计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 467-473.doi: 10.11896/JsJkx.190900128
包振山1, 郭俊南1, 谢源2, 张文博1
BAO Zhen-shan1, GUO Jun-nan1, XIE Yuan2 and ZHANG Wen-bo1
摘要： 如何准确地进行股票预测一直是量化金融领域的重要问题。长短期记忆细胞神经网络(LSTM)的出现较好地解决了股票预测这类的复杂序列化数据学习的问题。然而前期研究结果表明单一使用该方法仍存在预测不平衡、陷入局部极值导致能力不佳的问题。基于上述问题,文中利用将遗传算法(GA)解决调参问题来保证模型预测的平衡性,由此构建了新型股票预测模型。该模型分为三部分,首先利用LSTM网络进行收盘价的预测,再利用基于遗传算法的判别机制,最终获取下一刻股票的涨跌信号。这一模型不同于先前的研究,主要针对LSTM模型的输出模块进行了改进。文中使用了中证500的日内分钟数据进行测试验证。实验得出,改进模型的各方面指标均优于单独的LSTM模型。
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