CNN-LSTM Neural Network Model for Quantitative Strategy Analysis in Stock Markets

By Shuanglong Liu, Jinwen Ma. 2017

99 citations recorded by the source corpus.

How researchers see this paper

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

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “[91] Chinese Stocks 2007-2017 OCHLV 30d 1.”
Deep Learning for Financial Applications : A Survey (2020, background): “[37] Chinese Stocks 2007-2017 OCHLV CNN + LSTM Annualized Return, Mxm Retracement Python”
Short-term stock market price trend prediction using a comprehensive deep learning system (2020, methodology): “in [24] proposed a convolutional neural network (CNN) as well as a long shortterm memory (LSTM) neural network based model to analyze different quantitative strategies in stock markets.”
A Survey of Forex and Stock Price Prediction Using Deep Learning (2021, methodology): “[11], [13],[15],[18], [22], [49], [50],[59], [60], [65],[68],[69],[70] 1.0-1.2 [22], [49], [50],[60],[68],[69], [74] 1.2-1.4 [11],[13],[15],[18] 1.4-1.6 [65],[72],[79] 1.6-1.8 [70] 1.8-2.0 [59] It could be found that paper [59] performed the best which used Reinforcement learning model, and the…”
Forecasting stock prices with long-short term memory neural network based on attention mechanism (2020, methodology): “[12] CNN was used to develop a quantitative stock selection strategy to determine stock trends and then predict stock prices using LSTM to promote a hybrid neural network model for quantitative timing strategies to increase profits.[13] A time-weighted function was added to an LSTM neural network, a”