【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。更多还原

【Abstract】 How to make an accurate financial time series prediction is one of the important quantitative financial problems.Long and short term memory neural network(LSTM) has solved the complex serialized data learning problems such as stock prediction much better.However,the results of previous studies show that there are still some problems such as unbalanced prediction and local minimum value,which lead to poor prediction ability.Based on the above problems,the genetic algorithm(GA) is used to solve the parameter adjustment problem to ensure the balance of model prediction,and a new stock prediction model is constructed.First,LSTM neural network is used to predict closing price.Then,the prediction results are calculated to the judgment method based on genetic algorithm.Finally,the predicted stock’s ups and downs signals are gained as the output.This model is different from the previous state-of-the-art and is mainly improved for the output module of the LSTM model.High-frequency trading data of Index China are used for verification.The results show that the improved model is better than the LSTM model.更多还原