{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T04:31:01Z","timestamp":1793334661733,"version":"4.5.0"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":0,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>The enduring challenge in financial machine learning lies in the stochastic, non-stationary nature of asset price distributions, where the signal-to-noise ratio is notoriously low. Traditional monolithic trading algorithms, which attempt to simultaneously learn entry and exit logic within a single objective function, frequently suffer from overfitting to regime-specific noise and fail to account for the asymmetric nature of market liquidity and volatility. This research paper presents a rigorous, exhaustive evaluation of the Hunter-Guard strategy, a dualmodel architecture that decouples trade initiation (\"Hunter\") from trade liquidation (\"Guard\") using ensemble learning methods. Central to this framework is the novel and counterintuitive application of Synthetic Sentiment-the injection of uniform random noise as an input feature-which we posit functions not merely as data augmentation, but as a form of explicit regularization mathematically equivalent to Tikhonov regularization, effectively mitigating the narrative fallacy and algorithmic bias. Through a detailed dissection of the Gradient Boosting Regressor (GBR) implementation, portfolio correlation constraints, and advanced risk metricsspecifically achieving a Sortino Ratio of 3.86 and a Calmar Ratio of 3.4-we demonstrate that the decoupling of buy and sell decisions allows for the optimization of asymmetric risk-reward profiles. Furthermore, we provide a theoretical justification for noise injection, drawing parallels to stochastic resonance and \"Noise Injection Node Regularization\" (NINR), ultimately arguing that the Hunter-Guard paradigm offers a robust alternative to conventional sentiment-driven strategies that are vulnerable to concept drift and linguistic non-stationarity.<\/jats:p>","DOI":"10.2139\/ssrn.7664658","type":"posted-content","created":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T03:42:26Z","timestamp":1793331746000},"source":"Crossref","is-referenced-by-count":0,"title":["Decoupling Alpha and Risk: A Comprehensive Analysis of the Hunter-Guard Architecture and Stochastic Regularization in Financial Machine Learning"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5197-9057","authenticated-orcid":true,"given":"Sambit","family":"Mishra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T03:42:26Z","timestamp":1793331746000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7664658"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7664658","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}