Strasmore Research
学习 Matt Connor作者: Matt Connor · 更新于 2026-07-23 · data as of July 23, 2026 · refreshed weekly

美股除息日日历:本周与下周即将除息的股票

查看本周与下周即将除息的美股:将要除息的最大公司、每股派息金额与派息日,以及除息日早晨股价的实际走势和“股息捕获”交易的实测结果。全部由已宣布的派息记录计算,每周刷新。

即将到来的除息日决定这笔钱归谁:在除息日之前持有股票,下一笔派息就是您的;在除息日当天或之后买入,这笔现金归卖方所有。本页是一份滚动更新的除息日历——本周每一天将要除息的标的、未来两周内将要除息的最大公司——全部由已宣布的派息记录计算得出,每周刷新。本页还测量了除息日当天早晨股价的实际表现,以及所谓“股息捕获”交易在真实数据面前是否站得住脚。

本周的除息日

未来七天内将要除息的每一笔已宣布现金股息,按日期分组,并按派息频率拆分:

查询逐日统计的除息标的数量——未来七天的已宣布派息记录
每个数字背后的完整 SQL
SELECT toString(ex_dividend_date) AS ex_date,
       formatDateTime(ex_dividend_date, '%b %e') AS ex_date_label,
       count() AS names_going_ex,
       countIf(frequency = 12) AS monthly_payers,
       countIf(frequency = 4) AS quarterly_payers
FROM global_markets.stocks_dividends
WHERE ex_dividend_date > today()
  AND ex_dividend_date <= today() + 7
  AND cash_amount > 0
GROUP BY ex_dividend_date
ORDER BY ex_dividend_date

近期日历上共有5个除息日,从Jul 24开始,当天有99只标的除息——其中52只按月派息,28只按季度派息。扫日历时这个拆分很重要:按月派息的标的(收益型基金、抵押贷款信托、部分REITs)每四周就重新出现一次,且集中在月中前后;按季度派息的标的——多数实体经营公司——一个季度才出现一次。要拿到其中任何一笔派息,必须在所列日期前一个交易日收盘时已经持有该仓位。具体的时间规则见我们的除息日详解,而股权登记日与除息日的区别则说明了为什么对买方来说真正起作用的是除息日。

未来两周将要除息的大公司

十四天内将要除息的最大公司(市值100亿美元及以上)——派息金额、派息日,以及这笔派息按当前股价推算出的年化股息率:

查询未来14天内将要除息的最大公司——派息金额、派息日与指示股息率
每个数字背后的完整 SQL
WITH latest AS (
    SELECT ticker, argMax(market_cap, date) AS mcap, argMax(price, date) AS px
    FROM global_markets.stocks_ratios
    WHERE date >= today() - 10
    GROUP BY ticker
)
SELECT d.ticker AS ticker,
       toString(d.ex_dividend_date) AS ex_date,
       formatDateTime(d.ex_dividend_date, '%b %e') AS ex_date_label,
       round(max(d.cash_amount), 4) AS per_share_usd,
       toString(any(d.pay_date)) AS pay_date,
       formatDateTime(any(d.pay_date), '%b %e') AS pay_date_label,
       round(any(l.mcap) / 1e9, 0) AS mcap_bn,
       round(100 * max(d.cash_amount) * max(d.frequency) / any(l.px), 2) AS indicated_yield_pct
FROM global_markets.stocks_dividends d
JOIN latest l ON d.ticker = l.ticker
WHERE d.ex_dividend_date > today()
  AND d.ex_dividend_date <= today() + 14
  AND d.cash_amount > 0
  AND d.distribution_type = 'recurring'
  AND d.frequency > 0
  AND l.mcap >= 10000000000
  AND d.ticker NOT IN ('SPCX')
GROUP BY d.ticker, d.ex_dividend_date
ORDER BY any(l.mcap) DESC
LIMIT 12

名单上体量最大的是COST,将于Jul 24除息,每股派息1.47美元,派息日为Aug 7——按其近期股价折算,指示年化股息率为0.63%。股息率这一列要仔细读:它是把已宣布的这笔派息按其标明的频率年化得出的——一次快照式的算术,不是承诺。每股派息金额大和股息率高是两回事,这一点什么是股息率里有说明。有一个代码被排除在排名表之外(过滤条件可在面板的SQL中看到):它的股票代码曾被第二家发行人重新使用,因此该代码名下的记录横跨两家公司,与其打印一行无法归属的数据,我们选择直接剔除。

