Lakonishok and Smidt (1988) were the first to have reported a turn-of-the-month seasonal in equity returns. The beginning of the turn-of-the-month period is defined as the last trading day of the month and ending with the third trading day of the following month. More precisely, the researchers have found that, on average, the four days at the turn-of-the-month account for all of the positive return to the DJIA over the period of 1897-1986. Since then, a lot of research has been made on this topic and this paper also deals with this anomaly, but with a more recent and wider set of data. The pattern in returns over the period of the paper is remarkably similar to the pattern over the earlier time period. Interestingly, in the more recent period, we can conclude the same pattern as in the work of Lakonishok and Smidt, virtually all of the excess market return is accrued during the four-day turn-of-the-month period, and investors received little or no reward for bearing the market risk over the other 16 trading days of the month.

Despite the simplicity of the trading strategy based on this anomaly (for example to buy SPY ETF 1day before the end of the month and to sell it 3rd trading day of the new month at the close), the strategy is both profitable and statistically significant. Moreover, this anomaly cannot be explained by the known asset pricing models. To sum it up, the turn of the month is a well-known effect on stock indexes, with a simple idea that stock prices usually increase during the last four days and the first three days of each month. This supports, for example, the Carchano and Tornero in the “Calendar Anomalies in Stock Index Futures”. Quoting the authors: “Our analysis reveals that the turn-of-the-month effect in S&P 500 futures contracts is the only calendar effect that is statistically and economically significant and persistent over time.”

Fundamental reason

Although the turn of the month is a really simple anomaly, it is a big challenge for the academic world to explain the potential reasons for the functionality. Although the effect is more pronounced among small-cap and low-price stocks, it also exists for large-cap and for high-price stocks. The effect could exist because of returns at the turn-of-the-year; however, it does not. The effect occurs at turns-of-the-month that coincides with turns-of-the-year, but it also occurs during other months. Likewise, the turn-of-the month effect is not concentrated at calendar-year quarter-ends. The reason for functionality also is not a risk-based; the paper has explored whether higher “risk” at the turn-of-the-month can explain this pattern. Using the standard deviation of return as a measure of risk, it was found that risk is not higher during the four turn-of-the-month days than over the other 16 trading days of the month. This implicates that higher risk does not appear to explain the turn-of-the-month effect. Moreover, also a systematic monthly shift in interest rates does not appear to explain the turn-of-the-month pattern in equity returns. Interestingly, the turn-of-the-month effect occurs in 30 different markets, so we can conclude that the effect is apparently not due to a factor unique to the U.S. market structure.

On the other hand, Ogden (1990) proposes that the turn-of-the-month effect is due to a “regularity in payment” dates in the U.S. The aforementioned is based on the fact that investors receive a preponderance of compensation from employment, dividends and interest at month-ends. Consequently, as investors seek to invest these funds, equity prices are pushed up. Unfortunately, the paper provided tests that reject this hypothesis. The overall problem of finding some reason to functionality is also supported by the work of McConnell and Xu: “Equity Returns at the Turn of the Month”. Quoting the authors: “This persistent peculiarity in returns remains a puzzle in search of an answer.”

However, most researchers ascribe this effect to the timing of monthly cash flows received by pension funds, which are reinvested in the stock market. The end of the month is also a natural point for portfolio/trading models rebalancing both for retail and professional investors. The aforementioned could also help this effect to become statistically significant. However, caution is needed if one implements this strategy as calendar effects tend to vanish or rotate to different days in a month.

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Markets Traded
equities

Financial instruments
CFDs, ETFs, funds, futures, options

Confidence in anomaly's validity
Strong

Backtest period from source paper
1926-2005

Notes to Confidence in Anomaly's Validity

Indicative Performance
7.2%

Period of Rebalancing
Daily

Notes to Indicative Performance

per annum, annualized daily return 0,15% (arithmetically) from table 3, panel A1 for (-1,+3) strategy, return without cash (during days not invested in market)


Notes to Period of Rebalancing

Estimated Volatility
6.9%

Number of Traded Instruments
1

Notes to Estimated Volatility

estimated from t-statistic from table 3


Notes to Number of Traded Instruments

Maximum Drawdown

Complexity Evaluation
Simple strategy

Notes to Maximum drawdown

Notes to Complexity Evaluation

Sharpe Ratio
1.04

Simple trading strategy

Buy SPY ETF 1 day (some papers says 4 days) before the end of the month and sell 3rd trading day of the new month at the close.

