The Effect of Sentiment on Stock Price Prediction
Research output: Chapter in Book/Report/Conference proceeding › Chapter
Electronic versions
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.
| Original language | English |
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| Title of host publication | Recent Trends and Future Technology in Applied Intelligence - 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Proceedings |
| Editors | Malek Mouhoub, Samira Sadaoui, Otmane Ait Mahamed, Moonis Ali |
| Place of Publication | Germany |
| Publisher | Springer |
| Pages | 551-559 |
| Number of pages | 9 |
| ISBN (print) | 978-3-319-92057-3 |
| DOIs | |
| Publication status | Published - 30 May 2018 |
| Externally published | Yes |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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| Publisher | Springer |