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The Effect of Sentiment on Stock Price Prediction

  • Bruce James Vanstone
  • Adrian GeppEmail author
  • Geoff Harris
Conference paper
  • 2.1k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10868)

Abstract

Accurately predicting stock prices is of great interest to both academics and practitioners. However, despite considerable efforts over the last few decades, it still remains an elusive challenge. For each of Australia’s 20 largest stocks, we build two neural network autoregressive (NNAR) models: one a basic NNAR model, and the other an NNAR model extended with sentiment inputs. By comparing the prediction accuracy of the two models, we find evidence that the inclusion of sentiment variables based on news articles and twitter sentiment can enhance the accuracy of the stock price prediction process.

Keywords

Stock prices Sentiment Auto Regressive Neural Networks Prediction 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Bond Business SchoolBond UniversityGold CoastAustralia

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