Turn of the Month — ^DJI

SeasonAlpha's turn-of-the-month tool reveals the turn-of-the-month effect (TOM): the historical observation that gains cluster around the month boundary. On average, the last trading days of a month and the first trading days of the next were markedly stronger than the rest of the month. The tool measures this by computing, for any ticker, the average return per trading day relative to month-end and presenting it as a curve, heatmap and statistics. You can see at a glance whether — and how strongly — this pattern held for the chosen market.

The core is the window around the month boundary. The last trading day of a month is the reference point t0; before it lie t-1, t-2, t-3 (the month's final trading days), after it t+1, t+2, t+3 (the new month's first trading days). The TOM curve starts at t0 = 0 percent. If it rises to both sides, the month turn was positive on average. Two sliders let you set the window freely (1 to 10 days before and after); the window optimization additionally shows which combination historically delivered the highest average return per month turn.

Methodically the tool uses normalized returns, not absolute price changes. For each individual month turn the daily log returns within the window are accumulated and normalized to the last trading day of the month (t0). Crucial is the indexing by trading day relative to month-end — counted in exchange trading days, not calendar days, so weekends and holidays do not distort the window. These individual curves are then averaged across all selected years and months. Win rate, median, standard deviation and the significance test each refer to the distribution of total returns across all captured windows.

The turn-of-the-month effect is a statistical pattern for context, not investment advice and no guarantee of returns. Possible explanations range from liquidity inflows (salaries, savings plans, fund rebalancing) to window-dressing by institutions — none is conclusively proven, and effects can fade over time. The analysis looks strictly backward at price moves that already happened; individual years can deviate sharply from the average, as the heatmap and streak analysis show. Use the window as additional context to your own research. Past patterns do not guarantee future results.

Frequently asked questions

What is the turn-of-the-month effect? The turn-of-the-month effect describes the observation that gains historically cluster around the month boundary. The last trading days of a month and the first of the next were, on average, stronger than the rest of the month. The tool measures this via the average return per trading day relative to month-end.

Which window counts as turn-of-the-month? Typically about three trading days before to three after the turn (t-3 to t+3), with t0 being the last trading day of the month. In the tool you set the window freely (1 to 10 days). The window optimization shows which combination historically gave the highest average return per month turn.

How is the turn-of-the-month effect calculated? On normalized returns: the daily log returns in the window are accumulated and normalized to t0 (last trading day = 0 percent). Days are indexed relative to month-end (t-3 … t0 … t+3), not by calendar date, then averaged across all selected years and months.