Dynamic fusion of multi-source heterogeneous data using MOE mechanism for stock prediction

Abstract

Stock prices are influenced by numerous factors, including social media, news, and financial reports, serving as indicators of financial market dynamics. However, harnessing diverse information from different sources and structures to predict price trends remains challenging. In this paper, we propose a dual-stage deep learning model based on the Mixture-of-Expert (MoE) mechanism. In stage one, three distinct expert networks encode information about price movements, financial news, and investor sentiments through multi-source interaction attention. In stage two, a gated network dynamically fuses outputs, capturing temporal relationships in windowed data. Experimental results on the Chinese stock market demonstrate our model outperforms existing ones in forecasting tasks.