Turn of the Month Effect: Do Stocks Really Do Better Around Month-End?

Every few months someone in a trading group forwards the same tip: buy just before the month ends, sell a few days into the next month, and you’ll beat the market. That idea has a name, the turn of the month effect, and unlike most forwarded tips it actually has decades of academic research behind it. The harder question is whether any of that survives contact with a real Indian brokerage account. Let’s go through what the studies found, what they didn’t, and why we think most retail traders should treat it as a curiosity rather than a strategy.

What the turn of the month effect actually means

The definition most researchers use comes from a 1988 study by Lakonishok and Smidt of the Dow Jones Industrial Average. They defined the turn of the month as a four-trading-day window: the last trading day of a month plus the first three trading days of the next one. The effect is the finding that stock returns inside that short window have been noticeably higher than returns on the other days of the month.

Notice what this is not. It isn’t about the calendar date as such, and it isn’t about the first half of the month. It’s a narrow window measured in trading days, so holidays and weekends shift it around. If the 31st falls on a Sunday, the “last trading day” is whatever day the exchange was last open.

The turn of the month sits in the same family as other calendar anomalies, patterns tied to the day of the week, the time of the month or the time of the year. We’ve covered two of its cousins already, the weekend effect and the January effect. Wikipedia’s overview of calendar effects lists several more.

What the original research found

Lakonishok and Smidt worked with 90 years of daily Dow data, from 1897 to 1986. The numbers they reported are striking, and they’re summarised in a later paper by Wei Xu and John McConnell of Purdue University, “Equity Returns at the Turn of the Month”:

• The average cumulative return over the four-day turn-of-the-month window was 0.473%.
• The average cumulative return over the full month was only 0.349%.

Read that twice. Four days delivered more than the whole month. That means the remaining days of the month, taken together, had a negative average return over that 90-year sample. On those numbers, everything an investor earned from holding the Dow came from a handful of days around each month-end.

Lakonishok and Smidt weren’t quite first, either. Xu and McConnell point out that Ariel (1987) had already found a turn-of-the-month pattern in daily US data for 1962 to 1986, although he didn’t label it that way.

Four trading days delivered more than the whole month. The rest of the month, on average, went backwards.

Did the pattern survive after it was published?

This is the test that kills most anomalies. Once a pattern is in a journal, traders pile in and arbitrage it away, or a closer look shows it was never there. Xu and McConnell flag exactly this risk, citing Schwert’s 2003 work on anomalies that fade after discovery.

So they checked the years after Lakonishok and Smidt’s sample ended, using broad US market returns for 1987 to 2005. The pattern held up:

• The average daily value-weighted excess market return was 0.14% during the four-day window, against -0.01% on the other 16 trading days of the month.
• With equal-weighted returns, the turn-of-the-month daily average was 0.24%, against 0.04% on the other 16 days.

“Excess” here means the return over and above a risk-free rate. In plain words, over that 19-year stretch, value-weighted investors earned essentially no reward for holding stocks outside the four-day window.

They also ran through the usual suspects and ruled them out. According to the paper, the effect wasn’t confined to small or low-priced stocks, it wasn’t just a December-January story, and it wasn’t only a calendar-quarter-end thing either. That last point matters for Indian readers who’ve heard about funds dressing up portfolios before quarterly disclosures (our explainer on window dressing in mutual funds covers that). Xu and McConnell found the effect at quarter-ends and at ordinary month-ends alike.

Is it a global pattern, and what about India?

Xu and McConnell looked at 34 non-US markets with reliable daily index data from at least 1990 onward. They report a turn-of-the-month effect in 30 of them. Markets they list as not showing a meaningful effect include Argentina, Colombia, Italy and Malaysia.

Here’s the catch for us. India wasn’t in that set. The paper says India was dropped because of infrequent trading in the years before 1994. So this particular study tells us nothing directly about the Nifty or the Sensex. Anyone who claims the turn of the month “works in India” needs to show their own data, and the honest answer is that you’d have to test it yourself on Indian index history before risking money on it.

Why would month-end be special?

Nobody has a fully convincing answer. The explanations people offer fall into a few buckets.

The payday idea

The most intuitive one is cash flow. Salaries, dividends and interest tend to land around month-end, people invest some of that money, and the buying pushes prices up. Xu and McConnell trace this “payday” explanation to a 1990 paper by Ogden. Wikipedia also mentions a 2018 study of the Eurozone that linked higher market values around month-end to the time employees get paid.

It sounds right. The trouble is that Xu and McConnell couldn’t find the footprints you’d expect. Net flows into the mutual funds they tracked showed no matching monthly pattern, and NYSE trading volume was spread evenly through the month. If a wave of salary money were driving prices, you’d expect to see it in flows or volume.

