Here’s How Uber Shares Typically Trade After Earnings

Uber Technologies Inc. (UBER) is expected to report fourth-quarter 2022 earnings on 02/08/2023, and the market’s initial reaction could be volatile.

Over the past 12 quarters, Uber’s adjusted EPS has beat consensus expectations five times, but the stock rose the next day on only two of those occasions. Its average post-earnings move of +2.66%, masks a nearly 9% drop in May 2021 when it beat expectations and an 18.9% gain in August 2022 when it missed estimates by 400%.

Clearly, an earnings beat or miss is not the sole basis for a stock moving higher or lower immediately after earnings are released. Many stocks end up losing ground despite an earnings beat due to other factors that disappoint investors, such as a poor outlook on future growth expectations, non-profit factors like DAUs (tech companies), load factors (airlines), etc. Similarly, unforeseen catalysts, like positive forward guidance or even oversold market conditions leading up to earnings can help a stock’s price gain despite an earnings miss.

Although past performance is not a guarantee of future results, understanding the distribution of Uber’s stock price performance on the trading day following its last 12 quarterly earnings announcements can provide active traders with context regarding how the stock price might react on the day following its next earnings release. This graph reveals that Uber has seen heightened volatility in response to the previous 12 earnings releases, with shares either rallying more than 3.0% or declining more than -3.2% the next day.

Normal Distribution for Beginners

Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean.

• The normal distribution is the proper term for a probability bell curve.
• In a normal distribution, the mean is zero and the standard deviation is 1. It has zero skew and a kurtosis of 3.
• Normal distributions are symmetrical, but not all symmetrical distributions are normal.
• Many naturally-occurring phenomena tend to approximate the normal distribution.
• In finance, most pricing distributions are not, however, perfectly normal