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

TL;DR

This paper introduces AlphaMLDigger, a two-phase machine learning framework that leverages NLP and ensemble models to identify excess returns in volatile markets, addressing pandemic-induced data shifts.

Contribution

The paper presents a novel two-phase approach combining NLP sentiment analysis with ensemble machine learning models for stock prediction during market fluctuations.

Findings

Ensemble models achieve 98.4% accuracy in stock movement prediction.

The approach effectively captures market sentiment from social media data.

COVID-19 causes significant data shifts in China's stock market.

Abstract

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.