Do You Even Need Reaction Data? An Honest Disqualification Guide

TL;DR

Reaction data answers one question: what has the market measurably done after classified news events. If your workflow never asks that question, you do not need this dataset, and this page will tell you so before a sales call does. Below: six cases where the honest answer is no, four where it is probably yes, and one where a cheaper surface of the same data (the MCP connector) is the right-sized purchase.

Why a vendor writes this page

Data licenses that do not get used do not get renewed, and unrenewed licenses cost more in reputation than they earned in revenue. Disqualifying poor fits early is self-interest wearing honesty's clothes, but it is still honesty, so here is the list.

You probably do not need reaction data if:

1. Your strategy never conditions on news

Purely technical systems, market-making, and flow-based strategies that would not change a parameter based on an event class have no join point for this data. Adding it would be decoration.

2. You trade on speed to the headline

If your edge is reacting in the first seconds, your binding constraint is latency infrastructure, not historical base rates. Reaction data describes the days after the moment you monetize.

3. You want a signal, not a distribution

NQ publishes measured distributions with wide interquartile ranges, because that is what markets produce. If the purchase requirement is "tell me what to buy," no honest dataset satisfies it and we will not pretend this one does.

4. Your horizon makes event noise irrelevant

Long-only allocators rebalancing on multi-year fundamentals can treat individual event reactions as noise that washes out. The exception is the risk office, which is case 3 below.

5. You need breadth we do not have

Multi-asset, global-macro, or non-US-equity workflows exceed the dataset's deliberate scope (US equities, corporate event classes). A breadth vendor fits better; our comparison pages name them.

6. You would be replicating what you already built

A handful of shops have internally maintained, point-in-time event datasets with outcome labels. If yours is one and it covers your universe honestly (including the four failure modes), the marginal value here is a cross-check, not a foundation.

You probably do need it if:

  1. You research event classes. Any backtest whose population is "all events of type X" needs labeled populations with point-in-time integrity and dead companies included. Building that yourself costs the years, not the code.
  2. You size or explain event risk. Risk teams quantifying tail exposure by event class, or attributing a realized gap against its class distribution, are using the data as designed.
  3. You are training or grounding financial AI. Models need outcome labels to stop confabulating market history, at training time or through inference-time retrieval.
  4. You answer to a board about stock reactions. IROs and CFOs benchmarking announcement reactions against sector base rates: IR Intelligence.

The right-sized middle case

If you are one trader who wants base rates on demand rather than a research dataset, do not license the dataset. Connect the MCP connector and ask your AI assistant instead. Smallest surface, same measured data.

Frequently asked questions

Who is News Quantified for?

Teams whose work conditions on news event classes: event-driven researchers, risk managers quantifying event exposure, AI builders needing outcome-labeled data, and IR teams benchmarking announcement reactions.

Who should not buy reaction data?

Speed-to-headline traders, purely technical strategies, buyers seeking trade signals rather than distributions, and workflows outside US corporate equity events. Details above.

Is there a smaller option than a full license?

Yes. The MCP connector provides query access to the same measured data through an AI assistant, sized for individuals.

Historical reaction data. Not investment advice. Past reactions do not determine future reactions.