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

By Ziniu Hu, Weiqing Liu, Jiang Bian, Xuanzhe Liu, Tie-Yan Liu. 2017

345 citations recorded by the source corpus.

Stock trend prediction plays a critical role in seeking maximized profit from the stock investment. However, precise trend prediction is very difficult since the highly volatile and non-stationary nature of the stock market. Exploding information on the Internet together with the advancing development of natural language processing and text mining techniques have enabled investors to unveil market trends and volatility from online content. Unfortunately, the quality, trustworthiness, and comprehensiveness of online content related to stock market vary 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(HAN) 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 framework. A further simulation illustrates that a straightforward trading strategy based on our proposed framework can significantly increase the annualized return.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research, Time-Series & Forecasting

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “The authors of [212] proposed a novel method that used text mining techniques and Hybrid Attention Networks based on financial news for the forecast of the trend of stocks.”
Deep Learning for Financial Applications : A Survey (2020, methodology): “[175] proposed a novel method that used text mining techniques and Hybrid Attention Networks based on the financial news for forecasting the trend of stocks.”
Stock closing price prediction based on sentiment analysis and LSTM (2019, background): “The nonlinear and non-stationary nature of stock prices limits the applicability of conventional models, such like AR [34].”
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data (2019, background): “In addition to forecasting, interpretable RNNs are desirable for gaining insights into the important part of data for RNNs achieving good prediction performance (Hu et al., 2018; Foerster et al., 2017; Lipton, 2016).”
Temporal Relational Ranking for Stock Prediction (2018, background): “devised a neural network-based solution, named Hybrid Attention Networks, which leverages a hybrid attention mechanism to attentively fuse multiple news reports mentioning a stock into a joint representation [17].”
Applications of deep learning in stock market prediction: recent progress (2020, methodology): “…(2018); Huang et al. (2018); Wu et al. (2018); Kumar et al. (2019); Li et al. (2017a); Tang et al. (2019); Huynh et al. (2017); Mohan et al. (2019); Hu et al. (2018a) Historical Prices + Technical Indicators + Macroeconomics Dingli & Fournier (2017); de Oliveira et al. (2013); Zhong & Enke…”
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends (2018, methodology): “[55] deployed a Hybrid Attention Network (HAN) for the predic-”