In the last years, compliance requirements for the banking sector have greatly augmented, making the current compliance processes difficult to maintain. Any process that allows to accelerate the identification and implementation of compliance requirements can help address this issues. The contributions of the paper are twofold: we propose a new NLP task that is the investment rule detection, and a group of methods identify them. We show that the proposed methods are highly performing and fast, thus can be deployed in production.

In this paper, we describe a methodology to infer Bullish or Bearish sentiment towards companies/brands. More specifically, our approach leverages affective lexica and word embeddings in combination with convolutional neural networks to infer the sentiment of financial news headlines towards a target company. Such architecture was used and evaluated in the context of the SemEval 2017 challenge (task 5, subtask 2), in which it obtained the best performance.