the trading simulation approach to analyse the evolution of the Halloween effect. The results suggest that in the US stock market the Halloween effect became more persistent since the middle of the 20th century. Despite the decline in its prevalence since that time, nowadays it is still present in the US stock market and provides opportunities to build a trading strategy which can beat the market. These results are well in line with other developed stock markets. Therefore, in the main, our results are inconsistent with the Efficient Market Hypothesis.

• Kamstra, Mark J. and Kramer, Lisa A. and Levi, Maurice D. and Wermers, Russ: Seasonal Asset Allocation: Evidence from Mutual Fund Flows

  Abstract: We analyze the flow of money between mutual fund categories, finding strong evidence of seasonality in investor risk aversion. Aggregate investor flow data reveal an investor preference for safe mutual funds in autumn and risky funds in spring. During September alone, outflows from equity funds average $13 billion, controlling for previously documented flow determinants (e.g., capital-gains overhang). This movement of large amounts of money between fund categories is correlated with seasonality in investor risk aversion, consistent with investors preferring safer (riskier) investments in autumn (spring). We find consistent evidence in Canada and also in Australia, where seasons are offset by 6 months.

• Mohamed, Hussein: Time-Based Trading Patterns

  Abstract: The research paper investigates the influence of various calendar anomalies on stock market returns across multiple indices, including the S&P 500, S&P 400, S&P 600, Russell 1,000 Growth, and Russell 1,000 Value. The study provides a comprehensive analysis of both individual and interaction effects of several well-known calendar anomalies, challenging the conventional understanding that these anomalies occur in isolation. The calendar effects studied include the Federal Open Market Committee (FOMC) meetings, Options Expiration Dates, the Holiday Effect, Sports Events Effect, Halloween Effect, Turn-of-the-Month Effect, January and September Effects, as well as Friday and Monday Effects. The research applies advanced regression techniques, incorporating an ARMA (1,1) model and an EGARCH (1,1) model with a t-distribution to account for both autocorrelation and volatility clustering in stock returns. The ARMA model, with exogenous variables representing the calendar anomalies and their interactions, captures the linear dependencies in return series. The EGARCH model further analyses the persistence and asymmetry in volatility, revealing how negative market shocks tend to increase volatility more than positive shocks. These models effectively highlight the impact of calendar anomalies on both the returns and volatility of the studied indices. The findings reveal that the Halloween Effect was the most significant anomaly observed, particularly impacting indices such as the S&P 500, S&P 400, S&P 600, and Russell 1,000 Value. These results suggest that this anomaly can be strategically leveraged by investors to optimize returns. While the research highlights the potential for using calendar anomalies as part of a time-based trading strategy, it also notes that the effects may vary across different indices, reflecting the unique characteristics of each market segment. The study contributes to the broader discourse on market efficiency by offering practical insights for investors and suggesting that these calendar-based anomalies may present exploitable opportunities within financial markets.

• Schroeder, Jan L. and Posch, Peter N.: A Puzzle Piece for The “Sell in May and Go Away” Anomaly: Regulatory Disclosures

  Abstract: We propose a new puzzle piece for the Halloween effect, “Sell in May, and Go Away”, by identifying a seasonal pattern in SEC regulatory disclosures aligning with the summer and winter periods of the effect. From 2004-2023, winter sees a 473% surge in annual reports and audited financial statements, along with a 22% rise in insider trading, an 11% increase in activist investor activity, and a 13% uptick in private securities offerings compared to the summer. February consistently shows the highest disclosure volume, while September has the lowest. Similar patterns in European markets suggest a cross-market consistency in disclosure timing.