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 returns 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 Carcano 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 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 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 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
0%

Complexity Evaluation
Simple strategy

Notes to Maximum drawdown

not stated


Notes to Complexity Evaluation

Sharpe Ratio
1.04

Simple trading strategy

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

Hedge for stocks during bear markets

Not known - The strategy is timing equity market but invests long-only into the equity market factor; therefore, it is not suitable as a hedge/diversification during market/economic crises. The strategy’s goal is to hold the equity market only in positive times for equity market factors and be out of equities otherwise. This logic can be may be used to create an 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 amended strategy can find out only by rigorous backtest and source academic research paper doesn’t give us any clues on how it will perform…

Source paper
Strategy's implementation in QuantConnect's framework (chart+statistics+code)
Other papers

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