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1. Shard Calculation

Query:

Response:

Calculated Shard: 148 (from laksa028)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa148.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://www.investopedia.com/day-trading/best-time-day-week-month-trade-stocks/\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.investopedia.com/day-trading/best-time-day-week-month-trade-stocks/\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/www.investopedia.com\/day-trading\/best-time-day-week-month-trade-stocks\/","crawl_time":1776343460,"first_indexed_time":1510311432,"http_code":200,"src_unparsed":"com,investopedia!www,\/day-trading\/best-time-day-week-month-trade-stocks\/ s443","src_root_hash":"7273739457760125548","history_drop_reason":null,"meta_title":"Best Times of the Day, Week, and Month to Trade Stocks","meta_descriptions":["Market behavior can change depending on the month, day of the week, and even hour. Here's how to time your stock trades to take advantage of price swings."],"attrs_boilerpipe_text":"Every second counts in the\nstock market\n, where fortunes can shift with each tick of the trading clock. Perhaps it’s no surprise, then, that many traders seek to gain an edge by identifying optimal times to enter and exit positions, whether that’s a particular hour of the day, day of the week, or month of the year. Research has shown that market activity often follows certain patterns tied to timing.\n1\nUnderstanding these patterns and their limitations can help traders make more informed decisions about when to trade—or at least, be better informed when other traders\nthink\nis the best time to trade. Below, we show you our analysis of stock market return by days of the week, months of the year, and other supposed patterns to see if there’s anything to prevailing myths about the best times to trade.\nKey Takeaways\nThe closest thing to a hard-and-fast rule is that the first hour and last hour of a trading day are the busiest, offering the most prospects, while the middle of the day tends to be the calmest and most stable period of most trading days.\nEarly-month trading days show stronger returns than the rest of the month, possibly because of regular investment flows like 401(k) contributions.\nTrading days before long weekends show higher returns than regular trading days, while post-holiday returns tend to be slightly negative.\nWhile statistically interesting, these and other calendar patterns are typically too small to exploit profitably once trading spreads and other costs are considered, supporting the case for systematic investment approaches like dollar-cost averaging.\nTrading Costs Matter: A Warning About Market Effects\nBefore diving into the data to see whether any benefits are to be found\ntrading\nat specific hours, on specific days, or in different months, we should underline that trading costs—including price spreads—would quickly erase many of the differences we found if you tried to trade based on them. For the S&P 500, you’ll pay about 0.025% (2.5\nbasis points\n) in spreads to buy or sell the largest stocks, and about 0.045% (4.5 basis points) for a portfolio tracking the full index.\n2\nSince you need to buy and sell to capture any effect, double these numbers for your round-trip trading cost. So, unless a market pattern shows returns significantly above 0.05% to 0.09% (5–9 basis points), it likely isn’t actionable—even before considering other trading costs, taxes, and the risk that the pattern might not hold in the future.\nBest Times of the Day to Buy or Sell Stocks\nThe opening and closing hours of the trading day tend to be the most\nvolatile\nand active periods. When U.S. markets open at 9:30 a.m. Eastern time, it processes all events and news releases since the previous day’s close, which can create significant price swings.\nMany professional traders focus on the opening period (9:30 a.m. to 10:30 a.m. ET), as it typically offers the most significant price moves in the shortest time. By 11:30 a.m., volatility and volume often decrease significantly, leading many day traders to close their positions.\nThe last hour of trading (3 p.m. to 4 p.m. ET) typically sees another surge in activity, as institutional investors and day traders close positions and react to late-breaking news. Like the morning session, this period can offer prospects but comes with increased risk.\nTip\nIt’s generally impossible to time the market. Doing your research, knowing the financials of the company that interests you, and the overall sector or wider economic climate will guide you far better in your decisions than the hour of the day or date on the calendar for most types of trading.\nHow Will Extended Trading Affect the Best Intraday Trading Times?\nIn a significant shift for U.S. markets, the\nNew York Stock Exchange (NYSE)\nplans to expand trading on its NYSE Arca Equities platform to 22 hours daily, from 1:30 a.m. to 11:30 p.m. ET on weekdays. About 56% of trading on NYSE Arca is in shares of exchange-traded funds (ETFs), with the rest for corporate stocks.\n3\nThe exchange already has broader opening hours than the regular NYSE, with sessions from 4 a.m. to 8 p.m. before and after regular trading hours.\n4\nWhile\ncurrencies\n, Treasurys, and stock index futures already trade outside regular hours, the shift—expected in 2025 and pending U.S. Securities and Exchange Commission (SEC) approval—will presumably reshape how\ntraders\napproach intraday strategies. The NYSE’s head of markets, Kevin Tyrrell, said the move was meant in part to enable “exchange-based trading for our U.S.-listed companies and funds to investors in time zones across the globe.”\n5\nThe proposed shift also responds to increasing demand within the U.S. for round-the-clock trading access, as many have already via cryptocurrency markets. Some retail brokers already provide overnight trading where they facilitate your trades beyond official trading hours, usually by matching them with those occurring in alternative trading pools, aka\ndark pools\n, and overseas (especially in Asia) where it’s daytime.\nWhile evidence from other markets that have extended their hours is limited, the extension of trading hours is likely to do the following:\nEvents-based trading will cause price moves\n: \nInvestors\ncan respond more promptly to global events and news releases outside traditional market hours. This should also relieve some of the trading volume that bottles up around the opening and closing of the regular trading day.\nEnhance liquidity\n: Extended hours may attract more participants, potentially improving liquidity during previously inactive periods.\nIntroduce more volatility into what was previously “off hours”\n: While increased trading hours can provide more prospects, they may also introduce periods of heightened volatility, especially during times when fewer participants are active.\nBest Day of the Week to Buy or Sell Stocks\nResearch long ago showed that Fridays were when you’d see the best gains for stocks, with Mondays generally coming out the worst.\n6\nLike other bits of Wall Street wisdom, though the data has long since been outdated, that hasn’t meant people don’t still advise that stocks tend to drop on Mondays. Some people think this is because a significant amount of bad news is often released over the weekend. Others point to investors’ gloomy mood at having to go back to work, which is especially evident during the early hours of Monday trading.\nWhat the Numbers Show\nBut nothing beats actual data. We pulled\nS&P 500\nclosing prices for 2000 to late 2024. While any findings would only apply to the broad market of highly capitalized stocks—one could imagine various nooks and crannies of the market where day-specific effects might be found—it’s worth seeing if there are calendar effects during the last 25 years worth most investors bothering about.\nWhile we did find day-specific patterns, we also found that they’re not of much practical significance, if any, for investors.\nOur analysis of over 6,200 trading days shows that Tuesday has historically produced the highest average daily returns at 0.062%, while Friday and Monday show the lowest average returns at about 0.009% each. Wednesday and Thursday fall in between, with average returns of 0.024% and 0.042%, respectively. However, what counts is whether these results are robust—otherwise, you risk seeing patterns in the noise where there aren’t any.\nTo verify the reliability of these patterns, several statistical measures were considered both for daily and the other effects we check below. First, the\nstandard deviation\nof daily returns was calculated for each day of the week. Here’s what we found:\nVolatility context\n: The daily standard deviation of returns ranges from 1.12% to 1.34%—about 20 times larger than the differences among the days themselves.\nNot much difference in the percentage of positive trading days\n: The percentage of positive days for the S&P 500 is remarkably consistent across weekdays, ranging from 52% to 54%. This indicates that any given day has about the same probability of producing positive returns.\nStatistical significance\n: The overall market average daily return during this period was 0.030%, with most daily variations falling well within normal statistical noise.\nThe small magnitude of day-of-week effects, measured in hundredths of a percent, means that trading spreads, taxes, and so on, would likely overwhelm any potential gains from trying to exploit gains from trading on specific days of the week. In short, there’s nothing to be gained by trading based on what day of the week it is—all else being equal.\nWhat About Trading Before or After Long Weekends?\nThere’s a traditional view that the market is generally positive before a long weekend. Here, we found reason to think this is generally the case. When the markets close for extended periods—whether for holidays like Thanksgiving, Christmas, or three-day weekends—a distinct pattern emerges in the returns for the S&P 500:\nBefore long weekends: Average daily return of +0.185%\nAfter long weekends: Average daily return of -0.059%\nRegular trading days: Average daily return of +0.033%\nPut more straightforwardly, the last trading day before a long weekend has been five times higher than normal trading days.\nThe pattern’s reliability is supported by other measures. Before long weekends, about 55% of trading days are positive, compared with 50% after long weekends and about 54% on regular days. The data covers 196 long weekends over almost 25 years—a robust sample size.\nCautions Are Necessary\nDespite these clear patterns, investors should tread carefully before trying to trade based on this information:\nSpreads are likely to cost more\n: While the differences in returns are statistically significant, they’re still measured in fractions of a percent. For most retail investors, trading costs (including\nbid-ask spreads\n) would likely erase any potential gains from trying to exploit these patterns.\nTax implications\n: Frequent trading around weekends could lead to higher short-term capital gains taxes, potentially overwhelming any small gains from the strategy.\nPractical limitations\n: The strategy would require precise timing and regular trading around every long weekend, which isn’t practical for most investors.\nBest Months to Buy or Sell Stocks\nOur analysis of S&P 500 data from 2000 to 2024 also revealed some clear monthly patterns. November is historically the strongest month, with an average daily return of 0.107% and positive returns 57% of the time. April and July are the next strongest months. This holds, too, when we broaden our data window to include back to 1928.\nThe data challenges some traditional market wisdom. While September does live up to its reputation as the weakest month—showing average monthly returns of -1.53% and -1.13% for the periods 2000 to 2024 and 1928 to 2024, respectively—the supposed positive “\nJanuary effect\n,” which is 1.17% going back to 1928, has disappeared in recent decades, with returns of -0.15% since 2000.\nThe seasonal effects become more dramatic when looking at cumulative returns over these periods. If you had invested only in specific months, November would have shown the strongest performance with about a 54% cumulative gain from 2000 to 2024 (or about two years of months), while September’s weakness would have added up to a -37% loss over time.\nHow Actionable Are These Differences?\nBefore getting too excited about these patterns, it’s crucial to understand their reliability. The day-to-day\nswings\nin the market are typically much larger than these monthly differences. Likewise, even in November (the strongest month), the market regularly moves up or down by amounts much larger than its typical monthly gain. This “\nnoise\n” in the data means that while these patterns are interesting, they’re not reliable enough to be the main factor in timing investment decisions.\nImportant\nIt’s important to consider that we found that the day-to-day swings in the market are typically much larger than these monthly differences. It’s like the difference between weather and climate in temperate zones—while we might say summer is warmer than winter, any given winter day could be warmer than a summer day. As such, daily shifts in the market spark waves that wipe out most small calendar differences.\nSo when is the best time to sell? The data confirms the traditional investor wariness of September, as it’s the only month stocks declined more often than they rose (49.9% positive days). February and June also emerge as historically weak months, with average daily returns of -0.023% and -0.010%, respectively.\nHowever, the\nOctober effect\n—the fear going back to the October 1929 crash of market crises that month—appears less justified in recent data. October has actually had a relatively strong performance, with average monthly gains of 1.34% since 2000 and positive returns 53% of the time. That said, it’s among the most volatile—about 20% and 15% more volatile than September and November, the months right before and after.\nFast Fact\nNot for nothing is October a month that’s salient for many investors. Even though October, on average, has been a positive month historically, many of the worst market crashes have occurred in this month, including\nBlack Monday\n.\n7\n8\nFrom 2000 to 2024, it was also the most volatile.\n9\nEarly-Month Returns\nMany traders believe that returns in the early part of the month are better than the rest. Our analysis of S&P 500 data since 2000 shows a compelling pattern: Trading days early in the month tend to show stronger performance than those in the latter part of the month.\nWe found a consistent advantage across several metrics. The average daily return during the first five trading days is +0.084%, compared with just +0.019% for the remainder of the month—a difference that amounts to more than four times higher average returns. This pattern holds up even when looking at median returns, which are less influenced by extreme market moves, with early-month median daily returns of +0.121% vs. +0.049% for other days.\nThe early-month advantage isn’t just about the size of returns; it has also been consistent. During these first five trading days, the market posts positive returns 56.4% of the time, compared with 53.0% for the rest of the month. This higher batting average suggests the early-month effect is stable and isn’t just the result of a few outlier days.\nWhy Early Parts of the Month Might Have Higher Returns\nWhat might explain this pattern? One possibility is the timing of monthly investment flows, such as\n401(k) plan\ncontributions and institutional portfolio rebalancing, which often occur near the start of each month. As this new money enters the market, it may create systematic buying pressure, leading to slightly higher returns.\nHowever, investors should approach this pattern with due caution. While the data shows a clear historical tendency, the magnitude of the difference is not large enough to overcome transaction spreads in a trading strategy. Moreover, as with any market pattern, there’s no guarantee that historical tendencies will persist. Still, this monthly cycle represents an interesting facet of market behavior that adds to our understanding of how calendar effects influence stock returns.\nHow to Trade in Light of Daily, Monthly, and Other Seasonal Effects\nWhile our analysis has revealed several intriguing patterns in market behavior—whether it’s the early-month effect, day-of-week variations, or monthly seasonality—converting these patterns into profitable trades comes with significant challenges. The differences in returns, when not simply statistical noise, are often too small to overcome trading costs.\nFor instance, the early-month advantage of 0.065% per day (the difference between early-month and rest-of-the-month returns) would be largely erased by typical trading spreads in a strategy attempting to capture this effect.\nThis leads us to an important insight: Rather than trying to time the market based on calendar effects, investors might be better served by implementing a systematic investment approach like\ndollar-cost averaging (DCA)\n. With DCA, investors make regular investments of equal amounts regardless of market conditions or timing. This approach naturally diversifies the entry points for investments across different market conditions, reducing the impact of market timing and volatility on long-term returns.