A high-frequency, market-neutral relative-value strategy designed to capture short-horizon statistical dislocations in U.S. equities.

The strategy operates on a dynamic universe of high-liquidity U.S. stocks, applying a multi-stage correlation filter driven by industry structure and recent return dynamics. Within these clusters, cointegration analysis is used to identify statistically stable equity pairs suitable for spread trading.

Each pair is modeled using a stochastic mean-reversion framework, estimating equilibrium levels, volatility regimes, and mean-reversion half-life. Trades are initiated when spreads deviate beyond statistically significant bounds, positioning for convergence back to modeled fair value.

Risk is managed through a dual-constraint system combining:

• • Price-based drawdown limits
• • Time-based stop losses derived from half-life estimates

Positions are exited upon mean reversion within the expected temporal window or automatically closed when risk constraints are violated, maintaining strict exposure control and execution discipline.

A medium-horizon, market-neutral equity strategy designed to generate alpha by conditioning directional signals on structurally predicted firm-level volatility.

The strategy operates on a broad universe of U.S. equities, beginning with sector-level business model analysis to identify persistent sources of risk such as operating leverage, cost rigidity, financial leverage, and exposure to macro or commodity shocks. These structural characteristics are translated into quantitative risk indicators and aggregated into a forward-looking volatility score, capturing cross-sectional differences in business-model fragility.

Predicted volatility is used as a state variable, not a traded asset. Directional signals—including earnings surprises, sentiment measures, and demand indicators—are scaled according to each firm's forecasted volatility, allocating risk toward names where information shocks are expected to transmit most strongly into price movement.

Portfolio construction enforces dollar and sector neutrality, with volatility-scaled position sizing and strict exposure controls. Risk management and exits are governed by a multi-trigger framework combining:

• • Volatility convergence criteria, exiting positions as realized volatility compresses toward forecasted levels
• • Signal decay thresholds to prevent overstaying positions after informational edge dissipates
• • Time-based exits aligned with fundamental information diffusion
• • Hard risk stops used solely as tail-risk controls

By separating volatility forecasting from return generation, the strategy systematically concentrates capital where signals are most impactful while avoiding overexposure to structurally stable firms, resulting in a robust, interpretable long/short framework adaptable across market regimes.