An optimized Deep Learning model via the Golden Jackal Optimizer for the precise stock price forecasting.
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- Stock markets have historically been the reference point of the worldwide financial environment throughout the years. Investing in the stock market carries risk; however, it can offer significant short- or long-term returns for the investor. Forecasting stock prices has been a vital topic among professionals and researchers; however, it has always been a difficult task. Machine learning and metaheuristic algorithms over the last years have shown remarkable performance in a variety of sectors, encompassing the financial sector. The task of stock price prediction has extensively applied long short-term memories (LSTM) and gated recurrent unit (GRU) neural networks, demonstrating their promising performance. In this work, we propose an optimized deep learning model via the Golden Jackal Optimizer (GJO) algorithm for the task of precise stock price prediction. We selected the five stocks with the highest market capitalization from the S&P 500 index as our benchmark dataset. We compared the proposed approach with other deep learning models, and the results demonstrated that GJO is able to enhance forecasting quality.
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