Quantitative Analysis: Momentum and Mean-Reversion
What is quantitative analysis?
Quantitative analysis (quant) uses statistical models and historical price data to detect patterns invisible to the naked eye. No gut feeling — pure mathematics. The two main quant strategies in the 360° system are momentum and mean-reversion.
Strategy 1: Momentum
Core thesis: What has risen strongly recently tends to keep rising — at least in the short term.
Momentum models measure how strongly a stock has performed relative to comparable stocks over a defined period (e.g. 20 or 60 trading days).
| Momentum score | Signal |
|---|---|
| Top 20 % of all candidates | Strong long signal |
| Middle 60 % | Neutral |
| Bottom 20 % | Short signal |
Momentum works best in trending conditions and fails in sideways markets.
Strategy 2: Mean-Reversion
Core thesis: Extreme price moves revert to the mean — an exaggerated drop is often corrected.
The Z-score measures how far the current price deviates from its historical average (in standard deviations).
| Z-score | Interpretation | Signal |
|---|---|---|
| < −2.0 | Strongly oversold | Long (reversion expected) |
| −2.0 to −1.0 | Slightly oversold | Mild long bias |
| −1.0 to +1.0 | Normal | Neutral |
| +1.0 to +2.0 | Slightly overbought | Mild short bias |
| > +2.0 | Strongly overbought | Short (reversion expected) |
Mean-reversion works best in sideways markets and for stocks without a strong trend.
Combination: Momentum vs. Mean-Reversion
The quant score combines both strategies — weighted by the current market regime:
Bull regime → Momentum weight 70 %, mean-reversion 30 %
Bear regime → Momentum weight 60 %, mean-reversion 40 %
Sideways → Momentum weight 40 %, mean-reversion 60 %
The market regime (→ market regime analysis) therefore influences how the quant score is calculated.
Additional quant metrics in the 360° system
- Sharpe ratio (rolling 30 days): Return relative to risk
- Beta: How much does the stock fluctuate relative to the S&P 500?
- Correlation to SPY: Does the stock move with the market or independently?
Limitations of quantitative analysis
- Models are based on historical data — past patterns do not guarantee the future
- Quant signals can be instantly invalidated by news or earnings
- Overfitting risk: models that work perfectly on old data fail on new data
- Candidates from the selection universe often have short trading histories (frequently small-caps) — statistical significance is limited
All 8 methods → | Market regime → | Liquidity analysis → | Pricing →