Improving Stock Closing Price Prediction Using Recurrent Neural Network and Technical Indicators

Neural Computation
Tingwei Gao, Yueting Chai

Abstract

This study focuses on predicting stock closing prices by using recurrent neural networks (RNNs). A long short-term memory (LSTM) model, a type of RNN coupled with stock basic trading data and technical indicators, is introduced as a novel method to predict the closing price of the stock market. We realize dimension reduction for the technical indicators by conducting principal component analysis (PCA). To train the model, some optimization strategies are followed, including adaptive moment estimation (Adam) and Glorot uniform initialization. Case studies are conducted on Standard & Poor's 500, NASDAQ, and Apple (AAPL). Plenty of comparison experiments are performed using a series of evaluation criteria to evaluate this model. Accurate prediction of stock market is considered an extremely challenging task because of the noisy environment and high volatility associated with the external factors. We hope the methodology we propose advances the research for analyzing and predicting stock time series. As the results of experiments suggest, the proposed model achieves a good level of fitness.

References

Oct 14, 2000·Neural Computation·F A GersF Cummins
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May 12, 2007·Journal of Immigrant and Minority Health·Stuart L LustigSamantha C Morse
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Citations

May 21, 2021·PeerJ. Computer Science·Shazia Usmani, Jawwad A Shamsi

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Methods Mentioned

BETA
PCA

Software Mentioned

Scikit
learn
Adam
Python
TensorFlow

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