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Predicting Stock Price Movements with News Sentiment: An Artificial Neural Network Approach

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Artificial Neural Network Modelling

Part of the book series: Studies in Computational Intelligence ((SCI,volume 628))

Abstract

Behavioural finance suggests that emotions, moods and sentiments in response to news play a significant role in the decision-making process of investors. In particular, research in behavioural finance apparently indicates that news sentiment is significantly related to stock price movements. Using news sentiment analytics from the unique database RavenPack Dow Jones News Analytics, this study develops an Artificial Neural Network (ANN) model to predict the stock price movements of Google Inc. (NASDAQ:GOOG) and test its potential profitability with out-of-sample prediction.

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Author information

Authors and Affiliations

  1. Research School of Finance Actuarial Studies and Statistics, The Australian National University, Canberra, 2601, Australia

    Kin-Yip Ho & Wanbin (Walter) Wang

Authors
  1. Kin-Yip Ho
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  2. Wanbin (Walter) Wang
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Correspondence to Wanbin (Walter) Wang .

Editor information

Editors and Affiliations

  1. School of Computer and Mathematical, Auckland University of Technology (, Auckland, New Zealand

    Subana Shanmuganathan

  2. Dept of Informatics & Enabling Tech, Lincoln University, Lincoln, New Zealand

    Sandhya Samarasinghe

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Ho, KY., Wang, W.(. (2016). Predicting Stock Price Movements with News Sentiment: An Artificial Neural Network Approach. In: Shanmuganathan, S., Samarasinghe, S. (eds) Artificial Neural Network Modelling. Studies in Computational Intelligence, vol 628. Springer, Cham. https://doi.org/10.1007/978-3-319-28495-8_18

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  • DOI: https://doi.org/10.1007/978-3-319-28495-8_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28493-4

  • Online ISBN: 978-3-319-28495-8

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