Deep learning for stock prediction using numerical and textual information

By Ryo Akita, Akira Yoshihara, Takashi Matsubara, Kuniaki Uehara. 2016

347 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Multimodal Learning, 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): “[103] 10 stocks in Nikkei 225 and news 2001-2008 Textual information and Stock prices 10d Paragraph Vector + LSTM Profit -”
Deep Learning for Financial Applications : A Survey (2020, methodology): “[171] implemented a method that used text mining and price prediction together for forecasting prices.”
Stock Price Prediction by Deep Neural Generative Model of News Articles (2018, methodology): “Based on this relationship, machine learning approaches have been directed to predict price movements by employing classifiers given news articles [8]–[14].”
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (2017, methodology): “To further model the long-term dependency in time series, recurrent neurual networks (RNN), especially Long Short-Term Memory (LSTM) network, have also been employed in financial predition [3, 7, 22].”
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns (2017, methodology): “In [3], the LSTM is adopted to predict prices with historical numerical and textual data.”
A Survey of Forex and Stock Price Prediction Using Deep Learning (2021, background): “proposed an approach that converted newspaper articles into their distributed representations via Paragraph Vector and modelled the temporal effects of past events on open prices about multiple companies with LSTM [4].”
A Stock Market Trading System Using Deep Neural Network (2017, methodology): “It has also been applied in financial markets for prediction of stock prices using textual news data and numerical data [4] [5].”
Applications of deep learning in stock market prediction: recent progress (2020, methodology): “(2018) DGM SVM, MLP Liu & Wang (2019) seq2seq model with attention AZFin Text (Schumaker & Chen, 2009), DL4S (Akita et al., 2016), DA-RNN (Qin et al., 2017), MKL, ELM (Li et al., 2016b) Karathanasopoulos & Osman (2019) DBN MACD, ARMA”
Evaluation of bidirectional LSTM for short-and long-term stock market prediction (2018, methodology): “[20] combined both textual and numerical information by representing data from newspaper articles using Paragraph Vector to model the impact of past events on opening prices.”