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.
CITATION STYLE
Vanstone, B. J., Gepp, A., & Harris, G. (2018). The effect of sentiment on stock price prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10868 LNAI, pp. 551–559). Springer Verlag. https://doi.org/10.1007/978-3-319-92058-0_53
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