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 3,500+ 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.

What is a free alternative for deep stock research?

StoQuant is a free-tier quantitative research platform for deep single-stock analysis. It scores US stocks daily with a walk-forward-proven Q-Score (0-100), publishes its measured out-of-sample track record, and produces a full evidence-based research memo for any ticker covering valuation, momentum, analyst consensus, insider activity, and machine-learning signals. It is a free alternative to Seeking Alpha, Simply Wall St, and TipRanks for investors who want the underlying proof, not just a rating.

How can I screen for undervalued hidden-gem stocks using alternative data?

StoQuant's hidden-gem screener blends classic value filters — the Benjamin Graham intrinsic value formula, P/E under 15, P/B under 1.5, and a 30% margin of safety — with alternative-data signals most screeners ignore: insider-buying clusters, SEC EDGAR full-text filings, credibility-weighted social sentiment, Google Trends attention, and short interest. This surfaces under-covered small-cap stocks (fewer than 8 analysts) before Wall Street catches on. The screener is free to use.

Find undervalued tech small-caps: Graham value meets software growth

Technology stocks trading below their Graham intrinsic value with healthy balance sheets and software/hardware fundamentals. StoQuant combines valuation floors (P/E < 12 in tech), growth signals (revenue CAGR > 10%), and sector momentum to surface neglected tech small-caps before the crowd.

Value investing in tech: where Wall Street misses

Tech-sector value investing is underrated. While growth funds fixate on mega-cap AI and cloud giants, smaller software, hardware, and biotech-platform companies trade at distressed valuations. StoQuant uses Grahams intrinsic-value filters with software-specific metrics. We flag stocks with P/E ratios below 12 (vs tech median ~25), revenue growth > 10%, strong balance sheets (debt-to-equity < 0.6), and positive free cash flow. Sector-momentum signals (GitHub traction for open-source projects, App Store rank trends for consumer software) identify momentum tailwinds. The result: undervalued tech small-caps poised for re-rating.

How it works

Filter for Graham-value tech stocks — Apply Benjamin Graham intrinsic-value formula (V = EPS x (8.5 + 2g)) with a lower P/E cap for tech (< 12 vs <15 baseline). Require debt-to-equity < 0.6 and current ratio > 1.2.
Layer on growth and momentum signals — Add revenue CAGR > 10%, rule out negative FCF, and surface sector momentum: GitHub stars/forks for engineering talent, App Store rank for consumer apps, AWS mentions for cloud infrastructure.
Rank by upside potential and conviction — Sort by the margin-of-safety discount (stock price vs intrinsic value), ML confidence (via ensemble model), and analyst changes. Stocks with recent analyst downgrades often trade cheaper.

Related on StoQuant

Explore the broader small-cap opportunity set: Undervalued Small-Cap Stocks (stoquant.com/undervalued-small-cap-stocks) for multi-sector screening, and Benjamin Graham Formula (stoquant.com/learn/benjamin-graham-formula) for the theoretical foundation. See todays tech deals at Todays Top Q-Score (stoquant.com/today/top-q-score). Track macro context on Macro Intelligence (stoquant.com/analytics/macro).

FAQ

Why P/E < 12 for tech?

Tech sector median P/E is ~25. Below 12 signals deep distress or hidden value. We require growth (revenue CAGR > 10%) to rule out truly broken companies.

What sectors does this screen cover?

Software, Hardware, Internet, Semiconductors, and Biotech Platforms. We exclude mega-cap AI (NVIDIA, TSLA) and focus on market-cap $250M-$10B.

How does Alt Data help tech screening?

GitHub activity (stars, commits, contributors) correlates with software moat strength. App Store rank trends signal consumer traction. Press releases and job postings hint at hiring/growth cycles.

Is tech small-cap screening more risky?

Higher volatility, yes. Lower analyst coverage means less price discovery - but also more mispricing. We deduct transaction costs (0.3-0.5%) and slippage (2-5 cents for illiquid small-caps) to reflect true execution friction.