2020/01/26 by Luciano Del Corro, Johannes Hoffart, Del Corro, Luciano +1
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Financial Markets and Investment Strategies #Forecasting Techniques and Applications #I.2.7 #Stock Market Forecasting Methods #cs.CL
paper · pdf · doi:10.48550/arxiv.2001.09466
openalex publication_date 2020/01/26 · arxiv created 2021/02/16 · arxiv updated 2021/02/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a method to automatically identify financially relevant news using stock price movements and news headlines as input. The method repurposes the attention weights of a neural network initially trained to predict stock prices to assign a relevance score to each headline, eliminating the need for manually labeled training data. Our experiments on the four most relevant US stock indices and 1.5M news headlines show that the method ranks relevant news highly, positively correlated with the accuracy of the initial stock price prediction task.