Strasmore Research
市场回顾 Matt Connor作者: Matt Connor · 更新于 2026-09-05 · data as of September 5, 2026 · refreshed weekly

每月派息股票完整名单

按真实派息记录筛选美股每月派息股票,查看哪些公司过去一年实际派息十至十四次及当前收益率,并按成交额比较名单。

月度分红股票每月派发现金,而不是每季一次;但真正符合条件的股票数量远少于各类榜单所暗示的规模。若直接筛查派息记录,而不是采用数据供应商的标签,过去十二个月内有 1569 只美股上市ticker完成了十至十四次定期现金派息,其中仅 32 只有公司基本面记录。本页所有数据均按 Sep 4 交易日收盘价计价。

每月派息股票名单

以下每家公司均为在美国上市、提交监管文件的公司。在过去十二个月中,这些公司记录了十至十四次定期现金派息,且日均股票成交额至少为一百万美元。排名依据为日均成交额。

查询每月派息股票——按每日交易额排名的申报公司
28 rows (showing 20)
股票代码月度支付(美元)年化收益率(%)按月支付年数日均成交额(百万美元)价格截至
O0.2715.314.2220.3Sep 4
AGNC0.1213.3711.9139.8Sep 4
DOC0.10175.841.570.7Sep 4
DX0.1715.787.652Sep 4
ADC0.2674.415.751.5Sep 4
ARR0.2417.66.245.7Sep 4
EPR0.316.25.128.3Sep 4
APLE0.086.134.727.8Sep 4
MAIN0.2655.4913.927.3Sep 4
ORC0.118.2913.422.9Sep 4
PECO0.10833.35519.4Sep 4
SMA0.13595.011.316.2Sep 4
IVR0.1219.940.714.7Sep 4
CSWC0.19349.381.214.4Sep 4
LTC0.195.5414.213.5Sep 4
EFC0.1311.567.311.3Sep 4
PFLT0.0812.9314.15.7Sep 4
LAND0.04675.7513.55.7Sep 4
GOOD0.19.1614.23.8Sep 4
PBT0.01870.6614.23.7Sep 4
每个数字背后的完整 SQL
SELECT m.ticker AS ticker,
       round(m.latest_payment, 4) AS monthly_payment_usd,
       round(100 * m.latest_payment * 12 / l.last_close, 2) AS annual_yield_pct,
       round(dateDiff('day', s.streak_start, s.last_ex) / 365.25, 1) AS years_paying_monthly,
       round(l.adv_usd / 1e6, 1) AS avg_daily_traded_musd,
       formatDateTime(l.last_session, '%b %e') AS priced_through
FROM (
    SELECT ticker, argMax(cash_amount, ex_dividend_date) AS latest_payment
    FROM global_markets.stocks_dividends
    WHERE ex_dividend_date > today() - 365
      AND ex_dividend_date <= today()
      AND cash_amount > 0
      AND distribution_type = 'recurring'
      AND ticker NOT IN ('SPCX')
      AND ticker NOT IN ('KORU','SOXL','SOXS','TQQQ','SQQQ','NVDL','NVDS','NVD','TSLL','TSLQ','TSLZ','SPXL','SPXS','UPRO','SPXU','LABU','LABD','FAS','FAZ','TNA','TZA','YINN','YANG','UDOW','SDOW','BOIL','KOLD','UCO','SCO','USD','SSO','SDS','QLD','QID','ERX','ERY','DRN','DRV','CURE','SOXY','MUU','SNXX','UVXY','SVXY','UVIX','SVIX','BULZ','WEBL','WEBS','DPST','DRIP','GUSH','AGQ','ZSL','BITX','ETHU','MSTX','MSTU','CONL','DUST','JNUG','JDST','NUGT')
      AND ticker NOT IN (SELECT ticker FROM global_markets.stocks_splits
                         WHERE execution_date BETWEEN today() - 400 AND today())
    GROUP BY ticker
    HAVING count() BETWEEN 10 AND 14
       AND argMax(frequency, ex_dividend_date) = 12
) AS m
INNER JOIN (
    SELECT ticker,
           maxIf(ed, gap_days > 70) AS streak_start,
           max(ed) AS last_ex
    FROM (
        SELECT ticker, ex_dividend_date AS ed,
               dateDiff('day',
                        lagInFrame(ex_dividend_date) OVER (PARTITION BY ticker ORDER BY ex_dividend_date),
                        ex_dividend_date) AS gap_days
        FROM (
            SELECT ticker, ex_dividend_date
            FROM global_markets.stocks_dividends
            WHERE distribution_type = 'recurring' AND cash_amount > 0 AND ex_dividend_date <= today()
            GROUP BY ticker, ex_dividend_date
        )
    )
    GROUP BY ticker
) AS s ON s.ticker = m.ticker
INNER JOIN (
    SELECT ticker,
           avg(dollar_vol) AS adv_usd,
           argMax(close_px, d) AS last_close,
           max(d) AS last_session,
           count() AS sessions
    FROM (
        SELECT ticker,
               toDate(toTimeZone(window_start, 'America/New_York')) AS d,
               sum(toFloat64(close) * volume) AS dollar_vol,
               argMax(toFloat64(close), window_start) AS close_px
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE window_start >= toDateTime(today() - 32)
          AND toDate(toTimeZone(window_start, 'America/New_York')) < today()
          AND toHour(toTimeZone(window_start, 'America/New_York')) * 60
              + toMinute(toTimeZone(window_start, 'America/New_York')) >= 570
          AND toHour(toTimeZone(window_start, 'America/New_York')) * 60
              + toMinute(toTimeZone(window_start, 'America/New_York')) < 960
        GROUP BY ticker, d
    )
    GROUP BY ticker
    HAVING sessions >= 15 AND adv_usd >= 1000000 AND last_close > 0
) AS l ON l.ticker = m.ticker
INNER JOIN (
    SELECT ticker
    FROM global_markets.stocks_ratios
    GROUP BY ticker
    HAVING argMax(market_cap, date) > 0
) AS f ON f.ticker = m.ticker
ORDER BY l.adv_usd DESC
LIMIT 60
自己运行这个查询

