Predicting Stock Market Trends by Recurrent Deep Neural Networks

By Akira Yoshihara, Kazuki Fujikawa, Kazuhiro Seki, Kuniaki Uehara. 2014

86 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “In [209], the authors proposed a novel method that uses a combination of RBM, DBN and word embedding to create word vectors for RNN-RBM-DBN network to predict the trend of stock prices.”
Deep Learning for Financial Applications : A Survey (2020, methodology): “Env. [177]TWSEindex, 4 stocks in TWSE 2001-2017 Technical indicators, Price data, News CNN+ LSTM RMSE,Proﬁt Keras, Python, TALIB [178] StockofTsugamiCorporation 2013 PricedataLSTMRMSEKeras, Tensorﬂow [179] News, Nikkei Stock Average and 10-Nikkei companies 1999-2008 news,MACDRNN, RBM+DBN Accuracy, P”
Stock Price Prediction by Deep Neural Generative Model of News Articles (2018, methodology): “, support vector machines (SVMs) [3], [4], [9], [10] and multilayer perceptrons (MLPs) [11]–[14].”
Improving stock market prediction via heterogeneous information fusion (2017, background): “Temporal properties of news events that have short-or long-term inﬂuences on stock prices are modeled in [22].”
Convolutional neural networks applied to high-frequency market microstructure forecasting (2017, unknown): “[9] were able to outperform”
Restricted Boltzmann machine based stock market trend prediction (2017, background): “[34] use a recurrent deep neural network to model temporal effects of past events.”