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Spurious Seasonality Detection: A Non-Parametric Test Proposal

Department of Business, Universitat Rovira i Virgili, Av. Universitat 1, 43204 Reus, Spain
IFLP-CONICET-UNLP, C. C. 727, 1900 La Plata, Argentina
SThAR-EPFL Innovation Park, 1015 Lausanne, Switzerland
Graduate College, 207 Giannini Hall, University of California Berkeley, Berkeley, CA 94720, USA
Author to whom correspondence should be addressed.
Econometrics 2018, 6(1), 3;
Received: 3 September 2017 / Revised: 6 January 2018 / Accepted: 12 January 2018 / Published: 19 January 2018
PDF [1010 KB, uploaded 19 January 2018]


This paper offers a general and comprehensive definition of the day-of-the-week effect. Using symbolic dynamics, we develop a unique test based on ordinal patterns in order to detect it. This test uncovers the fact that the so-called “day-of-the-week” effect is partly an artifact of the hidden correlation structure of the data. We present simulations based on artificial time series as well. While time series generated with long memory are prone to exhibit daily seasonality, pure white noise signals exhibit no pattern preference. Since ours is a non-parametric test, it requires no assumptions about the distribution of returns, so that it could be a practical alternative to conventional econometric tests. We also made an exhaustive application of the here-proposed technique to 83 stock indexes around the world. Finally, the paper highlights the relevance of symbolic analysis in economic time series studies. View Full-Text
Keywords: daily seasonality; ordinal patterns; stock market; symbolic analysis daily seasonality; ordinal patterns; stock market; symbolic analysis

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Bariviera, A.F.; Plastino, A.; Judge, G. Spurious Seasonality Detection: A Non-Parametric Test Proposal. Econometrics 2018, 6, 3.

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