Predict Stock Price with Financial News Based on Recurrent Convolutional Neural Networks
By Chen-Yu Lee, Von-Wun Soo. 2017
38 citations recorded by the source corpus.
People have been interested in making profits from financial stock market prediction. However, stock market forecast has always been a challenging problem because of its uncertainty and volatility. We take a different approach by a model called recurrent convolutional neural networks (RCN) that combines the advantages of convolutions, sequence modeling, word embedding for stock price analysis and information extraction from financial news. We then combine RCN with technical analysis indicators to predict stock price. The results show that the technical analysis model combining with RCN performs better than the technical analysis alone. Besides, the prediction error of RCN is lower than that of Long-short term memory networks.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research, Time-Series & Forecasting
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Citations in the corpus
- Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “[116] TWSE index, 4 stocks in TWSE 2001-2017 Technical indicators, Price data,”
- Deep Learning for Financial Applications : A Survey (2020, methodology): “In [177], financial news data and word embedding with Word2vec were implemented to create the inputs for Recurrent CNN (RCNN) to predict the stock price.”
- Applications of deep learning in stock market prediction: recent progress (2020, methodology): “…List Historical Prices + Technical Indicators + Text Vargas et al. (2017); Liu et al. (2019c); Oncharoen & Vateekul (2018); Lien Minh et al. (2018); Lee & Soo (2017); Chen et al. (2018b) Text Liu et al. (2018); Hu et al. (2018b); Ding et al. (2015, 2014) Image Sezer & Ozbayoglu (2018); Sim…”