Sébastien Laurent: sebastien.laurent[at]univ-amu.fr
The analysis of news in the financial context has gained a prominent interest in the last years. This because of the possible predictive power of such content especially in terms of associated sentiment/mood or specific informative content. In this talk we present two papers showing how to exploit textual information.
In the first paper (with G. Nicola, S Rȍnnqvist and P. Sarlin) we investigate one of the possible deep learning approaches, based on a doc2vec representation of the textual data, a kind of neural network able to map onto a reduced latent semantic space the sequential and symbolic text input. Afterwards, a second supervised neural network is trained combining news data with standard financial figures to classify banks whether in distressed or tranquil states, based on a small set of known distress events. Then the final aim is not only the improvement of the predictive performance of the classifier but also to assess the importance of news data in the classification process. Does news data really bring more useful information not contained in standard financial variables? Our results seem to confirm such hypothesis.
In the second paper (with G. Nicola) we focus on another specific aspect of financial news analysis: how the covered topics modify according to space and time dimensions. To this purpose, we employ a modified version of topic model LDA, the so called Structural Topic Model (STM), that takes into account covariates as well. Our aim is to study the possible evolution of topics extracted from two well known news archive - Reuters and Bloomberg - and to investigate a causal effect in the diffusion of the news by means of a Granger causality test.
Our results show that both the temporal dynamics and the spatial differentiation matter in the news contagion.