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How to quickly and automatically mine effective information and serveinvestment decisions has attracted more and more attention from academia andindustry. And new challenges have arisen with the global pandemic. This paperproposes a two-phase AlphaMLDigger that effectively finds excessive returns ina highly fluctuated market. In phase 1, a deep sequential natural languageprocessing (NLP) model is proposed to transfer Sina Microblog blogs to marketsentiment. In phase 2, the predicted market sentiment is combined with socialnetwork indicator features and stock market history features to predict thestock movements with different Machine Learning models and optimizers. Theresults show that the ensemble models achieve an accuracy of 0.984 andsignificantly outperform the baseline model. In addition, we find that COVID-19brings data shift to China's stock market.

AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess Return on Investment