现在是哪类公司在派息

派息数据源本身不带行业标签,因此两周窗口的结构只能从记录里确实存在的字段中还原:公司规模、派息节奏,以及每笔已宣布派息按当前股价推算出的股息率。下面是十四天内将要除息、且带有市值记录的全部常规派息标的,按市值分层分组:

查询未来14天内谁将除息:按市值分层看标的数量、派息节奏与指示股息率
每个数字背后的完整 SQL
WITH latest AS (
    SELECT ticker, argMax(market_cap, date) AS mcap, argMax(price, date) AS px
    FROM global_markets.stocks_ratios
    WHERE date >= today() - 10
    GROUP BY ticker
),
names AS (
    SELECT d.ticker AS ticker,
           multiIf(any(l.mcap) >= 1e11, '1 Mega cap ($100B+)',
                   any(l.mcap) >= 1e10, '2 Large cap ($10-100B)',
                   any(l.mcap) >= 2e9,  '3 Mid cap ($2-10B)',
                                        '4 Small cap (under $2B)') AS size_band,
           max(d.cash_amount) AS amt,
           max(d.frequency) AS freq,
           any(l.px) AS px
    FROM global_markets.stocks_dividends d
    JOIN latest l ON d.ticker = l.ticker
    WHERE d.ex_dividend_date > today()
      AND d.ex_dividend_date <= today() + 14
      AND d.cash_amount > 0
      AND d.distribution_type = 'recurring'
      AND d.frequency > 0
      AND l.px > 0
      AND l.mcap > 0
    GROUP BY d.ticker
)
SELECT size_band,
       count() AS names_going_ex,
       round(quantileDeterministic(0.5)(100 * amt * freq / px, cityHash64(ticker)), 2) AS median_indicated_yield_pct,
       countIf(freq = 12) AS monthly_payers,
       countIf(freq = 4) AS quarterly_payers
FROM names
GROUP BY size_band
ORDER BY size_band

这个窗口内有6只超大市值股(1000亿美元及以上)和30只大市值股(100亿至1000亿美元),另有27只中市值股和55只更小的标的。指示股息率中位数在超大市值层为2.07%,大市值层为2.55%,中市值层为2.45%,小市值层为4.15%。市值与股息率是两个彼此独立的维度:按股息率排序的日历,浮上来的是与按公司规模排序完全不同的一批标的,所以只用股息率做筛选,会在您不知不觉间一路把持仓拖向更小的市值。派息节奏的分化方式,和上面那张逐日表格一致。市场顶端由按季度派息的习惯主导:超大市值股中有6只按季度派息,0只按月派息;而按月派息的群体集中在市值表更靠下的位置——本窗口内的小市值标的中有17只每月派息。

要拿到这笔钱,您实际需要做什么

在几乎所有情况下:什么都不用做。不用填表,不用打电话给券商,也不用登记——只要在除息日前一个交易日收盘(美东时间 16:00,夏令时对应北京时间次日 04:00)时持有股票,现金就会在派息日(通常是几周之后)打进您的账户。在那最后一个交易日当天买入同样算数:美股交易在下一个交易日完成交收(T+1),除息日正是照着这一点设定的。唯一可选的动作是DRIP(股息再投资计划),这是券商端的一个设置,会把这笔现金自动买成同一只股票的碎股。

除息日真正影响到手收益的,是税务处理。在美国的应税账户里,一笔股息要算作合格股息、按更低的长期资本利得税率征税,前提是:在除息日前60天开始的121天窗口内,您持有该股票超过60天。除息日前一天买入、领到派息、一周后卖出,这项检验就通不过:这笔派息按普通所得征税。REITs和许多基金的分派无论如何都按普通所得处理,账户类型也会改变结论(IRA账户可以绕开这一点)。这里不构成税务建议——但短持有的“股息捕获”,税后价值比表面看上去要低。

除息日早晨股价的实际走势

教科书式的说法是,股票在除息日会“按股息金额向下调整”后开盘。交易所确实会在开盘(美东时间 9:30,夏令时对应北京时间 21:30)前把参考价下调——但开盘价是这次下调加上隔夜发生的其他一切。下面是三家家喻户晓的派息公司在其最近一次除息日的表现:

