Abstract: Much research has demonstrated the existence of patterns in high-frequency equity returns, return volatility, bid-ask spreads and trading volume. In this paper, we employ a new test for detecting periodicities based on a signal coherence function. The technique is applied to the returns, bid-ask spreads, and trading volume of thirty stocks traded on the NYSE. We are able to confirm previous findings of an inverse J-shaped pattern in spreads and volume through the day. We also demonstrate that such intraday effects dominate day of the week seasonalities in spreads and volumes, while there are virtually no significant periodicities in the returns data. Our approach can also leads to a natural method for forecasting the time series, and we find that, particularly in the case of the volume series, the predictions are considerably more accurate than those from naïve methods.