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.

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.

Small cap investing strategy: a quant guide to hidden gems

Small caps are the last corner of the market where disciplined quant investors can still find mispricing. Learn the filters, signals, and risk controls that separate real gems from value traps.

Why small caps still offer alpha

Large-cap stocks are covered by dozens of analysts and parsed by algorithms in milliseconds. Small caps are different. Lower analyst coverage, thinner liquidity, and limited institutional ownership mean information is incorporated more slowly. That creates opportunities for investors willing to do systematic, quant-driven research. The key is to combine classic value discipline with modern signals like insider buying, alternative data, and machine-learning forecasts.

How it works

Start with a quality universe — Limit the universe to stocks with market cap between $250M and $10B, positive operating cash flow, current ratio above 1.5, and debt-to-equity below 0.5. This removes distressed lottery tickets.
Apply Graham value filters — Target P/E below 15, P/B below 1.5, PEG below 1.0, and a 30% margin of safety to Graham intrinsic value (V = EPS × (8.5 + 2g)).
Add insider and alt-data signals — Require a recent insider cluster (multiple distinct filers buying) and non-negative alternative-data momentum (social sentiment, web attention, short-interest trend).
Score with ML — Run the remaining candidates through a 149-feature ML ensemble. Rank by Q-Score and review the driver scorecard to confirm the thesis.
Size positions by regime — Use Hidden Markov Model regime detection to know whether the current environment favors value or momentum. In bear/high-vol regimes, reduce gross exposure and tighten stops.

Risk management in small caps

Small-cap alpha comes with real risk: liquidity gaps, earnings misses, and fraud are more common than in large caps. Risk controls are not optional. Position size should be smaller per name, portfolio concentration should be capped, and stop-loss rules should account for volatility. StoQuant recommends backtesting any small-cap screen with realistic transaction costs (0.1-0.5% per trade) and slippage (1-2 cents) before deploying capital.

The StoQuant small-cap workflow

StoQuant automates the strategy above in the hidden-gem screener. Every day it scans 14,000+ US-listed stocks, applies the Graham filters, scores survivors with the Q-Score, and flags insider clusters. The output is a ranked list of under-covered candidates with margin-of-safety, Q-Score, and the top three drivers. You can then export the list to your portfolio optimizer or run it through the Black-Litterman allocator.

Related on StoQuant

Use the hidden-gem screener at /gems, read the Benjamin Graham Formula guide (stoquant.com/learn/benjamin-graham-formula), and review Walk-Forward Validation (stoquant.com/learn/walk-forward-validation).

FAQ

What is a small-cap stock?

Definitions vary, but StoQuant defines small-cap as market cap between $250M and $10B. This range captures under-covered companies while excluding illiquid micro-caps.

Why focus on low analyst coverage?

Stocks covered by fewer than eight analysts are more likely to be mispriced because institutional research has not fully digested the available information.

What is a value trap?

A value trap is a stock that looks cheap on traditional metrics but is actually deteriorating. Quality filters (cash flow, debt, earnings quality) help avoid them.

How does insider buying help?

Insiders have information outsiders do not. A cluster of distinct insiders buying in a short window is one of the strongest behavioral signals in small caps.

Can I backtest this strategy on StoQuant?

Yes. The /proof page shows the walk-forward out-of-sample performance of the Q-Score and hidden-gem pipeline against Russell 2000 buy-and-hold.