查询三家家喻户晓的派息公司在最近一次除息日——前收盘价、除息日开盘价,以及作为参照的派息金额
每个数字背后的完整 SQL
WITH last_ex AS (
    SELECT ticker,
           max(ex_dividend_date) AS ex_d,
           argMax(cash_amount, ex_dividend_date) AS div_amt
    FROM global_markets.stocks_dividends
    WHERE ticker IN ('KO', 'VZ', 'XOM')
      AND cash_amount > 0
      AND distribution_type = 'recurring'
      AND ex_dividend_date < today()
      AND ex_dividend_date >= today() - 120
    GROUP BY ticker
),
daily AS (
    SELECT ticker,
           toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           argMaxIf(toFloat64(close), window_start, (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) AS close_px,
           argMinIf(toFloat64(open), window_start, (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) AS open_px,
           countIf((toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) AS bars
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('KO', 'VZ', 'XOM')
      AND window_start >= toDateTime(today() - 130)
    GROUP BY ticker, d
    HAVING bars > 200
),
seq AS (
    SELECT ticker, d, open_px, close_px,
           lagInFrame(close_px, 1) OVER (PARTITION BY ticker ORDER BY d ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS pre_close
    FROM daily
)
SELECT s.ticker AS ticker,
       toString(e.ex_d) AS last_ex_date,
       formatDateTime(e.ex_d, '%b %e') AS last_ex_label,
       round(e.div_amt, 4) AS dividend_usd,
       round(s.pre_close, 2) AS close_before_ex_usd,
       round(s.open_px, 2) AS ex_morning_open_usd,
       round(100 * (s.open_px - s.pre_close) / s.pre_close, 2) AS open_gap_pct,
       round(100 * e.div_amt / s.pre_close, 2) AS dividend_pct_of_price
FROM seq s
JOIN last_ex e ON e.ticker = s.ticker AND e.ex_d = s.d
WHERE s.pre_close > 0
ORDER BY ticker

以可口可乐(KO)最近一次除息日Jun 15为例:每股派息0.53美元,相当于前一晚82.6美元收盘价的0.64%,而除息日早晨的首个开盘价为81.08美元——跳空幅度-1.84%。威瑞森(VZ)的派息相当于其42.22美元前收盘价的1.68%,开盘跳空-1.46%。埃克森美孚(XOM)的派息相当于股价的0.67%,开盘跳空0.64%。三家派息公司,三个大小不同的跳空幅度,没有一个正好等于派息金额。下调只是对参考价的一次记账式调整;而开盘价是真实成交出来的价格,价格会因为与股息无关的原因在隔夜发生变动。派息金额是提前知道的;除息日早晨的价格不是。

股息捕获交易真的有效吗?

“股息捕获”指的是在除息日前夕买入、领到派息、等股价涨回来后再卖出。这里面包含一个可以检验的主张。检验方法是:过去六个月里五十家超大市值派息公司的83次除息事件,每一次都从除息日前一个收盘价起,跟踪到除息日早晨的开盘价、除息日收盘价,以及此后1个、5个和10个交易日的收盘价。

