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Cerchiello, P., Nicola, G., Ronnqvist, S. and Sarlin, P. (2017) Deep Learning Bank Distress from News and Numerical Financial Data. arXiv: 1706.09627.
https://doi.org/10.2139/ssrn.3292485

has been cited by the following article:

• TITLE: Prediction of the Technology Company’s Stock Price through the Deep Learning Method

  AUTHORS: Meng Wang

  KEYWORDS: Deep Learning, CNN, LSTM, Stock Forecast

  JOURNAL NAME: Open Journal of Modelling and Simulation, Vol.10 No.4, October 24, 2022

  ABSTRACT: This work aims to utilize deep learning methods CNN and LSTM to predict the adjusted close price of eight technical companies. The proposed model is a CNN-LSTM hybrid model, which combined CNN and LSTM layers in the model. It was compared with the single LSTM model and double LSTM model to evaluate its performance. The results showed the CNN-LSTM made a great prediction and can predict a more accurate value than the other two models, but it still can be improved to reduce overfitting problems.