Risk and interest rates

Another possibility is that the window is simply riskier, so investors demand more return for it. The data doesn’t support that either. In the paper, the average daily standard deviation of value-weighted returns during the four-day window was 0.98%, compared with 1.02% across other days. They also found no consistent turn-of-the-month pattern in Treasury bills, Treasury bonds or corporate bonds.

It’s noise

Then there’s the sceptical view. Under the efficient-market hypothesis, a predictable calendar pattern shouldn’t exist, because traders would already have priced it in. Wikipedia notes that a 2001 study argued there’s no statistically significant evidence for calendar effects at all and that such patterns come from data dredging, meaning that if you search enough ways of slicing the calendar, something will look significant by chance. The authors of the Purdue paper don’t settle the debate. They call the effect a challenge to both “rational” and “behavioural” models of asset pricing.

Can you trade the turn of the month effect?

Researchers have tested simple switching strategies, and the results look decent on paper. Xu and McConnell cite two:

• Hensel and Ziemba (1996) held the S&P 500 during the turn of the month and Treasury bills on the other days. Over 1928 to 1993 that beat buy-and-hold by roughly 0.63% a year.
• Kunkel and Compton (1998) switched between an equity fund at the turn of the month and a money market fund on other days, and reported about 2.1% a year more than buy-and-hold equities.

Those are results from US data, and the summaries don’t net out what each switch would cost an individual today. Your real-world version involves twelve round trips a year, and every round trip costs money: brokerage, taxes and levies on the trade, and the slippage between the price you wanted and the price you got. That’s where the idea runs into trouble.

Worked example: what costs do to a small edge

Let’s put rough numbers on it. Everything below is hypothetical, built only to show the arithmetic. We’ll borrow Lakonishok and Smidt’s long-run Dow averages purely for illustration; they are not a forecast for Indian markets.

Say Meera has Rs 5,00,000 and compares two plans for an index fund or ETF:

• Plan A, buy and hold: she stays invested all month. At the 0.349% average monthly return, that’s Rs 1,745 a month.
• Plan B, turn-of-the-month timing: she buys at the close the day before the last trading day and sells at the close on the third trading day of the next month, sitting in cash the rest of the time. At the 0.473% four-day average, that’s Rs 2,365 a month, before costs.

So timing gives her Rs 620 more a month, gross, which is 0.124% of her capital. Now assume, hypothetically, that each round trip costs her 0.15% of the amount traded once you add up charges, taxes and slippage. That’s Rs 750 a month.

Over a year, that’s Rs 7,440 of extra gross return against Rs 9,000 of costs. The edge is real on paper and still loses money in her account. She might claw a little back by parking the idle cash in a liquid fund, which is what the Treasury bill leg did in the US studies. But the margin is thin, and it gets thinner if her cost assumption is too low or if Indian returns don’t follow the US pattern at all.

Taxes make it worse. Twelve round trips a year turns what would have been a long-term holding into a string of short-term trades, which can change how the gains are taxed. Check that with a tax professional before you even run a test.

An anomaly that adds a fraction of a percent a month can be fully real and still lose money once you pay for twelve round trips a year.

How to test it yourself before believing it

If you’re curious, run a proper test before trading it. A sensible approach:

1. Get daily closing data for an Indian index such as the Nifty 50 over as many years as you can.
2. Mark the last trading day of each month and the first three trading days of the next, using the actual exchange calendar, not calendar dates.
3. Compare the average daily return inside the window with the average on all other days, and check whether the gap is consistent across different sub-periods, not just on average.
4. Subtract realistic costs for every round trip, using your own contract notes as the guide.
5. Be suspicious of any result that only works in one decade or one market phase. That’s exactly the data-dredging trap the sceptics warn about.

Whatever you find, size it modestly. Calendar patterns are averages built from hundreds of months, and any single month can go the other way. Our pillar guide to risk management in trading covers position sizing and stop-losses, and it applies here as much as anywhere.

Where this leaves us

The turn of the month effect is one of the better-documented oddities in finance. It showed up in 90 years of Dow data, it persisted for nearly two decades after it was published, and it appeared in most of the foreign markets one major study checked. Nobody has a clean explanation, and the payday story doesn’t fit the flow and volume data as neatly as you’d expect.

For an Indian retail trader, though, three facts matter more than the history. The best-known evidence is from the US. India was left out of the big international test. And a gross edge of a fraction of a percent a month is easily eaten by the cost of trading in and out twelve times a year. If month-end does anything useful for most of us, it’s as a reminder to put our monthly SIP money to work, not as a signal to trade.

Disclaimer: This article is for education only and is not investment advice. Past patterns in market returns do not guarantee future results, and trading involves risk of loss. The investor, amounts and cost assumptions in the worked example are hypothetical. Consult a SEBI-registered investment adviser before making trading or investment decisions.