Intelligent stock prediction: A neural network approach

Mohamad H. Shahrour and Mostafa Dekmak
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Mostafa Dekmak: ��University of Sunderland, Sunderland, UK

International Journal of Financial Engineering (IJFE), 2023, vol. 10, issue 01, 1-14

Abstract: Ever since the existence of financial markets, predicting stocks’ movement has been crucial for investors in order to increase their investment returns. Despite the plethora of research, the outstanding literature provides mixed results concerning the choice of model. Are Artificial Intelligence systems valid techniques in predicting stock prices? Do deep learning models outperform machine learning models? Through developing different machine and deep learning models, the overall findings reveal that deep learning techniques (i.e., ANN and LSTM) outperform machine learning techniques (i.e., SVR) in price prediction. The results are validated using different accuracy measures.

Keywords: Stock prediction; artificial intelligence; deep learning; machine learning; neural networks (search for similar items in EconPapers)
JEL-codes: C45 C53 G17 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Working Paper: Intelligent Stock Prediction: A Neural Network Approach (2022)
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DOI: 10.1142/S2424786322500165

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