O是本名单中成交最活跃的每月派息股:每月派息为$0.271,连续每月派息记录已达14.2年;按其Sep 4收盘价计算,年化收益率为5.3%。其日均股票成交额为220.3百万美元。筛选结果包含28家公司。收益率一栏将最近一次单笔派息按每年十二次进行年化,再除以该收盘价,因此会随股价变动。该数据并非对未来十二个月现金派息的预测。

在除息日前持有股票,才能获得下一笔股息。即将到来的除息日日历列出下一批进入除息状态的股票;除息日详解介绍决定买方能否领取股息的截止时间。

如何识别按月派息的产品

股息数据流会标注派息频率,但仅依赖这一标签会在两个方向上都产生错误。这里的测试会统计某个 ticker 实际派息的次数,并同时设置上下限。

查询过去一年各股票的定期现金派付——每月派息区间所在位置
分组股票代码标记为月度公司申报文件
One a year39955530
Two a year24073644
Three to six a year44521821477
Seven to nine a year1641625
Ten to fourteen a year1569156532
Fifteen to thirty-nine a year7100
Forty or more a year11800
每个数字背后的完整 SQL
WITH payers AS (
    SELECT ticker,
           count() AS payments_12m,
           argMax(frequency, ex_dividend_date) AS freq_flag
    FROM global_markets.stocks_dividends
    WHERE ex_dividend_date > today() - 365
      AND ex_dividend_date <= today()
      AND cash_amount > 0
      AND distribution_type = 'recurring'
      AND ticker NOT IN ('SPCX')
    GROUP BY ticker
),
filers AS (
    SELECT ticker
    FROM global_markets.stocks_ratios
    GROUP BY ticker
    HAVING argMax(market_cap, date) > 0
)
SELECT multiIf(payments_12m = 1, 'One a year',
               payments_12m = 2, 'Two a year',
               payments_12m BETWEEN 3 AND 6, 'Three to six a year',
               payments_12m BETWEEN 7 AND 9, 'Seven to nine a year',
               payments_12m BETWEEN 10 AND 14, 'Ten to fourteen a year',
               payments_12m BETWEEN 15 AND 39, 'Fifteen to thirty-nine a year',
               'Forty or more a year') AS bucket,
       count() AS tickers,
       countIf(freq_flag = 12) AS labelled_monthly,
       countIf(ticker IN (SELECT ticker FROM filers)) AS company_filings
FROM payers
GROUP BY bucket
ORDER BY min(payments_12m)
自己运行这个查询

