A LSTM-based method for stock returns prediction: A case study of China stock market

By Kai Chen, Yi Zhou, Fangyan Dai. 2015

624 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research, Signal Processing, Time-Series & Forecasting

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “[81], Dezsi and Nistor [82] applied the raw price data as the input to LSTM models.”
Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations (2017, methodology): “[9] predicted China stock returns using LSTM network.”
Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications (2018, methodology): “A different approach is followed by Chen, Zhou and Dai [10] in order to predict the stock market returns by means of Long-Short Term Memory (LSTM).”
A Survey of Forex and Stock Price Prediction Using Deep Learning (2021, background): “Paper [41], [47] ,[83] also had great performance, but paper [45], [70] didn’t have the high accuracy.”
Stock market's price movement prediction with LSTM neural networks (2017, background): “But there is also some work using price data to foresee price movement, [17] employ historic price data in addition to stock indexes to predict if a stock price will go up, down or stay the same on the day.”
Fractional Neuro-Sequential ARFIMA-LSTM for Financial Market Forecasting (2020, methodology): “Chen et al. [16] predicted stock exchange data of China stock market using sequential features of LSTM, He used 30 days long sequential step with 10 learning features in the model.”
CNNpred: CNN-based stock market prediction using a diverse set of variables (2019, neutral): “In (Di Persio & Honchar, 2016), CNN, LSTM, and MLP were applied to the historical data of close prices of S&P 500 index.”
Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals (2017, background): “Several preprinted drafts consider deep learning for stock market prediction [4, 17, 8] however, in all cases, the empirical studies are limited to few stocks and short time periods.”
Stock market index prediction using deep neural network ensemble (2017, methodology): “The conventional sigmoid or tanh activation function is replaced with the recentlyproposed Leaky Rectified Linear Unit(LReLU) [18] in neural networks, which enables deep network to be trained efficiently.”
A graph-based CNN-LSTM stock price prediction algorithm with leading indicators (2021, background): “There is mainly recurrent unit [25], long-short term memory unit [26] in LSTM [27, 28].”
Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models (2018, methodology): “In Equation (11), the EWMA model is similar to the GARCH (1, 1) model but uses ρ, which has a different meaning from β1, that is, it gives more weight to the near past.”