{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T20:49:03Z","timestamp":1792183743598,"version":"4.4.0"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":53,"publisher":"Elsevier BV","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>The optimal regime detection filter for a trading strategy must match the distributional structure of the strategy's return sequence. Applying a mismatched filter produces degenerate or spurious regime signals; applying a matched filter extracts maximum information from the performance history. This paper formalises the Matched Filter Principle by connecting the signal processing matched filter theorem to the Neyman-Pearson framework for regime detection in trade outcome sequences. The tail index \u03b1 of the return distribution determines which filter design is optimal: strategies with \u03b1 &amp;gt; 2 (finite variance, approximately Gaussian) are optimally filtered by rollingmean estimators such as the Semi-Parametric Markov Chain (SPMC), while strategies with \u03b1 \u2264 2 (infinite or borderline-infinite variance, Pareto\/power-law) cause rolling-mean filters to degenerate because the sample mean is no longer a reliable estimator. Empirical validation uses 143 look-1 The Matched Filter Principle ahead-free strategy-filter combinations across six USD currency pairs (2019-2026). Meanreversion strategies produce tail index \u03b1 \u2248 3.7-4.4 and strong SPMC performance (\u0394 Sharpe = +10.5%); trend-following strategies produce \u03b1 \u2248 1.5-2.4 and degenerate SPMC signals (the filter labels the extreme tail as a \"regime\" rather than detecting genuine state persistence). The \u03b1 = 2 threshold that separates reliable from unreliable rolling-mean estimation may also separate meanreverting from trending market conditions, connecting filter design, strategy type, and market efficiency through a single distributional parameter.<\/jats:p>","DOI":"10.2139\/ssrn.6621520","type":"posted-content","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T13:36:40Z","timestamp":1777037800000},"source":"Crossref","is-referenced-by-count":0,"title":["The Matched Filter Principle for Trading Strategy Regime Detection: Tail Index as the Universal Threshold Between Filter Designs"],"prefix":"10.2139","author":[{"given":"Marijan","family":"anon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"473","key":"ref1","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1198\/016214505000000411","article-title":"Parameter estimation for the truncated Pareto 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