118 个 ticker 在过去一年实际进行了四十次或以上的定期现金派息,另有 71 个进行了十五至三十九次。这些产品属于按周和按日派息,是基金市场中增长迅速的一类。如果筛选条件只有“派息十次或以上”,它们都会被纳入按月派息名单。将上限设为十四次即可排除这些产品。

仅看标签同样不够可靠。标注为按月派息频率的 ticker 中,有 55 个在过去一年只派息一次,另有 182 个派息三至六次。在十至十四次的区间内,标签与实际记录有 1565 次相符,占 1569 次中的相应比例,因此本页面要求同时满足两项条件。

月度派息者大多是基金,而非经营性公司

这正是许多清单式文章略过的部分。该数据库没有基金、ETF或REIT类型标签,因此月度派息名单无法按标签筛选出经营性公司。不过,数据库包含一项公司基本面数据,报告其覆盖的经营性公司的市值。交易所交易基金和封闭式基金不在该数据覆盖范围内,因此,在本页面中,是否存在市值记录,便成为判断经营性公司的实际代理标准。在月度派息组的 1569 个ticker中,32 个有市值记录。其余ticker均没有此类记录。大多数属于交易所交易基金、封闭式基金和区间基金,此外还有少量该数据源未覆盖的单位、信托和规模较小的发行人。

其中少数提交相关数据的发行人,主要是房地产投资信托、抵押贷款REIT、商业发展公司和特许权使用费信托。月度收入的真实构成正是如此:能够每月派息的公司形式,通常就是为了将现金直接转付给投资者而设立的。在普通经营性企业中,几乎不存在持续二十年的股息增长标杆这样的月度版本,因为这类企业通常按季度派息。美国为何最初形成按季度派息的惯例,以及哪些组织形式打破了这一惯例,详见月度派息股票解析

月度派息股票的收益率

月度派息股票之间的收益率差距,远大于整个市场内部的差距。该表将所有满足流动性门槛、且拥有完整一年价格历史的月度派息股票分组,并增加了收益率筛选通常不会展示的一列:过去十二个月股价的表现。