Hedge for stocks during bear markets

Not known - Strategy is timing equity market but invests long-only into equity market factor therefore is not suitable as a hedge/diversification during market/economic crises. Strategy’s goal is to hold equity market only in a positive times for equity market factor and be out of equities otherwise. This logic can be maybe used to create amended market timing strategy (using original rules) which is out of equities during positive times and holds bonds (or goes short equities) during bad times. This new amended strategy can be maybe used as a hedge/diversification to equity market risk factor during bear markets. However, performance/risk characteristics and overall correlation and quality of suggested ammended strategy can be find out only by rigorous backtest and source academic research paper doesn’t give us any clues on how it will perform…

Source paper
Xu, McConnell: Equity Returns at the Turn of the Month
- Abstract

A turn-of-the-month effect in U.S. equity returns was initially identified by Lakonishok and Smidt (1988) using the DJIA for the period 1897-1986. According to the turn-of-the-month effect, equity returns over the interval beginning the last trading day of the month and ending three days later are significantly higher than over other days. Using CRSP daily returns, we find that the turn-of-the-month effect persists over the recent interval of 1987-2005: in essence, over this 19-year period (and over the 109-year period of 1897-2005) all of the excess market return occurred during the four-day turn-of-the-month interval. Thus, during the other 16 trading days of the month, on average, investors received no reward for bearing market risk. We further find that the turn-of-the-month effect is not confined to small or low-priced stocks; it is not confined to the December-January turn-of-the-month; it is not confined to calendar-quarter-ends; it is not confined to the U.S.; and it is not due to market risk as traditionally measured: the standard deviation of returns at the turn-of-the-month is no higher than during other days. This persistent peculiarity in equity returns poses a challenge to both “rational” and “behavioral” models of asset pricing.

Strategy's implementation in QuantConnect's framework (chart+statistics+code)
Other papers
Reschenhofer: Further Evidence on the Turn of the Month Effect
- Abstract

This paper points out that even distinct patterns in financial time series, which persist over long periods of time, cannot immediately be taken as genuine. In view of the large number of possible patterns, the only way to avoid any data- snooping bias is to use a formal statistical test, which has not been tailored to the specific patterns present in the data. Adopting a universal frequency domain test for the detection of synchronous cycles, we find clear evidence for within-month patterns in daily returns on the S&P 500 index, which corroborates earlier findings obtained simply by comparing different days of the month.

Dzhabarov, Ziemba: Do Seasonal Anomalies Still Work?
- Abstract

Dzhabarov and Ziemba investigate whether traditional seasonal anomalies, such as the January effect,monthly effect, January barometer, sell-in-May-and-go-away phenomenon, holiday effect, and turn-of-the-month effect, still exist in the turbulent markets of the early part of the 21st century.The evidence indicates that there is still value in these anomalies. In their study, the authors use futures data from 1993 to 2009 and from 2004 to 2009 for small-cap stocks measured by the Russell 2000 Index and for large-cap stocks measured by the S&P 500 Index.As was true in the 1990s, the effects tend to be stronger in small-cap stocks.The results are useful for investors who wish to tilt portfolios and for speculators who wish to trade the effects.

Grimbracher, Swinkles, Vliet
- Abstract

This paper studies the interaction of the five most well-established calendar effects: the Halloween effect, January effect, turn-of-the-month effect, weekend effect and holiday effect. We find that Halloween and turn-of-the-month (TOM) are the strongest effects fully diminishing the other three effects to zero. The equity premium over the sample 1963-2008 is 7.2% if there is a Halloween or TOM effect, and -2.8% in all other cases. These findings are robust across different samples over time and stock markets.

