This study represents an initial endeavor to harness the potential of the semantic space within the Twitter news flow to forecast financial anomalies. In pursuit of this objective, approximately two million entities were extracted from the news text disseminated by the most widely followed news...

The study develops an original interdisciplinary approach, leveraging complex networks through which it identifies groups of investors and projects in equity crowdfunding, investigates whether clientele effects arise resulting in specific investor-entrepreneur matching, and explores which...

A time series can often be characterized using machine learning techniques, which require feature vectors as input. The quality of the feature vectors reflects the accuracy of the utilized machine learning techniques. We propose a method for combining features extracted from two popular...

The network structure of non-centrally cleared derivative markets, uncovered via the European Market Infrastructure Regulation (EMIR), is investigated with a focus on the Covid-19 market turmoil period. Initial and variation margin networks are reconstructed to analyze channels of potential...