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The Effect of Sentiment on Stock Price Prediction

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Recent Trends and Future Technology in Applied Intelligence (IEA/AIE 2018)


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.

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Correspondence to Adrian Gepp .

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Vanstone, B.J., Gepp, A., Harris, G. (2018). The Effect of Sentiment on Stock Price Prediction. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds) Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science(), vol 10868. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92057-3

  • Online ISBN: 978-3-319-92058-0

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