Backpropagation and recurrent neural networks in financial analysis of multiple stock market returns

By Jovina Roman, A. Jameel. 1996

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Citations in the corpus

NSE Stock Market Prediction Using Deep-Learning Models (2018, methodology): “al performed an analysis on multiple stock market return using Back propagation and RNN [13].”
Grey system theory-based models in time series prediction (2010, methodology): “…models (Box & Jenkins, 1976) can be mentioned as statistical models, neural network (NN) based models (Quah & Srinivasan, 1999; Rabiner, 1989; Roman & Jameel, 1996) are widely used as an artiﬁcial intel-ligence-based approach, back propagation being the most widely used technique for…”
Discovering golden nuggets: data mining in financial application (2004, background): “However, those models can predict linear patterns only [ 8 ].”
Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models (2016, methodology): “Nonlinear Autoregressive Neural Network with Exogenous Inputs (NARX-NN) Neural Networks are employed due to their advantages such as their numeric nature, the absence of data distribution assumptions, the ability to insert new data or update inputs into a trained network, and their free model estima”
A recurrent neural network approach in predicting daily stock prices an application to the Sri Lankan stock market (2017, background): “Because of that RNN models can predict stock prices based on more recent history [12].”
Forecasting Intraday stock price trends with text mining techniques (2004, background): “Numerous publications describe data mining applications that try to predict the immediate future of stock prices or indices [1][2][3].”
Stock price prediction using LSTM, RNN and CNN-sliding window model (2017, methodology): “In 1996, [15] used back propagation and RNN models for the prediction of stock index for five different stock markets.”