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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)

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.

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References

  1. Rapach, D.E., Zhou, G.: Forecasting stock returns. In: Handbook of Economic Forecasting, vol. 2(Part A), pp. 328–383 (2013)

    Google Scholar 

  2. Atsalakis, G.S., Valavanis, K.P.: Surveying stock market forecasting techniques - part II: soft computing methods. Expert Syst. Appl. 36(3), 5932–5941 (2009)

    Article  Google Scholar 

  3. Fama, E.: The behaviour of stock market prices. J. Bus. 38, 34–105 (1965)

    Article  Google Scholar 

  4. Yen, G., Lee, C.F.: Efficient market hypothesis (EMH): past, present and future. Rev. Pac. Basin Financ. Markets Policies 11(02), 305–329 (2008)

    Article  Google Scholar 

  5. Long, J.B.D., Shleifer, A., Summers, L.H., Waldmann, R.J.: Noise trader risk in financial markets. J. Polit. Econ. 98(4), 703–738 (1990)

    Article  Google Scholar 

  6. Tetlock, P.C.: Giving content to investor sentiment: the role of media in the stock market. J. Financ. LXII(3), 1139–1168 (2007)

    Article  Google Scholar 

  7. Hyndman, R., Athanasopoulos, G.: Forecasting: Principles and Practice. OTexts, Melbourne (2013)

    Google Scholar 

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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. https://doi.org/10.1007/978-3-319-92058-0_53

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  • DOI: https://doi.org/10.1007/978-3-319-92058-0_53

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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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