\n10\nDCA works well for many investors since it removes the emotional and tactical complexity of trying to time the market. It also aligns well with how most people actually invest—through regular contributions to\nindividual retirement accounts (IRAs)\nor investment plans.\nAre There Really Best Times to Buy or Sell Stocks?\nHistorically, some days or months have tended to be better or worse for stocks. These so-called\nmarket anomalies\nchallenged theories of efficient markets. However, research shows that as these anomalies became more well-known and trading became more automated, these have largely all disappeared.\n11\nOthers persist, but trading spreads tend to wash out your ability to trade on most of them.\nWhat Is the Super Bowl Indicator?\nThis suggests that the stock market will rise the year an NFC team wins and fall if an AFC team wins. While this “indicator” had an accuracy rate above 80% for many years, it’s a classic example of\ncorrelation without causation\n(when two things coincidentally happen together, but one can’t cause the other).\nWhat Is the Strangest Calendar Effect Claimed by Investors?\nUnusual patterns people have studied include the adage for investors to “\nsell in May and go away\n,” the presidential election cycle, and even the effect of lunar phases on stock returns. While some have shown statistical significance historically, none have proved reliable enough for actual trading, given spreads and other costs.\nThe Bottom Line\nOur analysis confirms some traditional beliefs (like September’s weakness) while challenging others (like the supposed January effect). While certain patterns show statistical significance, like the early-month effect or preholiday strength, trading spreads and market efficiency largely annul the practical value of these findings. Any\nabnormal returns\nare generally short-lived, as these prospects are quickly\narbitraged\naway, and markets become more efficient as traders and investors increasingly learn about the patterns.\n12\nThat is, once other traders know there’s a benefit, they’ll move to take advantage, which soon removes it.\nThe most valuable insight from studying these patterns is not about when to trade, but the futility of trying to time the market based on the calendar alone. The data suggests that while timing patterns exist, they’re typically too small to exploit profitably. This reinforces the wisdom of systematic investment approaches like dollar-cost averaging, which succeed not by timing the market perfectly but by participating in it consistently.","attrs_markdown":"\n\n[Skip to content](https:\/\/www.investopedia.com\/day-trading\/best-time-day-week-month-trade-stocks\/#main)\n\n[Investopedia](https:\/\/www.investopedia.com\/)\n\n[Live](https:\/\/www.investopedia.com\/stock-market-today-dow-jones-s-and-p-500-04162026-11951125)\n\n- Please fill out this field.\n- [Newsletters](https:\/\/www.investopedia.com\/day-trading\/best-time-day-week-month-trade-stocks\/)\n\nPlease fill out this field.\n\n- [News](https:\/\/www.investopedia.com\/news-4427706)\n  News\n  \n  - [Markets](https:\/\/www.investopedia.com\/markets-news-4427704)\n  - [Companies](https:\/\/www.investopedia.com\/company-news-4427705)\n  - [Earnings](https:\/\/www.investopedia.com\/earnings-reports-5218419)\n  - [CD Rates](https:\/\/www.investopedia.com\/certificates-deposit-news-7111770)\n  - 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Peter began covering markets at Multex (Reuters) and has expanded his coverage to include investments, ethics, public policy, and the health and travel industries.\n\nLearn about our  [editorial policies](https:\/\/www.investopedia.com\/legal-4768893#editorial-policy)\n\nUpdated August 28, 2025\n\nReviewed by\n\n[Samantha Silberstein](https:\/\/www.investopedia.com\/samantha-silberstein-5115118)\n\n![Sam Silberstein]()\n\n![Sam Silberstein](https:\/\/www.investopedia.com\/thmb\/Qsd1ISirwYHu9DMn0zIRjorVz9g=\/200x200\/filters:no_upscale\\(\\):max_bytes\\(150000\\):strip_icc\\(\\)\/00100lrPORTRAIT_00100_BURST20200515171729630_COVER-SamSilberstein-8e793dbafa8845ebb749a0275c34bdef.JPG)\n\nReviewed by Samantha Silberstein\n\n[Full Bio](https:\/\/www.investopedia.com\/samantha-silberstein-5115118)\n\nSamantha (Sam) Silberstein, CFP®, CSLP®, EA, is an experienced financial consultant. She has a demonstrated history of working in both institutional and retail environments, from broker-dealers to RIAs. She is a current CFA level 3 candidate and also has her FINRA Series 7 and 63 licenses. Throughout her career, Samantha has used her expertise and various licenses and certifications to provide in-depth advice about household and business-specific financial planning, investing, credit cards, debt, student loans, taxes, retirement, and income strategies.\n\nLearn about our  [Financial Review Board](https:\/\/www.investopedia.com\/investopedia-financial-review-board-5076269)\n\nFact checked by\n\n[Ryan Eichler](https:\/\/www.investopedia.com\/ryan-eichler-5217138)\n\n![Headshot]()\n\n![Headshot](https:\/\/www.investopedia.com\/thmb\/0OW9HfIR55KN5h8kIlkqUtmufnc=\/200x200\/filters:no_upscale\\(\\):max_bytes\\(150000\\):strip_icc\\(\\)\/InvestopediaHeadShot-988693bb87b54bb095bd3789cd117a50.jpg)\n\nFact checked by Ryan Eichler\n\n[Full Bio](https:\/\/www.investopedia.com\/ryan-eichler-5217138)\n\nRyan Eichler holds a B.S.B.A with a concentration in Finance from Boston University. He has held positions in, and has deep experience with, expense auditing, personal finance, real estate, as well as fact checking & editing.\n\nLearn about our  [editorial policies](https:\/\/www.investopedia.com\/legal-4768893#editorial-policy)\n\n![Woman tracking and trading stocks on computer screens](https:\/\/www.investopedia.com\/thmb\/a3ffZqtlFLgjx7P1jQevvrHg8Xg=\/1500x0\/filters:no_upscale\\(\\):max_bytes\\(150000\\):strip_icc\\(\\)\/INV_SP500Gainers_Losers_2023_GettyImages-1223455297-196b1bea36f64240a70a0ce37ba6d1bc.jpg)\n\n![Woman tracking and trading stocks on computer screens](https:\/\/www.investopedia.com\/thmb\/a3ffZqtlFLgjx7P1jQevvrHg8Xg=\/1500x0\/filters:no_upscale\\(\\):max_bytes\\(150000\\):strip_icc\\(\\)\/INV_SP500Gainers_Losers_2023_GettyImages-1223455297-196b1bea36f64240a70a0ce37ba6d1bc.jpg)\n\nFatCamera \/ Getty Images\n\nClose\n\nEvery second counts in the [stock market](https:\/\/www.investopedia.com\/terms\/s\/stockmarket.asp), where fortunes can shift with each tick of the trading clock. Perhaps it’s no surprise, then, that many traders seek to gain an edge by identifying optimal times to enter and exit positions, whether that’s a particular hour of the day, day of the week, or month of the year. Research has shown that market activity often follows certain patterns tied to timing.1\n\nUnderstanding these patterns and their limitations can help traders make more informed decisions about when to trade—or at least, be better informed when other traders *think* is the best time to trade. Below, we show you our analysis of stock market return by days of the week, months of the year, and other supposed patterns to see if there’s anything to prevailing myths about the best times to trade.\n\n### Key Takeaways\n- The closest thing to a hard-and-fast rule is that the first hour and last hour of a trading day are the busiest, offering the most prospects, while the middle of the day tends to be the calmest and most stable period of most trading days.\n- Early-month trading days show stronger returns than the rest of the month, possibly because of regular investment flows like 401(k) contributions.\n- Trading days before long weekends show higher returns than regular trading days, while post-holiday returns tend to be slightly negative.\n- While statistically interesting, these and other calendar patterns are typically too small to exploit profitably once trading spreads and other costs are considered, supporting the case for systematic investment approaches like dollar-cost averaging.\n## Trading Costs Matter: A Warning About Market Effects\nBefore diving into the data to see whether any benefits are to be found [trading](https:\/\/www.investopedia.com\/trading-4427765) at specific hours, on specific days, or in different months, we should underline that trading costs—including price spreads—would quickly erase many of the differences we found if you tried to trade based on them. For the S\\&P 500, you’ll pay about 0.025% (2.5 [basis points](https:\/\/www.investopedia.com\/terms\/b\/basispoint.asp)) in spreads to buy or sell the largest stocks, and about 0.045% (4.5 basis points) for a portfolio tracking the full index.2\n\nSince you need to buy and sell to capture any effect, double these numbers for your round-trip trading cost. So, unless a market pattern shows returns significantly above 0.05% to 0.09% (5–9 basis points), it likely isn’t actionable—even before considering other trading costs, taxes, and the risk that the pattern might not hold in the future.\n\nTake the Next Step to Invest\n\nAdvertiser Disclosure\n\n×\n\nThe offers that appear in this table are from partnerships from which Investopedia receives compensation. This compensation may impact how and where listings appear. Investopedia does not include all offers available in the marketplace.\n## Best Times of the Day to Buy or Sell Stocks\nThe opening and closing hours of the trading day tend to be the most [volatile](https:\/\/www.investopedia.com\/terms\/v\/volatility.asp) and active periods. When U.S. markets open at 9:30 a.m. Eastern time, it processes all events and news releases since the previous day’s close, which can create significant price swings.\n\nMany professional traders focus on the opening period (9:30 a.m. to 10:30 a.m. ET), as it typically offers the most significant price moves in the shortest time. By 11:30 a.m., volatility and volume often decrease significantly, leading many day traders to close their positions.\n\nThe last hour of trading (3 p.m. to 4 p.m. ET) typically sees another surge in activity, as institutional investors and day traders close positions and react to late-breaking news. Like the morning session, this period can offer prospects but comes with increased risk.\n\n### Tip\nIt’s generally impossible to time the market. Doing your research, knowing the financials of the company that interests you, and the overall sector or wider economic climate will guide you far better in your decisions than the hour of the day or date on the calendar for most types of trading.\n## How Will Extended Trading Affect the Best Intraday Trading Times?\nIn a significant shift for U.S. markets, the [New York Stock Exchange (NYSE)](https:\/\/www.investopedia.com\/terms\/n\/nyse.asp) plans to expand trading on its NYSE Arca Equities platform to 22 hours daily, from 1:30 a.m. to 11:30 p.m. ET on weekdays. About 56% of trading on NYSE Arca is in shares of exchange-traded funds (ETFs), with the rest for corporate stocks.3 The exchange already has broader opening hours than the regular NYSE, with sessions from 4 a.m. to 8 p.m. before and after regular trading hours.4\n\nWhile [currencies](https:\/\/www.investopedia.com\/terms\/c\/currency.asp), Treasurys, and stock index futures already trade outside regular hours, the shift—expected in 2025 and pending U.S. Securities and Exchange Commission (SEC) approval—will presumably reshape how [traders](https:\/\/www.investopedia.com\/terms\/t\/trader.asp) approach intraday strategies. The NYSE’s head of markets, Kevin Tyrrell, said the move was meant in part to enable “exchange-based trading for our U.S.-listed companies and funds to investors in time zones across the globe.”5\n\nThe proposed shift also responds to increasing demand within the U.S. for round-the-clock trading access, as many have already via cryptocurrency markets. Some retail brokers already provide overnight trading where they facilitate your trades beyond official trading hours, usually by matching them with those occurring in alternative trading pools, aka [dark pools](https:\/\/www.investopedia.com\/terms\/d\/dark-pool.asp), and overseas (especially in Asia) where it’s daytime.\n\nWhile evidence from other markets that have extended their hours is limited, the extension of trading hours is likely to do the following:\n\n- **Events-based trading will cause price moves**: [Investors](https:\/\/www.investopedia.com\/terms\/i\/investor.asp) can respond more promptly to global events and news releases outside traditional market hours. This should also relieve some of the trading volume that bottles up around the opening and closing of the regular trading day.\n- **Enhance liquidity**: Extended hours may attract more participants, potentially improving liquidity during previously inactive periods.\n- **Introduce more volatility into what was previously “off hours”**: While increased trading hours can provide more prospects, they may also introduce periods of heightened volatility, especially during times when fewer participants are active.\n## Best Day of the Week to Buy or Sell Stocks\nResearch long ago showed that Fridays were when you’d see the best gains for stocks, with Mondays generally coming out the worst.6 Like other bits of Wall Street wisdom, though the data has long since been outdated, that hasn’t meant people don’t still advise that stocks tend to drop on Mondays. Some people think this is because a significant amount of bad news is often released over the weekend. Others point to investors’ gloomy mood at having to go back to work, which is especially evident during the early hours of Monday trading.\n\n### What the Numbers Show\nBut nothing beats actual data. We pulled [S\\&P 500](https:\/\/www.investopedia.com\/terms\/s\/sp500.asp) closing prices for 2000 to late 2024. While any findings would only apply to the broad market of highly capitalized stocks—one could imagine various nooks and crannies of the market where day-specific effects might be found—it’s worth seeing if there are calendar effects during the last 25 years worth most investors bothering about.\n\nWhile we did find day-specific patterns, we also found that they’re not of much practical significance, if any, for investors.\n\nOur analysis of over 6,200 trading days shows that Tuesday has historically produced the highest average daily returns at 0.062%, while Friday and Monday show the lowest average returns at about 0.009% each. Wednesday and Thursday fall in between, with average returns of 0.024% and 0.042%, respectively. However, what counts is whether these results are robust—otherwise, you risk seeing patterns in the noise where there aren’t any.\n\nTo verify the reliability of these patterns, several statistical measures were considered both for daily and the other effects we check below. First, the [standard deviation](https:\/\/www.investopedia.com\/terms\/s\/standarddeviation.asp) of daily returns was calculated for each day of the week. Here’s what we found:\n\n- **Volatility context**: The daily standard deviation of returns ranges from 1.12% to 1.34%—about 20 times larger than the differences among the days themselves.\n- **Not much difference in the percentage of positive trading days**: The percentage of positive days for the S\\&P 500 is remarkably consistent across weekdays, ranging from 52% to 54%. This indicates that any given day has about the same probability of producing positive returns.\n- **Statistical significance**: The overall market average daily return during this period was 0.030%, with most daily variations falling well within normal statistical noise.\n\nThe small magnitude of day-of-week effects, measured in hundredths of a percent, means that trading spreads, taxes, and so on, would likely overwhelm any potential gains from trying to exploit gains from trading on specific days of the week. In short, there’s nothing to be gained by trading based on what day of the week it is—all else being equal.\n\n## What About Trading Before or After Long Weekends?\nThere’s a traditional view that the market is generally positive before a long weekend. Here, we found reason to think this is generally the case. When the markets close for extended periods—whether for holidays like Thanksgiving, Christmas, or three-day weekends—a distinct pattern emerges in the returns for the S\\&P 500:\n\n- Before long weekends: Average daily return of +0.185%\n- After long weekends: Average daily return of -0.059%\n- Regular trading days: Average daily return of +0.033%\n\nPut more straightforwardly, the last trading day before a long weekend has been five times higher than normal trading days.\n\nThe pattern’s reliability is supported by other measures. Before long weekends, about 55% of trading days are positive, compared with 50% after long weekends and about 54% on regular days. The data covers 196 long weekends over almost 25 years—a robust sample size.\n\n### Cautions Are Necessary\nDespite these clear patterns, investors should tread carefully before trying to trade based on this information:\n\n1. **Spreads are likely to cost more**: While the differences in returns are statistically significant, they’re still measured in fractions of a percent. For most retail investors, trading costs (including [bid-ask spreads](https:\/\/www.investopedia.com\/terms\/b\/bid-askspread.asp)) would likely erase any potential gains from trying to exploit these patterns.