2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 17 , Issue 1 , PP: 196-208, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Zinah Kareem Mansoor 1 * , Ali Yakoob Al-Sultan 2
Doi: https://doi.org/10.54216/FPA.170114
The stock price exhibits quick and extremely nonlinear fluctuations in the financial market. A prominent worry among scholars and investors is the correct prediction of short-term stock prices and the corresponding upward and downward trends. Financial organizations have successfully incorporated machine learning and deep learning techniques to anticipate time series data accurately. Nevertheless, the precision of these models' predictions still needs improvement. Most current studies employ single prediction algorithms that cannot overcome intrinsic limitations. This paper proposes a methodology that utilizes the MUTUAL, principal component analysis (PCA), and Long Short-Term Memory (LSTM) model to accurately simulate and predict the variations in stock prices. The technology is utilized for the three global stock market datasets: TSLA, S&P500, and NASDAQ. The highest level of improvement achieved is a correlation of 99%. Furthermore, there is a reduction in error for the metrics MSE, MAPE, and RMSE, with improvements of 0.0001, 0.009, and 0.01 correspondingly.
PCA , LSTM , Deep learning , Stock Market Prediction, Deep Learning, Neural Networks, Sentiment Analysis, Financial Markets, Trading Strategies
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