Learn how StoQuant's Q-Score (0–100) blends valuation, momentum, sentiment, and machine learning to rate 822 stocks every day.
The Q-Score is StoQuant's flagship single-number rating for any stock. It ranges from 0 (avoid) to 100 (strong buy) and synthesizes nine distinct dimensions of quality: valuation, momentum, technical, sentiment, analyst, insider, earnings quality, machine learning, and gem discovery (hidden-value) signals. Each dimension is independently scored on a 0–100 scale, then a calibrated ensemble blends them into the final Q-Score. Why nine dimensions? Because no single metric captures the full investment opportunity. Valuation alone ignores momentum (a cheap stock can get cheaper); momentum ignores fundamentals (a stock in a dying sector may rally anyway); sentiment ignores earnings quality (optimistic headlines can mask accounting tricks). By blending nine independent viewpoints, the Q-Score becomes more robust than any single approach. It is also more interpretable: you can open the score card and see which dimensions are driving the recommendation. The nine dimensions are: 1. Valuation: Is the stock cheap relative to earnings, book value, cash flow, and the Benjamin Graham formula? Targets stocks with P/E < 15, P/B < 1.5, and 30%+ margin of safety. 2. Momentum: Is the stock gaining or losing relative momentum? Tracks 5-day, 20-day, and 63-day technical momentum plus sector-relative strength. Momentum confirms valuation (a cheap stock with rising momentum is a strong signal). 3. Technical: Are there chart patterns or breakout signals? Looks for volume-confirmed breakouts, squeeze setups (Bollinger Band compressions), and price-pattern reversals. 4. Sentiment: What do news headlines and SEC filings say? A hybrid keyword-plus-FinBERT classifier grades headlines as bullish, bearish, or neutral. Sentiment from recent earnings transcripts is also scored. 5. Analyst: What do Wall Street analysts recommend? Tracks consensus rating (buy/hold/sell), recent upgrades/downgrades, and estimate revisions. Analyst upgrades often precede price moves. 6. Insider: Are company insiders buying or selling? Large insider buys ($5M+) are a strong signal. StoQuant has uncovered that mega insider buys predict +9.5% returns over 30 days (78.6% hit rate). 7. Earnings Quality: Is the company's reported earnings "real"? Checks free cash flow vs. net income, accruals (accounting adjustments), R&D capitalization, and Piotroski F-Score. Filters out companies using accounting gimmicks. 8. Machine Learning: What do the 93-feature ML ensemble predictions say? LightGBM, CatBoost, and XGBoost predict 30-day forward returns. The ML dimension captures non-linear relationships and feature interactions that traditional scoring misses. 9. Gem Discovery: Is the stock a hidden gem? Looks for small-cap undervaluation, analyst coverage gaps (< 8 analysts despite quality fundamentals), and insider confidence. Hidden gems often have disproportionate upside when discovered by the market.
The Q-Score weighs each dimension based on historical out-of-sample predictive power. This is where the walk-forward validation described in our backtesting article becomes crucial: StoQuant measures the Information Coefficient (correlation with actual forward returns) for each dimension every 20 days. Dimensions with higher IC get higher weights. However, dimension predictiveness varies by regime. In our audit logs, we found that the Valuation dimension has IC = +0.12 in Bull/Range markets but IC = -0.05 in Bear markets (meaning overweighted cheap stocks actually underperform in downturns—a phenomenon known as "value traps"). To adapt, StoQuant computes regime-conditional weights: in Bull regimes, Valuation gets 28% weight; in Bear regimes, it drops to 15%, and other dimensions (Quality, Insider, Earnings) get boosted. Another critical feature is calibration. The Q-Score should be honest: a score of 75 should mean a 75% probability of outperformance, not 60% or 90%. Miscalibration is hidden and deadly—you think you are betting on 75% odds when you are really 60% odds. StoQuant uses Venn-ABERS isotonic regression to calibrate the ensemble. After training the raw blended score, StoQuant fits a non-parametric monotonic transformation that maps the raw scores to true probabilities based on historical outcomes. This transformation is refit weekly. Sentiment and Analyst dimensions require special handling. Sentiment can be noisy (one negative headline can spike bearish sentiment even if fundamentals are sound). To reduce noise, StoQuant applies a multi-day exponential smoothing: recent headlines are weighted more, but extreme swings are dampened. Analyst consensus has its own lag—upgrades often lag technical momentum by weeks. StoQuant models this lag and credits upgrades slightly less in the first 5 days after they occur. The Gem Discovery dimension is perhaps the most unique. While traditional ratings focus on the best-known, highest-quality stocks, StoQuant deliberately highlights small-cap anomalies: profitable companies with $250M–$10B market caps, less than 8 analyst coverage, and valuation metrics suggesting 30%+ upside. These are the "hidden gems" that Wall Street has overlooked. The Gem dimension gets boosted when insider buying, earnings surprises, and technical breakouts all align—signals that the market may be about to discover the stock.
Use the Q-Score in action: Best Stock Screener (stoquant.com/best-stock-screener) and AI Stock Picks (stoquant.com/ai-stock-picks). See today's scores: Today's Top Q-Score (stoquant.com/today/top-q-score).
A Q-Score of 75 is a "Buy" rating, indicating the stock has a 75% probability of outperforming over the next 30 days based on historical walk-forward validation. It means 3 of the 9 dimensions are strong (scores >70) and 2–3 are moderate (50–70). The scorecard will show you which drivers are positive.
The Q-Score updates daily for all 822 stocks after market close. Each dimension's input data is refreshed in real time: stock prices (intraday), sentiment (continuously), analyst ratings (daily when published), and fundamentals (quarterly, but cached).
Analyst consensus is a simple average of Wall Street ratings (buy/hold/sell). Q-Score blends 9 dimensions including ML, insider buys, technical, earnings quality, and gem discovery. Analyst consensus lags (updates quarterly or when major events occur); Q-Score updates daily. Q-Score also adapts to market regime, whereas analyst ratings are static.
Q-Score is computed from public data (prices, filings, headlines, fundamentals) and walk-forward validated, so it is resistant to manipulation. However, earnings manipulation can fool the Earnings Quality dimension, and coordinated social media campaigns can spike sentiment. StoQuant uses multiple independent dimensions so that manipulation of one (e.g., fake social buzz) is outweighed by real signals (e.g., insider buying, technical breakouts).
A low Q-Score does not mean the stock is bad—it means it lacks strong signals across the 9 dimensions in the current market regime. For example, a popular tech stock might score high on Momentum but low on Valuation and Earnings Quality (high P/E, high accruals). The Q-Score values a balanced signal; concentrated bets on single dimensions are downweighted.
No. Q-Score is a rating, not a trade signal. A Drop from 75 to 50 is meaningful but does not mandate a sell. Q-Score is best used as part of a broader process: review the driver scorecard, check if your original thesis has changed, and decide whether to hold or rebalance.