\n2. **Tax implications**: Frequent trading around weekends could lead to higher short-term capital gains taxes, potentially overwhelming any small gains from the strategy.\n3. **Practical limitations**: The strategy would require precise timing and regular trading around every long weekend, which isn’t practical for most investors.\n## Best Months to Buy or Sell Stocks\nOur analysis of S\\&P 500 data from 2000 to 2024 also revealed some clear monthly patterns. November is historically the strongest month, with an average daily return of 0.107% and positive returns 57% of the time. April and July are the next strongest months. This holds, too, when we broaden our data window to include back to 1928.\n\nThe data challenges some traditional market wisdom. While September does live up to its reputation as the weakest month—showing average monthly returns of -1.53% and -1.13% for the periods 2000 to 2024 and 1928 to 2024, respectively—the supposed positive “[January effect](https:\/\/www.investopedia.com\/terms\/j\/januaryeffect.asp),” which is 1.17% going back to 1928, has disappeared in recent decades, with returns of -0.15% since 2000.\n\nThe seasonal effects become more dramatic when looking at cumulative returns over these periods. If you had invested only in specific months, November would have shown the strongest performance with about a 54% cumulative gain from 2000 to 2024 (or about two years of months), while September’s weakness would have added up to a -37% loss over time.\n\n### How Actionable Are These Differences?\nBefore getting too excited about these patterns, it’s crucial to understand their reliability. The day-to-day [swings](https:\/\/www.investopedia.com\/terms\/s\/swing.asp) in the market are typically much larger than these monthly differences. Likewise, even in November (the strongest month), the market regularly moves up or down by amounts much larger than its typical monthly gain. This “[noise](https:\/\/www.investopedia.com\/terms\/n\/noise.asp)” in the data means that while these patterns are interesting, they’re not reliable enough to be the main factor in timing investment decisions.\n\n### Important\nIt’s important to consider that we found that the day-to-day swings in the market are typically much larger than these monthly differences. It’s like the difference between weather and climate in temperate zones—while we might say summer is warmer than winter, any given winter day could be warmer than a summer day. As such, daily shifts in the market spark waves that wipe out most small calendar differences.\n\nSo when is the best time to sell? The data confirms the traditional investor wariness of September, as it’s the only month stocks declined more often than they rose (49.9% positive days). February and June also emerge as historically weak months, with average daily returns of -0.023% and -0.010%, respectively.\n\nHowever, the [October effect](https:\/\/www.investopedia.com\/articles\/financial-theory\/09\/october-effect.asp)—the fear going back to the October 1929 crash of market crises that month—appears less justified in recent data. October has actually had a relatively strong performance, with average monthly gains of 1.34% since 2000 and positive returns 53% of the time. That said, it’s among the most volatile—about 20% and 15% more volatile than September and November, the months right before and after.\n\n### Fast Fact\nNot for nothing is October a month that’s salient for many investors. Even though October, on average, has been a positive month historically, many of the worst market crashes have occurred in this month, including [Black Monday](https:\/\/www.investopedia.com\/terms\/b\/blackmonday.asp).78 From 2000 to 2024, it was also the most volatile.9\n## Early-Month Returns\nMany traders believe that returns in the early part of the month are better than the rest. Our analysis of S\\&P 500 data since 2000 shows a compelling pattern: Trading days early in the month tend to show stronger performance than those in the latter part of the month.\n\nWe found a consistent advantage across several metrics. The average daily return during the first five trading days is +0.084%, compared with just +0.019% for the remainder of the month—a difference that amounts to more than four times higher average returns. This pattern holds up even when looking at median returns, which are less influenced by extreme market moves, with early-month median daily returns of +0.121% vs. +0.049% for other days.\n\nThe early-month advantage isn’t just about the size of returns; it has also been consistent. During these first five trading days, the market posts positive returns 56.4% of the time, compared with 53.0% for the rest of the month. This higher batting average suggests the early-month effect is stable and isn’t just the result of a few outlier days.\n\n### Why Early Parts of the Month Might Have Higher Returns\nWhat might explain this pattern? One possibility is the timing of monthly investment flows, such as [401(k) plan](https:\/\/www.investopedia.com\/terms\/1\/401kplan.asp) contributions and institutional portfolio rebalancing, which often occur near the start of each month. As this new money enters the market, it may create systematic buying pressure, leading to slightly higher returns.\n\nHowever, investors should approach this pattern with due caution. While the data shows a clear historical tendency, the magnitude of the difference is not large enough to overcome transaction spreads in a trading strategy. Moreover, as with any market pattern, there’s no guarantee that historical tendencies will persist. Still, this monthly cycle represents an interesting facet of market behavior that adds to our understanding of how calendar effects influence stock returns.\n\n## How to Trade in Light of Daily, Monthly, and Other Seasonal Effects\nWhile our analysis has revealed several intriguing patterns in market behavior—whether it’s the early-month effect, day-of-week variations, or monthly seasonality—converting these patterns into profitable trades comes with significant challenges. The differences in returns, when not simply statistical noise, are often too small to overcome trading costs.\n\nFor instance, the early-month advantage of 0.065% per day (the difference between early-month and rest-of-the-month returns) would be largely erased by typical trading spreads in a strategy attempting to capture this effect.\n\nThis leads us to an important insight: Rather than trying to time the market based on calendar effects, investors might be better served by implementing a systematic investment approach like [dollar-cost averaging (DCA)](https:\/\/www.investopedia.com\/terms\/d\/dollarcostaveraging.asp). With DCA, investors make regular investments of equal amounts regardless of market conditions or timing. This approach naturally diversifies the entry points for investments across different market conditions, reducing the impact of market timing and volatility on long-term returns.10\n\nDCA works well for many investors since it removes the emotional and tactical complexity of trying to time the market. It also aligns well with how most people actually invest—through regular contributions to [individual retirement accounts (IRAs)](https:\/\/www.investopedia.com\/terms\/i\/ira.asp) or investment plans.\n\n## Are There Really Best Times to Buy or Sell Stocks?\nHistorically, some days or months have tended to be better or worse for stocks. These so-called [market anomalies](https:\/\/www.investopedia.com\/articles\/stocks\/08\/market-anomaly-efficient-market.asp) challenged theories of efficient markets. However, research shows that as these anomalies became more well-known and trading became more automated, these have largely all disappeared.11 Others persist, but trading spreads tend to wash out your ability to trade on most of them.\n\n## What Is the Super Bowl Indicator?\nThis suggests that the stock market will rise the year an NFC team wins and fall if an AFC team wins. While this “indicator” had an accuracy rate above 80% for many years, it’s a classic example of [correlation without causation](https:\/\/web.archive.org\/web\/20150523013856\/https:\/\/www.investopedia.com\/terms\/s\/spurious_correlation.asp) (when two things coincidentally happen together, but one can’t cause the other).\n\n## What Is the Strangest Calendar Effect Claimed by Investors?\nUnusual patterns people have studied include the adage for investors to “[sell in May and go away](https:\/\/www.investopedia.com\/terms\/s\/sell-in-may-and-go-away.asp),” the presidential election cycle, and even the effect of lunar phases on stock returns. While some have shown statistical significance historically, none have proved reliable enough for actual trading, given spreads and other costs.\n## The Bottom Line\nOur analysis confirms some traditional beliefs (like September’s weakness) while challenging others (like the supposed January effect). While certain patterns show statistical significance, like the early-month effect or preholiday strength, trading spreads and market efficiency largely annul the practical value of these findings. Any [abnormal returns](https:\/\/www.investopedia.com\/terms\/a\/abnormalreturn.asp) are generally short-lived, as these prospects are quickly [arbitraged](https:\/\/www.investopedia.com\/terms\/a\/arbitrage.asp) away, and markets become more efficient as traders and investors increasingly learn about the patterns.12 That is, once other traders know there’s a benefit, they’ll move to take advantage, which soon removes it.\n\nThe most valuable insight from studying these patterns is not about when to trade, but the futility of trying to time the market based on the calendar alone. The data suggests that while timing patterns exist, they’re typically too small to exploit profitably. This reinforces the wisdom of systematic investment approaches like dollar-cost averaging, which succeed not by timing the market perfectly but by participating in it consistently.\n\nArticle Sources\n\nInvestopedia requires writers to use primary sources to support their work. These include white papers, government data, original reporting, and interviews with industry experts. We also reference original research from other reputable publishers where appropriate. You can learn more about the standards we follow in producing accurate, unbiased content in our [editorial policy.](https:\/\/www.investopedia.com\/legal-4768893#EditorialPolicy)\n\n1. Charles Schwab. “[Trading Near the Bells](https:\/\/www.schwab.com\/learn\/story\/trading-near-bells).”\n2. Nasdaq. “[Sampling the S\\&P 500 to Minimize Spreads](https:\/\/www.nasdaq.com\/articles\/sampling-sp-500-minimize-spreads).”\n3. Financial Times. \"[NYSE Plans to Extend Daily Trading to 22 Hours on Its Arca Exchange](https:\/\/www.ft.com\/content\/59dc109d-d385-412a-bcc6-b671db9ce680).\"\n4. New York Stock Exchange. “[Market Information](https:\/\/www.nyse.com\/markets\/nyse-arca\/market-info).”\n5. Intercontinental Exchange. “[The New York Stock Exchange Plans to Extend Weekday Trading on Its NYSE Arca Equities Exchange to 22 Hours a Day](https:\/\/ir.theice.com\/press\/news-details\/2024\/The-New-York-Stock-Exchange-Plans-to-Extend-Weekday-Trading-on-its-NYSE-Arca-Equities-Exchange-to-22-Hours-a-Day\/default.aspx).”\n6. Cross, Frank, via JSTOR. “[The Behavior of Stock Prices on Fridays and Mondays](https:\/\/www.jstor.org\/stable\/4529641).” *Financial Analysts Journal*, vol. 29, no. 6, 1973, pp. 67–69.\n7. Federal Reserve History. “[Stock Market Crash of 1929](https:\/\/www.federalreservehistory.org\/essays\/stock-market-crash-of-1929).”\n8. Federal Reserve History. “[Stock Market Crash of 1987](https:\/\/www.federalreservehistory.org\/essays\/stock-market-crash-of-1987).”\n9. Barron's. \"[Forget September. This Is the Stock Market’s Most Volatile Month](https:\/\/www.barrons.com\/articles\/stocks-most-volatile-month-61786737).\"\n10. Financial Industry Regulatory Authority. “[The Pros and Cons of Dollar-Cost Averaging](https:\/\/www.finra.org\/investors\/insights\/dollar-cost-averaging).”\n11. Schwert, G. William, via SSRN. “[Anomalies and Market Efficiency](https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=338080).” *The Bradley Policy Research CenterFinancial Research and Policy*, Working Paper No. FR 02-13, October 2002, pp. 2–3.\n12. Schwert, G. William, via SSRN. “[Anomalies and Market Efficiency](https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=338080).” *The Bradley Policy Research CenterFinancial Research and Policy*, Working Paper No. FR 02-13, October 2002, pp. 1–52.\n\nCompare Accounts\n\nAdvertiser Disclosure\n\n×\n\nThe offers that appear in this table are from partnerships from which Investopedia receives compensation. This compensation may impact how and where listings appear. Investopedia does not include all offers available in the marketplace.\n\nRead more\n\n- [Trading](https:\/\/www.investopedia.com\/trading-4427765)\n\nPartner Links\n\n## Related Articles\n[![A close-up of a person's hands holding a cell phone displaying stock market data.](https:\/\/www.investopedia.com\/thmb\/SY1p2t3TS7zGPtwVQDnksLbW_hQ=\/282x188\/filters:no_upscale\\(\\):max_bytes\\(150000\\):strip_icc\\(\\)\/GettyImages-1323094570-fbb8bf2a57c2488c8e44f6005b77e5a0.jpg) ![A close-up of a person's hands holding a cell phone displaying stock market data.](https:\/\/www.investopedia.com\/thmb\/SY1p2t3TS7zGPtwVQDnksLbW_hQ=\/282x188\/filters:no_upscale\\(\\):max_bytes\\(150000\\):strip_icc\\(\\)\/GettyImages-1323094570-fbb8bf2a57c2488c8e44f6005b77e5a0.jpg) Gap Risk Explained: Definition, Functionality, and Example](https:\/\/www.investopedia.com\/terms\/g\/gaprisk.asp)\n\n[![An accountant works with a computer to double check the listed price of foreign assets that will be included in the 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Perhaps it’s no surprise, then, that many traders seek to gain an edge by identifying optimal times to enter and exit positions, whether that’s a particular hour of the day, day of the week, or month of the year. Research has shown that market activity often follows certain patterns tied to timing.1\n\nUnderstanding these patterns and their limitations can help traders make more informed decisions about when to trade—or at least, be better informed when other traders *think* is the best time to trade. Below, we show you our analysis of stock market return by days of the week, months of the year, and other supposed patterns to see if there’s anything to prevailing myths about the best times to trade.\n\n### Key Takeaways\n- The closest thing to a hard-and-fast rule is that the first hour and last hour of a trading day are the busiest, offering the most prospects, while the middle of the day tends to be the calmest and most stable period of most trading days.\n- Early-month trading days show stronger returns than the rest of the month, possibly because of regular investment flows like 401(k) contributions.\n- Trading days before long weekends show higher returns than regular trading days, while post-holiday returns tend to be slightly negative.\n- While statistically interesting, these and other calendar patterns are typically too small to exploit profitably once trading spreads and other costs are considered, supporting the case for systematic investment approaches like dollar-cost averaging.\n## Trading Costs Matter: A Warning About Market Effects\nBefore diving into the data to see whether any benefits are to be found [trading](https:\/\/www.investopedia.com\/trading-4427765) at specific hours, on specific days, or in different months, we should underline that trading costs—including price spreads—would quickly erase many of the differences we found if you tried to trade based on them. For the S\\&P 500, you’ll pay about 0.025% (2.5 [basis points](https:\/\/www.investopedia.com\/terms\/b\/basispoint.asp)) in spreads to buy or sell the largest stocks, and about 0.045% (4.5 basis points) for a portfolio tracking the full index.2\n\nSince you need to buy and sell to capture any effect, double these numbers for your round-trip trading cost. So, unless a market pattern shows returns significantly above 0.05% to 0.09% (5–9 basis points), it likely isn’t actionable—even before considering other trading costs, taxes, and the risk that the pattern might not hold in the future.\n\n## Best Times of the Day to Buy or Sell Stocks\nThe opening and closing hours of the trading day tend to be the most [volatile](https:\/\/www.investopedia.com\/terms\/v\/volatility.asp) and active periods. When U.S. markets open at 9:30 a.m. Eastern time, it processes all events and news releases since the previous day’s close, which can create significant price swings.\n\nMany professional traders focus on the opening period (9:30 a.m. to 10:30 a.m. ET), as it typically offers the most significant price moves in the shortest time. By 11:30 a.m., volatility and volume often decrease significantly, leading many day traders to close their positions.\n\nThe last hour of trading (3 p.m. to 4 p.m. ET) typically sees another surge in activity, as institutional investors and day traders close positions and react to late-breaking news. Like the morning session, this period can offer prospects but comes with increased risk.