Out-of-Distribution-Gated Momentum: Controlling Exposure via Feature-Based Wasserstein Distance

Standard trend-following strategies rely on volatility scaling to size positions, inherently ignoring the knowledge about uncertainty when deployed in out-of-distribution (OOD) market conditions. We propose a distribution-aware familiarity score to explicitly quantify this uncertainty without relying on learned representations. Our method measures the optimal-transport distance between current market return distributions and their closest analogues in the training data, acting as a smooth, continuous throttle on position exposure. By isolating this sizing mechanism from the directional signal, we guarantee that performance differences stem purely from OOD exposure control. Across five asset classes and multiple validation schemes, our optimal-transport throttle consistently reduces portfolio drawdowns, with ablations confirming the mechanism captures OOD dynamics distinct from standard volatility. We also report critical negative results: jointly learning direction and size fails to converge, and enforcing strict statistical calibration actively degrades performance-highlighting a fundamental tension in OOD detection. Finally, we demonstrate that distribution-aware familiarity serves as a robust, interpretable principle for controlling exposure under domain shift.