Taiwan institutional-flow breadth seasonality

By calendar month, the average share of stocks the three institutional groups were net buyers of versus net sellers of, since 2012 — a market-breadth read on institutional appetite. The signal is mild (~2pp): only December's net-buy share edges above net-sell. Descriptive statistics only, not a forecast.

as_of
2026-09-04
Sample period
2012-05-022026-09-04
Rows
12

Methodology

Each trading day gets a "breadth" reading: of the stocks the three institutional groups took any net position in that day, what share they were net buyers of. Those daily readings are averaged within each calendar month, then across years, giving a seasonal profile. It counts STOCKS, not money, so a single heavyweight name cannot tilt the reading the way a NT$ total would. Days that carry only placeholder data are excluded.

Starts May 2012 — about 14 years; the latest month covered is shown as the sample period above, and placeholder months are excluded from it. Read the size honestly: the seasonal effect is mild, around 2 percentage points. Only December's net-buy share edges above its net-sell share, and October–November are the weakest. A version measured in NT$ rather than in number of stocks is a separate statistic. Historical statistics only, not a forecast.

What this means, and how to use it

Use it to size an expectation, and mostly to lower one. The seasonal effect here is about 2 percentage points — only December's net-buy share edges above its net-sell share, and October–November are the weakest. That is far too small to trade on its own, and the honest use is the opposite: if a strategy claims a large seasonal institutional-flow edge, this is the baseline it has to beat. It counts stocks rather than money, so a single heavyweight cannot tilt it. Historical statistics, not a forecast.

Net-buy breadth by month

Net-buy breadth by month
MonthNet-buy breadthNet-sell breadthYears buy>50%Years
Jan48.4%48.8%514
Feb48.6%48.7%414
Mar48.6%48.7%414
Apr48.4%49%514
May48%49.4%315
Jun47.6%49.9%415
Jul49.1%48.5%415
Aug48.7%48.7%515
Sep48.1%49.1%215
Oct47.1%50.1%214
Nov47.5%49.7%114
Dec49%47.9%414

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The figures on this page come from the Institutional flow dataset.

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