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.

由于与股票数据相关的复杂性和不确定性，因此无法准确预测股票期货价格。要成功分析每个公司的股票未来价格，需要一个有效的预测系统。对于研究人员来说，分析大型股票的未来价格以获得更好的准确性更为复杂。因此，提出了一种具有增强LSTM模型的深层CNN，用于基于大数据预测股票的未来价格。此外，使用纳斯达克（Nasdaq），富时（FTSE），太艾（TAIEX）和牛熊证（BSE）等四个实时股票期货价格来分析采用增强LSTM模型的拟议深层CNN的效率。通过执行不同的实验（例如提前1个月，提前1周和提前1天）评估模型的性能。连续一年，收集所有工作日数据，并根据建议的模型进行实验。仿真结果表明，所提出的模型在各种指标方面都具有更好的性能，例如POCID的获得率超过85％，与其他现有技术相比，\（\ hbox {R} ^ 2 \）大于80％，ARV小于0.024％，MAPE小于0.04％。