AI-Powered Screening
We use machine learning to scan thousands of businesses for anomalies in valuation, balance sheet strength, and earnings power.
The Scale Problem
There are tens of thousands of publicly traded companies across global markets. No investment team, regardless of size, can manually evaluate every one of them with the depth required to make sound investment decisions. The traditional response to this constraint is to focus on a narrow universe — typically large-cap companies in familiar industries — and ignore the rest.
This creates opportunity. The businesses that are under-analyzed and under-followed are often the ones where the best pricing inefficiencies exist.
How Our Screening Works
We have built machine learning models trained on decades of financial data that scan thousands of businesses continuously, looking for anomalies that suggest a business may be mispriced.
Our models evaluate:
- Valuation anomalies: Businesses trading at prices that appear inconsistent with their historical earnings power, balance sheet strength, or competitive position.
- Balance sheet quality: Companies with unusually strong or deteriorating financial positions relative to their industry peers.
- Earnings power trends: Businesses where reported earnings may be understating or overstating true economic earnings.
- Capital allocation signals: Management behaviors — buybacks, dividends, acquisitions — that historically predict future returns.
From Screen to Research
The output of our AI screening is a shortlist of businesses that warrant deeper investigation. The machine identifies what is potentially interesting; human analysts determine what is genuinely valuable. This combination allows us to cover a vastly larger opportunity set than traditional methods while maintaining the depth of analysis that good investing requires.