查询高流动性每月派息股票的收益率分布及各区间12个月价格变化中位数
分组支付方收益率中位数(%)过去12个月价格变动中位数(%)公司申报文件
Under 3%672.28-0.41
From 3% to 5%2894.17-2.32
From 5% to 8%2216.36-2.19
From 8% to 12%929.36-3.26
Above 12%7314.51-8.910
每个数字背后的完整 SQL
WITH monthly AS (
    SELECT ticker, argMax(cash_amount, ex_dividend_date) AS latest_payment
    FROM global_markets.stocks_dividends
    WHERE ex_dividend_date > today() - 365
      AND ex_dividend_date <= today()
      AND cash_amount > 0
      AND distribution_type = 'recurring'
      AND ticker NOT IN ('SPCX')
      AND ticker NOT IN ('KORU','SOXL','SOXS','TQQQ','SQQQ','NVDL','NVDS','NVD','TSLL','TSLQ','TSLZ','SPXL','SPXS','UPRO','SPXU','LABU','LABD','FAS','FAZ','TNA','TZA','YINN','YANG','UDOW','SDOW','BOIL','KOLD','UCO','SCO','USD','SSO','SDS','QLD','QID','ERX','ERY','DRN','DRV','CURE','SOXY','MUU','SNXX','UVXY','SVXY','UVIX','SVIX','BULZ','WEBL','WEBS','DPST','DRIP','GUSH','AGQ','ZSL','BITX','ETHU','MSTX','MSTU','CONL','DUST','JNUG','JDST','NUGT')
      AND ticker NOT IN (SELECT ticker FROM global_markets.stocks_splits
                         WHERE execution_date BETWEEN today() - 400 AND today())
    GROUP BY ticker
    HAVING count() BETWEEN 10 AND 14
       AND argMax(frequency, ex_dividend_date) = 12
),
filers AS (
    SELECT ticker
    FROM global_markets.stocks_ratios
    GROUP BY ticker
    HAVING argMax(market_cap, date) > 0
),
tape AS (
    SELECT ticker,
           toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           sum(toFloat64(close) * volume) AS dollar_vol,
           argMax(toFloat64(close), window_start) AS close_px
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN (SELECT ticker FROM monthly)
      AND (window_start >= toDateTime(today() - 32)
           OR (window_start >= toDateTime(today() - 378) AND window_start < toDateTime(today() - 358)))
      AND toDate(toTimeZone(window_start, 'America/New_York')) < today()
      AND toHour(toTimeZone(window_start, 'America/New_York')) * 60
          + toMinute(toTimeZone(window_start, 'America/New_York')) >= 570
      AND toHour(toTimeZone(window_start, 'America/New_York')) * 60
          + toMinute(toTimeZone(window_start, 'America/New_York')) < 960
    GROUP BY ticker, d
),
px AS (
    SELECT ticker,
           avgIf(dollar_vol, d > today() - 32) AS adv_usd,
           argMaxIf(close_px, d, d > today() - 32) AS last_close,
           argMaxIf(close_px, d, d <= today() - 358) AS close_year_ago,
           countIf(d > today() - 32) AS recent_sessions,
           countIf(d <= today() - 358) AS old_sessions
    FROM tape
    GROUP BY ticker
    HAVING recent_sessions >= 15
       AND old_sessions > 0
       AND adv_usd >= 1000000
       AND last_close > 0
       AND close_year_ago > 0
),
named AS (
    SELECT m.ticker AS ticker,
           100 * m.latest_payment * 12 / p.last_close AS yld,
           100 * (p.last_close - p.close_year_ago) / p.close_year_ago AS price_change,
           m.ticker IN (SELECT ticker FROM filers) AS is_filer
    FROM monthly m
    INNER JOIN px p ON p.ticker = m.ticker
)
SELECT multiIf(yld < 3, 'Under 3%',
               yld < 5, 'From 3% to 5%',
               yld < 8, 'From 5% to 8%',
               yld < 12, 'From 8% to 12%',
               'Above 12%') AS bucket,
       count() AS payers,
       round(quantileDeterministic(0.5)(yld, cityHash64(ticker)), 2) AS median_yield_pct,
       round(quantileDeterministic(0.5)(price_change, cityHash64(ticker)), 1) AS median_price_change_12m_pct,
       countIf(is_filer) AS company_filings
FROM named
GROUP BY bucket
ORDER BY median_yield_pct
自己运行这个查询

最大的一组是 From 3% to 5%,包含 289 只股票;相比之下,From 5% to 8%组有 221只,Under 3%组有 67只。收益率中位数从最低组的 2.28%,依次稳步升至最高组的 14.51%。

现在看旁边的价格变化列。最低收益率组的十二个月价格变化中位数为 -0.4%,From 3% to 5%组为 -2.3%,随后在 Above 12%组变为 -8.9%。收益率的分母是股价,因此即使派息金额不变,股价下跌也可能使股票进入最高收益率组。极高收益率通常反映的是股价下跌带来的计算结果,而不是公司慷慨派息的证据。股息收益率与美国国债收益率的比较说明了高收益率需要满足哪些条件。

