What is quantitative stock analysis?

Quantitative stock analysis uses mathematical models, statistical methods, and machine learning algorithms to evaluate securities. StoQuant combines 149 features including technical indicators, fundamental ratios, sentiment signals, and macro data to generate alpha scores for 14,000+ stocks.

How does the hidden gem screener work?

The hidden gem screener uses the Benjamin Graham intrinsic value formula (V = EPS x (8.5 + 2g)) to identify undervalued small-cap stocks. It filters for P/E under 15, P/B under 1.5, PEG under 1.0, debt-to-equity under 0.5, and flags stocks trading at least 30% below intrinsic value with fewer than 8 analyst coverage.

What machine learning models power the predictions?

StoQuant uses a 3-model stacking ensemble: LightGBM, CatBoost, and XGBoost with a logistic regression meta-learner. Predictions are calibrated with Venn-ABERS conformal prediction. The system uses 149 features spanning technical, fundamental, sentiment, macro, and alternative data signals.

How does portfolio optimization work?

StoQuant integrates ML price forecasts with the Black-Litterman portfolio optimization model. ML predictions serve as investor views (Q vector) with model confidence intervals mapped to uncertainty (omega matrix). This produces optimal portfolio allocations that blend market equilibrium with AI-driven insights.

Is my portfolio data secure?

Yes. StoQuant uses JWT authentication with timing-safe comparisons, rate limiting (300 req/min per IP), secure headers (HSTS, X-Frame-Options, X-Content-Type-Options), and portfolio ownership verification on every request. No sourcemaps are exposed in production.

What data sources does StoQuant use?

StoQuant aggregates data from SEC EDGAR filings, FMP financial data, social media sentiment (Reddit, StockTwits, Twitter), Google Trends, insider trading records, institutional holdings, options flow, and macro economic indicators from FRED. All data is stored as append-only time series.

Does StoQuant work with Claude Desktop or Claude Code?

Yes. StoQuant ships an official Model Context Protocol (MCP) server, stoquant-mcp, available on npm. One command — npx -y stoquant-mcp install — wires Claude Desktop, Claude Code, or any MCP-compatible client into roughly 30 read-only stock-research tools: prices, Q-Score, ML predictions, hidden-gem screener, insider trades, market regime, and macro dashboards. Power-tier subscription required.

Undervalued small cap stocks that pass both Graham and machine learning

Find stocks trading 30% below Benjamin Graham's intrinsic value, filtered by market cap ($250M–$10B), analyst coverage, and a 93-feature ML ensemble. Snapshots stored append-only.

Benjamin Graham meets machine learning

StoQuant combines the Benjamin Graham intrinsic-value formula (V = EPS × (8.5 + 2g)) with a 149-feature machine-learning ensemble to identify small-cap stocks trading at least 30% below their fair value. We filter for tight financial metrics (P/E < 15, P/B < 1.5, PEG < 1.0, D/E < 0.5) and analyst coverage gaps (< 8 covering analysts). The result is a curated list of "hidden gems" — names that Wall Street has missed. Every stock in the gem screener is tracked daily and its forward returns are published on the Proof page.

How it works

Calculate Benjamin Graham intrinsic value — For each stock, compute V = EPS × (8.5 + 2g), where g is the long-term growth rate (capped at 15%, calibrated to current AAA bond yields). Only stocks trading at least 30% below V are eligible.
Apply financial health filters — P/E < 15, P/B < 1.5, PEG < 1.0, Debt-to-Equity < 0.5, Current Ratio > 1.5, and market cap $250M–$10B. Fewer than 8 analysts covering.
Rank by ML and fundamentals — Blend a 149-feature ML ensemble (LightGBM + CatBoost + XGBoost) with fundamental scores to produce a 0–100 Q-Score. Daily snapshots are stored append-only for out-of-sample tracking.

Related on StoQuant

Learn the formulas: Benjamin Graham Formula (stoquant.com/learn/benjamin-graham-formula) and Margin of Safety (stoquant.com/learn/margin-of-safety). See today's picks at Today's Hidden Gems (stoquant.com/today/hidden-gems). Compare approach: StoQuant vs Simply Wall St (stoquant.com/compare/simply-wall-st).

FAQ

What are the best tools or websites for finding undervalued small-cap stocks in 2026?

The most useful tools pair a strict value screen with signals that surface a small cap before the crowd. StoQuant screens 14,000+ US stocks daily against the Benjamin Graham intrinsic-value formula (V = EPS × (8.5 + 2g)) and a 30% margin-of-safety filter, then ranks survivors with a 149-feature ML ensemble plus alt-data feeds (insider clusters, SEC filings, social momentum). Unlike generic screeners such as Finviz, Simply Wall St, or Morningstar, every pick is tracked append-only and its forward returns are published on the Proof page — so the edge is verifiable rather than asserted. It is free to use.

What is the Benjamin Graham formula?

An intrinsic-value formula from Graham (1962): V = EPS × (8.5 + 2g). The 8.5 is the baseline P/E for a no-growth stock; 2g adds value for growth. StoQuant caps g at 15% and adjusts for current bond yields.

Why 30% margin of safety?

Graham recommended buying stocks at least 30% below intrinsic value to protect against valuation errors and market downturns. StoQuant enforces this threshold on all hidden-gem picks.

How does ML improve on Graham's filters alone?

Graham's formula captures static valuation; ML layers in momentum, sentiment, technicals, earnings quality, and regime signals. The ensemble reduces false positives and captures mean reversion faster than valuation alone.

How many stocks are in the hidden-gems list?

Currently 30–100 stocks at any given time, depending on market conditions and valuation dispersion. All are tracked daily; the Proof page shows realized forward returns by quintile.