International Journal of Advances in Applied Computational Intelligence
Journal DOI
2833-5600ISSN (Online)
Volume 5 , Issue 2 , PP: 08-23, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Barbara Charchekhandra 1 , Rashel Abu Hakmeh 2 , Murat Ozcek 3
Doi: https://doi.org/10.54216/IJAACI.050201
Stock is a financial product considered by flexible trading, high risk, and high return that can preferred by several investors. Investors may get an abundance of returns through the accurate prediction of stock price trends. Nevertheless, the stock price can be influenced by certain factors including market conditions, companies’ managerial decisions, macroeconomic situation, and investors’ preferences for major economic and social events. Econometric and Statistical models are widely utilized in classical stock price prediction; however, these techniques could not handle the complex and dynamic environments of the stock market. Researchers have begun using deep learning (DL) and machine learning (ML) to estimate stock fluctuations and prices with the rapid evolution of artificial intelligence (AI), serving investors to define investment strategies to increase returns and decrease risk. Therefore, this manuscript presents a new dung beetle optimization with deep learning based stock price prediction (DBODL-SPP) methodology. The purpose of the DBODL-SPP algorithm is to predict the rise or fall of stock prices using the optimal DL model. In the DBODL-SPP technique, the min-max scalar can be deployed for pre-processing the input data. Besides, the DBODL-SPP approach applies the DBO algorithm for electing an optimal subset of features. The DBODL-SPP technique makes use of a multi-head attention long short-term memory (MHA-LSTM) model for the stock price prediction. Finally, by using the equilibrium optimizer (EO) algorithm, the parameter tuning of the MHA-LSTM algorithm can be carried out. A detailed set of experimentations has been applied to evaluate the enriched performance of the DBODL-SPP technique. The simulation values emphasized that the DBODL-SPP algorithm achieves better results than other techniques for stock price prediction
Stock Price Prediction , Dung Beetle Optimization , Hyperparameter Tuning , Multi-Head Attention , Deep Learning
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