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Prediction of Stock Market Returns using LSTM Model and Traditional Statistical Model
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 37 |
| Year of Publication: 2021 |
| Authors: Seth Gyamerah, Dennis Redeemer Korda |
10.5120/ijca2021921773
|
Seth Gyamerah, Dennis Redeemer Korda . Prediction of Stock Market Returns using LSTM Model and Traditional Statistical Model. International Journal of Computer Applications. 183, 37 ( Nov 2021), 57-61. DOI=10.5120/ijca2021921773
Abstract
Despite the growing interest in time series data specifically, stock market predictions in the financial world as well as its development stages of most related studies, this article aim to provide a good structure and suitable model for predicting the trend movement of stock market returns. This is done through a classification wavelet LSTM network model and the result compare to a baseline model. The results show that there are high returns of S&P500 stock market prices with a 15minutes interval range as compared to the wavelet-logistic (W-LR) regression model. It is therefore obvious that the enhanced deep neural network (W-LSTM, LSTM model) performs better in stock market prediction as compared to the traditional statistical models (LR W-LR).
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