\n\n### Tip\nIt’s generally impossible to time the market. Doing your research, knowing the financials of the company that interests you, and the overall sector or wider economic climate will guide you far better in your decisions than the hour of the day or date on the calendar for most types of trading.\n## How Will Extended Trading Affect the Best Intraday Trading Times?\nIn a significant shift for U.S. markets, the [New York Stock Exchange (NYSE)](https:\/\/www.investopedia.com\/terms\/n\/nyse.asp) plans to expand trading on its NYSE Arca Equities platform to 22 hours daily, from 1:30 a.m. to 11:30 p.m. ET on weekdays. About 56% of trading on NYSE Arca is in shares of exchange-traded funds (ETFs), with the rest for corporate stocks.3 The exchange already has broader opening hours than the regular NYSE, with sessions from 4 a.m. to 8 p.m. before and after regular trading hours.4\n\nWhile [currencies](https:\/\/www.investopedia.com\/terms\/c\/currency.asp), Treasurys, and stock index futures already trade outside regular hours, the shift—expected in 2025 and pending U.S. Securities and Exchange Commission (SEC) approval—will presumably reshape how [traders](https:\/\/www.investopedia.com\/terms\/t\/trader.asp) approach intraday strategies. The NYSE’s head of markets, Kevin Tyrrell, said the move was meant in part to enable “exchange-based trading for our U.S.-listed companies and funds to investors in time zones across the globe.”5\n\nThe proposed shift also responds to increasing demand within the U.S. for round-the-clock trading access, as many have already via cryptocurrency markets. Some retail brokers already provide overnight trading where they facilitate your trades beyond official trading hours, usually by matching them with those occurring in alternative trading pools, aka [dark pools](https:\/\/www.investopedia.com\/terms\/d\/dark-pool.asp), and overseas (especially in Asia) where it’s daytime.\n\nWhile evidence from other markets that have extended their hours is limited, the extension of trading hours is likely to do the following:\n\n- **Events-based trading will cause price moves**: [Investors](https:\/\/www.investopedia.com\/terms\/i\/investor.asp) can respond more promptly to global events and news releases outside traditional market hours. This should also relieve some of the trading volume that bottles up around the opening and closing of the regular trading day.\n- **Enhance liquidity**: Extended hours may attract more participants, potentially improving liquidity during previously inactive periods.\n- **Introduce more volatility into what was previously “off hours”**: While increased trading hours can provide more prospects, they may also introduce periods of heightened volatility, especially during times when fewer participants are active.\n## Best Day of the Week to Buy or Sell Stocks\nResearch long ago showed that Fridays were when you’d see the best gains for stocks, with Mondays generally coming out the worst.6 Like other bits of Wall Street wisdom, though the data has long since been outdated, that hasn’t meant people don’t still advise that stocks tend to drop on Mondays. Some people think this is because a significant amount of bad news is often released over the weekend. Others point to investors’ gloomy mood at having to go back to work, which is especially evident during the early hours of Monday trading.\n\n### What the Numbers Show\nBut nothing beats actual data. We pulled [S\\&P 500](https:\/\/www.investopedia.com\/terms\/s\/sp500.asp) closing prices for 2000 to late 2024. While any findings would only apply to the broad market of highly capitalized stocks—one could imagine various nooks and crannies of the market where day-specific effects might be found—it’s worth seeing if there are calendar effects during the last 25 years worth most investors bothering about.\n\nWhile we did find day-specific patterns, we also found that they’re not of much practical significance, if any, for investors.\n\nOur analysis of over 6,200 trading days shows that Tuesday has historically produced the highest average daily returns at 0.062%, while Friday and Monday show the lowest average returns at about 0.009% each. Wednesday and Thursday fall in between, with average returns of 0.024% and 0.042%, respectively. However, what counts is whether these results are robust—otherwise, you risk seeing patterns in the noise where there aren’t any.\n\nTo verify the reliability of these patterns, several statistical measures were considered both for daily and the other effects we check below. First, the [standard deviation](https:\/\/www.investopedia.com\/terms\/s\/standarddeviation.asp) of daily returns was calculated for each day of the week. Here’s what we found:\n\n- **Volatility context**: The daily standard deviation of returns ranges from 1.12% to 1.34%—about 20 times larger than the differences among the days themselves.\n- **Not much difference in the percentage of positive trading days**: The percentage of positive days for the S\\&P 500 is remarkably consistent across weekdays, ranging from 52% to 54%. This indicates that any given day has about the same probability of producing positive returns.\n- **Statistical significance**: The overall market average daily return during this period was 0.030%, with most daily variations falling well within normal statistical noise.\n\nThe small magnitude of day-of-week effects, measured in hundredths of a percent, means that trading spreads, taxes, and so on, would likely overwhelm any potential gains from trying to exploit gains from trading on specific days of the week. In short, there’s nothing to be gained by trading based on what day of the week it is—all else being equal.\n\n## What About Trading Before or After Long Weekends?\nThere’s a traditional view that the market is generally positive before a long weekend. Here, we found reason to think this is generally the case. When the markets close for extended periods—whether for holidays like Thanksgiving, Christmas, or three-day weekends—a distinct pattern emerges in the returns for the S\\&P 500:\n\n- Before long weekends: Average daily return of +0.185%\n- After long weekends: Average daily return of -0.059%\n- Regular trading days: Average daily return of +0.033%\n\nPut more straightforwardly, the last trading day before a long weekend has been five times higher than normal trading days.\n\nThe pattern’s reliability is supported by other measures. Before long weekends, about 55% of trading days are positive, compared with 50% after long weekends and about 54% on regular days. The data covers 196 long weekends over almost 25 years—a robust sample size.\n\n### Cautions Are Necessary\nDespite these clear patterns, investors should tread carefully before trying to trade based on this information:\n\n1. **Spreads are likely to cost more**: While the differences in returns are statistically significant, they’re still measured in fractions of a percent. For most retail investors, trading costs (including [bid-ask spreads](https:\/\/www.investopedia.com\/terms\/b\/bid-askspread.asp)) would likely erase any potential gains from trying to exploit these patterns.\n2. **Tax implications**: Frequent trading around weekends could lead to higher short-term capital gains taxes, potentially overwhelming any small gains from the strategy.\n3. **Practical limitations**: The strategy would require precise timing and regular trading around every long weekend, which isn’t practical for most investors.\n## Best Months to Buy or Sell Stocks\nOur analysis of S\\&P 500 data from 2000 to 2024 also revealed some clear monthly patterns. November is historically the strongest month, with an average daily return of 0.107% and positive returns 57% of the time. April and July are the next strongest months. This holds, too, when we broaden our data window to include back to 1928.\n\nThe data challenges some traditional market wisdom. While September does live up to its reputation as the weakest month—showing average monthly returns of -1.53% and -1.13% for the periods 2000 to 2024 and 1928 to 2024, respectively—the supposed positive “[January effect](https:\/\/www.investopedia.com\/terms\/j\/januaryeffect.asp),” which is 1.17% going back to 1928, has disappeared in recent decades, with returns of -0.15% since 2000.\n\nThe seasonal effects become more dramatic when looking at cumulative returns over these periods. If you had invested only in specific months, November would have shown the strongest performance with about a 54% cumulative gain from 2000 to 2024 (or about two years of months), while September’s weakness would have added up to a -37% loss over time.\n\n### How Actionable Are These Differences?\nBefore getting too excited about these patterns, it’s crucial to understand their reliability. The day-to-day [swings](https:\/\/www.investopedia.com\/terms\/s\/swing.asp) in the market are typically much larger than these monthly differences. Likewise, even in November (the strongest month), the market regularly moves up or down by amounts much larger than its typical monthly gain. This “[noise](https:\/\/www.investopedia.com\/terms\/n\/noise.asp)” in the data means that while these patterns are interesting, they’re not reliable enough to be the main factor in timing investment decisions.\n\n### Important\nIt’s important to consider that we found that the day-to-day swings in the market are typically much larger than these monthly differences. It’s like the difference between weather and climate in temperate zones—while we might say summer is warmer than winter, any given winter day could be warmer than a summer day. As such, daily shifts in the market spark waves that wipe out most small calendar differences.\n\nSo when is the best time to sell? The data confirms the traditional investor wariness of September, as it’s the only month stocks declined more often than they rose (49.9% positive days). February and June also emerge as historically weak months, with average daily returns of -0.023% and -0.010%, respectively.\n\nHowever, the [October effect](https:\/\/www.investopedia.com\/articles\/financial-theory\/09\/october-effect.asp)—the fear going back to the October 1929 crash of market crises that month—appears less justified in recent data. October has actually had a relatively strong performance, with average monthly gains of 1.34% since 2000 and positive returns 53% of the time. That said, it’s among the most volatile—about 20% and 15% more volatile than September and November, the months right before and after.\n\n### Fast Fact\nNot for nothing is October a month that’s salient for many investors. Even though October, on average, has been a positive month historically, many of the worst market crashes have occurred in this month, including [Black Monday](https:\/\/www.investopedia.com\/terms\/b\/blackmonday.asp).78 From 2000 to 2024, it was also the most volatile.9\n## Early-Month Returns\nMany traders believe that returns in the early part of the month are better than the rest. Our analysis of S\\&P 500 data since 2000 shows a compelling pattern: Trading days early in the month tend to show stronger performance than those in the latter part of the month.\n\nWe found a consistent advantage across several metrics. The average daily return during the first five trading days is +0.084%, compared with just +0.019% for the remainder of the month—a difference that amounts to more than four times higher average returns. This pattern holds up even when looking at median returns, which are less influenced by extreme market moves, with early-month median daily returns of +0.121% vs. +0.049% for other days.\n\nThe early-month advantage isn’t just about the size of returns; it has also been consistent. During these first five trading days, the market posts positive returns 56.4% of the time, compared with 53.0% for the rest of the month. This higher batting average suggests the early-month effect is stable and isn’t just the result of a few outlier days.\n\n### Why Early Parts of the Month Might Have Higher Returns\nWhat might explain this pattern? One possibility is the timing of monthly investment flows, such as [401(k) plan](https:\/\/www.investopedia.com\/terms\/1\/401kplan.asp) contributions and institutional portfolio rebalancing, which often occur near the start of each month. As this new money enters the market, it may create systematic buying pressure, leading to slightly higher returns.\n\nHowever, investors should approach this pattern with due caution. While the data shows a clear historical tendency, the magnitude of the difference is not large enough to overcome transaction spreads in a trading strategy. Moreover, as with any market pattern, there’s no guarantee that historical tendencies will persist. Still, this monthly cycle represents an interesting facet of market behavior that adds to our understanding of how calendar effects influence stock returns.\n\n## How to Trade in Light of Daily, Monthly, and Other Seasonal Effects\nWhile our analysis has revealed several intriguing patterns in market behavior—whether it’s the early-month effect, day-of-week variations, or monthly seasonality—converting these patterns into profitable trades comes with significant challenges. The differences in returns, when not simply statistical noise, are often too small to overcome trading costs.\n\nFor instance, the early-month advantage of 0.065% per day (the difference between early-month and rest-of-the-month returns) would be largely erased by typical trading spreads in a strategy attempting to capture this effect.\n\nThis leads us to an important insight: Rather than trying to time the market based on calendar effects, investors might be better served by implementing a systematic investment approach like [dollar-cost averaging (DCA)](https:\/\/www.investopedia.com\/terms\/d\/dollarcostaveraging.asp). With DCA, investors make regular investments of equal amounts regardless of market conditions or timing. This approach naturally diversifies the entry points for investments across different market conditions, reducing the impact of market timing and volatility on long-term returns.10\n\nDCA works well for many investors since it removes the emotional and tactical complexity of trying to time the market. It also aligns well with how most people actually invest—through regular contributions to [individual retirement accounts (IRAs)](https:\/\/www.investopedia.com\/terms\/i\/ira.asp) or investment plans.\n\n## Are There Really Best Times to Buy or Sell Stocks?\nHistorically, some days or months have tended to be better or worse for stocks. These so-called [market anomalies](https:\/\/www.investopedia.com\/articles\/stocks\/08\/market-anomaly-efficient-market.asp) challenged theories of efficient markets. However, research shows that as these anomalies became more well-known and trading became more automated, these have largely all disappeared.11 Others persist, but trading spreads tend to wash out your ability to trade on most of them.\n\n## What Is the Super Bowl Indicator?\nThis suggests that the stock market will rise the year an NFC team wins and fall if an AFC team wins. While this “indicator” had an accuracy rate above 80% for many years, it’s a classic example of [correlation without causation](https:\/\/web.archive.org\/web\/20150523013856\/https:\/\/www.investopedia.com\/terms\/s\/spurious_correlation.asp) (when two things coincidentally happen together, but one can’t cause the other).\n\n## What Is the Strangest Calendar Effect Claimed by Investors?\nUnusual patterns people have studied include the adage for investors to “[sell in May and go away](https:\/\/www.investopedia.com\/terms\/s\/sell-in-may-and-go-away.asp),” the presidential election cycle, and even the effect of lunar phases on stock returns. While some have shown statistical significance historically, none have proved reliable enough for actual trading, given spreads and other costs.\n## The Bottom Line\nOur analysis confirms some traditional beliefs (like September’s weakness) while challenging others (like the supposed January effect). While certain patterns show statistical significance, like the early-month effect or preholiday strength, trading spreads and market efficiency largely annul the practical value of these findings. Any [abnormal returns](https:\/\/www.investopedia.com\/terms\/a\/abnormalreturn.asp) are generally short-lived, as these prospects are quickly [arbitraged](https:\/\/www.investopedia.com\/terms\/a\/arbitrage.asp) away, and markets become more efficient as traders and investors increasingly learn about the patterns.12 That is, once other traders know there’s a benefit, they’ll move to take advantage, which soon removes it.\n\nThe most valuable insight from studying these patterns is not about when to trade, but the futility of trying to time the market based on the calendar alone. The data suggests that while timing patterns exist, they’re typically too small to exploit profitably. This reinforces the wisdom of systematic investment approaches like dollar-cost averaging, which succeed not by timing the market perfectly but by participating in it consistently.","meta_canonical":null}

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Meta Title	Best Times of the Day, Week, and Month to Trade Stocks
Meta Description	Market behavior can change depending on the month, day of the week, and even hour. Here's how to time your stock trades to take advantage of price swings.
