Multi-modal Attention Network for Stock Movements Prediction

TL;DR

This paper introduces a multi-modal attention network that combines social media semantics and historical stock data to improve prediction accuracy and trading profits in stock movement forecasting.

Contribution

It presents a novel multi-modality attention network that effectively fuses social media semantics with numeric historical data for stock prediction.

Findings

Outperforms previous methods in prediction accuracy (61.20%)

Achieves higher trading profits (9.13%)

Demonstrates the effectiveness of multi-modality fusion in stock prediction

Abstract

Stock prices move as piece-wise trending fluctuation rather than a purely random walk. Traditionally, the prediction of future stock movements is based on the historical trading record. Nowadays, with the development of social media, many active participants in the market choose to publicize their strategies, which provides a window to glimpse over the whole market's attitude towards future movements by extracting the semantics behind social media. However, social media contains conflicting information and cannot replace historical records completely. In this work, we propose a multi-modality attention network to reduce conflicts and integrate semantic and numeric features to predict future stock movements comprehensively. Specifically, we first extract semantic information from social media and estimate their credibility based on posters' identity and public reputation. Then we incorporate the semantic from online posts and numeric features from historical records to make the trading strategy. Experimental results show that our approach outperforms previous methods by a significant margin in both prediction accuracy (61.20\%) and trading profits (9.13\%). It demonstrates that our method improves the performance of stock movements prediction and informs future research on multi-modality fusion towards stock prediction.