Predicting Stock Price Volatility by Analyzing Semantic Content in Media.

Research output: Working paper

Abstract

Current models for predicting volatility do not incorporate information flow and are solely based on historical volatilities. We suggest a method to quantify the semantic content of words in news articles about a company and use this as a predictor of its stock volatility. The results show that future stock volatility is better predicted by our method than the conventional models. We also analyze the functional role of text in media either as a passive documentation of past information flow or as an active source for new information influencing future volatility. Our data suggest that semantic content may take both roles.

Details

Authors
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Psychology
  • Economics

Keywords

  • latent semantic analysis, information flow, volatility, GARCH
Original languageEnglish
PublisherDepartment of Economics, Lund Universtiy
Number of pages43
Publication statusPublished - 2014
Publication categoryResearch

Publication series

NameWorking Paper / Department of Economics, School of Economics and Management, Lund University
No.38