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Boilerpipe Text	Every second counts in the stock market , where fortunes can shift with each tick of the trading clock. Perhaps it’s no surprise, then, that many traders seek to gain an edge by identifying optimal times to enter and exit positions, whether that’s a particular hour of the day, day of the week, or month of the year. Research has shown that market activity often follows certain patterns tied to timing. 1 Understanding these patterns and their limitations can help traders make more informed decisions about when to trade—or at least, be better informed when other traders think is the best time to trade. Below, we show you our analysis of stock market return by days of the week, months of the year, and other supposed patterns to see if there’s anything to prevailing myths about the best times to trade. Key Takeaways The closest thing to a hard-and-fast rule is that the first hour and last hour of a trading day are the busiest, offering the most prospects, while the middle of the day tends to be the calmest and most stable period of most trading days. Early-month trading days show stronger returns than the rest of the month, possibly because of regular investment flows like 401(k) contributions. Trading days before long weekends show higher returns than regular trading days, while post-holiday returns tend to be slightly negative. While statistically interesting, these and other calendar patterns are typically too small to exploit profitably once trading spreads and other costs are considered, supporting the case for systematic investment approaches like dollar-cost averaging. Trading Costs Matter: A Warning About Market Effects Before diving into the data to see whether any benefits are to be found trading at specific hours, on specific days, or in different months, we should underline that trading costs—including price spreads—would quickly erase many of the differences we found if you tried to trade based on them. For the S&P 500, you’ll pay about 0.025% (2.5 basis points ) in spreads to buy or sell the largest stocks, and about 0.045% (4.5 basis points) for a portfolio tracking the full index. 2 Since you need to buy and sell to capture any effect, double these numbers for your round-trip trading cost. So, unless a market pattern shows returns significantly above 0.05% to 0.09% (5–9 basis points), it likely isn’t actionable—even before considering other trading costs, taxes, and the risk that the pattern might not hold in the future. Best Times of the Day to Buy or Sell Stocks The opening and closing hours of the trading day tend to be the most volatile and active periods. When U.S. markets open at 9:30 a.m. Eastern time, it processes all events and news releases since the previous day’s close, which can create significant price swings. Many professional traders focus on the opening period (9:30 a.m. to 10:30 a.m. ET), as it typically offers the most significant price moves in the shortest time. By 11:30 a.m., volatility and volume often decrease significantly, leading many day traders to close their positions. The last hour of trading (3 p.m. to 4 p.m. ET) typically sees another surge in activity, as institutional investors and day traders close positions and react to late-breaking news. Like the morning session, this period can offer prospects but comes with increased risk. Tip It’s generally impossible to time the market. Doing your research, knowing the financials of the company that interests you, and the overall sector or wider economic climate will guide you far better in your decisions than the hour of the day or date on the calendar for most types of trading. How Will Extended Trading Affect the Best Intraday Trading Times? In a significant shift for U.S. markets, the New York Stock Exchange (NYSE) plans to expand trading on its NYSE Arca Equities platform to 22 hours daily, from 1:30 a.m. to 11:30 p.m. ET on weekdays. About 56% of trading on NYSE Arca is in shares of exchange-traded funds (ETFs), with the rest for corporate stocks. 3 The exchange already has broader opening hours than the regular NYSE, with sessions from 4 a.m. to 8 p.m. before and after regular trading hours. 4 While currencies , Treasurys, and stock index futures already trade outside regular hours, the shift—expected in 2025 and pending U.S. Securities and Exchange Commission (SEC) approval—will presumably reshape how traders approach intraday strategies. The NYSE’s head of markets, Kevin Tyrrell, said the move was meant in part to enable “exchange-based trading for our U.S.-listed companies and funds to investors in time zones across the globe.” 5 The proposed shift also responds to increasing demand within the U.S. for round-the-clock trading access, as many have already via cryptocurrency markets. Some retail brokers already provide overnight trading where they facilitate your trades beyond official trading hours, usually by matching them with those occurring in alternative trading pools, aka dark pools , and overseas (especially in Asia) where it’s daytime. While evidence from other markets that have extended their hours is limited, the extension of trading hours is likely to do the following: Events-based trading will cause price moves : Investors can respond more promptly to global events and news releases outside traditional market hours. This should also relieve some of the trading volume that bottles up around the opening and closing of the regular trading day. Enhance liquidity : Extended hours may attract more participants, potentially improving liquidity during previously inactive periods. Introduce more volatility into what was previously “off hours” : While increased trading hours can provide more prospects, they may also introduce periods of heightened volatility, especially during times when fewer participants are active. Best Day of the Week to Buy or Sell Stocks Research long ago showed that Fridays were when you’d see the best gains for stocks, with Mondays generally coming out the worst. 6 Like other bits of Wall Street wisdom, though the data has long since been outdated, that hasn’t meant people don’t still advise that stocks tend to drop on Mondays. Some people think this is because a significant amount of bad news is often released over the weekend. Others point to investors’ gloomy mood at having to go back to work, which is especially evident during the early hours of Monday trading. What the Numbers Show But nothing beats actual data. We pulled S&P 500 closing prices for 2000 to late 2024. While any findings would only apply to the broad market of highly capitalized stocks—one could imagine various nooks and crannies of the market where day-specific effects might be found—it’s worth seeing if there are calendar effects during the last 25 years worth most investors bothering about. While we did find day-specific patterns, we also found that they’re not of much practical significance, if any, for investors. Our analysis of over 6,200 trading days shows that Tuesday has historically produced the highest average daily returns at 0.062%, while Friday and Monday show the lowest average returns at about 0.009% each. Wednesday and Thursday fall in between, with average returns of 0.024% and 0.042%, respectively. However, what counts is whether these results are robust—otherwise, you risk seeing patterns in the noise where there aren’t any. To verify the reliability of these patterns, several statistical measures were considered both for daily and the other effects we check below. First, the standard deviation of daily returns was calculated for each day of the week. Here’s what we found: Volatility context : The daily standard deviation of returns ranges from 1.12% to 1.34%—about 20 times larger than the differences among the days themselves. Not much difference in the percentage of positive trading days : The percentage of positive days for the S&P 500 is remarkably consistent across weekdays, ranging from 52% to 54%. This indicates that any given day has about the same probability of producing positive returns. Statistical significance : The overall market average daily return during this period was 0.030%, with most daily variations falling well within normal statistical noise. The small magnitude of day-of-week effects, measured in hundredths of a percent, means that trading spreads, taxes, and so on, would likely overwhelm any potential gains from trying to exploit gains from trading on specific days of the week. In short, there’s nothing to be gained by trading based on what day of the week it is—all else being equal. What About Trading Before or After Long Weekends? There’s a traditional view that the market is generally positive before a long weekend. Here, we found reason to think this is generally the case. When the markets close for extended periods—whether for holidays like Thanksgiving, Christmas, or three-day weekends—a distinct pattern emerges in the returns for the S&P 500: Before long weekends: Average daily return of +0.185% After long weekends: Average daily return of -0.059% Regular trading days: Average daily return of +0.033% Put more straightforwardly, the last trading day before a long weekend has been five times higher than normal trading days. The pattern’s reliability is supported by other measures. Before long weekends, about 55% of trading days are positive, compared with 50% after long weekends and about 54% on regular days. The data covers 196 long weekends over almost 25 years—a robust sample size. Cautions Are Necessary Despite these clear patterns, investors should tread carefully before trying to trade based on this information: Spreads are likely to cost more : While the differences in returns are statistically significant, they’re still measured in fractions of a percent. For most retail investors, trading costs (including bid-ask spreads ) would likely erase any potential gains from trying to exploit these patterns. Tax implications : Frequent trading around weekends could lead to higher short-term capital gains taxes, potentially overwhelming any small gains from the strategy. Practical limitations : The strategy would require precise timing and regular trading around every long weekend, which isn’t practical for most investors. Best Months to Buy or Sell Stocks Our analysis of S&P 500 data from 2000 to 2024 also revealed some clear monthly patterns. November is historically the strongest month, with an average daily return of 0.107% and positive returns 57% of the time. April and July are the next strongest months. This holds, too, when we broaden our data window to include back to 1928. The data challenges some traditional market wisdom. While September does live up to its reputation as the weakest month—showing average monthly returns of -1.53% and -1.13% for the periods 2000 to 2024 and 1928 to 2024, respectively—the supposed positive “ January effect ,” which is 1.17% going back to 1928, has disappeared in recent decades, with returns of -0.15% since 2000. The seasonal effects become more dramatic when looking at cumulative returns over these periods. If you had invested only in specific months, November would have shown the strongest performance with about a 54% cumulative gain from 2000 to 2024 (or about two years of months), while September’s weakness would have added up to a -37% loss over time. How Actionable Are These Differences? Before getting too excited about these patterns, it’s crucial to understand their reliability. The day-to-day swings in the market are typically much larger than these monthly differences. Likewise, even in November (the strongest month), the market regularly moves up or down by amounts much larger than its typical monthly gain. This “ noise ” in the data means that while these patterns are interesting, they’re not reliable enough to be the main factor in timing investment decisions. Important It’s important to consider that we found that the day-to-day swings in the market are typically much larger than these monthly differences. It’s like the difference between weather and climate in temperate zones—while we might say summer is warmer than winter, any given winter day could be warmer than a summer day. As such, daily shifts in the market spark waves that wipe out most small calendar differences. So when is the best time to sell? The data confirms the traditional investor wariness of September, as it’s the only month stocks declined more often than they rose (49.9% positive days). February and June also emerge as historically weak months, with average daily returns of -0.023% and -0.010%, respectively. However, the October effect —the fear going back to the October 1929 crash of market crises that month—appears less justified in recent data. October has actually had a relatively strong performance, with average monthly gains of 1.34% since 2000 and positive returns 53% of the time. That said, it’s among the most volatile—about 20% and 15% more volatile than September and November, the months right before and after. Fast Fact Not for nothing is October a month that’s salient for many investors. Even though October, on average, has been a positive month historically, many of the worst market crashes have occurred in this month, including Black Monday . 7 8 From 2000 to 2024, it was also the most volatile. 9 Early-Month Returns Many traders believe that returns in the early part of the month are better than the rest. Our analysis of S&P 500 data since 2000 shows a compelling pattern: Trading days early in the month tend to show stronger performance than those in the latter part of the month. We found a consistent advantage across several metrics. The average daily return during the first five trading days is +0.084%, compared with just +0.019% for the remainder of the month—a difference that amounts to more than four times higher average returns. This pattern holds up even when looking at median returns, which are less influenced by extreme market moves, with early-month median daily returns of +0.121% vs. +0.049% for other days. The early-month advantage isn’t just about the size of returns; it has also been consistent. During these first five trading days, the market posts positive returns 56.4% of the time, compared with 53.0% for the rest of the month. This higher batting average suggests the early-month effect is stable and isn’t just the result of a few outlier days. Why Early Parts of the Month Might Have Higher Returns What might explain this pattern? One possibility is the timing of monthly investment flows, such as 401(k) plan contributions and institutional portfolio rebalancing, which often occur near the start of each month. As this new money enters the market, it may create systematic buying pressure, leading to slightly higher returns. However, investors should approach this pattern with due caution. While the data shows a clear historical tendency, the magnitude of the difference is not large enough to overcome transaction spreads in a trading strategy. Moreover, as with any market pattern, there’s no guarantee that historical tendencies will persist. Still, this monthly cycle represents an interesting facet of market behavior that adds to our understanding of how calendar effects influence stock returns. How to Trade in Light of Daily, Monthly, and Other Seasonal Effects While our analysis has revealed several intriguing patterns in market behavior—whether it’s the early-month effect, day-of-week variations, or monthly seasonality—converting these patterns into profitable trades comes with significant challenges. The differences in returns, when not simply statistical noise, are often too small to overcome trading costs. For instance, the early-month advantage of 0.065% per day (the difference between early-month and rest-of-the-month returns) would be largely erased by typical trading spreads in a strategy attempting to capture this effect. This leads us to an important insight: Rather than trying to time the market based on calendar effects, investors might be better served by implementing a systematic investment approach like dollar-cost averaging (DCA) . With DCA, investors make regular investments of equal amounts regardless of market conditions or timing. This approach naturally diversifies the entry points for investments across different market conditions, reducing the impact of market timing and volatility on long-term returns. 10 DCA works well for many investors since it removes the emotional and tactical complexity of trying to time the market. It also aligns well with how most people actually invest—through regular contributions to individual retirement accounts (IRAs) or investment plans. Are There Really Best Times to Buy or Sell Stocks? Historically, some days or months have tended to be better or worse for stocks. These so-called market anomalies challenged theories of efficient markets. However, research shows that as these anomalies became more well-known and trading became more automated, these have largely all disappeared. 11 Others persist, but trading spreads tend to wash out your ability to trade on most of them. What Is the Super Bowl Indicator? This suggests that the stock market will rise the year an NFC team wins and fall if an AFC team wins. While this “indicator” had an accuracy rate above 80% for many years, it’s a classic example of correlation without causation (when two things coincidentally happen together, but one can’t cause the other). What Is the Strangest Calendar Effect Claimed by Investors? Unusual patterns people have studied include the adage for investors to “ sell in May and go away ,” the presidential election cycle, and even the effect of lunar phases on stock returns. While some have shown statistical significance historically, none have proved reliable enough for actual trading, given spreads and other costs. The Bottom Line Our analysis confirms some traditional beliefs (like September’s weakness) while challenging others (like the supposed January effect). While certain patterns show statistical significance, like the early-month effect or preholiday strength, trading spreads and market efficiency largely annul the practical value of these findings. Any abnormal returns are generally short-lived, as these prospects are quickly arbitraged away, and markets become more efficient as traders and investors increasingly learn about the patterns. 12 That is, once other traders know there’s a benefit, they’ll move to take advantage, which soon removes it. The most valuable insight from studying these patterns is not about when to trade, but the futility of trying to time the market based on the calendar alone. The data suggests that while timing patterns exist, they’re typically too small to exploit profitably. This reinforces the wisdom of systematic investment approaches like dollar-cost averaging, which succeed not by timing the market perfectly but by participating in it consistently.
