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Further Evidence on the Turn-of-the-Month Effect

Keywords: Data snooping , Synchronous cycles , Optimal test for periodicities

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Abstract:

This paper points out that even distinct patterns in financial time series, which persist over long periods of time, cannot immediately be taken asgenuine. In view of the large number of possible patterns, the only way to avoid any data- snooping bias is to use a formal statistical test, which hasnot been tailored to the specific patterns present in the data. Adopting a universal frequency domain test for the detection of synchronous cycles,we find clear evidence for within-month patterns in daily returns on the S&P 500 index, which corroborates earlier findings obtained simply bycomparing different days of the month.

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