Earnings announcements play a key role in financial market dynamics. These announcements, often eagerly awaited by investors, can significantly influence share prices. In quantitative trading, the impact of these announcements is of crucial importance.

An earnings announcement is an official statement of a company's profitability for a given period, usually a quarter or a year. These announcements are regulated by the Securities and Exchange Commission (SEC) to ensure their accuracy. They are preceded by estimates made by market analysts. These estimates can fluctuate in the run-up to the announcement, influencing the share price.

Since 1970, companies listed in the USA have been required to file detailed financial statements with the SEC. However, most companies also choose to announce their results to investors before this official filing. These voluntary announcements vary considerably in detail, from a simple earnings summary to a complete set of financial accounts.

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Earnings announcements have a notorious effect on markets.

Beaver (1968) highlighted the crucial role of earnings announcements as a vector of information for investors. These announcements can induce significant movements in share prices. Indeed, a study by Bernard and Thomas (1989) revealed that it can take from 6 to 12 months for the information contained in these announcements to be fully integrated into share prices.

Post-earnings announcement drift (PEAD) is a phenomenon in which stocks continue to appreciate or depreciate after an earnings announcement, depending on whether the announcement is positive or negative. This trend is considered a market anomaly. PEAD can be exploited in algorithmic trading by buying stocks with positive surprises and shorting those with negative surprises.

One of the main debates surrounding PEAD concerns the market's under- or over-reaction to announcements. Some researchers, such as Barberis, Shleifer and Vishny (1998), attribute PEAD to market under-reaction due to behavioral biases. Others, like Zhang (2010), believe that market over-reaction can be exacerbated by factors such as high-frequency trading.

With the rise of algo trading, results announcements have taken on even greater importance. Algorithmic traders often use quantitative models to anticipate the market's reaction to these announcements and adjust their strategies accordingly. For example, a PEAD-based strategy could be programmed to buy stocks after a positive earnings announcement and sell them after a defined period, thus exploiting the observed trend.

As with any trading strategy, exploiting anomalies around earnings announcements entails risks. PEAD, although well documented, may not occur every time. In addition, transaction costs and increased volatility during the announcement period can erode potential profits. Nevertheless, for informed and cautious traders, earnings announcements can offer lucrative opportunities.

Earnings announcements continue to play a central role in the financial landscape. For algorithmic traders, understanding the impact of these announcements is essential to developing effective strategies. With the advent of artificial intelligence and machine learning techniques, it is likely that strategies based on earnings announcements will become increasingly sophisticated, offering traders new opportunities to exploit market inefficiencies. Ultimately, success lies in the ability to anticipate, understand and react appropriately to earnings announcements as part of a well-designed trading strategy.

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