The Team Behind the Earnings Surprise Framework
We are practitioners, academics, and former analysts who grew tired of watching rigorous
quantitative research stay locked behind institutional walls, and decided to do something
about it.
Empirically grounded methodology
Practitioner-built curriculum
Cross-sectional research focus
The Principles That Shape Every Decision We Make
Our Mission in Plain Terms
We exist to make rigorous cross-sectional earnings surprise modeling accessible to analytical minds beyond the largest institutions. Our approach draws on empirical research, systematic thinking, and a frank acknowledgment that no model predicts the future with certainty. We present frameworks honestly, with all their limitations visible.
Where We Are Heading
We envision a research community where analyst forecast error patterns are studied with the same discipline applied to any other systematic phenomenon in markets — openly, collaboratively, and with a healthy skepticism toward any single framework that claims to have all the answers.
Empirical Rigor Above All
Every framework we present is traced back to observable data and testable hypotheses. We do not offer intuition dressed as analysis — we demand evidence, and we show our reasoning at each step.
Practitioner Relevance Always
Academic elegance matters little if a framework cannot be applied by someone sitting at a real desk with real constraints. We translate theoretical constructs into tools that practitioners can actually use and critique.
Intellectual Honesty as a Standard
We present what the research actually shows, including the cases where cross-sectional models underperform, fail to generalize, or rest on assumptions that deserve scrutiny. Honest limitations build more trust than polished omissions.
Continuous Methodological Improvement
Markets evolve, analyst behavior shifts, and modeling techniques advance. We revisit our frameworks regularly, incorporating new research and community feedback rather than treating any approach as permanently settled.
Community-Driven Learning
The most durable analytical insights emerge from dialogue, not monologue. We actively cultivate a community where practitioners challenge assumptions, share findings, and collectively push the quality of earnings surprise modeling forward.
The Faculty and Advisory Minds Behind the Platform
-
01
Quantitative Research Director and Co-Founder
Dr. Miriam Calloway, Research Director and Co-Founder
Miriam spent over a decade building cross-sectional factor models at a systematic asset management firm before turning her attention to what she calls the analyst forecast error problem. She holds a doctorate in financial econometrics and leads the platform's core research agenda, ensuring every framework published meets the empirical standards she applied in practice. Her particular focus is on how consensus revision dynamics create predictable cross-sectional dispersion in realized surprises.
-
02
Curriculum Architect and Co-Founder
James Okafor, Lead Curriculum Architect and Co-Founder
James built quantitative analyst training programs for two major buy-side institutions before co-founding the platform. His frustration with the gap between textbook earnings models and actual practitioner workflows drove the platform's founding philosophy. He translates dense academic research into structured analytical frameworks that practitioners can interrogate, adapt, and apply without losing methodological integrity. He holds a master's degree in applied finance. -
03 Academic Advisor and Faculty Lead
Professor Anita Rao, Academic Advisor and Faculty Lead
Anita is an associate professor of finance whose published research covers analyst forecast bias, post-earnings announcement drift, and the behavioral underpinnings of systematic forecast errors. She joined the platform's advisory board to ensure the curriculum remains anchored to the best available empirical evidence. Her role bridges the academic literature and the practitioner-facing content, flagging where research consensus is strong and where it remains genuinely contested. -
04Senior Quantitative Analyst and Content Lead
David Strömberg, Senior Quantitative Analyst and Content Lead
David brings a sell-side research background to the platform, having spent several years as a quantitative earnings analyst at a mid-tier investment bank. He understands the institutional pressures that shape analyst forecast behavior from the inside, which informs his work translating earnings surprise patterns into cross-sectional modeling case studies. He is responsible for the platform's analytical content library and keeps the research examples current and regionally relevant for Australian market participants.
Recognition, Affiliations, and Community Standing
2022
Emerging Fintech Education Platform Recognition
Acknowledged by the Australian Financial Technology Association for contributions to practitioner-focused quantitative research dissemination and cross-sectional modeling frameworks.
Academic Partnership — Finance Faculty Collaboration
Established a formal research collaboration with a leading Australian university finance department to co-develop empirically grounded earnings surprise modeling content.
2023
Featured Research — Quantitative Finance Publication
Platform methodology cited in a practitioner-facing quantitative finance journal as a reference framework for cross-sectional analyst forecast error modeling in the Asia-Pacific region.
CFA Institute Chapter Endorsement — Practitioner Content
Recognized by a CFA Institute chapter in Australia for the quality and intellectual honesty of the platform's content covering earnings surprise dynamics and systematic forecast bias.
2024
Community Choice — Quant Research Platform of the Year
Selected by a community vote of independent quantitative finance practitioners across Australia and New Zealand as a preferred reference platform for earnings surprise research frameworks.
Industry Panel Recognition — Methodological Transparency
Highlighted by an independent fintech industry panel for the platform's commitment to presenting model limitations and empirical boundaries alongside its core analytical frameworks.
Join the Conversation
Ready to Engage With Rigorous Earnings Surprise Research?
Have you ever sat with a model that looked elegant on paper and then watched it struggle against actual earnings announcement data? We have, more than once, and that experience is precisely what shaped this platform. Whether you are a practitioner exploring cross-sectional modeling for the first time or an experienced analyst looking for a community that takes the empirical details seriously, we want to hear from you. Reach out about institutional partnerships, curriculum questions, or simply to challenge an assumption we have made. That kind of dialogue is what makes the research better for everyone involved. Past performance does not guarantee future results, and we say that not as a disclaimer but as a core conviction.
Get in touch01Institutional partnership enquiries welcome
02Open to academic collaboration and peer review
03Community of independent quant practitioners