AI-based stock market trending analysis
In this paper, we present two hierarchical neural networks for market trends predictions. These models utilised sentiment analysis of news as well as past information of returns and prices to predict the next day trend (i.e. bullish, stagnant, bearish). The hierarchical models were trained in the...
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| 主要作者: | |
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| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2020
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/138093 |
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| 總結: | In this paper, we present two hierarchical neural networks for market trends
predictions. These models utilised sentiment analysis of news as well as past
information of returns and prices to predict the next day trend (i.e. bullish,
stagnant, bearish). The hierarchical models were trained in the context of swing
trading of 2 days using price actions of Dow Jones Industrial Average (Ticker:
DJI). Experimental studies showed an F1-accuracy of 0.53 on this 3-class problem
with Hierarchical LSTM. This was a considerable improvement over the
industry-standard model, ARIMA. The Hierarchical LSTM came out as the best
performing model. |
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