Correlation is not the same thing as causation. Even if two things change in tandem, it doesn’t automatically mean that one causes the other. It could be a common underlying factor driving both, or alternatively, it could be pure coincidence, where a statistical connection arises without any real dependency. Therefore, mere statistical correlation is not enough to prove a cause-and-effect relationship.
A classic example of the first case is ice cream consumption and drownings, which correlate, but neither causes the other; instead, the common underlying factor driving both is warm weather.
As an example of the second, consider this graph from Spurious Correlations: Popularity of the first name Sunny correlates with Salesforce's stock price (CRM) (r=0.985)
Thirdly, even if a cause-and-effect relationship had existed in the past, we cannot be certain of its continuity if, for instance, the party responsible for buybacks has changed, guidance has changed, etc.
This is a bit of a company-unrelated “zero-post,” but it’s worth being careful about what kind of conclusions one draws when conducting analysis with incomplete information.