This registered report presents the most comprehensive empirical challenge to weak-form market efficiency ever assembled. We introduce Temporal Structural Forecasting (TSF), a methodology that identifies exploitable patterns in historical price data through multi-cycle seasonal decomposition. The study tests 44 preregistered hypotheses across four papers using 346 S TSF compared to signal benchmarks only), Study 3a H3. 0a-H3. 0d (split by exposure type for independent validation), Study 3b (signal benchmarks only notation added). Version 2. 0 (Pre-Analysis Methodology Revision) This revision corrects the benchmark methodology prior to primary data analysis. No results from the 346-stock universe have been analyzed. Change: The buy-and-hold benchmark comparison has been revised to use an exposure-matched benchmark. The original methodology compared strategy returns against a 100% continuously invested buy-and-hold benchmark, creating an invalid comparison for timing strategies that hold cash between signals. Revised methodology: Excess returns are now calculated against an exposure-matched benchmark: EMB = (avgₑxposure × B&Hᵣeturn) + ( (1 − avgₑxposure) × rfᵣeturn), where avgₑxposure is the strategy's realized average capital deployment and rfᵣeturn is 5% annualized. This ensures valid comparison between strategies with different exposure profiles. Affected sections: Section 1. 5 Key Terms: "Excess Return" definition updated Section 2. 8 Performance Metrics: Exposure-matched benchmark formula added Hypotheses H2. 3 and H2. 13: "buy-and-hold" replaced with "exposure-matched benchmark" Rationale: Identified during external review prior to primary data analysis. The correction ensures that a strategy averaging 40% exposure is compared against 40% B&H + 60% risk-free, not 100% B&H.
Kevin Burk (Sat,) studied this question.