| Summary: | Whilst many studies have examined the efficiency of the US stock market reaction to earnings announcements there is a significant gap in the literature for the UK stock market, particularly in recent years. The major aim of this study
is to examine whether the stock market reacts efficiently to earnings announcements, and test whether the post-earnings announcement drift phenomenon is present in the UK for the years 2014-2019. The minor aims of this study are to investigate whether the stock price reactions to earnings
announcements are influenced by factors such as the magnitude of the earnings surprise, firm size, and capital structure.
This study considers a sample of 936 earnings announcements, utilising the market model to predict normal and abnormal returns for each stock surrounding the event. The events are grouped according to the direction and magnitude of the earnings surprises, and the size and capital structures of the firms. Hypothesis tests are then performed to determine the significance of average abnormal returns and cumulative average abnormal returns for each group.
I find that the UK stock market does not always rapidly and accurately incorporate the information contained in earnings announcements into the stock
price as the semi-strong form of the Efficient Market Hypothesis suggests. Furthermore, positive relationships are observed between the magnitude of the
earnings surprise and positive earnings announcements, and debt-to-equity ratio and positive earnings announcements. A negative relationship is observed between market capitalisation and the magnitude of the stock market reaction.
I conclude that the UK stock market is not entirely efficient in reacting to earnings announcements, with the market reacting more efficiently to positive earnings surprises than negative earnings surprises. I attribute my findings to the behaviour of investors and highlight several biases which can influence their reaction to earnings announcements, such as overconfidence bias and conservatism bias.
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