查询过去六个月每一次超大市值股除息事件——从除息前收盘价起的价格路径
每个数字背后的完整 SQL
WITH rth AS (
    SELECT ticker,
           toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           argMaxIf(toFloat64(close), window_start, (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) AS close_px,
           argMinIf(toFloat64(open), window_start, (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) AS open_px,
           countIf((toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) AS bars
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('AAPL', 'MSFT', 'JNJ', 'KO', 'PG', 'XOM', 'CVX', 'JPM', 'HD', 'MCD',
                     'PEP', 'ABBV', 'MRK', 'PFE', 'VZ', 'T', 'CSCO', 'IBM', 'WMT', 'CAT',
                     'BAC', 'WFC', 'C', 'GS', 'MS', 'UNH', 'LLY', 'AMGN', 'BMY', 'GILD',
                     'TXN', 'QCOM', 'AVGO', 'ADP', 'LMT', 'RTX', 'HON', 'GE', 'MMM', 'UPS',
                     'LOW', 'TGT', 'COST', 'SBUX', 'NKE', 'DIS', 'CMCSA', 'DUK', 'SO', 'NEE')
      AND window_start >= toDateTime(today() - 190)
    GROUP BY ticker, d
    HAVING bars > 200
),
seq AS (
    SELECT ticker, d, open_px,
           lagInFrame(close_px, 1) OVER w AS pre_close,
           close_px AS ex_close,
           leadInFrame(close_px, 1) OVER w AS c1,
           leadInFrame(close_px, 5) OVER w AS c5,
           leadInFrame(close_px, 10) OVER w AS c10
    FROM rth
    WINDOW w AS (PARTITION BY ticker ORDER BY d ROWS BETWEEN 1 PRECEDING AND 10 FOLLOWING)
),
ev AS (
    SELECT s.ticker AS ticker, s.d AS ex_d, s.pre_close AS pre_close, s.open_px AS ex_open,
           s.ex_close AS ex_close, s.c1 AS c1, s.c5 AS c5, s.c10 AS c10,
           max(dv.cash_amount) AS div_amt
    FROM seq s
    JOIN global_markets.stocks_dividends dv ON dv.ticker = s.ticker AND dv.ex_dividend_date = s.d
    WHERE dv.cash_amount > 0
      AND dv.distribution_type = 'recurring'
      AND s.d >= today() - 160
      AND s.pre_close > 0 AND s.c1 > 0 AND s.c5 > 0 AND s.c10 > 0
    GROUP BY s.ticker, s.d, s.pre_close, s.open_px, s.ex_close, s.c1, s.c5, s.c10
),
paths AS (
    SELECT ticker, ex_d, pre_close, div_amt,
           arrayJoin([('1 ex-day open', ex_open),
                      ('2 ex-day close', ex_close),
                      ('3 one session later', c1),
                      ('4 five sessions later', c5),
                      ('5 ten sessions later', c10)]) AS chk
    FROM ev
)
SELECT chk.1 AS checkpoint,
       count() AS events,
       round(avg(100 * div_amt / pre_close), 2) AS avg_dividend_pct,
       round(avg(100 * (chk.2 - pre_close) / pre_close), 2) AS avg_move_pct,
       round(quantileDeterministic(0.5)(100 * (chk.2 - pre_close) / pre_close, cityHash64(ticker, ex_d)), 2) AS median_move_pct,
       round(100 * countIf(chk.2 >= pre_close) / count(), 1) AS pct_back_above_pre_ex
FROM paths
GROUP BY checkpoint
ORDER BY checkpoint

这些事件的平均派息金额,相当于除息前股价的0.64%。在除息日早晨开盘时,股票相对除息前收盘价平均变动-0.51%(中位数-0.45%),只有25.3%的事件开在前收盘价或以上:这次下调是真实且即时发生的。接下来才是推销话术略过的部分。到除息日收盘时,事件平均距离起始价-0.67%;一个交易日后为-1.05%;五个交易日后为-1.17%;十个交易日后为-0.24%——其中44.6%回到了起始价或以上。

等股价“涨回来”,意味着要扛过十个交易日的正常波动——这种双向波动的幅度,远远盖过一笔0.64%的派息——而短期持有又会失去合格股息的税务待遇。股息是把钱从股价转移到您的账户,不是成交记录上凭空多出来的钱,而之后的价格走势属于市场。

基金与ETF:它们为什么不在前瞻日历上

追求现金流的投资者常常持有基金而不是个股,而上面那份前瞻日历里基金很少,原因是结构性的:多数交易所交易基金(ETF)不像公司董事会那样提前很久宣布除息日——它们的分派往往在除息日当天、或者仅仅提前一点点才进入记录。但它们有一样东西:节拍器一般规律的派息节奏,足以据此提前安排。下面是十三只被广泛持有的收益型基金,含最近一次除息日、除息日之间的典型间隔,以及按该节奏推算出的下一个日期:

