Various explanations have been investigated to the January effect in existent literature, but no conclusive explanation has been given to distinguish particular explanation from others. A time-series GARCH-M model with the conditional variance as proxies for market systematic risk is applied in this paper to investigate the seasonal effects in the USA, the UK, China and Australia with different tax system and tax year end. Empirical evidence showed January effect in the USA, January and April effect in the UK, July effect in Australia and no significant seasonal effect in China. The pattern consistently links to tax year end and tax system in the sample countries. But no clear evidence has been found to support the proposition that market risk is higher or priced highly only in certain calendar month with seasonal effect. However, with an interactive dummy variable to reflect the seasonal effect added into the time-series GARCH-M model, the seasonal effects are explained away. The results in the sampled countries support the proposition that market volatility increases when it is close to the date of financial statement performance due to the uncertainty of the financial information
