Title: Dynamic Industry-specific Lexicon Generation for Stock Market Forecast
Citation: LECTURE NOTES IN COMPUTER SCIENCE vol. 12565 p. 162-176
Publisher: Springer Verlag
Publication Year: 2020
JRC N°: JRC120225
ISSN: 1611-3349 (online),0302-9743 (print)
URI: https://link.springer.com/chapter/10.1007%2F978-3-030-64583-0_16
DOI: 10.1007/978-3-030-64583-0_16
Type: Articles in periodicals and books
Abstract: Press releases represent a valuable resource for financial trading and have long been exploited by researchers for the development of automatic stock price predictors. We hereby propose an NLP-based approach to generate industry-specific lexicons from news documents, with the goal of dynamically capturing, on a daily basis, the correlation between words used in these documents and stock price fluctuations. Furthermore, we design a binary classification algorithm that leverages on our lexicons to predict the magnitude of future price changes, for individual companies. Then, we validate our approach through an experimental study conducted on three different industries of the Standard & Poor's 500 index, by processing press news published by globally renowned sources, and collected within the Dow Jones DNA dataset. Classification results let us quantify the mutual dependence between words and prices, and help us estimate the predictive power of our lexicons.
JRC Directorate:Joint Research Centre Corporate Activities

Files in This Item:
There are no files associated with this item.

Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.