查询十三只大型股息与收益型基金——最近除息日、派息节奏与推算的下一个除息日
每个数字背后的完整 SQL
WITH hist AS (
    SELECT ticker, ex_dividend_date, cash_amount, frequency,
           dateDiff('day', lagInFrame(ex_dividend_date) OVER (PARTITION BY ticker ORDER BY ex_dividend_date), ex_dividend_date) AS gap_days
    FROM global_markets.stocks_dividends
    WHERE ticker IN ('SCHD', 'VYM', 'VIG', 'DVY', 'SPYD', 'JEPI', 'JEPQ', 'QYLD', 'SPHD', 'NOBL', 'DGRO', 'HDV', 'SPY')
      AND cash_amount > 0
      AND ex_dividend_date >= today() - 800
)
SELECT ticker,
       any(frequency) AS payments_per_year,
       toString(max(ex_dividend_date)) AS last_ex_date,
       formatDateTime(max(ex_dividend_date), '%b %e') AS last_ex_label,
       round(argMax(cash_amount, ex_dividend_date), 4) AS last_per_share_usd,
       round(quantileDeterministicIf(0.5)(gap_days, cityHash64(ticker, ex_dividend_date), gap_days BETWEEN 5 AND 200)) AS typical_gap_days,
       toString(max(ex_dividend_date) + toIntervalDay(round(quantileDeterministicIf(0.5)(gap_days, cityHash64(ticker, ex_dividend_date), gap_days BETWEEN 5 AND 200)))) AS implied_next_ex,
       formatDateTime(max(ex_dividend_date) + toIntervalDay(round(quantileDeterministicIf(0.5)(gap_days, cityHash64(ticker, ex_dividend_date), gap_days BETWEEN 5 AND 200))), '%b %e') AS implied_next_label
FROM hist
GROUP BY ticker
HAVING countIf(gap_days BETWEEN 5 AND 200) > 0
ORDER BY implied_next_ex, ticker

这组里最快到来的是JEPI,最近一次除息日为Jul 1,每股派息0.3872美元,节奏为30天一次——据此推算,它的下一个除息日在Jul 31前后。最靠后的是HDV,它的节奏是91天一次,指向Oct 14。“每年派息次数”一列把这些基金分成两类:一年派息十二次的标的大约每四周除息一次;一年派息四次的基金则一个季度一次,落在季末月份。推算出的日期只能当作估计值——只有基金发行方的正式通知才是最终结论。

这个月是派息旺季还是淡季?

除息日在一年之中并不是均匀分布的——下面是三年记录按自然月取的平均值:

查询按自然月统计的除息日数量——三年平均值,季度派息与月度派息对比
每个数字背后的完整 SQL
SELECT multiIf(toMonth(ex_dividend_date) = 1, '01 Jan', toMonth(ex_dividend_date) = 2, '02 Feb', toMonth(ex_dividend_date) = 3, '03 Mar',
               toMonth(ex_dividend_date) = 4, '04 Apr', toMonth(ex_dividend_date) = 5, '05 May', toMonth(ex_dividend_date) = 6, '06 Jun',
               toMonth(ex_dividend_date) = 7, '07 Jul', toMonth(ex_dividend_date) = 8, '08 Aug', toMonth(ex_dividend_date) = 9, '09 Sep',
               toMonth(ex_dividend_date) = 10, '10 Oct', toMonth(ex_dividend_date) = 11, '11 Nov', '12 Dec') AS month,
       round(count() / 3.0) AS ex_dates_per_year,
       round(countIf(frequency = 4) / 3.0) AS quarterly_payers,
       round(countIf(frequency = 12) / 3.0) AS monthly_payers
FROM global_markets.stocks_dividends
WHERE ex_dividend_date >= toStartOfMonth(today()) - INTERVAL 36 MONTH
  AND ex_dividend_date < toStartOfMonth(today())
  AND cash_amount > 0
GROUP BY month
ORDER BY month

季末月份承担了绝大部分:3月平均有5605个除息日,6月5792个,9月4973个,12月6380个,每个月都容纳了两千家以上的季度派息标的(仅6月就有2640家)。每个季度的第一个月则很清淡——1月平均2043个,7月2683个,只有季末月份的一半甚至更少。托住淡季的是按月派息的群体:7月平均值中有1470个来自月度派息标的,它们完全无视季度日历。某一周名单很短,是日历本身的特征,而不是对市场下的判断。

各家日历为什么会不一致——以及如何读这一份

把本页和券商的日历对照,您会发现一些细小的差异。每一份日历都建立在公司公告之上,而不同数据源在这几件事上并不相同:吸收公告的速度、是否展示修订过的日期,以及除了实体经营公司之外是否把基金、信托和外国上市公司也计算在内(上面按日统计的数字把它们全部包含在内,只有排名表设了市值门槛)。两份日历不一致时,以公司发布的派息公告原文为准。下面是实时计算出的数据深度凭据:

