Predicting the stock market remains a difficult field because of its inherent volatility. With the development of artificial intelligence, research using deep learning for stock price prediction is increasing, but the importance of applying a prediction system consisting of preparing verified data and selecting an optimal feature set is lacking. Accordingly, this study proposes a GA optimization-based deep learning technique (CNN-LSTM) that predicts the next day's closing price based on an artificial intelligence model to more accurately predict future stock values. In this study, CNN extracts features related to stock price prediction, and LSTM reflects the long-term history process of input time series data. Basic stock price data and technical indicator data for the last 20 days prepare a data set to predict the next day's closing price, and then a CNN-LSTM hybrid model is set. In order to apply the optimal parameters of this model, GA was used in combination. The Korea Stock Index (KOSPI) data was selected for model evaluation. Experimental results showed that GA-based CNN-LSTM has higher prediction accuracy than single CNN, LSTM models, and CNN-LSTM model. This study helps investors and policy makers who want to use stock price fluctuations as more accurate predictive data using deep learning models.

由于股票市场固有的波动性，预测股票市场仍然是一个困难的领域。随着人工智能的发展，利用深度学习进行股价预测的研究不断增加，但缺乏应用由准备验证数据和选择最佳特征集组成的预测系统的重要性。据此，本研究提出了一种基于遗传算法优化的深度学习技术（CNN-LSTM），基于人工智能模型预测次日收盘价，以更准确地预测未来股票价值。在本研究中，CNN提取与股票价格预测相关的特征，LSTM反映了输入时间序列数据的长期历史过程。近20天的基础股价数据和技术指标数据准备一个数据集来预测第二天的收盘价，然后设置CNN-LSTM混合模型。为了应用该模型的最优参数，结合使用了遗传算法。选择韩国股票指数（KOSPI）数据进行模型评估。实验结果表明，基于遗传算法的CNN-LSTM比单一CNN、LSTM模型和CNN-LSTM模型具有更高的预测精度。这项研究可以帮助那些希望使用深度学习模型将股价波动作为更准确的预测数据的投资者和政策制定者。