Premium prediction built around how bankers actually frame deals.
The current product shell uses simulated outputs, but the methodology is designed for a real transaction dataset: train on historical public M&A, engineer strategic and financial variables, and estimate the premium that comparable deals would imply.
The Overpay Index
Most M&A analysis asks whether a transaction made strategic sense. ImpliedAI asks a sharper question: given everything observable about the transaction, what premium should the acquirer have paid? The Overpay Index converts the gap between predicted fair premium and actual premium into a 0-100 score.
Unaffected share price premium
Sector median precedent transactions
Revenue growth and margin quality
Strategic buyer versus financial sponsor behavior
Competitive process intensity
Market cycle and cost of capital
Target scarcity and category leadership
Synergy justification and execution risk
01
Historical training data
Build a clean public M&A dataset with deal terms, unaffected prices, premiums, sector tags, and market conditions.
02
Feature engineering
Translate deal narratives into variables: strategic fit, scarcity, buyer type, process intensity, target quality, and financing environment.
03
Premium prediction
Use gradient boosting or similar supervised models to estimate warranted premium ranges from historical patterns.
04
Overpay classification
Compare actual premium to predicted fair premium and classify the transaction as fair, stretched, or materially overpaid.
The current website intentionally uses fake data first. That is the right order: prove the product, workflow, research framing, and institutional aesthetic before investing in SEC scraping, data licensing, Python pipelines, and production machine learning.