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Title: AI-based stock market trending analysis
Authors: Ko, Johann
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2020
Publisher: Nanyang Technological University
Project: SCSE19-0125
Abstract: 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.
Schools: School of Computer Science and Engineering 
Organisations: Agency for Science, Technology and Research (A*STAR)
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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