Recurrent neural network and a hybrid model for prediction of stock returns

By A. Rather, A. Agarwal, V. N. Sastry. 2015

490 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research, Economics & Operations Research, Time-Series & Forecasting

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Citations in the corpus

Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets (2018, background): “Some literatures have established various models based on deep neural networks to improve the prediction ability of high-frequency financial time series [32, 33].”
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].”
A deep learning framework for financial time series using stacked autoencoders and long-short term memory (2017, methodology): “As it has been successfully applied to predicting financial time series in previous literature [23, 37, 38], it helps us to get more knowledge regarding how well our proposed model can improve performance compared with the conventional neural network.”
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting (2020, background): “Univariate techniques analyze each individual time-series separately without considering the correlations between different time-series [22].”
Predicting the Price of Bitcoin Using Machine Learning (2018, methodology): “[21] successfully took this approach in predicting stock returns combining a RNN with a genetic algorithm for network optimization.”
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns (2017, methodology): “[25] proposed a hybrid model combing RNN and AR to predict the stock returns.”
AI in Finance: Challenges, Techniques, and Opportunities (2021, methodology): “The direct applications of DNNs consist of CNNs to model multi-market/sector/factor correlations and dependencies by embedding each factor as an input, RNNs like LSTM and bidirectional RNN to sequentially model multi-market/sector/factor time series, deep transfer learning to model cross-sectional i”
Object detection using YOLO: challenges, architectural successors, datasets and applications (2022, background): “The success stories of deep learning in various domains includes weather forecasting [80], stock market prediction [53], speech recognition [59], object detection [37], character recognition [83], intrusion detection [32], automatic landslide detection [48], time series prediction [8], text…”
A systematic review of fundamental and technical analysis of stock market predictions (2019, background): “The history and components involved in fundamental and technical analysis for decision making in the stock market were examined by Renu and Christie (2018). A review of technical analysis on stock-markets to categorise and code published articles, to offer a summary of research works that have added”
A LSTM-based method for stock returns prediction: A case study of China stock market (2015, methodology): “Some designs of RNNs were used to predict stock market [4] [5].”