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Hypotheses

Nasdaq Half-Year Hurst Selection: Reduced Fixed Risk Budget, Forward Validation

Derived by the Research Lead from Chang, Lizardi and Shah: retain the six-stock daily Hurst selector between SMA momentum and Bollinger reversion, but re-register a smaller fixed allocation. Use ADBE, AMGN, CSCO, INTC,…

Hypothesis

Derived by the Research Lead from Chang, Lizardi and Shah: retain the six-stock daily Hurst selector between SMA momentum and Bollinger reversion, but re-register a smaller fixed allocation. Use ADBE, AMGN, CSCO, INTC, MSFT and QCOM on USEQ, long/cash, 1-DAY split-adjusted bars, without dividend income. These are six explicitly selected case studies, not a historical Nasdaq constituent universe or a reproduction of the paper. Change risk_per_name to 0.0025 of equity; with the retained 8% stop this targets 3.125%……Show moreShow less

Derived by the Research Lead from Chang, Lizardi and Shah: retain the six-stock daily Hurst selector between SMA momentum and Bollinger reversion, but re-register a smaller fixed allocation. Use ADBE, AMGN, CSCO, INTC, MSFT and QCOM on USEQ, long/cash, 1-DAY split-adjusted bars, without dividend income. These are six explicitly selected case studies, not a historical Nasdaq constituent universe or a reproduction of the paper. Change risk_per_name to 0.0025 of equity; with the retained 8% stop this targets 3.125% of equity per name and 18.75% across six concurrent entries before rounding. These allocations describe entry sizing, not guaranteed losses or continuously maintained weights. Retain all signal definitions and the 15% drawdown limit. Daily-close decisions fill at the next session open; protective stops trigger intrabar in the platform, including gap-through fills at the open. No exchange-hosted offline protection is required. Freeze the revision before collecting new forward evidence; prior optimization, holdout exposures and lineage looks remain counted. Previously exposed periods are development diagnostics only, never a fresh exam. Engineering must restore the bound optimization artifact and repair the contradictory no-report gate before another eligibility decision. The supplied source paper's full text was not provided or independently reviewed here; this revision preserves the existing operationalization rather than asserting the author's exact method.

Analysis

Optimizing Returns Using the Hurst Exponent and Q Learning on Momentum and Mean Reversion Strategies

Backtest and paper results are hypothetical. Trading involves risk of loss.