基于深度学习的股价预测与量化选股策略研究

Jiahao Ji
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引用次数: 0

摘要

随着国内股票市场的不断发展和金融体系制度的不断完善,同时国内股票市场在金融体系中的地位逐渐上升,基于国内股票市场的预测研究将变得越来越重要。为了解决短期股价预测精度低、准确性差的问题,本文选用鲸鱼优化算法下的注意力机制双向长短期记忆网络(WOA-BiLSTM-Attenion)模型进行股价预测。注意机制的双向长短期记忆网络模型可以减少历史信息的损失,增加重要信息的影响力。在此基础上,利用鲸鱼优化算法(WOA)进行超参数选择,减少人为干扰。实验结果表明,与BP、LSTM、BiLSTM、BiLSTM-Attention相比,WOA-BiLSTM-Atttenion模型对股票收盘价的预测效果更好,预测值为13.9446,预测值为0.9477,具有较高的准确性,以期为其他领域的预测研究提供一定的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Stock Price Prediction and Quantitative Stock Picking Strategy Based on Deep Learning
With the continuous development of the domestic stock market and the continuous improvement of the financial system system, and at the same time, the domestic stock market gradually rises in the financial system, based on the prediction research of the domestic stock market will become more and more important. In order to solve the problems of low precision and poor accuracy of short-term stock price prediction, this paper selects the bi-directional long- and short-term memory network of attention mechanism (WOA-BiLSTM-Attenion) model under the whale optimization algorithm for stock price prediction. The modeling of bi-directional long- and short-term memory network with attention mechanism can reduce the loss of historical information and increase the influence of important information. On this basis, Whale Optimization Algorithm (WOA) is then used for hyperparameter selection to reduce human interference. The experimental results show that compared with BP, LSTM, BiLSTM, BiLSTM-Attention, the WOA-BiLSTM-Attenion model has a better effect on stock closing price prediction, with a value of 13.9446, and the value of 0.9477, which has a higher accuracy, with a view to providing certain reference for the prediction research in other fields.
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