计算机与现代化 ›› 2023, Vol. 0 ›› Issue (01): 95-102.

• 算法设计与分析 • 上一篇    下一篇

纠正学习策略下LightGBM-GRU模型的股票波动率预测

  

  1. (天津职业技术师范大学信息技术工程学院(软件工程学院),天津 300222)
  • 出版日期:2023-03-02 发布日期:2023-03-02
  • 作者简介:石志伟(1995—),男,山东鄄城县人,CCF会员,硕士研究生,研究方向:人工智能,大数据,量化交易,E-mail: zhiweishi_china@163.com; 通信作者:武志峰(1974—),男,教授,博士,研究方向:机器学习,复杂网络社区发现,链接预测,E-mail: zhifeng.wu@163.com; 张哲(1996—),男,硕士研究生,研究方向:人工智能,自然语言处理,E-mail: zachary_zhang1616@163.com。 石志伟,等:纠正学习策略下LightGBM-GRU模型的股票波动率预测
  • 基金资助:
    国家自然科学基金青年科学基金资助项目(61601331);天津市自然科学基金青年科学基金资助项目(18JCQNJC04700); 天津市研究生科研创新项目(2021YJSS226)

Stock Volatility Prediction of LightGBM-GRU Model under Corrective Learning Strategy

  1. (School of Information and Technology (School of Software Engineering), Tianjin University of Technology and Education, Tianjin 300222, China)
  • Online:2023-03-02 Published:2023-03-02

摘要: 为提高传统智能算法进行时间序列预测时的精度和解决工程数据问题时的适应性,提出一种纠正学习策略。波动性广泛应用于金融领域,对股票的波动性进行预测具有重要的价值。由于股票价格的时间序列是非线性和非平稳的,预测股票市场波动成为时间序列预测中的难点。本文通过纠正学习策略进行仿真实验,设计出LightGBM-GRU模型,以LightGBM和GRU作为基模型和纠正器,预测3年内126只来自不同行业的股票在未来10 min的波动率,根据RMSPE、MAE、MSE、RMSE等指标表明:即使经典的效果比较好的集成学习模型,也能通过纠正学习策略同时提高精度和泛化能力。本文指出在算法富集和大数据的时代,智能算法的矛盾转变为智能算法通用性有限与工程问题多样性之间的矛盾,纠正学习策略可以为数据仿真提供新思路。

关键词: 纠正学习, 股票波动率, 时间序列预测, 机器学习, 神经网络

Abstract: In order to improve the accuracy of traditional intelligent algorithms in time series prediction and the adaptability of solving engineering data problems, a corrective learning strategy is proposed. Volatility is widely used in the financial field, so it is of great value to predict the volatility of stocks. Since the time series of stock prices are non-linear and non-stationary, predicting the volatility of the stock market has become a difficult point in time series forecasting. In this paper, a simulation experiment is carried out by corrective learning strategy, and a LightGBM-GRU model is designed. Using LightGBM and GRU as the base model and corrector, we predict the volatility of 126 stocks from different industries in the next 10 minutes within 3 years. According to RMSPE,MAE,MSE,RMSE and other indicators: even the classical integrated learning model with good effect, the accuracy and generalization ability also can be improved at the same time by the corrective learning strategy. This paper points out that in the era of algorithm enrichment and big data, the contradiction of intelligent algorithms has turned into a contradiction between the limited versatility of intelligent algorithms and the diversity of engineering problems. Correcting learning strategies can provide new ideas for data simulation.

Key words: corrective learning, stock volatility, time series forecasting, machine learning, neural networks