What it means
At its heart, herding looks like a sensible response to uncertainty. If you do not trust your own read on a situation, copying people who appear better informed feels safer than backing your own judgement, and the trouble only appears when everyone reasons the same way.
At that point the crowd carries almost no independent information, yet it looks more confident than ever. For a business, herding matters because it distorts both the price of capital and the timing of decisions.
When money floods into a sector, funding is cheap and valuations look generous; when the crowd reverses, perfectly healthy companies find their credit lines repriced overnight. Founders who raised on crowd enthusiasm often discover that the same crowd disappears exactly when they need a follow-on round.
Analysts measure herding by comparing how many investors traded in the same direction in a given security against the average direction of trading across the whole market. The best-known measure then subtracts an adjustment for the clustering you would expect from chance alone, so that only the excess counts as genuine herding.
Anything left over is treated as evidence that investors were watching each other rather than the fundamentals. It is worth separating deliberate herding from what economists call spurious herding, where everyone reacts to the same public news at the same moment.
Only the first is a behavioural problem; the second is simply a market processing information quickly. Career risk is the most common driver of the deliberate kind, because a manager who is wrong alongside everyone else rarely loses their job.
In practice
Real-world examples.
Example
A regional bank sees three competitors announce identical cuts to small business lending after one bad quarter. Its credit committee follows within six weeks, not because its own loan book has deteriorated, but because approving loans nobody else is approving feels career-threatening. Two years later the bank has lost market share to a lender that stayed put.
Example
An equity fund manager notices that 80% of her peer group has added the same three semiconductor names. She buys them too, reasoning that if the trade fails she will be wrong in good company, and if she skips it and the trade works she will be the only underperformer in her category. Her allocation now mirrors the crowd rather than her own research.
Example
A software company delays a price rise for a full year because no competitor has moved first. When one rival finally raises list prices by 9%, four others follow within a quarter, and the company discovers it had left roughly $2,000,000 of revenue on the table waiting for permission from the market.
Think of it
“Herding is following the crowd-doing what everyone else does.
Formula
Calculation
A standard herding measure is: H = |B / (B + S) - E| - A, where B is the number of investors buying a given security in a period, S the number selling, E the average buy ratio across all securities in that period, and A an adjustment for random clustering.
Suppose 40 institutional investors trade shares in a mid-cap logistics company during one quarter. Of those, 34 buy and 6 sell, so the buy ratio is 34 / 40 = 0.85, or 85%. Across every security in the market that quarter the average buy ratio was 60%, and the expected random clustering adjustment for a group of 40 traders is 0.05.
The calculation is |0.85 - 0.60| - 0.05 = 0.25 - 0.05 = 0.20. In plain terms, 20 percentage points more of the trading crowd moved in the same direction than chance alone would explain, which is a strong herding signal for that stock.Case study
Seen in the real world.
This is an illustrative, fictional example. Northwind Freight Partners, an invented logistics operator, spent 2021 watching every listed peer announce large investments in electric delivery vans. Its finance director could not make the payback maths work at current battery prices, but the board grew uncomfortable being the only name in the sector without an announcement.
Northwind committed $18,000,000 to a fleet order it had internally scored as marginal. Eighteen months later, two of the peers quietly wrote down their own fleets when charging infrastructure lagged behind, and Northwind was left with vehicles it could not deploy on its longest routes.
The postmortem, in this fictional account, found that no individual decision had been irrational: each manager was responding to what the others had already done. Northwind's response was to require that any capital request above $5,000,000 include a written case that does not reference competitor behaviour at all.
Watch out
Common mistakes.
- Assuming herding only affects retail investors. Professional fund managers herd at least as much, because they are measured against a peer benchmark and being wrong alone is punished far more harshly than being wrong together.
- Treating any correlated trading as herding. If a central bank changes rates and every investor reprices bonds within an hour, that is shared information being processed, not investors copying each other.
- Believing you can spot herding in yourself in real time. Herding feels like ordinary prudence from the inside, which is why the practical defence is a written investment thesis you commit to before you check what others are doing.
Questions
People also ask.
Is herding always harmful?
No, following better-informed participants can be sensible in genuinely unfamiliar markets, but it becomes damaging once so many people copy that prices no longer reflect any independent analysis.
How can a management team reduce herding in its own decisions?
Ask each participant to write down their view privately before the meeting starts, so the first opinion spoken does not anchor everyone else.
Does herding explain market crashes?
It amplifies them rather than causing them, because selling pressure attracts more selling, but the trigger is usually a real shift in fundamentals, liquidity or interest rates.
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