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[Best Months to Buy or Sell Stocks](https://www.investopedia.com/day-trading/best-time-day-week-month-trade-stocks/#toc-best-months-to-buy-or-sell-stocks) - [Early-Month Returns](https://www.investopedia.com/day-trading/best-time-day-week-month-trade-stocks/#toc-early-month-returns) - [Daily, Monthly, and Other Seasonal Effects](https://www.investopedia.com/day-trading/best-time-day-week-month-trade-stocks/#toc-how-to-trade-in-light-of-daily-monthly-and-other-seasonal-effects) - [FAQs](https://www.investopedia.com/day-trading/best-time-day-week-month-trade-stocks/#toc-are-there-really-best-times-to-buy-or-sell-stocks) - [The Bottom Line](https://www.investopedia.com/day-trading/best-time-day-week-month-trade-stocks/#toc-the-bottom-line) # Best Times of the Day, Week, and Month to Trade Stocks What our analysis says about when and when not to enter or exit the market By [Peter Gratton](https://www.investopedia.com/peter-gratton-7968386) ![Peter Gratton portrait]() ![Peter Gratton portrait](https://www.investopedia.com/thmb/Tx-Ks42pE2ZOzQzV-QuIWtsDGHw=/200x200/filters:no_upscale\(\):max_bytes\(150000\):strip_icc\(\)/PGphotobestversion-c7f87d2cc1ef41459cf33551c7bd3530.jpg) [Full Bio](https://www.investopedia.com/peter-gratton-7968386) Peter Gratton, Ph.D., is a New Orleans-based editor and professor with over 20 years of experience in investing, economics, and public policy. Peter began covering markets at Multex (Reuters) and has expanded his coverage to include investments, ethics, public policy, and the health and travel industries. Learn about our [editorial policies](https://www.investopedia.com/legal-4768893#editorial-policy) Updated August 28, 2025 Reviewed by [Samantha Silberstein](https://www.investopedia.com/samantha-silberstein-5115118) ![Sam Silberstein]() ![Sam Silberstein](https://www.investopedia.com/thmb/Qsd1ISirwYHu9DMn0zIRjorVz9g=/200x200/filters:no_upscale\(\):max_bytes\(150000\):strip_icc\(\)/00100lrPORTRAIT_00100_BURST20200515171729630_COVER-SamSilberstein-8e793dbafa8845ebb749a0275c34bdef.JPG) Reviewed by Samantha Silberstein [Full Bio](https://www.investopedia.com/samantha-silberstein-5115118) Samantha (Sam) Silberstein, CFP®, CSLP®, EA, is an experienced financial consultant. She has a demonstrated history of working in both institutional and retail environments, from broker-dealers to RIAs. She is a current CFA level 3 candidate and also has her FINRA Series 7 and 63 licenses. Throughout her career, Samantha has used her expertise and various licenses and certifications to provide in-depth advice about household and business-specific financial planning, investing, credit cards, debt, student loans, taxes, retirement, and income strategies. Learn about our [Financial Review Board](https://www.investopedia.com/investopedia-financial-review-board-5076269) Fact checked by [Ryan Eichler](https://www.investopedia.com/ryan-eichler-5217138) ![Headshot]() ![Headshot](https://www.investopedia.com/thmb/0OW9HfIR55KN5h8kIlkqUtmufnc=/200x200/filters:no_upscale\(\):max_bytes\(150000\):strip_icc\(\)/InvestopediaHeadShot-988693bb87b54bb095bd3789cd117a50.jpg) Fact checked by Ryan Eichler [Full Bio](https://www.investopedia.com/ryan-eichler-5217138) Ryan Eichler holds a B.S.B.A with a concentration in Finance from Boston University. He has held positions in, and has deep experience with, expense auditing, personal finance, real estate, as well as fact checking & editing. Learn about our [editorial policies](https://www.investopedia.com/legal-4768893#editorial-policy) ![Woman tracking and trading stocks on computer screens](https://www.investopedia.com/thmb/a3ffZqtlFLgjx7P1jQevvrHg8Xg=/1500x0/filters:no_upscale\(\):max_bytes\(150000\):strip_icc\(\)/INV_SP500Gainers_Losers_2023_GettyImages-1223455297-196b1bea36f64240a70a0ce37ba6d1bc.jpg) ![Woman tracking and trading stocks on computer screens](https://www.investopedia.com/thmb/a3ffZqtlFLgjx7P1jQevvrHg8Xg=/1500x0/filters:no_upscale\(\):max_bytes\(150000\):strip_icc\(\)/INV_SP500Gainers_Losers_2023_GettyImages-1223455297-196b1bea36f64240a70a0ce37ba6d1bc.jpg) FatCamera / Getty Images Close Every second counts in the [stock market](https://www.investopedia.com/terms/s/stockmarket.asp), where fortunes can shift with each tick of the trading clock. Perhaps it’s no surprise, then, that many traders seek to gain an edge by identifying optimal times to enter and exit positions, whether that’s a particular hour of the day, day of the week, or month of the year. Research has shown that market activity often follows certain patterns tied to timing.1 Understanding these patterns and their limitations can help traders make more informed decisions about when to trade—or at least, be better informed when other traders think is the best time to trade. Below, we show you our analysis of stock market return by days of the week, months of the year, and other supposed patterns to see if there’s anything to prevailing myths about the best times to trade. ### Key Takeaways - The closest thing to a hard-and-fast rule is that the first hour and last hour of a trading day are the busiest, offering the most prospects, while the middle of the day tends to be the calmest and most stable period of most trading days. - Early-month trading days show stronger returns than the rest of the month, possibly because of regular investment flows like 401(k) contributions. - Trading days before long weekends show higher returns than regular trading days, while post-holiday returns tend to be slightly negative. - While statistically interesting, these and other calendar patterns are typically too small to exploit profitably once trading spreads and other costs are considered, supporting the case for systematic investment approaches like dollar-cost averaging. ## Trading Costs Matter: A Warning About Market Effects Before diving into the data to see whether any benefits are to be found [trading](https://www.investopedia.com/trading-4427765) at specific hours, on specific days, or in different months, we should underline that trading costs—including price spreads—would quickly erase many of the differences we found if you tried to trade based on them. For the S\&P 500, you’ll pay about 0.025% (2.5 [basis points](https://www.investopedia.com/terms/b/basispoint.asp)) in spreads to buy or sell the largest stocks, and about 0.045% (4.5 basis points) for a portfolio tracking the full index.2 Since you need to buy and sell to capture any effect, double these numbers for your round-trip trading cost. So, unless a market pattern shows returns significantly above 0.05% to 0.09% (5–9 basis points), it likely isn’t actionable—even before considering other trading costs, taxes, and the risk that the pattern might not hold in the future. Take the Next Step to Invest Advertiser Disclosure × The offers that appear in this table are from partnerships from which Investopedia receives compensation. This compensation may impact how and where listings appear. Investopedia does not include all offers available in the marketplace. ## Best Times of the Day to Buy or Sell Stocks The opening and closing hours of the trading day tend to be the most [volatile](https://www.investopedia.com/terms/v/volatility.asp) and active periods. When U.S. markets open at 9:30 a.m. Eastern time, it processes all events and news releases since the previous day’s close, which can create significant price swings. Many professional traders focus on the opening period (9:30 a.m. to 10:30 a.m. ET), as it typically offers the most significant price moves in the shortest time. By 11:30 a.m., volatility and volume often decrease significantly, leading many day traders to close their positions. The last hour of trading (3 p.m. to 4 p.m. ET) typically sees another surge in activity, as institutional investors and day traders close positions and react to late-breaking news. Like the morning session, this period can offer prospects but comes with increased risk. ### Tip It’s generally impossible to time the market. Doing your research, knowing the financials of the company that interests you, and the overall sector or wider economic climate will guide you far better in your decisions than the hour of the day or date on the calendar for most types of trading. ## How Will Extended Trading Affect the Best Intraday Trading Times? In a significant shift for U.S. markets, the [New York Stock Exchange (NYSE)](https://www.investopedia.com/terms/n/nyse.asp) plans to expand trading on its NYSE Arca Equities platform to 22 hours daily, from 1:30 a.m. to 11:30 p.m. ET on weekdays. About 56% of trading on NYSE Arca is in shares of exchange-traded funds (ETFs), with the rest for corporate stocks.3 The exchange already has broader opening hours than the regular NYSE, with sessions from 4 a.m. to 8 p.m. before and after regular trading hours.4 While [currencies](https://www.investopedia.com/terms/c/currency.asp), Treasurys, and stock index futures already trade outside regular hours, the shift—expected in 2025 and pending U.S. Securities and Exchange Commission (SEC) approval—will presumably reshape how [traders](https://www.investopedia.com/terms/t/trader.asp) approach intraday strategies. The NYSE’s head of markets, Kevin Tyrrell, said the move was meant in part to enable “exchange-based trading for our U.S.-listed companies and funds to investors in time zones across the globe.”5 The proposed shift also responds to increasing demand within the U.S. for round-the-clock trading access, as many have already via cryptocurrency markets. Some retail brokers already provide overnight trading where they facilitate your trades beyond official trading hours, usually by matching them with those occurring in alternative trading pools, aka [dark pools](https://www.investopedia.com/terms/d/dark-pool.asp), and overseas (especially in Asia) where it’s daytime. While evidence from other markets that have extended their hours is limited, the extension of trading hours is likely to do the following: - Events-based trading will cause price moves: [Investors](https://www.investopedia.com/terms/i/investor.asp) can respond more promptly to global events and news releases outside traditional market hours. This should also relieve some of the trading volume that bottles up around the opening and closing of the regular trading day. - Enhance liquidity: Extended hours may attract more participants, potentially improving liquidity during previously inactive periods. - Introduce more volatility into what was previously “off hours”: While increased trading hours can provide more prospects, they may also introduce periods of heightened volatility, especially during times when fewer participants are active. ## Best Day of the Week to Buy or Sell Stocks Research long ago showed that Fridays were when you’d see the best gains for stocks, with Mondays generally coming out the worst.6 Like other bits of Wall Street wisdom, though the data has long since been outdated, that hasn’t meant people don’t still advise that stocks tend to drop on Mondays. Some people think this is because a significant amount of bad news is often released over the weekend. Others point to investors’ gloomy mood at having to go back to work, which is especially evident during the early hours of Monday trading. ### What the Numbers Show But nothing beats actual data. We pulled [S\&P 500](https://www.investopedia.com/terms/s/sp500.asp) closing prices for 2000 to late 2024. While any findings would only apply to the broad market of highly capitalized stocks—one could imagine various nooks and crannies of the market where day-specific effects might be found—it’s worth seeing if there are calendar effects during the last 25 years worth most investors bothering about. While we did find day-specific patterns, we also found that they’re not of much practical significance, if any, for investors. Our analysis of over 6,200 trading days shows that Tuesday has historically produced the highest average daily returns at 0.062%, while Friday and Monday show the lowest average returns at about 0.009% each. Wednesday and Thursday fall in between, with average returns of 0.024% and 0.042%, respectively. However, what counts is whether these results are robust—otherwise, you risk seeing patterns in the noise where there aren’t any. To verify the reliability of these patterns, several statistical measures were considered both for daily and the other effects we check below. First, the [standard deviation](https://www.investopedia.com/terms/s/standarddeviation.asp) of daily returns was calculated for each day of the week. Here’s what we found: - Volatility context: The daily standard deviation of returns ranges from 1.12% to 1.34%—about 20 times larger than the differences among the days themselves. - Not much difference in the percentage of positive trading days: The percentage of positive days for the S\&P 500 is remarkably consistent across weekdays, ranging from 52% to 54%. This indicates that any given day has about the same probability of producing positive returns. - Statistical significance: The overall market average daily return during this period was 0.030%, with most daily variations falling well within normal statistical noise. The small magnitude of day-of-week effects, measured in hundredths of a percent, means that trading spreads, taxes, and so on, would likely overwhelm any potential gains from trying to exploit gains from trading on specific days of the week. In short, there’s nothing to be gained by trading based on what day of the week it is—all else being equal. ## What About Trading Before or After Long Weekends? There’s a traditional view that the market is generally positive before a long weekend. Here, we found reason to think this is generally the case. When the markets close for extended periods—whether for holidays like Thanksgiving, Christmas, or three-day weekends—a distinct pattern emerges in the returns for the S\&P 500: - Before long weekends: Average daily return of +0.185% - After long weekends: Average daily return of -0.059% - Regular trading days: Average daily return of +0.033% Put more straightforwardly, the last trading day before a long weekend has been five times higher than normal trading days. The pattern’s reliability is supported by other measures. Before long weekends, about 55% of trading days are positive, compared with 50% after long weekends and about 54% on regular days. The data covers 196 long weekends over almost 25 years—a robust sample size. ### Cautions Are Necessary Despite these clear patterns, investors should tread carefully before trying to trade based on this information: 1. Spreads are likely to cost more: While the differences in returns are statistically significant, they’re still measured in fractions of a percent. For most retail investors, trading costs (including [bid-ask spreads](https://www.investopedia.com/terms/b/bid-askspread.asp)) would likely erase any potential gains from trying to exploit these patterns. 2. Tax implications: Frequent trading around weekends could lead to higher short-term capital gains taxes, potentially overwhelming any small gains from the strategy. 