Stock price prediction using LSTM, RNN and CNN-sliding window model

By Sreelekshmy Selvin, R. Vinayakumar, E. Gopalakrishnan, V. Menon, Kritik Soman. 2017

858 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Model Architectures, Time-Series & Forecasting

How researchers see this paper

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

In Economics and Econometrics

In Management Science and Operations Research

In Model Architectures

In Time-Series & Forecasting

Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “[85] Stocks of Infosys, TCS and CIPLA from NSE 2014 Price data RNN, LSTM and CNN Accuracy -”
Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network (2018, methodology): “In this section, we evaluate the effect of news articles on stock prices on different time periods, by changing the i value (i ∈ [1,2,5,7,10]) of the Open-to-Close price return which indicates the time intervals (1 day, 2 days, 5 days, 7 days, and 10 days).”
NSE Stock Market Prediction Using Deep-Learning Models (2018, neutral): “Comparison study of different DL models of stock market prediction has already been done as we can see in [1].”
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018, background): “Unlike CNNs which are less widely applied in ﬁnancial markets, the LSTM has been popular in recent years, [42, 28, 43, 44, 45, 46, 47, 20] all utilising LSTMs to analyse ﬁnancial data.”
Applications of deep learning in stock market prediction: recent progress (2020, background): “…Ding et al. (2014, 2015); Chen et al. (2017); Dingli & Fournier (2017); Huynh et al. (2017); Li & Tam (2017); Liu & Song (2017); Nelson et al. (2017); Selvin et al. (2017); Singh & Srivastava (2017); Sun et al. (2017); Vargas et al. (2017); Weng et al. (2017); Yang et al. (2017); Zhang et al.…”
A Survey of Forex and Stock Price Prediction Using Deep Learning (2021, background): “RMSE [8,12,24,27,29,31,32,35–39,45–47,71–73,86] MAPE [10,12,29,31,35,38,40,41,60,65,73] MAE [12,24,29,32,35,37–38,40–42,66,71–72] Accuracy [3,13,16,19,22–23,25–26,28–29,34,36,45,50,52–54,56,58,61–64,67,68,74,87,89] F-measure [3,14,18–19,26,28,53,68] Sharpe ratio [14,18,71,77,82,84,88,90,91] CEQ [14]”
Deep Learning for Stock Market Prediction (2020, background): “Contrary to a simple artificial neural network, recurrent neural networks (RNNs) have achieved considerable success in the financial area on account of their great performance [14,15].”