Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction

TL;DR

This paper introduces a deep learning framework that leverages hybrid attention networks and self-paced learning to improve stock trend prediction from chaotic online news, addressing the challenges of market volatility and content quality.

Contribution

The paper proposes a novel hybrid attention network combined with self-paced learning to enhance stock trend prediction from online news data.

Findings

The approach outperforms existing models on real-world stock data.

Hybrid attention networks effectively capture content dependencies.

Self-paced learning improves model robustness against noisy data.

Abstract

Stock trend prediction plays a critical role in seeking maximized profit from stock investment. However, precise trend prediction is very difficult since the highly volatile and non-stationary nature of stock market. Exploding information on Internet together with advancing development of natural language processing and text mining techniques have enable investors to unveil market trends and volatility from online content. Unfortunately, the quality, trustworthiness and comprehensiveness of online content related to stock market varies drastically, and a large portion consists of the low-quality news, comments, or even rumors. To address this challenge, we imitate the learning process of human beings facing such chaotic online news, driven by three principles: sequential content dependency, diverse influence, and effective and efficient learning. In this paper, to capture the first two principles, we designed a Hybrid Attention Networks to predict the stock trend based on the sequence of recent related news. Moreover, we apply the self-paced learning mechanism to imitate the third principle. Extensive experiments on real-world stock market data demonstrate the effectiveness of our approach.