3. Practical limitations: The strategy would require precise timing and regular trading around every long weekend, which isn’t practical for most investors. ## Best Months to Buy or Sell Stocks Our analysis of S\&P 500 data from 2000 to 2024 also revealed some clear monthly patterns. November is historically the strongest month, with an average daily return of 0.107% and positive returns 57% of the time. April and July are the next strongest months. This holds, too, when we broaden our data window to include back to 1928. The data challenges some traditional market wisdom. While September does live up to its reputation as the weakest month—showing average monthly returns of -1.53% and -1.13% for the periods 2000 to 2024 and 1928 to 2024, respectively—the supposed positive “[January effect](https://www.investopedia.com/terms/j/januaryeffect.asp),” which is 1.17% going back to 1928, has disappeared in recent decades, with returns of -0.15% since 2000. The seasonal effects become more dramatic when looking at cumulative returns over these periods. If you had invested only in specific months, November would have shown the strongest performance with about a 54% cumulative gain from 2000 to 2024 (or about two years of months), while September’s weakness would have added up to a -37% loss over time. ### How Actionable Are These Differences? Before getting too excited about these patterns, it’s crucial to understand their reliability. The day-to-day [swings](https://www.investopedia.com/terms/s/swing.asp) in the market are typically much larger than these monthly differences. Likewise, even in November (the strongest month), the market regularly moves up or down by amounts much larger than its typical monthly gain. This “[noise](https://www.investopedia.com/terms/n/noise.asp)” in the data means that while these patterns are interesting, they’re not reliable enough to be the main factor in timing investment decisions. ### Important It’s important to consider that we found that the day-to-day swings in the market are typically much larger than these monthly differences. It’s like the difference between weather and climate in temperate zones—while we might say summer is warmer than winter, any given winter day could be warmer than a summer day. As such, daily shifts in the market spark waves that wipe out most small calendar differences. So when is the best time to sell? The data confirms the traditional investor wariness of September, as it’s the only month stocks declined more often than they rose (49.9% positive days). February and June also emerge as historically weak months, with average daily returns of -0.023% and -0.010%, respectively. However, the [October effect](https://www.investopedia.com/articles/financial-theory/09/october-effect.asp)—the fear going back to the October 1929 crash of market crises that month—appears less justified in recent data. October has actually had a relatively strong performance, with average monthly gains of 1.34% since 2000 and positive returns 53% of the time. That said, it’s among the most volatile—about 20% and 15% more volatile than September and November, the months right before and after. ### Fast Fact Not for nothing is October a month that’s salient for many investors. Even though October, on average, has been a positive month historically, many of the worst market crashes have occurred in this month, including [Black Monday](https://www.investopedia.com/terms/b/blackmonday.asp).78 From 2000 to 2024, it was also the most volatile.9 ## Early-Month Returns Many traders believe that returns in the early part of the month are better than the rest. Our analysis of S\&P 500 data since 2000 shows a compelling pattern: Trading days early in the month tend to show stronger performance than those in the latter part of the month. We found a consistent advantage across several metrics. The average daily return during the first five trading days is +0.084%, compared with just +0.019% for the remainder of the month—a difference that amounts to more than four times higher average returns. This pattern holds up even when looking at median returns, which are less influenced by extreme market moves, with early-month median daily returns of +0.121% vs. +0.049% for other days. The early-month advantage isn’t just about the size of returns; it has also been consistent. During these first five trading days, the market posts positive returns 56.4% of the time, compared with 53.0% for the rest of the month. This higher batting average suggests the early-month effect is stable and isn’t just the result of a few outlier days. ### Why Early Parts of the Month Might Have Higher Returns What might explain this pattern? One possibility is the timing of monthly investment flows, such as [401(k) plan](https://www.investopedia.com/terms/1/401kplan.asp) contributions and institutional portfolio rebalancing, which often occur near the start of each month. As this new money enters the market, it may create systematic buying pressure, leading to slightly higher returns. However, investors should approach this pattern with due caution. While the data shows a clear historical tendency, the magnitude of the difference is not large enough to overcome transaction spreads in a trading strategy. Moreover, as with any market pattern, there’s no guarantee that historical tendencies will persist. Still, this monthly cycle represents an interesting facet of market behavior that adds to our understanding of how calendar effects influence stock returns. ## How to Trade in Light of Daily, Monthly, and Other Seasonal Effects While our analysis has revealed several intriguing patterns in market behavior—whether it’s the early-month effect, day-of-week variations, or monthly seasonality—converting these patterns into profitable trades comes with significant challenges. The differences in returns, when not simply statistical noise, are often too small to overcome trading costs. For instance, the early-month advantage of 0.065% per day (the difference between early-month and rest-of-the-month returns) would be largely erased by typical trading spreads in a strategy attempting to capture this effect. This leads us to an important insight: Rather than trying to time the market based on calendar effects, investors might be better served by implementing a systematic investment approach like [dollar-cost averaging (DCA)](https://www.investopedia.com/terms/d/dollarcostaveraging.asp). With DCA, investors make regular investments of equal amounts regardless of market conditions or timing. This approach naturally diversifies the entry points for investments across different market conditions, reducing the impact of market timing and volatility on long-term returns.10 DCA works well for many investors since it removes the emotional and tactical complexity of trying to time the market. It also aligns well with how most people actually invest—through regular contributions to [individual retirement accounts (IRAs)](https://www.investopedia.com/terms/i/ira.asp) or investment plans. ## Are There Really Best Times to Buy or Sell Stocks? Historically, some days or months have tended to be better or worse for stocks. These so-called [market anomalies](https://www.investopedia.com/articles/stocks/08/market-anomaly-efficient-market.asp) challenged theories of efficient markets. However, research shows that as these anomalies became more well-known and trading became more automated, these have largely all disappeared.11 Others persist, but trading spreads tend to wash out your ability to trade on most of them. ## What Is the Super Bowl Indicator? This suggests that the stock market will rise the year an NFC team wins and fall if an AFC team wins. While this “indicator” had an accuracy rate above 80% for many years, it’s a classic example of [correlation without causation](https://web.archive.org/web/20150523013856/https://www.investopedia.com/terms/s/spurious_correlation.asp) (when two things coincidentally happen together, but one can’t cause the other). ## What Is the Strangest Calendar Effect Claimed by Investors? Unusual patterns people have studied include the adage for investors to “[sell in May and go away](https://www.investopedia.com/terms/s/sell-in-may-and-go-away.asp),” the presidential election cycle, and even the effect of lunar phases on stock returns. While some have shown statistical significance historically, none have proved reliable enough for actual trading, given spreads and other costs. ## The Bottom Line Our analysis confirms some traditional beliefs (like September’s weakness) while challenging others (like the supposed January effect). While certain patterns show statistical significance, like the early-month effect or preholiday strength, trading spreads and market efficiency largely annul the practical value of these findings. Any [abnormal returns](https://www.investopedia.com/terms/a/abnormalreturn.asp) are generally short-lived, as these prospects are quickly [arbitraged](https://www.investopedia.com/terms/a/arbitrage.asp) away, and markets become more efficient as traders and investors increasingly learn about the patterns.12 That is, once other traders know there’s a benefit, they’ll move to take advantage, which soon removes it. The most valuable insight from studying these patterns is not about when to trade, but the futility of trying to time the market based on the calendar alone. The data suggests that while timing patterns exist, they’re typically too small to exploit profitably. This reinforces the wisdom of systematic investment approaches like dollar-cost averaging, which succeed not by timing the market perfectly but by participating in it consistently. Article Sources Investopedia requires writers to use primary sources to support their work. These include white papers, government data, original reporting, and interviews with industry experts. We also reference original research from other reputable publishers where appropriate. You can learn more about the standards we follow in producing accurate, unbiased content in our [editorial policy.](https://www.investopedia.com/legal-4768893#EditorialPolicy) 1. Charles Schwab. “[Trading Near the Bells](https://www.schwab.com/learn/story/trading-near-bells).” 2. Nasdaq. “[Sampling the S\&P 500 to Minimize Spreads](https://www.nasdaq.com/articles/sampling-sp-500-minimize-spreads).” 3. Financial Times. "[NYSE Plans to Extend Daily Trading to 22 Hours on Its Arca Exchange](https://www.ft.com/content/59dc109d-d385-412a-bcc6-b671db9ce680)." 4. New York Stock Exchange. “[Market Information](https://www.nyse.com/markets/nyse-arca/market-info).” 5. Intercontinental Exchange. “[The New York Stock Exchange Plans to Extend Weekday Trading on Its NYSE Arca Equities Exchange to 22 Hours a Day](https://ir.theice.com/press/news-details/2024/The-New-York-Stock-Exchange-Plans-to-Extend-Weekday-Trading-on-its-NYSE-Arca-Equities-Exchange-to-22-Hours-a-Day/default.aspx).” 6. Cross, Frank, via JSTOR. “[The Behavior of Stock Prices on Fridays and Mondays](https://www.jstor.org/stable/4529641).” Financial Analysts Journal, vol. 29, no. 6, 1973, pp. 67–69. 7. Federal Reserve History. “[Stock Market Crash of 1929](https://www.federalreservehistory.org/essays/stock-market-crash-of-1929).” 8. Federal Reserve History. “[Stock Market Crash of 1987](https://www.federalreservehistory.org/essays/stock-market-crash-of-1987).” 9. Barron's. "[Forget September. This Is the Stock Market’s Most Volatile Month](https://www.barrons.com/articles/stocks-most-volatile-month-61786737)." 10. Financial Industry Regulatory Authority. “[The Pros and Cons of Dollar-Cost Averaging](https://www.finra.org/investors/insights/dollar-cost-averaging).” 11. Schwert, G. William, via SSRN. “[Anomalies and Market Efficiency](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=338080).” The Bradley Policy Research CenterFinancial Research and Policy, Working Paper No. FR 02-13, October 2002, pp. 2–3. 12. Schwert, G. William, via SSRN. “[Anomalies and Market Efficiency](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=338080).” The Bradley Policy Research CenterFinancial Research and Policy, Working Paper No. FR 02-13, October 2002, pp. 1–52. 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Readable Markdown	Every second counts in the [stock market](https://www.investopedia.com/terms/s/stockmarket.asp), where fortunes can shift with each tick of the trading clock. Perhaps it’s no surprise, then, that many traders seek to gain an edge by identifying optimal times to enter and exit positions, whether that’s a particular hour of the day, day of the week, or month of the year. Research has shown that market activity often follows certain patterns tied to timing.1 Understanding these patterns and their limitations can help traders make more informed decisions about when to trade—or at least, be better informed when other traders think is the best time to trade. Below, we show you our analysis of stock market return by days of the week, months of the year, and other supposed patterns to see if there’s anything to prevailing myths about the best times to trade. ### Key Takeaways - The closest thing to a hard-and-fast rule is that the first hour and last hour of a trading day are the busiest, offering the most prospects, while the middle of the day tends to be the calmest and most stable period of most trading days. - Early-month trading days show stronger returns than the rest of the month, possibly because of regular investment flows like 401(k) contributions. - Trading days before long weekends show higher returns than regular trading days, while post-holiday returns tend to be slightly negative. - While statistically interesting, these and other calendar patterns are typically too small to exploit profitably once trading spreads and other costs are considered, supporting the case for systematic investment approaches like dollar-cost averaging. ## Trading Costs Matter: A Warning About Market Effects Before diving into the data to see whether any benefits are to be found [trading](https://www.investopedia.com/trading-4427765) at specific hours, on specific days, or in different months, we should underline that trading costs—including price spreads—would quickly erase many of the differences we found if you tried to trade based on them. For the S\&P 500, you’ll pay about 0.025% (2.5 [basis points](https://www.investopedia.com/terms/b/basispoint.asp)) in spreads to buy or sell the largest stocks, and about 0.045% (4.5 basis points) for a portfolio tracking the full index.2 Since you need to buy and sell to capture any effect, double these numbers for your round-trip trading cost. So, unless a market pattern shows returns significantly above 0.05% to 0.09% (5–9 basis points), it likely isn’t actionable—even before considering other trading costs, taxes, and the risk that the pattern might not hold in the future. ## Best Times of the Day to Buy or Sell Stocks The opening and closing hours of the trading day tend to be the most [volatile](https://www.investopedia.com/terms/v/volatility.asp) and active periods. When U.S. markets open at 9:30 a.m. Eastern time, it processes all events and news releases since the previous day’s close, which can create significant price swings. Many professional traders focus on the opening period (9:30 a.m. to 10:30 a.m. ET), as it typically offers the most significant price moves in the shortest time. By 11:30 a.m., volatility and volume often decrease significantly, leading many day traders to close their positions. The last hour of trading (3 p.m. to 4 p.m. ET) typically sees another surge in activity, as institutional investors and day traders close positions and react to late-breaking news. Like the morning session, this period can offer prospects but comes with increased risk. ### Tip It’s generally impossible to time the market. Doing your research, knowing the financials of the company that interests you, and the overall sector or wider economic climate will guide you far better in your decisions than the hour of the day or date on the calendar for most types of trading. ## How Will Extended Trading Affect the Best Intraday Trading Times? In a significant shift for U.S. markets, the [New York Stock Exchange (NYSE)](https://www.investopedia.com/terms/n/nyse.asp) plans to expand trading on its NYSE Arca Equities platform to 22 hours daily, from 1:30 a.m. to 11:30 p.m. ET on weekdays. About 56% of trading on NYSE Arca is in shares of exchange-traded funds (ETFs), with the rest for corporate stocks.3 The exchange already has broader opening hours than the regular NYSE, with sessions from 4 a.m. to 8 p.m. before and after regular trading hours.4 While [currencies](https://www.investopedia.com/terms/c/currency.asp), Treasurys, and stock index futures already trade outside regular hours, the shift—expected in 2025 and pending U.S. Securities and Exchange Commission (SEC) approval—will presumably reshape how [traders](https://www.investopedia.com/terms/t/trader.asp) approach intraday strategies. The NYSE’s head of markets, Kevin Tyrrell, said the move was meant in part to enable “exchange-based trading for our U.S.