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Distance between trailing and forward dividend yield, by payment schedule

Answered against 22 years of US equities and 12 years of US options data and published with the query that produced it. This result is stored as of 2026-08-22, from Trailing vs Forward Dividend Yield Explained.

as of table 4×7read in context →
Distance between trailing and forward dividend yield, by payment schedule — 4 rows by 7 columns, computed from US exchange, SIP and OPRA data.
schedulepayersmedian_trailing_pctmedian_forward_pctmedian_gap_ppp90_gap_pppct_gap_over_half_point
quarterly11211.9320.0310.3798.8
monthly205.515.80.4421.17540
semi-annual190.970.9700.4875.3
annual100.60.6000
Rows × columns
4 × 7
Computed
Completeness
No missing values
Source
US exchange, SIP and OPRA market data
Licence
Strasmore terms · free, no signup
Formats
JSON · CSV · the SQL below

What each column holds

Column definitions for Distance between trailing and forward dividend yield, by payment schedule, derived from the stored result.
ColumnTypeRangeNotes
schedule text 4 distinct values (annual, monthly, quarterly…)
payers number 10 to 1,121
median_trailing_pct number 0.6 to 5.51 percent
median_forward_pct number 0.6 to 5.8 percent
median_gap_pp number 0 to 0.442
p90_gap_pp number 0 to 1.175
pct_gap_over_half_point number 0 to 40 percent

Computed from Strasmore's warehouse of US exchange, SIP and OPRA market data. Equity prices are delayed; options greeks and implied volatility are end-of-day. This result is stored, not recomputed on load — it is exactly the numbers that were returned on , and the query below is what returned them.

the exact SQL behind every number 
WITH universe AS (
    SELECT ticker,
           argMax(price, date) AS last_price
    FROM global_markets.stocks_ratios
    WHERE date = (SELECT max(date) FROM global_markets.stocks_ratios)
      AND price >= 5
      AND market_cap >= 1000000000
    GROUP BY ticker
),
paid AS (
    SELECT ticker,
           sum(cash_amount) AS ttm_cash,
           argMax(cash_amount, ex_dividend_date) AS latest_cash,
           argMax(frequency, ex_dividend_date) AS pay_frequency
    FROM global_markets.stocks_dividends
    WHERE distribution_type = 'recurring'
      AND cash_amount > 0
      AND frequency IN (1, 2, 4, 12)
      AND ex_dividend_date > today() - INTERVAL 1 YEAR
      AND ex_dividend_date <= today()
    GROUP BY ticker
)
SELECT multiIf(d.pay_frequency = 12, 'monthly',
               d.pay_frequency = 4, 'quarterly',
               d.pay_frequency = 2, 'semi-annual',
               'annual') AS schedule,
       count() AS payers,
       round(quantileDeterministic(0.5)(toFloat64(d.ttm_cash) / toFloat64(u.last_price) * 100,
                                        cityHash64(u.ticker)), 2) AS median_trailing_pct,
       round(quantileDeterministic(0.5)(toFloat64(d.latest_cash) * d.pay_frequency / toFloat64(u.last_price) * 100,
                                        cityHash64(u.ticker)), 2) AS median_forward_pct,
       round(quantileDeterministic(0.5)(abs(toFloat64(d.latest_cash) * d.pay_frequency - toFloat64(d.ttm_cash))
                                        / toFloat64(u.last_price) * 100,
                                        cityHash64(u.ticker)), 3) AS median_gap_pp,
       round(quantileDeterministic(0.9)(abs(toFloat64(d.latest_cash) * d.pay_frequency - toFloat64(d.ttm_cash))
                                        / toFloat64(u.last_price) * 100,
                                        cityHash64(u.ticker)), 3) AS p90_gap_pp,
       round(100 * countIf(abs(toFloat64(d.latest_cash) * d.pay_frequency - toFloat64(d.ttm_cash))
                           / toFloat64(u.last_price) * 100 >= 0.5) / count(), 1) AS pct_gap_over_half_point
FROM universe AS u
INNER JOIN paid AS d ON u.ticker = d.ticker
GROUP BY schedule
ORDER BY payers DESC

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