Multi-Factor Stock Scorecard Template

Quick Navigation

• Overview
• Step-by-Step Framework
• What Data to Track
• Common Mistakes
• FAQ

Overview

This guide focuses on Stocks research through a repeatable, evidence-first workflow.

Stock research should combine business quality, valuation context, and scenario-based risk mapping.

The workflow works best when each thesis includes both upside logic and explicit invalidation conditions.

Core angle: Use consistent scoring before ranking opportunities.

Step-by-Step Framework

1. Define your objective and time horizon for this Stocks topic before reviewing signals.
2. Collect primary sources first (official filings, policy releases, market data) and use AI for structuring notes.
3. Build a written base-case, upside-case, and downside-case before position sizing.
4. Write explicit risk limits and invalidation points before taking action.
5. Review outcomes on a schedule and update process rules instead of reacting to short-term noise.

What Data to Track

• Revenue quality, margin durability, and capital efficiency metrics.
• Valuation regime context relative to peers and history.
• Catalyst calendar and downside scenario mapping.

Validation Checks Before Action

• Cross-check AI outputs with at least one primary source.
• Confirm that position size still fits current drawdown tolerance.
• Re-read invalidation criteria before any incremental exposure.

Common Mistakes to Avoid

• Treating AI summaries as final truth without source verification.
• Changing position size without updating downside assumptions.
• Overweighting recent headlines while ignoring broader regime context.
• Entering positions without a pre-defined review cadence.

Key Takeaways

• Define objective and time horizon before interpreting signals
• Use AI as an acceleration layer, then verify primary sources
• Document invalidation points and downside assumptions

FAQ