-listed companies and funds to investors in time zones across the globe.”5 The proposed shift also responds to increasing demand within the U.S. for round-the-clock trading access, as many have already via cryptocurrency markets. Some retail brokers already provide overnight trading where they facilitate your trades beyond official trading hours, usually by matching them with those occurring in alternative trading pools, aka [dark pools](https://www.investopedia.com/terms/d/dark-pool.asp), and overseas (especially in Asia) where it’s daytime. While evidence from other markets that have extended their hours is limited, the extension of trading hours is likely to do the following: - Events-based trading will cause price moves: [Investors](https://www.investopedia.com/terms/i/investor.asp) can respond more promptly to global events and news releases outside traditional market hours. This should also relieve some of the trading volume that bottles up around the opening and closing of the regular trading day. - Enhance liquidity: Extended hours may attract more participants, potentially improving liquidity during previously inactive periods. - Introduce more volatility into what was previously “off hours”: While increased trading hours can provide more prospects, they may also introduce periods of heightened volatility, especially during times when fewer participants are active. ## Best Day of the Week to Buy or Sell Stocks Research long ago showed that Fridays were when you’d see the best gains for stocks, with Mondays generally coming out the worst.6 Like other bits of Wall Street wisdom, though the data has long since been outdated, that hasn’t meant people don’t still advise that stocks tend to drop on Mondays. Some people think this is because a significant amount of bad news is often released over the weekend. Others point to investors’ gloomy mood at having to go back to work, which is especially evident during the early hours of Monday trading. ### What the Numbers Show But nothing beats actual data. We pulled [S\&P 500](https://www.investopedia.com/terms/s/sp500.asp) closing prices for 2000 to late 2024. While any findings would only apply to the broad market of highly capitalized stocks—one could imagine various nooks and crannies of the market where day-specific effects might be found—it’s worth seeing if there are calendar effects during the last 25 years worth most investors bothering about. While we did find day-specific patterns, we also found that they’re not of much practical significance, if any, for investors. Our analysis of over 6,200 trading days shows that Tuesday has historically produced the highest average daily returns at 0.062%, while Friday and Monday show the lowest average returns at about 0.009% each. Wednesday and Thursday fall in between, with average returns of 0.024% and 0.042%, respectively. However, what counts is whether these results are robust—otherwise, you risk seeing patterns in the noise where there aren’t any. To verify the reliability of these patterns, several statistical measures were considered both for daily and the other effects we check below. First, the [standard deviation](https://www.investopedia.com/terms/s/standarddeviation.asp) of daily returns was calculated for each day of the week. Here’s what we found: - Volatility context: The daily standard deviation of returns ranges from 1.12% to 1.34%—about 20 times larger than the differences among the days themselves. - Not much difference in the percentage of positive trading days: The percentage of positive days for the S\&P 500 is remarkably consistent across weekdays, ranging from 52% to 54%. This indicates that any given day has about the same probability of producing positive returns. - Statistical significance: The overall market average daily return during this period was 0.030%, with most daily variations falling well within normal statistical noise. The small magnitude of day-of-week effects, measured in hundredths of a percent, means that trading spreads, taxes, and so on, would likely overwhelm any potential gains from trying to exploit gains from trading on specific days of the week. In short, there’s nothing to be gained by trading based on what day of the week it is—all else being equal. ## What About Trading Before or After Long Weekends? There’s a traditional view that the market is generally positive before a long weekend. Here, we found reason to think this is generally the case. When the markets close for extended periods—whether for holidays like Thanksgiving, Christmas, or three-day weekends—a distinct pattern emerges in the returns for the S\&P 500: - Before long weekends: Average daily return of +0.185% - After long weekends: Average daily return of -0.059% - Regular trading days: Average daily return of +0.033% Put more straightforwardly, the last trading day before a long weekend has been five times higher than normal trading days. The pattern’s reliability is supported by other measures. Before long weekends, about 55% of trading days are positive, compared with 50% after long weekends and about 54% on regular days. The data covers 196 long weekends over almost 25 years—a robust sample size. ### Cautions Are Necessary Despite these clear patterns, investors should tread carefully before trying to trade based on this information: 1. Spreads are likely to cost more: While the differences in returns are statistically significant, they’re still measured in fractions of a percent. For most retail investors, trading costs (including [bid-ask spreads](https://www.investopedia.com/terms/b/bid-askspread.asp)) would likely erase any potential gains from trying to exploit these patterns. 2. Tax implications: Frequent trading around weekends could lead to higher short-term capital gains taxes, potentially overwhelming any small gains from the strategy. 3. Practical limitations: The strategy would require precise timing and regular trading around every long weekend, which isn’t practical for most investors. ## Best Months to Buy or Sell Stocks Our analysis of S\&P 500 data from 2000 to 2024 also revealed some clear monthly patterns. November is historically the strongest month, with an average daily return of 0.107% and positive returns 57% of the time. April and July are the next strongest months. This holds, too, when we broaden our data window to include back to 1928. The data challenges some traditional market wisdom. While September does live up to its reputation as the weakest month—showing average monthly returns of -1.53% and -1.13% for the periods 2000 to 2024 and 1928 to 2024, respectively—the supposed positive “[January effect](https://www.investopedia.com/terms/j/januaryeffect.asp),” which is 1.17% going back to 1928, has disappeared in recent decades, with returns of -0.15% since 2000. The seasonal effects become more dramatic when looking at cumulative returns over these periods. If you had invested only in specific months, November would have shown the strongest performance with about a 54% cumulative gain from 2000 to 2024 (or about two years of months), while September’s weakness would have added up to a -37% loss over time. ### How Actionable Are These Differences? Before getting too excited about these patterns, it’s crucial to understand their reliability. The day-to-day [swings](https://www.investopedia.com/terms/s/swing.asp) in the market are typically much larger than these monthly differences. Likewise, even in November (the strongest month), the market regularly moves up or down by amounts much larger than its typical monthly gain. This “[noise](https://www.investopedia.com/terms/n/noise.asp)” in the data means that while these patterns are interesting, they’re not reliable enough to be the main factor in timing investment decisions. ### Important It’s important to consider that we found that the day-to-day swings in the market are typically much larger than these monthly differences. It’s like the difference between weather and climate in temperate zones—while we might say summer is warmer than winter, any given winter day could be warmer than a summer day. As such, daily shifts in the market spark waves that wipe out most small calendar differences. So when is the best time to sell? The data confirms the traditional investor wariness of September, as it’s the only month stocks declined more often than they rose (49.9% positive days). February and June also emerge as historically weak months, with average daily returns of -0.023% and -0.010%, respectively. However, the [October effect](https://www.investopedia.com/articles/financial-theory/09/october-effect.asp)—the fear going back to the October 1929 crash of market crises that month—appears less justified in recent data. October has actually had a relatively strong performance, with average monthly gains of 1.34% since 2000 and positive returns 53% of the time. That said, it’s among the most volatile—about 20% and 15% more volatile than September and November, the months right before and after. ### Fast Fact Not for nothing is October a month that’s salient for many investors. Even though October, on average, has been a positive month historically, many of the worst market crashes have occurred in this month, including [Black Monday](https://www.investopedia.com/terms/b/blackmonday.asp).78 From 2000 to 2024, it was also the most volatile.9 ## Early-Month Returns Many traders believe that returns in the early part of the month are better than the rest. Our analysis of S\&P 500 data since 2000 shows a compelling pattern: Trading days early in the month tend to show stronger performance than those in the latter part of the month. We found a consistent advantage across several metrics. The average daily return during the first five trading days is +0.084%, compared with just +0.019% for the remainder of the month—a difference that amounts to more than four times higher average returns. This pattern holds up even when looking at median returns, which are less influenced by extreme market moves, with early-month median daily returns of +0.121% vs. +0.049% for other days. The early-month advantage isn’t just about the size of returns; it has also been consistent. During these first five trading days, the market posts positive returns 56.4% of the time, compared with 53.0% for the rest of the month. This higher batting average suggests the early-month effect is stable and isn’t just the result of a few outlier days. ### Why Early Parts of the Month Might Have Higher Returns What might explain this pattern? One possibility is the timing of monthly investment flows, such as [401(k) plan](https://www.investopedia.com/terms/1/401kplan.asp) contributions and institutional portfolio rebalancing, which often occur near the start of each month. As this new money enters the market, it may create systematic buying pressure, leading to slightly higher returns. However, investors should approach this pattern with due caution. While the data shows a clear historical tendency, the magnitude of the difference is not large enough to overcome transaction spreads in a trading strategy. Moreover, as with any market pattern, there’s no guarantee that historical tendencies will persist. Still, this monthly cycle represents an interesting facet of market behavior that adds to our understanding of how calendar effects influence stock returns. ## How to Trade in Light of Daily, Monthly, and Other Seasonal Effects While our analysis has revealed several intriguing patterns in market behavior—whether it’s the early-month effect, day-of-week variations, or monthly seasonality—converting these patterns into profitable trades comes with significant challenges. The differences in returns, when not simply statistical noise, are often too small to overcome trading costs. For instance, the early-month advantage of 0.065% per day (the difference between early-month and rest-of-the-month returns) would be largely erased by typical trading spreads in a strategy attempting to capture this effect. This leads us to an important insight: Rather than trying to time the market based on calendar effects, investors might be better served by implementing a systematic investment approach like [dollar-cost averaging (DCA)](https://www.investopedia.com/terms/d/dollarcostaveraging.asp). With DCA, investors make regular investments of equal amounts regardless of market conditions or timing. This approach naturally diversifies the entry points for investments across different market conditions, reducing the impact of market timing and volatility on long-term returns.10 DCA works well for many investors since it removes the emotional and tactical complexity of trying to time the market. It also aligns well with how most people actually invest—through regular contributions to [individual retirement accounts (IRAs)](https://www.investopedia.com/terms/i/ira.asp) or investment plans. ## Are There Really Best Times to Buy or Sell Stocks? Historically, some days or months have tended to be better or worse for stocks. These so-called [market anomalies](https://www.investopedia.com/articles/stocks/08/market-anomaly-efficient-market.asp) challenged theories of efficient markets. However, research shows that as these anomalies became more well-known and trading became more automated, these have largely all disappeared.11 Others persist, but trading spreads tend to wash out your ability to trade on most of them. ## What Is the Super Bowl Indicator? This suggests that the stock market will rise the year an NFC team wins and fall if an AFC team wins. While this “indicator” had an accuracy rate above 80% for many years, it’s a classic example of [correlation without causation](https://web.archive.org/web/20150523013856/https://www.investopedia.com/terms/s/spurious_correlation.asp) (when two things coincidentally happen together, but one can’t cause the other). ## What Is the Strangest Calendar Effect Claimed by Investors? Unusual patterns people have studied include the adage for investors to “[sell in May and go away](https://www.investopedia.com/terms/s/sell-in-may-and-go-away.asp),” the presidential election cycle, and even the effect of lunar phases on stock returns. While some have shown statistical significance historically, none have proved reliable enough for actual trading, given spreads and other costs. ## The Bottom Line Our analysis confirms some traditional beliefs (like September’s weakness) while challenging others (like the supposed January effect). While certain patterns show statistical significance, like the early-month effect or preholiday strength, trading spreads and market efficiency largely annul the practical value of these findings. Any [abnormal returns](https://www.investopedia.com/terms/a/abnormalreturn.asp) are generally short-lived, as these prospects are quickly [arbitraged](https://www.investopedia.com/terms/a/arbitrage.asp) away, and markets become more efficient as traders and investors increasingly learn about the patterns.12 That is, once other traders know there’s a benefit, they’ll move to take advantage, which soon removes it. The most valuable insight from studying these patterns is not about when to trade, but the futility of trying to time the market based on the calendar alone. The data suggests that while timing patterns exist, they’re typically too small to exploit profitably. This reinforces the wisdom of systematic investment approaches like dollar-cost averaging, which succeed not by timing the market perfectly but by participating in it consistently.
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