Abstract
The stock market has an important role in the development of modern society. They allow the deployment of economic resources. Changes in stock prices reflect changes in the market. With powerful data processing capabilities in many fields, deep learning is also widely used in the financial field such as: stock market prediction, optimal investment, financial information processing, and execute financial trading strategies. Therefore, stock market prediction is considered one of the most popular and valuable areas in the financial sector. In this study, we propose using multi deep learning algorithms for stock prediction: RNN, LSTM, CNN, and BiLSTM. We do experiments on a stock that has a wide range of trading days and use them to predict daily closing prices. The experimental results show that the multi deep learning models can achieve good results in predicting stock prices compared to many traditional prediction models.
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Hung, B.T., Chakrabarti, P., Chatterjee, P. (2024). Stock Prediction Using Multi Deep Learning Algorithms. In: Kautish, S., Chatterjee, P., Pamucar, D., Pradeep, N., Singh, D. (eds) Computational Intelligence for Modern Business Systems . Disruptive Technologies and Digital Transformations for Society 5.0. Springer, Singapore. https://doi.org/10.1007/978-981-99-5354-7_6
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DOI: https://doi.org/10.1007/978-981-99-5354-7_6
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