Stock prediction using deep learning

By Ritika Singh, S. Srivastava. 2016

296 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Management Science and Operations Research, Signal Processing, 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): “[107] Google’s daily stock data 2004-2015 OCHLV, Technical indicators 20d 1d (2D)(2) PCA + DNN SMAPE, PCD, MAPE, RMSE, HR, TR, R(2) R, Matlab”
Applications of deep learning in stock market prediction: recent progress (2020, methodology): “…(NMSE) Chong et al. (2017) Root Mean Squared Error (RMSE) de Oliveira et al. (2013); Chong et al. (2017); Lee & Soo (2017); Li et al. (2017b); Qin et al. (2017); Singh & Srivastava (2017); Althelaya et al. (2018); Althelaya et al. (2018); Chen et al. (2018a,b); Gao & Chai (2018); Lei (2018);…”
Variational Quantum Circuits for Deep Reinforcement Learning (2019, background): “ed in many academic ﬁelds and has successful commercial applications. It has been successful in computer vision [32] [36] [39] , natural language processing [34], ﬁnancial trading [12] [15] [28] [29] [33] and even the game of Go[30] which is previously considered formidable for machines. It also hel”
Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions (2017, methodology): “Though some deep learning models, such as LSTM [13] and RNN [14], have been applied to optimise portfolio, they use raw time series rather than charts as input.”
Deep Learning of Subsurface Flow via Theory-guided Neural Network (2019, background): “…also been utilized in diverse scientific disciplines, including the fields of biomedicine (Liang et al., 2018; Shashikumar et al., 2018), economics (Singh & Srivastava, 2017; Yong et al., 2017), chemistry (Fooshee et al., 2018; Sun et al., 2019), and physics (Bhimji et al., 2018; Sadowski & Baldi,…”
A Survey of Forex and Stock Price Prediction Using Deep Learning (2021, methodology): “And paper [29] had the highest order of magnitude of MAPE.”