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Brownlee, J. (2017) Long Short-Term Memory Networks with Python Develop Sequence Prediction Models with Deep Learning. Machine Learning Mastery, EBook.

has been cited by the following article:

• TITLE: Predicting Equity Price with Corporate Action Events Using LSTM-RNN

  AUTHORS: Shotaro Minami

  KEYWORDS: LSTM, Long-Short Term Memory(LSTM-RNN), Recurrent Neural Network (RNN), Prediction of Single Stock Price, Artificial Intelligence Finance

  JOURNAL NAME: Journal of Mathematical Finance, Vol.8 No.1, January 31, 2018

  ABSTRACT: Forecasting the stock price of a particular has been a difficult task for many analysts and researchers. In fact, investors are highly interested in the research area of stock price prediction. However, to improve the accuracy of forecasting a single stock price is a really challenging task; therefore in this paper, I propose a sequential learning model for prediction of a single stock price with corporate action event information and Macro-Economic indices using LTSM-RNN method. The results show that the proposed model is expected to be a promising method in the stock price prediction of a single stock with variables like corporate action and corporate publishing.