Stock prices often respond drastically to the information firms release to the market on quarterly earnings announcement dates (EADs) (see Figure 1). For instance, almost 20% of Google’s total annual volatility is accumulated on EADs. Estimating the stock market risk ex ante (before the news release) is difficult as each of these announcements is unique. Our paper proposes a new way of measuring the uncertainty of stock returns around EADs using option price data.
The economic reason for the importance of EADs is related to the large amount of valuation relevant information and past company performance that is released to the market. Since this information is not known to investors before the earnings release, stock market uncertainty around EADs is particularly large. The main contribution of the paper is to develop a theoretical framework and practical estimators which capture the uncertainty around Earnings Announcement Days. We develop an option pricing theory which provides academics and practitioner alike with a new tool to study the risk around corporate events such as EADs.
After developing and introducing a new EAD uncertainty measure, we study how it may help forecasting stock market risk around EADs. First, we find that our measure produces accurate risk predictions and improves over widely used risk measures such as the dispersion in analyst forecasts. Second, our measure is robust and very easy to implement and is therefore straightforward to be applied in practice without the need for complicated mathematical or computational machinery. Fourth, we show that our proposed model taking into account EADs improves option pricing performance of between 15-20%.