Work

On the Importance of Text Analysis for Stock Price Prediction

Year: 2014

Type: conference-paper

Abstract: We investigate the importance of text analysis for stock price prediction.In particular, we introduce a system that forecasts companies' stock price changes (UP, DOWN, STAY) in response to financial events reported in 8-K documents.Our results indicate that using text boosts prediction accuracy over 10% (relative) over a strong baseline that incorporates many financially-rooted features.This impact is most important in the short term (i.e., the next day after the financial event) but persists for up to five days.

Source: Language Resources and Evaluation Conference (LREC)

DOI: 10.63317/387ydnp6mtf6

Authors: Heeyoung Lee, Mihai Surdeanu, Bill MacCartney, Dan Jurafsky

Institutions: Stanford University, University of Arizona, Google (United States)

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Funders: National Science Foundation, Center for Advanced Study in the Behavioral Sciences, Stanford University, Center for Advanced Study, University of Illinois at Urbana-Champaign

Award: RI: Medium: Bringing Sentiment Analysis and Social Network Analysis Together