Financial Forecasting with Word Embeddings Extracted from News: A Preliminary Analysis

Abstract

News represents a rich source of information about financial agents actions and expectations. We rely on word embedding methods to summarize the daily content of news. We assess the added value of the word embeddings extracted from US news, as a case study, by using different language approaches while forecasting the US S&P500 index by means of DeepAR, an advanced neural forecasting method based on auto-regressive Recurrent Neural Networks operating in a probabilistic setting. Although this is currently on-going work, the obtained preliminary results look promising, suggesting an overall validity of the employed methodology.