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

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arxiv 1712.02136 v3 pith:TND6J5HJ submitted 2017-12-06 cs.SI cs.LGq-fin.ST

Ziniu Hu , Weiqing Liu , Jiang Bian , Xuanzhe Liu , Tie-Yan Liu This is my paper · ORCID
classification cs.SIcs.LGq-fin.ST
keywords stocklearningmarkettrendcontentnewsonlineprediction
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data

    cs.LG 2024-12 conditional novelty 4.0 of 10

    A factorized 'higher-order' transformer with kernelized linear attention and tweet plus price inputs reaches 72.94% accuracy and 0.516 MCC on StockNet, behind only NL-LSTM among the baselines compared.

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