mlsanityData to 7 Oct 2026

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

arXiv 1712.02136 ↗First posted 6 Dec 2017cs.SI cs.LG q-fin.STcited by 60

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Abstract (arXiv)

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.

Key facts. Cited by 60 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Dec 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 12; 2023: 11; 2024: 10; 2025: 7; 2026: 9.

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  1. 2024A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalistcited by 137
  2. 2021HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Informationcited by 43
  3. 2024Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Modelscited by 35
  4. 2021Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transportcited by 32
  5. 2021REST: Relational Event-driven Stock Trend Forecastingcited by 30
  6. 2021Reinforcement Learning for Quantitative Tradingcited by 24
  7. 2023DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecastingcited by 21
  8. 2022Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Predictioncited by 18
  9. 2022NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecastingcited by 16
  10. 2023Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Predictioncited by 16
  11. 2024Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysiscited by 15
  12. 2021DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunitiescited by 14
38 more
  1. 2022Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networkscited by 13
  2. 2021Stock Movement Prediction with Financial News using Contextualized Embedding from BERTcited by 12
  3. 2022Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Modelcited by 12
  4. 2024FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Modelcited by 11
  5. 2022MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimizationcited by 10
  6. 2023Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Predictioncited by 10
  7. 2023StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Seriescited by 9
  8. 2024Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Modelscited by 9
  9. 2025From Deep Learning to LLMs: A survey of AI in Quantitative Investmentcited by 9
  10. 2021Graph-Based Learning for Stock Movement Prediction with Textual and Relational Datacited by 8
  11. 2024Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflowcited by 8
  12. 2022GCNET: graph-based prediction of stock price movement using graph convolutional networkcited by 6
  13. 2025LLM-Based Routing in Mixture of Experts: A Novel Framework for Tradingcited by 6
  14. 2022Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligencecited by 5
  15. 2022Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Modelscited by 4
  16. 2026FinDeepForecast: A Live Multi-Agent System for Benchmarking Deep Research Agents in Financial Forecastingcited by 4
  17. 2023Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Surveycited by 3
  18. 2023ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Predictioncited by 3
  19. 2023Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Pricescited by 3
  20. 2021Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approachcited by 2
  21. 2022Multimodal Neural Network For Demand Forecastingcited by 2
  22. 2023Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Financecited by 2
  23. 2024CausalStock: Deep End-to-end Causal Discovery for News-driven Stock Movement Predictioncited by 2
  24. 2021Stock Market Analysis with Text Data: A Reviewcited by 1
  25. 2022Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approachcited by 1
  26. 2022Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Modelscited by 1
  27. 2024Pre-Finetuning with Impact Duration Awareness for Stock Movement Predictioncited by 1
  28. 2024Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Datacited by 1
  29. 2025Exploring the Synergy of Quantitative Factors and Newsflow Representations from Large Language Models for Stock Return Predictioncited by 1
  30. 2021COVID19-HPSMP: COVID-19 Adopted Hybrid and Parallel Deep Information Fusion Framework for Stock Price Movement Predictioncited by 0
  31. 2021Multivariate Realized Volatility Forecasting with Graph Neural Networkcited by 0
  32. 2022Deep Multiple Instance Learning For Forecasting Stock Trends Using Financial Newscited by 0
  33. 2023Improved Stock Price Movement Classification Using News Articles Based on Embeddings and Label Smoothingcited by 0
  34. 2023The Battle of Information Representations: Comparing Sentiment and Semantic Features for Forecasting Market Trendscited by 0
  35. 2023Towards systematic intraday news screening: a liquidity-focused approachcited by 0
  36. 2024Large Investment Modelcited by 0
  37. 2025Predicting Stock Movement with BERTweet and Transformerscited by 0
  38. 2025Cross-Modal Temporal Fusion for Financial Market Forecastingcited by 0

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