News-Driven Price Movement Forecasting with Label-Prior Graph Attention
Start Page
569
End Page
572
Date Issued
2024-05-13
Author(s)
Abstract
This paper introduces a novel approach to stock movement prediction using multi-label classification, leveraging the interconnections between news articles and related company stocks. We present the Label-Prior Graph Attention (LPGA) model, which significantly enhances the performance of news-driven stock price movement forecasting. This model is comprised of a unique graph attention architecture, incorporating a label encoder and a text encoder, designed to effectively capture and utilize the relationships between labels in a graph-based context. Our model demonstrates superior performance over several benchmark models. The LPGA model's efficacy is further validated through experiments on two multi-label datasets. The model outperforms established baseline models across various evaluation metrics. The success of the LPGA model in both stock movement prediction and general multi-label classification tasks indicates its potential as a versatile tool in the realm of machine learning and financial analysis.
Event(s)
WWW 2024 Companion - Companion Proceedings of the ACM Web Conference
Publisher
ACM
Type
conference paper