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 stocks like your favorite company: AI peer discovery

Love a stock but want to diversify? StoQuant uses machine learning to find peer companies with similar valuation, growth, quality, and momentum profiles. Input any ticker and get a ranked list of comparable stocks - across sectors, market caps, and geographies.

Find hidden peers: the stocks most like your favorite company

Finding stocks similar to a company you like sounds simple - just filter the same industry and market cap. But that misses the real insight: two tech companies trading at the same P/E ratio but with opposite growth trajectories are not peers. StoQuant peer-discovery engine analyzes 149 quantitative and qualitative features - valuation (P/E, P/B, Graham discount), growth (revenue, EPS, FCF CAGR), profitability (ROE, ROIC), balance-sheet strength, momentum (3/6/12-month returns), sector rotation alpha, sentiment (news + social), insider activity, and macro regime probabilities - to compute a multi-dimensional similarity score. When you input a stock, the engine finds peers ranked by overall similarity, sector similarity, and size-adjusted similarity. The result: discovery of hidden peer stocks in overlooked sectors or market-cap ranges.

How it works

Enter a ticker — Input any US stock you like (Apple, Tesla, a small-cap biotech, etc.). StoQuant loads its profile across 93 quantitative features.
Compute similarity scores — Using a combination of Euclidean distance (normalized), cosine similarity (on categorical factors like sector), and sector-residualized factor matching, StoQuant ranks all other stocks by overall similarity, sector peer similarity, and size-adjusted peer similarity.
Review and compare — See the top 20 peer stocks ranked by overall match score. Each peer shows valuation (margin of safety), growth trajectory, Q-Score, insider activity, and realized forward returns vs your input stock.

Use cases: when stocks-like discovery matters

Portfolio concentration risk? Find peers to dilute a single holding. Sector rotation thesis? Find peer companies in your target sector that match your style (growth, value, quality). Wanting to go ex-US? Enter a foreign ADR and find domestic peers with matching fundamentals. Tail-risk hedging? Find uncorrelated peers with opposite momentum.

Related on StoQuant

Understand the full multi-dimensional scoring in Q-Score Methodology (stoquant.com/learn/q-score-methodology). Use peer discovery to assemble diversified watchlists and screens. Compare real-time peer returns on the Proof page (stoquant.com/proof). Try the live comparison tool at stoquant.com/compare.

FAQ

How does StoQuant compute stock similarity?

Multi-dimensional distance: normalized Euclidean distance on continuous factors (valuation, growth, profitability), cosine similarity on sector and categorical factors, and cross-sector factor matching. The engine weights each dimension by its historical predictive power (permutation importance from the ML ensemble).

Are peers always in the same sector?

No. A high-growth software company might have a similar profile (10% FCF yield, 30% revenue growth, high momentum) to a high-growth biotech or consumer-discretionary stock. StoQuant shows overall peers first, then sector-constrained peers.

Can I compare peers on individual dimensions (just valuation, just growth)?

Yes. The full feature comparison view lets you sort peers by any single dimension: "show me all peers with higher growth but lower P/E", etc.

How does peer discovery help portfolio construction?

If you love a $200B mega-cap, finding peers in the $5-10B range lets you bet the same thesis with higher volatility/upside. Peers across sectors reduce concentration risk. Momentum-adjusted peers help rotate into uptrends.