Notice Research outputs are not investment advice.
Past performance does not predict future results.
Research Methodology About Contact us
analyst team reviewing forecast data
Analyst disagreement drives returns

Why Consensus Fails and Dispersion Wins

Most investors treat analyst forecasts as a single number. That is the mistake. When twenty researchers look at the same company and reach wildly different conclusions, that spread is not noise — it is signal. Our platform maps how forecast dispersion shapes cross-sectional returns, giving practitioners a lens most models ignore. We do not sell certainty. We measure disagreement and show where it predicts price movement.
Explore our approach

Built by Researchers, for Practitioners

We started with a simple observation: the academic literature on analyst dispersion was rich, but few practitioners knew how to apply it. Our platform closes that gap.
Meet the team
research papers and financial charts on desk 0% research papers mapped

From Research Paper to Portfolio Tool

Our journey began in university finance departments, where cross-sectional studies sat behind paywalls and dense notation. We translated those findings into interactive models practitioners could actually use.
See the models
dashboard showing forecast dispersion metrics 0% dispersion metrics tracked

Connecting Forecasts to Market Movement

We built tools that link analyst disagreement directly to return patterns. Users see which dispersion metrics matter, how they interact, and where historical relationships hold or break.

View the data
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Everyday Support for Serious Work

Markets do not wait for office hours. Our team monitors model outputs, flags structural shifts, and publishes regular commentary so users are never interpreting stale signals.
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Smart Tools, No Black Boxes

Every model we publish comes with full methodology documentation. Users can replicate our steps, challenge our assumptions, and adapt the framework to their own research process.
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The People Behind the Platform

Our team blends academic finance researchers with former sell-side strategists and quantitative educators. We have spent years in libraries, on trading floors, and in classrooms — and we built this platform because we kept seeing the same gap between theory and practice.
Dr. Sarah Chen-Whitmore

Dr. Sarah Chen-Whitmore

Lead Research Director

Auckland, New Zealand

Sarah spent a decade in university finance departments studying how disagreement among equity researchers predicts future returns. She leads our research agenda and ensures every model we publish meets rigorous standards.

Core specializations

cross-sectional econometrics analyst forecast modeling empirical asset pricing academic peer review
James Okonkwo-Ramirez

James Okonkwo-Ramirez

Quantitative Educator & Strategist

Wellington, New Zealand

James moved from sell-side strategy desks to building tools that help investors understand complex signals. He translates our research into practical workflows that analysts can adopt without a PhD.

Core specializations

sell-side strategy research quantitative communication model implementation practitioner training
Dr. Mei-Lin Parkhurst

Dr. Mei-Lin Parkhurst

Senior Methodologist

Christchurch, New Zealand

Mei-Lin designs the frameworks that keep our models transparent and testable. She insists that every output can be traced back to its inputs, assumptions, and parameter choices.

Core specializations

statistical model design robustness testing data architecture reproducible research
Thomas Brennan-Hale

Thomas Brennan-Hale

Platform Operations Lead

Auckland, New Zealand

Thomas keeps the platform running and the data flowing. He bridges our research team and the technology that delivers models to practitioners around the world.

Core specializations

platform infrastructure data pipeline management user experience design research operations

What Guides Our Work

We are not a fund, a bank, or a vendor of secret formulas. We are researchers who believe that understanding analyst disagreement is a durable edge — and that sharing that understanding openly builds better markets.

Intellectual Integrity

We publish results that contradict our priors. We document failures alongside successes. Our credibility depends on never cherry-picking the data that flatters our models.

Empirical Rigor

Every claim we make is testable. We specify hypotheses before running tests, report full sample periods, and subject our models to out-of-sample validation and structural break analysis.

Practitioner Relevance

Academic insight that sits unread in journals helps no one. We design our research outputs for the time-pressed analyst who needs signal, not a literature review.

Continuous Innovation

Markets evolve and so do analyst behaviors. We revisit our frameworks regularly, incorporate new data sources, and retire models when their predictive power decays.

Radical Transparency

No black boxes, no proprietary magic. Users see our code, our assumptions, and our errors. We believe sunlight is the best disinfectant for model risk.

Recognition for Research Excellence

1

Best Paper in Empirical Finance

NZ Finance Society

Awarded for original research demonstrating that forecast dispersion predicts cross-sectional returns even after controlling for standard risk factors and liquidity measures.

2023
2

Research Innovation Prize

Asia-Pacific Quant Alliance

Recognized for developing an open-source methodology that allows practitioners to replicate and extend academic findings on analyst disagreement without proprietary data licenses.

2022
3

Outstanding Dissertation Award

University of Auckland

Honored for doctoral thesis examining how changes in analyst coverage breadth interact with forecast dispersion to generate predictable return patterns.

2021
4

Open Science in Finance Medal

Global Research Transparency Institute

Awarded for commitment to publishing full methodology documentation, replication code, and out-of-sample test results alongside all model releases.

2024
5

Practitioner Impact Citation

Institutional Investor Forum

Recognized by industry peers for research outputs that have been adopted by institutional research teams to improve their cross-sectional stock selection frameworks.

2025

Our Journey

Our Journey

  1. 01
    2019

    First Research Publication

    Our founding researchers published the initial paper linking analyst forecast dispersion to cross-sectional return predictability in New Zealand equity markets. The response from practitioners was immediate — they wanted tools, not just theory.

  2. 02
    2020

    Prototype Model Launch

    We built the first interactive dispersion dashboard, allowing users to explore how disagreement metrics varied across sectors and market conditions. Early adopters included local fund researchers and independent analysts.

    3. 03
    2021

    Institutional Partnership Program

    Three New Zealand research departments adopted our framework for their graduate curriculum. We learned that teaching dispersion effects required better visualization tools than academia typically produces.

  3. 04
    2023

    Methodology Breakthrough

    We developed a robust approach to handling structural breaks in analyst behavior, solving a problem that had plagued out-of-sample dispersion research for years. This became the backbone of our current model suite.

  4. 05
    2025

    Global Platform Release

    After years of refinement with a closed user group, we opened the platform to practitioners worldwide. The core mission remained unchanged: bridge academic research on analyst disagreement with real-world application.

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