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Mean Reversion

Technical Specification, Mathematical Formulation, Market Data Inputs, and Quantitative Factor Logic.

Reports & Scenarios Guide →
Strategy Docs: Momentum Momentum Omega Mean Reversion Statistical Arbitrage Trend Following Volatility Breakout
1

Strategy Objective, Rationale & Market Regimes

Investment Objective: The Mean Reversion strategy identifies statistical overextensions in equity prices away from their historical central moving averages, generating counter-trend positions that capture profits as prices revert to equilibrium.

Foundational Economic & Behavioral Rationale: Rooted in the Ornstein-Uhlenbeck stochastic mean-reverting process and empirical studies by Fama and French (1988), Poterba and Summers (1988), and Lo and MacKinlay (1990). Market participants frequently overreact to short-term news, quarterly guidance revisions, or temporary liquidity imbalances, pushing prices beyond intrinsic statistical boundaries.

Market Regime Suitability:

• Optimal Regimes

Range-bound, sideways, choppy, and low-trend consolidating market environments with well-defined support/resistance bounds.

• Challenging Regimes

Strong secular bull runs or catastrophic structural breakdowns where prices trend persistently without mean reversion.

2

Market Data Inputs

The quantitative engine processes high-fidelity financial market data stored in high-performance QuestDB time-series tables across dual resolution layers:

1. End-of-Day (EOD) OHLCV Dataset (Primary)

Primary data source utilized for indicator calculations, mathematical factor modeling, and primary quantitative model scoring.

  • • Open, High, Low, Close (OHLC): Split & dividend adjusted.
  • • Volume: Total daily traded share volume.
  • • Universe Coverage: Active US equities spanning Russell 3000 and S&P 500.

2. 15-Minute Intraday Bar Dataset (Auxiliary)

High-frequency intraday bars utilized for auxiliary multi-timeframe confirmation and higher-timeframe regime alignment.

  • • 15m Interval Bars: Intraday pricing sequence.
  • • EMA(50, 200, 800) 15m: Triple Exponential Moving Average pattern detection.
  • • Purpose: Auxiliary regime evaluation and multi-timeframe confirmation filter.
3

Indicators and Mathematical Formulations

The Mean Reversion model evaluates rolling statistical standard normal Z-scores to gauge distance from historical equilibrium:

$$\mu_K(t) = \frac{1}{K} \sum_{i=0}^{K-1} \text{Close}(t - i)$$
$$\sigma_K(t) = \sqrt{\frac{1}{K-1} \sum_{i=0}^{K-1} \left(\text{Close}(t - i) - \mu_K(t)\right)^2}$$
$$Z(t) = \frac{\text{Close}(t) - \mu_K(t)}{\sigma_K(t) + \epsilon}$$
$$\text{RawScore}(t) = -Z(t)$$

Where \(K = 20\) trading days is the standard rolling lookback window.

4

Factor Classification & Scoring Thresholds (Model Logic)

The quantitative factor model classifies equities into mean-reverting states at statistical extremes:

• Positive Factor Classification (+1.0 Score - Oversold Divergence)

Condition: Rolling Z-score satisfies \(Z(t) \le -\text{entry\_threshold}\) (default \(Z \le -2.0\)). Price is \(\ge 2.0\) standard deviations below its 20-day mean.

• Negative Factor Classification (-1.0 Score - Overbought Divergence)

Condition: Rolling Z-score satisfies \(Z(t) \ge +\text{entry\_threshold}\) (default \(Z \ge +2.0\)). Price is \(\ge 2.0\) standard deviations above its 20-day mean.

• Equilibrium / Neutral State (0.0 Score - Mean Convergence)

Condition: \(|Z(t)| \le \text{exit\_threshold}\) (default \(|Z| \le 0.5\)). Factor reverts to neutral once price returns within 0.5 standard deviations of its mean.

Empirical Research Notes & Case Studies

Real-world historical simulations and market research examining Mean Reversion factor performance.

Backtesting 15 min read

Signals and themes from quant strategies after last week’s earnings: FTNT, F5 and healthcare

We reviewed 300 signals from our six quant strategies together. Strategies split into two families (trend and reversion) that read last week’s earnings in opposite ways. The trend family (Momentum, Momentum Omega, Trend Following, Volatility Breakout) is buying the stocks that jumped, such as Grid Dynamics (+24.5% on 30 Oct) and Regeneron (+11.5%). The reversion family (Mean Reversion, Statistical Arbitrage) is shorting them and buying the stocks that fell, such as Reddit (−22%). The most valuable signals are the ones both families back: F5 and Fortinet, each long across both families with no votes against them, each down about 20% after beating on the quarter and guiding to slower growth. Beyond the stocks, two themes stand out: healthcare catalysts lead momentum, and the market is selling companies that guide lower or pay heavily for AI.

Nov 02, 2026 Read Analysis →
Semiconductors 12 min read

Intel Inc (INTC) earnings: data center jumps 78%, but Q4 guidance cut triggers 25% sell-off

Intel’s Q3 print delivered a stark contrast between a surging Data Center business (+78% YoY to $7.3B) and a severe guidance reset. While Q3 beat top- and bottom-line estimates ($16.5B revenue, $0.52 non-GAAP EPS), Q4 revenue guidance ($15.0B–$16.0B vs. $17.1B consensus) sent shares down ~25% pre-market. Management highlighted supply bottlenecks in substrate packaging, a memory-driven PC downturn running into H1 2027, and a $300M revenue hit from the Fab 28 explosion in Israel. With capex ramping for 18A/14A and 2027 expected to be cash-flow negative, we adjust the score from -2/10 to -1/10 Neutral.

Oct 30, 2026 Read Analysis →