Abstract
The exact prediction of stock future prices are impossible due to complexity and uncertainty related with the stock data. An effective prediction system is required for the successful analysis of future price of stocks for every company. It is more complex for the researchers to analyze the large stock future prices for obtaining better accuracy. For this reason, a deep CNN with reinforcement-LSTM model is proposed for forecasting stock future prices based on big data. Furthermore, four real-time stock future prices such as NASDAQ, FTSE, TAIEX, and BSE are used for analyzing the efficiency of the proposed deep CNN with reinforcement-LSTM model. The models performance is evaluated conducting different experiments like 1-month ahead, 1-week ahead, and 1-day ahead. In a consecutive year, all working days data is collected and conducted the experiments based on proposed model. The simulation results show that the proposed model gives better performance in terms of various metrics such as POCID obtains more than 85%, \(\hbox {R}^2\) more than 80%, ARV by less than 0.024%, and MAPE is lesser than 0.04% when compared with other existing techniques.
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Ishwarappa, Anuradha, J. Big data based stock trend prediction using deep CNN with reinforcement-LSTM model. Int J Syst Assur Eng Manag (2021). https://doi.org/10.1007/s13198-021-01074-2
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DOI: https://doi.org/10.1007/s13198-021-01074-2