收益率超过12%的标的

任何月度派息、收益率大致超过12%的标的,都值得进一步研究。以下是申报数据中达到这一标准的公司,以及过去一年股价和月度派息的变化。

查询收益率超过12%的每月派息股票——推动该数字的价格变动与派息变动
股票代码年化收益率(%)过去12个月价格变动(%)过去12个月支付变动(%)日均成交额(百万美元)
EARN21.99-22.701.6
IVR19.94-5.4-64.714.4
ORC18.29-7.2-16.722.2
ARR17.67.3046
SAR16.96-28.102.1
DX15.782.9050.4
HRZN14.88-30.4-45.53.5
AGNC13.375.30135
PFLT12.93-27.4-225.7
PNNT12.85-47.5-501.8
每个数字背后的完整 SQL
WITH monthly AS (
    SELECT ticker,
           argMax(cash_amount, ex_dividend_date) AS latest_payment,
           argMin(cash_amount, ex_dividend_date) AS oldest_payment
    FROM global_markets.stocks_dividends
    WHERE ex_dividend_date > today() - 400
      AND ex_dividend_date <= today()
      AND cash_amount > 0
      AND distribution_type = 'recurring'
      AND ticker NOT IN ('SPCX')
      AND ticker NOT IN ('KORU','SOXL','SOXS','TQQQ','SQQQ','NVDL','NVDS','NVD','TSLL','TSLQ','TSLZ','SPXL','SPXS','UPRO','SPXU','LABU','LABD','FAS','FAZ','TNA','TZA','YINN','YANG','UDOW','SDOW','BOIL','KOLD','UCO','SCO','USD','SSO','SDS','QLD','QID','ERX','ERY','DRN','DRV','CURE','SOXY','MUU','SNXX','UVXY','SVXY','UVIX','SVIX','BULZ','WEBL','WEBS','DPST','DRIP','GUSH','AGQ','ZSL','BITX','ETHU','MSTX','MSTU','CONL','DUST','JNUG','JDST','NUGT')
      AND ticker NOT IN (SELECT ticker FROM global_markets.stocks_splits
                         WHERE execution_date BETWEEN today() - 400 AND today())
    GROUP BY ticker
    HAVING countIf(ex_dividend_date > today() - 365) BETWEEN 10 AND 14
       AND argMax(frequency, ex_dividend_date) = 12
       AND oldest_payment > 0
),
filers AS (
    SELECT ticker
    FROM global_markets.stocks_ratios
    GROUP BY ticker
    HAVING argMax(market_cap, date) > 0
),
tape AS (
    SELECT ticker,
           toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           sum(toFloat64(close) * volume) AS dollar_vol,
           argMax(toFloat64(close), window_start) AS close_px
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN (SELECT ticker FROM monthly)
      AND (window_start >= toDateTime(today() - 32)
           OR (window_start >= toDateTime(today() - 378) AND window_start < toDateTime(today() - 358)))
      AND toDate(toTimeZone(window_start, 'America/New_York')) < today()
      AND toHour(toTimeZone(window_start, 'America/New_York')) * 60
          + toMinute(toTimeZone(window_start, 'America/New_York')) >= 570
      AND toHour(toTimeZone(window_start, 'America/New_York')) * 60
          + toMinute(toTimeZone(window_start, 'America/New_York')) < 960
    GROUP BY ticker, d
),
px AS (
    SELECT ticker,
           avgIf(dollar_vol, d > today() - 32) AS adv_usd,
           argMaxIf(close_px, d, d > today() - 32) AS last_close,
           argMaxIf(close_px, d, d <= today() - 358) AS close_year_ago,
           countIf(d > today() - 32) AS recent_sessions,
           countIf(d <= today() - 358) AS old_sessions
    FROM tape
    GROUP BY ticker
    HAVING recent_sessions >= 15
       AND old_sessions > 0
       AND adv_usd >= 1000000
       AND last_close > 0
       AND close_year_ago > 0
)
SELECT m.ticker AS ticker,
       round(100 * m.latest_payment * 12 / p.last_close, 2) AS annual_yield_pct,
       round(100 * (p.last_close - p.close_year_ago) / p.close_year_ago, 1) AS price_change_12m_pct,
       round(100 * (m.latest_payment - m.oldest_payment) / m.oldest_payment, 1) AS payment_change_12m_pct,
       round(p.adv_usd / 1e6, 1) AS avg_daily_traded_musd
FROM monthly m
INNER JOIN px p ON p.ticker = m.ticker
INNER JOIN filers f ON f.ticker = m.ticker
WHERE 100 * m.latest_payment * 12 / p.last_close >= 12
ORDER BY annual_yield_pct DESC
LIMIT 25
自己运行这个查询

