  Studies conducted on financial market prediction lack a comprehensive feature
set that can carry a broad range of contributing factors; therefore, leading to
imprecise results. Furthermore, while cooperating with the most recent
innovations in explainable AI, studies have not provided an illustrative
summary of market-driving factors using this powerful tool. Therefore, in this
study, we propose a novel feature matrix that holds a broad range of features
including Twitter content and market historical data to perform a binary
classification task of one step ahead prediction. The utilization of our
proposed feature matrix not only leads to improved prediction accuracy when
compared to existing feature representations, but also its combination with
explainable AI allows us to introduce a fresh analysis approach regarding the
importance of the market-driving factors included. Thanks to the Lime
interpretation technique, our interpretation study shows that the volume of
tweets is the most important factor included in our feature matrix that drives
the market's movements.
