Market-data correlation & trend prediction

Find the correlation. Predict the trend.

Pear Data Corp finds the relationships hidden in market data — pairing streams, stitching them into aggregates, and surfacing where one signal reliably moves ahead of another. The result is an earlier read on where markets are heading.

  • Correlation, not coincidence — relationships tested to hold out of sample.
  • Aggregates, not single series — several streams stitched into one signal.
  • Lead, not lag — find what moves first, and act before the market catches on.
What we do

Relationships in the data, made usable.

The core work PDC was built on — now run at scale. We look for where markets move together, and turn stable structure into a forward signal.

Pair & correlation discovery

Surface stream pairs whose movements track — the way gold and the dollar once did — and test whether the relationship holds up.

Multi-stream aggregation

Stitch several data streams into a single aggregate index, then measure how that composite correlates with the target.

Lead-lag & predictive signal

Identify which series moves first and turn stable lead-lag structure into a forward read on the trend.

Monitoring & alerts

Track correlations over time and flag the moment a relationship breaks down or a market regime shifts.

How we work

From raw streams to a forward signal.

The same four movements on every engagement — a repeatable path from data to a decision you can time.

01

Ingest

Pull the market data streams that bear on your question, and clean them to a common clock.

02

Pair

Generate candidate relationships — single series and stitched aggregates alike.

03

Correlate

Test each pair for strength, stability, and lead-lag — and whether it survives out of sample.

04

Predict

Turn the relationships that hold into a forward read, then monitor for breaks.

Also from PDC — marketing

Marketing that ships, for teams that measure.

The same operator's discipline, applied to go-to-market: strategy, stakeholder buy-in, campaign execution, and cross-channel optimization. A sample of the work:

Why “Pear”

It started with a pair.

Pear Data Corp began with two data streams and one question: how tightly do they move together? The classic case was gold and the US dollar — for years, a near-mirror relationship. “Pear” is our nod to that origin: a pair. Today, with AI, we do it at scale — many streams, stitched and compared, to find the relationships worth acting on.

Veteran operators Data & process driven Measurable · time-based
Start a conversation

Tell us the streams you’re curious about…

Send a note with the markets or metrics you’re watching, and we’ll come back with how we’d look for the correlation.

info@peardatacorp.com