10个标的达到这一标准。EARN的年化收益率最高,为21.99%;其股价在过去12个月上涨或下跌了-22.7%,月度派息变动了0%。其次是IVR,收益率为19.94%,股价变动为-5.4%,派息变动为-64.7%。派息削减和股价下跌经常同时出现在同一标的上,收益率也会因此双双推高。

收益率本身并不能说明全部情况,这正是什么是股息收益率一文要完整解释的内容。

Realty Income月度股息,逐年变化

Realty Income是这一类别的参考标的,也是多数月度股息相关文章的起点。下表列出其公布的月度股息率、每个自然年度公布的最高派息额、相较上一年度的增幅,以及该年度内上调股息率的次数:

查询Realty Income按日历年公布的月度派息率、较上年的增幅及各年内的上调步骤
年份月度支付(美元)年度涨幅涨价次数最小年度涨幅
20140.18340.6940.69
20150.1914.1350.69
20160.20256.0260.69
20170.21254.9450.69
20180.221450.69
20190.22752.9450.69
20200.23453.0850.69
20210.24655.1250.69
20220.24850.8140.69
20230.25653.2250.69
20240.26352.7340.69
20250.272.4760.69
每个数字背后的完整 SQL
WITH steps AS (
    SELECT ed, amt,
           lagInFrame(amt) OVER (ORDER BY ed ASC) AS prior_amt
    FROM (
        SELECT ex_dividend_date AS ed, max(cash_amount) AS amt
        FROM global_markets.stocks_dividends
        WHERE ticker = 'O'
          AND distribution_type = 'recurring'
          AND cash_amount > 0
          AND ex_dividend_date >= '2013-01-01'
          AND ex_dividend_date < toStartOfYear(today())
        GROUP BY ed
    )
),
yearly AS (
    SELECT toYear(ed) AS year,
           max(amt) AS monthly_rate,
           countIf(prior_amt > 0 AND amt > prior_amt) AS raise_count
    FROM steps
    GROUP BY year
),
seq AS (
    SELECT year, monthly_rate, raise_count,
           lagInFrame(monthly_rate) OVER (ORDER BY year ASC) AS prior_rate
    FROM yearly
),
panel AS (
    SELECT year, monthly_rate, raise_count,
           100 * (monthly_rate - prior_rate) / prior_rate AS raise_pct
    FROM seq
    WHERE prior_rate > 0 AND year >= 2014
)
SELECT year,
       round(monthly_rate, 4) AS monthly_payment_usd,
       round(raise_pct, 2) AS annual_raise_pct,
       raise_count,
       round(min(raise_pct) OVER (), 2) AS smallest_annual_raise_pct
FROM panel
ORDER BY year
自己运行这个查询

月度股息率从2014年的$0.1834升至2025年的$0.27。表中最小的年度增幅为0.69%,因此所列各年的股息率均有所上升。最近一个完整年度的增幅为2.47%,而2014年的增幅为0.69%。旁边的上调次数显示了这一增幅如何实现:仅在2025年内就有6次单独上调。按月派息的公司通常会在一年内多次小幅上调派息率,而不是一次性大幅上调。完整的统计情况请参阅Realty Income股息页面。

月度分配记录可追溯多久

本页的每项连续记录均按股息记录起算,而不是按公司首次按月分配的年份起算。因此,这一记录比最早开始按月分配的公司更短。统计每个完整日历年内有十至十四个定期除息日的 ticker 数量:

查询股息记录中按已完成日历年统计的每月派息股票
21 rows (showing 20)
年份月度付款人数月度占比
200500
200670.25
2007692.32
2008401.2
2009471.32
2010611.53
2011601.44
20121351.88
201373010.2
201483111.12
201585711.5
201683910.92
201786910.98
20189348.76
20199838.94
20209719.44
202110049.49
202210949.88
2023116910.44
2024129911.35
每个数字背后的完整 SQL
WITH per_year AS (
    SELECT ticker,
           toYear(ex_dividend_date) AS year,
           count(DISTINCT ex_dividend_date) AS payments
    FROM global_markets.stocks_dividends
    WHERE distribution_type = 'recurring'
      AND cash_amount > 0
      AND ex_dividend_date >= '2005-01-01'
      AND ex_dividend_date < toStartOfYear(today())
    GROUP BY ticker, year
)
SELECT year,
       countIf(payments BETWEEN 10 AND 14) AS monthly_payers,
       round(100 * countIf(payments BETWEEN 10 AND 14) / count(), 2) AS monthly_share_pct
FROM per_year
GROUP BY year
ORDER BY year
自己运行这个查询

