How each yield band got there: median price and dividend change over the prior 12 months
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 Dividend Yield Traps: How to Spot One.
| yield_band | names | median_yield_pct | median_price_change_pct | median_dividend_change_pct |
|---|---|---|---|---|
| under 2% | 1220 | 1.08 | 11.9 | 5.5 |
| 2-4% | 836 | 2.88 | 8.1 | 4.2 |
| 4-6% | 403 | 4.91 | 5.1 | 1.9 |
| 6-8% | 394 | 6.74 | -0.4 | 0 |
| 8% and up | 304 | 9.69 | -2.4 | 0 |
- Rows × columns
- 5 × 5
- Computed
- Completeness
- No missing values
- Source
- US exchange, SIP and OPRA market data
- Licence
- Strasmore terms · free, no signup
What each column holds
| Column | Type | Range | Notes |
|---|---|---|---|
yield_band |
text | 5 distinct values (2-4%, 4-6%, 6-8%…) | |
names |
number | 304 to 1,220 | |
median_yield_pct |
number | 1.08 to 9.69 | percent |
median_price_change_pct |
number | -2.4 to 11.9 | percent |
median_dividend_change_pct |
number | 0 to 5.5 | 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 px_base AS (
SELECT ticker,
argMax(toFloat64(close), window_start) AS price
FROM global_markets.delayed_stocks_minute_aggs
WHERE toDate(toTimeZone(window_start, 'America/New_York')) = toDate('2025-06-30')
AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
+ toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
GROUP BY ticker
HAVING price >= 5
),
px_prior AS (
SELECT ticker,
argMax(toFloat64(close), window_start) AS price
FROM global_markets.delayed_stocks_minute_aggs
WHERE toDate(toTimeZone(window_start, 'America/New_York')) = toDate('2024-06-28')
AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
+ toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
GROUP BY ticker
HAVING price > 0
),
rate_base AS (
SELECT ticker,
argMax(toFloat64(cash_amount) * frequency, ex_dividend_date) AS annual_rate
FROM global_markets.stocks_dividends
WHERE distribution_type = 'recurring'
AND frequency = 4
AND cash_amount > 0
AND ex_dividend_date > toDate('2024-06-30')
AND ex_dividend_date <= toDate('2025-06-30')
GROUP BY ticker
),
rate_prior AS (
SELECT ticker,
argMax(toFloat64(cash_amount) * frequency, ex_dividend_date) AS annual_rate
FROM global_markets.stocks_dividends
WHERE distribution_type = 'recurring'
AND frequency = 4
AND cash_amount > 0
AND ex_dividend_date > toDate('2023-06-30')
AND ex_dividend_date <= toDate('2024-06-28')
GROUP BY ticker
)
SELECT multiIf(100 * rb.annual_rate / b.price >= 8, '8% and up',
100 * rb.annual_rate / b.price >= 6, '6-8%',
100 * rb.annual_rate / b.price >= 4, '4-6%',
100 * rb.annual_rate / b.price >= 2, '2-4%',
'under 2%') AS yield_band,
count() AS names,
round(quantileDeterministic(0.5)(100 * rb.annual_rate / b.price, cityHash64(b.ticker)), 2) AS median_yield_pct,
round(quantileDeterministic(0.5)(100 * (b.price / p.price - 1), cityHash64(b.ticker)), 1) AS median_price_change_pct,
round(quantileDeterministic(0.5)(100 * (rb.annual_rate / rp.annual_rate - 1), cityHash64(b.ticker)), 1) AS median_dividend_change_pct
FROM px_base AS b
INNER JOIN px_prior AS p ON b.ticker = p.ticker
INNER JOIN rate_base AS rb ON b.ticker = rb.ticker
INNER JOIN rate_prior AS rp ON b.ticker = rp.ticker
GROUP BY yield_band
ORDER BY median_yield_pct
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