Carchano, Tornero: Calendar Anomalies in Stock Index Futures
- Abstract

There exist a large and increasing number of papers that describe different calendar anomalies in stock markets. Although empirical evidence suggests that seasonal effects disappeared after the early 1990s, new studies and approaches assert the continuation of some anomalies in stock indexes. In this paper, we present a comprehensive study of 188 possible cyclical anomalies in S&P 500, DAX and Nikkei stock index futures contracts from 1991 to 2008. Frictions in futures markets, unlike spot markets frictions, make it feasible to produce economically significant profits from trading rules based on calendar effects. By applying a percentile-t-bootstrap and Monte Carlo methods, our analysis reveals that the turn-of-the-month effect in S&P 500 futures contracts is the only calendar effect that is statistically and economically significant and persistent over time.

Desai, Trivedi: A Survey of Day of the Month Effect in World Stock Markets
- Abstract

A curious seasonal anomaly found in finance is the turn of the month effect, where the daily mean return of stock market at the end of a month and beginning of a month is significantly higher than the average daily return of all the days of a month. There have been evidences that certain months in a year deliver significantly higher returns. Similar anomalies are found for week days also, where some days in a week deliver above average returns. Seasonal anomalies for researchers have been a subject of great interest and lot of literature is available worldwide. This paper examines presence of day of the month effect on ten stock markets, geographically located in different corners of the world. This paper is not intended to study only the anomalies and inefficiencies present in various world markets, it is intended to highlight the profit potential available to individual investors and professional fund managers. The date wise daily returns are calculated in percentage terms to make the phenomena easy to understand. The statistical significance of daily returns is tested with Z-Statistics, in total 310 hypotheses are tested in the research. We found day of the month effect present in all the stock markets tested across the world, some days in a month historically are found to have delivered significantly higher returns.

Giovanis: The Turn-of-The-Month-Effect: Evidence from Periodic Generalized Autoregressive Conditional Heteroskedasticity (PGARCH) Model
- Abstract

The current study examines the turn of the month effect on stock returns in 20 countries. This will allow us to explore whether the seasonal patterns usually found in global data; America, Australia, Europe and Asia. Ordinary Least Squares (OLS) is problematic as it leads to unreliable estimations; because of the autocorrelation and Autoregressive Conditional Heteroskedasticity (ARCH) effect existence. For this reason Generalized GARCH models are estimated. Two approaches are followed. The first is the symmetric Generalized ARCH (1,1) model. However, previous studies found that volatility tends to increase more when the stock market index decreases than when the stock market index increases by the same amount. In addition there is higher seasonality in volatility rather on average returns. For this reason the Periodic-GARCH (1,1) is estimated. The findings support the persistence of the specific calendar effect in 19 out of 20 countries examined.

McConnell, Xu: Equity Returns at the Turn of the Month
- Abstract

The turn-of-the-month effect in U.S. equities is found to be so powerful in the 1926-2005 period that, on average, investors received no reward for bearing market risk except at turns of the month. The effect is not confined to small-capitalization or low-price stocks, to calendar year-ends or quarter-ends, or to the United States: This study finds that it occurs in 31 of the 35 countries examined. Furthermore, it is not caused by month-end buying pressure as measured by trading volume or net flows to equity funds. This persistent peculiarity in returns remains a puzzle in search of an answer.

Giovanis: The Turn-of-the-Month-Effect: Evidence from Periodic Generalized Autoregressive Conditional Heteroskedasticity (PGARCH) Model
- Abstract

The current study examines the turn of the month effect on stock returns in 20 countries. This will allow us to explore whether the seasonal patterns usually found in global data; America, Australia, Europe and Asia. Ordinary Least Squares (OLS) is problematic as it leads to unreliable estimations; because of the autocorrelation and Autoregressive Conditional Heteroskedasticity (ARCH) effects existence. For this reason Generalized GARCH models are estimated. Two approaches are followed. The first is the symmetric Generalized ARCH (1,1) model. However, previous studies found that volatility tends to increase more when the stock market index decreases than when the stock market index increases by the same amount. In addition there is higher seasonality in volatility rather on average returns. For this reason the Periodic-GARCH (1,1) is estimated. The findings support the persistence of the specific calendar effect in 19 out of 20 countries examined.

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.

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