Interval-Based Abnormality Detection and Event Returns in Daily Stock Data
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| Format: | Recurso digital |
| Langue: | anglais |
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Zenodo
2025
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| _version_ | 1866901210470023168 |
|---|---|
| author | Burk, Kevin |
| author_facet | Burk, Kevin |
| contents | <p>This record contains the preregistration for the study <em>Interval-Based Abnormality Detection and Event Returns in Daily Stock Data</em>. The document specifies all methodological decisions made prior to analysis, including the forecast-generation process, interval construction, abnormality definitions, BUY/SELL event rules, portfolio mechanics, outcome measures, statistical tests, and all figures to be produced.</p> <p>The preregistration fixes a 10-year evaluation window (2015–2024) using daily forecasts constructed from historical data beginning in 2005. The study evaluates the economic characteristics of interval-defined abnormality events across 30 U.S. equities, using a deterministic, non-adaptive, multi-model forecasting approach.</p> <p>The preregistration freezes all analytic choices in advance of data analysis. The full protocol is provided in the attached PDF.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17644819 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Interval-Based Abnormality Detection and Event Returns in Daily Stock Data Burk, Kevin abnormality detection interval forecasting empirical residuals daily stock data event returns nonparametric analysis backtesting forecasting evaluation financial time series preregistered study <p>This record contains the preregistration for the study <em>Interval-Based Abnormality Detection and Event Returns in Daily Stock Data</em>. The document specifies all methodological decisions made prior to analysis, including the forecast-generation process, interval construction, abnormality definitions, BUY/SELL event rules, portfolio mechanics, outcome measures, statistical tests, and all figures to be produced.</p> <p>The preregistration fixes a 10-year evaluation window (2015–2024) using daily forecasts constructed from historical data beginning in 2005. The study evaluates the economic characteristics of interval-defined abnormality events across 30 U.S. equities, using a deterministic, non-adaptive, multi-model forecasting approach.</p> <p>The preregistration freezes all analytic choices in advance of data analysis. The full protocol is provided in the attached PDF.</p> |
| title | Interval-Based Abnormality Detection and Event Returns in Daily Stock Data |
| topic | abnormality detection interval forecasting empirical residuals daily stock data event returns nonparametric analysis backtesting forecasting evaluation financial time series preregistered study |
| url | https://doi.org/10.5281/zenodo.17644819 |