倾听混沌耳语：面向新闻的股票趋势预测的深度学习框架
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
摘要：
股票走势预测是实现股票投资收益最大化的关键。然而，由于股票市场的高度波动性和非平稳性，精确的趋势预测是非常困难的。互联网上爆炸式的信息，加上自然语言处理和文本挖掘技术的不断发展，使投资者能够从在线内容中揭示市场趋势和波动。遗憾的是，与股市相关的网络内容的质量、可信度和全面性差异很大，很大一部分是低质量的新闻、评论甚至谣言。为了应对这一挑战，我们模仿人类面对如此混乱的在线新闻的学习过程，由三个原则驱动：顺序内容依赖，多样影响，以及有效和高效的学习。本文针对前两个原则，设计了一个混合注意力网络，根据最近的相关新闻序列来预测股票走势。此外，我们采用自定进度的学习机制来模仿第三个原则。在真实股市数据上的大量实验证明了该方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
