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

arXiv: https://arxiv.org/abs/1712.02136 · HTML: https://mlsanity.com/paper/1712.02136/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2017.

## Built on by (most cited first, 30 of 60)

- [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://mlsanity.com/paper/2402.18485/) (2024)
- [HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information](https://mlsanity.com/paper/2110.13716/) (2021)
- [Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models](https://mlsanity.com/paper/2402.03659/) (2024)
- [Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport](https://mlsanity.com/paper/2106.12950/) (2021)
- [REST: Relational Event-driven Stock Trend Forecasting](https://mlsanity.com/paper/2102.07372/) (2021)
- [Reinforcement Learning for Quantitative Trading](https://mlsanity.com/paper/2109.13851/) (2021)
- [DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting](https://mlsanity.com/paper/2306.09862/) (2023)
- [Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction](https://mlsanity.com/paper/2211.07400/) (2022)
- [Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction](https://mlsanity.com/paper/2310.05627/) (2023)
- [NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting](https://mlsanity.com/paper/2201.01770/) (2022)
- [DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities](https://mlsanity.com/paper/2201.09058/) (2021)
- [Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis](https://mlsanity.com/paper/2407.16150/) (2024)
- [Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks](https://mlsanity.com/paper/2201.04965/) (2022)
- [Stock Movement Prediction with Financial News using Contextualized Embedding from BERT](https://mlsanity.com/paper/2107.08721/) (2021)
- [Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model](https://mlsanity.com/paper/2206.06606/) (2022)
- [FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model](https://mlsanity.com/paper/2403.02647/) (2024)
- [MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization](https://mlsanity.com/paper/2210.01774/) (2022)
- [Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction](https://mlsanity.com/paper/2309.00073/) (2023)
- [StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series](https://mlsanity.com/paper/2301.09279/) (2023)
- [Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models](https://mlsanity.com/paper/2407.03689/) (2024)
- [From Deep Learning to LLMs: A survey of AI in Quantitative Investment](https://mlsanity.com/paper/2503.21422/) (2025)
- [Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data](https://mlsanity.com/paper/2107.10941/) (2021)
- [Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow](https://mlsanity.com/paper/2407.18103/) (2024)
- [LLM-Based Routing in Mixture of Experts: A Novel Framework for Trading](https://mlsanity.com/paper/2501.09636/) (2025)
- [GCNET: graph-based prediction of stock price movement using graph convolutional network](https://mlsanity.com/paper/2203.11091/) (2022)
- [Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence](https://mlsanity.com/paper/2301.04020/) (2022)
- [Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models](https://mlsanity.com/paper/2206.06320/) (2022)
- [FinDeepForecast: A Live Multi-Agent System for Benchmarking Deep Research Agents in Financial Forecasting](https://mlsanity.com/paper/2601.05039/) (2026)
- [Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey](https://mlsanity.com/paper/2308.04947/) (2023)
- [ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction](https://mlsanity.com/paper/2310.18706/) (2023)

## 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.