A Stock Market Trading System Using Deep Neural Network

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Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “[144] Singapore Stock Market Index 2010-2017 OCHL of last 10 days of index 10d 3d Feed-forward DNN RMSE, MAPE, Profit, SR -”
Deep Learning for Financial Applications : A Survey (2020, methodology): “[44] Singapore Stock Market Index 2010-2017 OCHL of last 10 days of Index DNN RMSE, MAPE, Profit, SR -”
Deep Learning of Subsurface Flow via Theory-guided Neural Network (2019, background): “, 2018), economics (Singh & Srivastava, 2017; Yong et al., 2017), chemistry (Fooshee et al.”
CNNpred: CNN-based stock market prediction using a diverse set of variables (2019, methodology): “Algorithms like deep multilayer perceptron (MLP) (Yong et al., 2017), restricted Boltzmann machine (RBM) (Cai et al., 2012; Zhu et al., 2014), long short-term memory (LSTM) (Chen et al., 2015; Fischer & Krauss, 2018), autoencoder (AE) (Bao et al., 2017) and convolutional neural network (CNN)…”
A graph-based CNN-LSTM stock price prediction algorithm with leading indicators (2021, methodology): “In addition, there are some algorithm, such as, Restricted Boltzmann Machine (RBM) [29, 30], deep multilayer perceptron (MLP) [22], and autoencoder (AE) [31], among others.”
A Survey of Forex and Stock Price Prediction Using Deep Learning (2021, methodology): “proposed a DNN model with 40 nodes which showed reasonable results and appeared to be a highly profitable model [75].”