融合技术与基本面数据的混合模型提升股市预测表现
Integration of LSTM Networks in Random Forest Algorithms for Stock Market Trading Predictions
- 用LSTM分析价格走势，随机森林处理公司经济数据
- 结合两类信息的模型在10个交易日预测中表现更优
- 适合关注量化交易策略优化的研究者与从业者
本文旨在分析并选择融合不同模型与多源数据（如金融与微观经济信息）的股票交易系统。基于作者前期工作，采用机器学习与深度学习先进技术，构建具有实证统计优势的交易算法，以改进现有文献成果。方法将长短期记忆网络（LSTM）与基于决策树的算法（如随机森林、梯度提升）结合：前者用于分析资产价格模式，后者输入企业经济数据。基于国际公司数据的算法交易数值模拟显示，同时使用基本面与技术面变量的混合方法优于仅使用单一类型变量的传统方法。在决策树模型中，随机森林表现最佳。此外，通过筛选关键技术变量可进一步提升该混合方法的预测性能。
原文摘要 · Abstract (English)
The aim of this paper is the analysis and selection of stock trading systems that combine different models with data of different nature, such as financial and microeconomic information. Specifically, based on previous work by the authors and applying advanced techniques of Machine Learning and Deep Learning, our objective is to formulate trading algorithms for the stock market with empirically tested statistical advantages, thus improving results published in the literature. Our approach integrates Long Short-Term Memory (LSTM) networks with algorithms based on decision trees, such as Random Forest and Gradient Boosting. While the former analyze price patterns of financial assets, the latter are fed with economic data of companies. Numerical simulations of algorithmic trading with data from international companies and 10-weekday predictions confirm that an approach based on both fundamental and technical variables can outperform the usual approaches, which do not combine those two types of variables. In doing so, Random Forest turned out to be the best performer among the decision trees. We also discuss how the prediction performance of such a hybrid approach can be boosted by selecting the technical variables.
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