AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess Return on Investment
This addresses the challenge of making investment decisions in fluctuating markets, particularly during the COVID-19 pandemic, but is incremental as it combines existing methods like NLP and ensemble models for a specific domain.
The paper tackled the problem of automatically mining information for investment decisions by proposing AlphaMLDigger, a two-phase machine learning solution that predicts stock movements using market sentiment from social media and other features, achieving an accuracy of 0.984 and outperforming baselines.
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.