About Stock Ranks

Stock Ranks is an AI-driven analytics platform designed to simplify and systematize the investment process. Each month, it evaluates approximately 4,000 US-listed companies, producing objective, data-driven rankings that help investors make more informed decisions.

The ranks are generated using an ensemble of advanced machine learning models trained on fundamental company data such as income statements, balance sheets, cash flow statements, and stock price-based features.

Why Stock Ranks Was Created

Stock Ranks was founded by a data scientist and lifelong market enthusiast who spent years developing advanced fraud detection models for top U.S. Fintech companies. The same techniques used to identify anomalies, patterns, and risk factors in payments/banking were adapted to analyze corporate fundamentals and market data.

The motivation behind Stock Ranks was simple — to build a system that could analyze financial statements more efficiently and objectively than humans, reducing emotional decision-making and cognitive bias that often hinder investors. By leveraging machine learning, Stock Ranks delivers a disciplined, quantitative view of company performance and market potential.

The goal is to provide actionable, transparent, and repeatable insights that empower investors to make consistent, data-backed decisions — transforming what is often a subjective process into one grounded in evidence and probability.

How Recommendations Are Generated

Each month, all stocks are ranked based on their expected performance over the next 12 months. The ranking system is a five-tier structure as shown below:

“Strong Buy” and “Strong Sell” ratings are relatively rare — typically representing only the most extreme model predictions each month.