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| Type | Working Paper |
| Scope | Discipline-based scholarship |
| Title | A Large-Dimensional Test for Cross-Sectional Anomalies: Efficient Sorting Revisited |
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| Institution | University of Zurich |
| Series Name | SSRN |
| Number | 3560178 |
| Date | 2020 |
| Abstract Text | Many researchers seek factors that predict the cross-section of stock returns. In finance, the key is to replicate anomalies by long-short portfolios based on their factor scores, with microcaps alleviated via New York Stock Exchange (NYSE) breakpoints and value-weighted returns. In econometrics, the key is to include a covariance matrix estimator of stock returns for the (mimicking) portfolio construction. This paper marries these two strands of literature in order to test the zoo of cross-sectional anomalies by injecting size controls, basically NYSE breakpoints and value-weighted returns, into efficient sorting. Thus, we propose to use a covariance matrix estimator for ultra-high dimensions (up to 5,000) taking into account large, small and microcap stocks. We demonstrate that using a nonlinear shrinkage estimator of the covariance matrix substantially enhances the power of tests for cross-sectional anomalies: On average, ‘Student’ t-statistics more than double. |
| Official URL | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3560178 |
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