查询在档的前瞻已宣布除息记录——这份日历背后的数据凭据
每个数字背后的完整 SQL
SELECT countIf(ex_dividend_date > today()) AS future_ex_dates_declared,
       countIf(ex_dividend_date > today() AND ex_dividend_date <= today() + 7) AS in_the_next_7_days,
       countIf(ex_dividend_date > today() AND ticker IN ('SCHD', 'VYM', 'VIG', 'DVY', 'SPYD', 'JEPI', 'JEPQ', 'QYLD', 'SPHD', 'NOBL', 'DGRO', 'HDV', 'SPY')) AS fund_records_declared_ahead,
       toString(max(ex_dividend_date)) AS furthest_declared_date,
       formatDateTime(max(ex_dividend_date), '%b %e, %Y') AS furthest_declared_label
FROM global_markets.stocks_dividends
WHERE ex_dividend_date > today() - 1
  AND cash_amount > 0

目前在档的未来除息日共有3202个,其中213个落在一周之内,已宣布的日期最远排到Nov 12, 2027。而上面那十三只基金,提前宣布的除息日总共只有0个——正是这个结构性的缺口,使得预判基金的下一个除息日只能靠派息节奏,而不是靠公告。董事会一次会议只宣布一笔派息,所以远期的日历要等公告陆续落地才会填满:近期的日期可以视为可靠,远期的则只能视为暂定。

除息日日历 FAQ

美股即将到来的除息日在哪里查?

公司在宣布每笔股息时会同时公布除息日、股权登记日和派息日,这些公告随后进入市场数据源。本页直接用这些记录计算日历——目前在档的未来除息日有3202个——并每周重建一次。

除息日当天买入股票,还能拿到这笔股息吗?

拿不到。除息日是股票第一次不带这笔派息交易的日子。要拿到派息,您必须在除息日前一个交易日收盘时已经持有该股票;在除息日当天买入,派息归卖方所有。

领取美股股息需要自己做什么操作吗?

不需要。只要在除息日前一个交易日收盘时持有股票,现金就会在派息日自动打进您的券商账户——不用填表,不用申领,也不用打电话。唯一可选的动作是DRIP,即券商端把派息自动再投资、买入更多股份的设置。

除息日前买入“抢股息”,这招有用吗?

数据并不支持这种做法。过去六个月的83次超大市值股除息事件中,股票开盘价相对除息前收盘价平均变动-0.51%,而平均派息金额只相当于股价的0.64%;十个交易日之后,也只有44.6%回到了起始价或以上。短期持有还会通不过合格股息的持有期检验。

美股股息怎么交税?买入时间会影响税率吗?

在美国的应税账户里,会。一笔股息要算作合格股息、按长期资本利得税率征税,前提是在除息日前60天开始的121天窗口内,您持有该股票超过60天。除息前买入、除息后马上卖出的做法达不到这个窗口,派息按普通所得征税。REITs和许多基金的分派无论如何都按普通所得处理。这里讲的是机制,不是税务建议。

按股票代码查看股息概况

本日历上的每一只大市值派息股,都有自己的实测股息概况——按最新收盘价计算的当前股息率、成交记录上完整的派息历史、连续增长的年数,以及它历次除息日早晨的实际交易表现:

AAPL, ABBV, ABT, ACN, ADP, AMGN, AVGO, AXP, BAC, BLK, BMY, C, CAT, CL, CMCSA, COP, COST, CRM, CSCO, CVX, DE, DHR, DUK, GE, GILD, GIS, GOOGL, GS, HD, HON, IBM, JNJ, JPM, KMB, KO, LIN, LLY, LMT, LOW, MA, MCD, MDLZ, MDT, META, MMM, MO, MRK, MS, MSFT, NEE, NKE, O, ORCL, PEP, PFE, PG, PM, QCOM, RTX, SBUX, SO, SPGI, T, TGT, TMO, TXN, UNH, UNP, USB, V, VZ, WFC, WMT, XOM


上面每一张表格,都是对已宣布派息记录和真实分钟K线数据运行的一条存储、带版本的查询——您可以展开任意面板下方的SQL,也可以在Strasmore终端上自己筛选这份日历。