| Publication |
2013.
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| Summary/Abstract |
Various explanations have been advanced for the January effect in the existing literature, but no consensus has been arrived at to distinguish one particular explanation from any others. In this paper, a time-series GARCH-M model with conditional variance as a proxy for market systematic risk is applied to investigate the seasonal effects in four countries with different tax system and tax year end: the USA, the UK, China and Australia. Empirical evidence showed a January effect in the USA, a January and an April effect in the UK, a July effect in Australia and no significant seasonal effect in China. This pattern consistently links to tax year end and the tax system in the sample countries; however, no clear evidence has been found to support the proposition that market risk is higher or priced highly only in calendar months with a seasonal effect. However, to reflect the seasonal effect, an interactive dummy variable is added into the time-series GARCH-M model, and the seasonal effects are explained away. The results of 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.
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