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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
Year of Publication: 2021
Seth Gyamerah, Dennis Redeemer Korda

Seth Gyamerah and Dennis Redeemer Korda. Prediction of Stock Market Returns using LSTM Model and Traditional Statistical Model. International Journal of Computer Applications 183(37):57-61, November 2021. BibTeX

	author = {Seth Gyamerah and Dennis Redeemer Korda},
	title = {Prediction of Stock Market Returns using LSTM Model and Traditional Statistical Model},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2021},
	volume = {183},
	number = {37},
	month = {Nov},
	year = {2021},
	issn = {0975-8887},
	pages = {57-61},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2021921773},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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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LSTM model, Logistic regression model, Stock market (S&P500) and classification problem, Wavelet transform function.