  This thesis investigates share buybacks, specifically share buyback
announcements. It addresses how to recognize such announcements, the excess
return of share buybacks, and the prediction of returns after a share buyback
announcement. We illustrate two NLP approaches for the automated detection of
share buyback announcements. Even with very small amounts of training data, we
can achieve an accuracy of up to 90%. This thesis utilizes these NLP methods to
generate a large dataset consisting of 57,155 share buyback announcements. By
analyzing this dataset, this thesis aims to show that most companies, which
have a share buyback announced are underperforming the MSCI World. A minority
of companies, however, significantly outperform the MSCI World. This
significant overperformance leads to a net gain when looking at the averages of
all companies. If the benchmark index is adjusted for the respective size of
the companies, the average overperformance disappears, and the majority
underperforms even greater. However, it was found that companies that announce
a share buyback with a volume of at least 1% of their market cap, deliver, on
average, a significant overperformance, even when using an adjusted benchmark.
It was also found that companies that announce share buybacks in times of
crisis emerge better than the overall market. Additionally, the generated
dataset was used to train 72 machine learning models. Through this, it was able
to find many strategies that could achieve an accuracy of up to 77% and
generate great excess returns. A variety of performance indicators could be
improved across six different time frames and a significant overperformance was
identified. This was achieved by training several models for different tasks
and time frames as well as combining these different models, generating
significant improvement by fusing weak learners, in order to create one strong
learner.
