SPACs AI Research Model

Problem

SPAC filings and enforcement cases contain the data needed to study deal quality and regulatory risk, but it's locked in unstructured text across hundreds of filings.

Approach

Built an AI-driven data extraction pipeline to analyze 900+ SPAC filings and enforcement cases, translating research needs into PRDs and standardizing 10+ key deal attributes from unstructured text.

Outcome

Surfaced $1B+ in tracked transactions across issuers, supporting analysis of valuation errors and regulatory gaps. Research under Professor Saad Siddiqui.

My role

Researcher — pipeline design and stakeholder translation.