Forecasting Stock Market Prices through Long Short-Term Memory Method
Keywords:
Forecasting stock market, stock market, sharesAbstract
Prediction of futuristic price of stocks (shares) of listed companies at exchange has always been a fascinating area of interest for all kind of market participants. Whether short term traders. long term investors, or risk managers, everyone interest lies in forecasting the market accurately and in time. This study explores the application of Long Short-Term Memory (LSTM) networks, a class of Recurrent Neural Networks (RNNs), for predicting stock prices of major technology companies, Apple (AAPL), Google (GOOG), Microsoft (MSFT), and Amazon (AMZN) using historical data from 2005 to 2024. This work of mine “Stock Prediction using LSTM (Long Short-Term Memory”) method is a sincere effort in same direction, and I hope it will immensely help all market participants and serve them with more accuracy in forecasting share prices in near future period.
References
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8),1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Chen, K., Zhou, Y., & Dai, F. (2015). A LSTM-based method for stock price prediction. Expert Systems with Applications, 42(8), 3903–3910.
https://doi.org/10.1016/j.eswa.2014.11.047
Li Qi, et al. (2024). Integrating Symbolic Genetic Programming With Lstm for Forecasting Cross-Sectional Price Returns: A Comparative Analysis of Chinese And Japanese Stock Market. Advances in Artificial Intelligence and Machine Learning, 4(4), 2981-3005.
Yahoo Finance. (n.d.). Historical market data. Retrieved from https://www.yfinance.com
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This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles in the Graduate Journal of Interdisciplinary Research, Reports and Reviews (Grad. J. InteR3) by Vyom Hans Publications are published and licensed under a Creative Commons Attribution- CC-BY 4.0 International License.