该记录显示,60年有2011家月度分配者,135年有2012家;随后,730年为2013家,1481年为2025家。占比栏将这一步变化与数据流中的单纯增长区分开来:月度分配者占所有定期分配者的比例从1.44%升至10.2%。一家早在这一步之前就按月分配的公司,其连续记录年数不可能超过该记录本身涵盖的年限。

如何衡量这份名单

用直白的语言说明所有门槛和排除条件。

  • 发放频率。 某个 ticker 只有在过去365天内记录了十至十四次定期现金分配,且最新频率标签显示为月度时,才符合月度标准。上下限都很重要:下限允许偶尔缺少一条记录,上限则排除按周和按日分配的产品。
  • 分配类型。 只统计数据源标记为定期的分配。特别分配、额外分配和不定期的一次性分配,既不计入频率测试,也不计入收益率。
  • 连续记录。 连续月度分配年数,是根据连续的定期除息日计算的;相邻除息日之间不得有超过七十天的间隔。数据源偶尔会缺少某个月公司实际发放的记录,因此如果要求每年必须有十二条完整记录,就会低估所有连续分配记录的长度。若频率切换为季度,间隔会接近三个月,这通常意味着连续记录已经中断。记录本身决定了可计算的上限,正如上方覆盖情况面板所示。
  • 流动性门槛。 在过去一个月内,至少十五个正常交易时段的日均股票成交额须达到一百万美元。低于这一水平,报价价差会使收益率缺乏实际参考价值。
  • 定价。 收益率使用已完成交易时段的最后收盘价。页面重建时仍在进行的交易时段不计入,因此日期标记可能比日历日期晚一两天。
  • 公司筛选。 主名单只保留存在公司基本面记录的 ticker。收益率分布面板则包含所有月度分配者,包括基金。
  • 排除项。 按名称排除杠杆型和反向交易所交易产品。过去一年内发生过股票拆分的 ticker 也会被排除,因为拆分会造成虚假的价格变动,破坏每股数据的可比性。由两家发行方重复使用的一个 symbol 在所有结果中都会被排除。
  • 未纳入的内容。 本数据不包含基金费用率,也不包含分配的税务性质(返还资本还是收入)。一些高收益月度分配基金返还的是资本,而非收益;这不会体现在收益率中。

常见问题

哪些股票每月派发股息?

按过去十二个月的派息记录计算,1569只美国上市的股票代码按月派息,但其中仅有32条具备公司基本面记录。其余几乎都是基金。在提交申报的标的中,交易最活跃的是O;通过流动性门槛的名单共有28只。

每月派息股票安全吗?

按月派息并不意味着安全,这类标的主要集中在抵押贷款REIT和期权收益基金,其派息会随信贷状况和利率变化。在此处统计的每月派息标的中,过去十二个月的价格变动中位数为-8.9%(收益率高于12%),而收益率低于3%的标的为-0.4%。

哪只每月派息股票的收益率最高?

在通过流动性门槛的每月派息申报公司中,截至Sep 4收盘,EARN的年化收益率最高,为21.99%。其股价在此前十二个月内变动了-22.7%,这也是大多数两位数收益率的计算基础。

本页的月度收益率如何计算?

将最近一次宣布的月度派息乘以十二,再除以最近一个完整交易日的收盘价。该方法是将当前派息率年化,而不是汇总过去十二个月的现金派息。因此,年中提高派息的公司在本页显示的收益率会高于其过去十二个月收益率。


上方每张表均为基于已宣布股息记录和真实分钟级行情数据的、已存储并版本化的查询。您可以展开任意面板下方的SQL,或在Strasmore终端上自行构建每月派息标的筛选。