Predicting Stock Price Movements Using Sentiment and Subjectivity Analyses

Author

Andrew Kirby, The Graduate Center, City University of New YorkFollow

Date of Degree

6-2021

Document Type

Thesis

Degree Name

M.A.

Program

Linguistics

Advisor

Kyle Gorman

Subject Categories

Computational Linguistics

Keywords

Natural Language Processing, Computational Linguistics

Abstract

In a quick search online, one can find many tools which use information from news headlines to make predictions concerning the trajectory of a given stock. But what if we went further, looking instead into the text of the article, to extract this and other information? Here, the goal is to extract the sentence in which a stock ticker symbol is mentioned from a news article, then determine sentiment and subjectivity values from that sentence, and finally make a prediction on whether or not the value of that stock will go up or not in a 24-hour timespan. Bloomberg News articles published between 2008 and 2013 were used as a data source, and prices of stocks were acquired using Yahoo Finance. News and information influence human behavior; constantly changing, the effects of this information on the market can be observed daily. This technology could assist people in making better decisions on how to invest their money.

Recommended Citation

Kirby, Andrew, "Predicting Stock Price Movements Using Sentiment and Subjectivity Analyses" (2021). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/4398