论文标题
Alphamldigger：一种新型的机器学习解决方案，旨在探索超额投资回报
AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess Return on Investment
论文作者
论文摘要
如何快速自动自动挖掘有效的信息并提供投资决策，吸引了学术界和行业的关注。全球大流行也带来了新的挑战。本文提出了一个两阶段的alphamldigger，在高度波动的市场中有效地发现了过多的回报。在第1阶段中，提出了深层顺序自然语言处理（NLP）模型将Sina Microblog博客转移到市场情绪。在第2阶段，预测的市场情绪与社交网络指标功能和股票市场历史记录功能相结合，可以通过不同的机器学习模型和优化器来预测股票运动。结果表明，整体模型的精度为0.984，并且明显优于基线模型。此外，我们发现Covid-19将数据转移到了中国的股票市场。
How to quickly and automatically mine effective information and serve investment decisions has attracted more and more attention from academia and industry. And new challenges have arisen with the global pandemic. This paper proposes a two-phase AlphaMLDigger that effectively finds excessive returns in a highly fluctuated market. In phase 1, a deep sequential natural language processing (NLP) model is proposed to transfer Sina Microblog blogs to market sentiment. In phase 2, the predicted market sentiment is combined with social network indicator features and stock market history features to predict the stock movements with different Machine Learning models and optimizers. The results show that the ensemble models achieve an accuracy of 0.984 and significantly outperform the baseline model. In addition, we find that COVID-19 brings data shift to China's stock market.
