# REST: Relational Event-driven Stock Trend Forecasting

arXiv: https://arxiv.org/abs/2102.07372 · HTML: https://mlsanity.com/paper/2102.07372/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [Temporal Relational Ranking for Stock Prediction](https://mlsanity.com/paper/1809.09441/) (2018)
- [Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction](https://mlsanity.com/paper/1712.02136/) (2017)
- [HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction](https://mlsanity.com/paper/1908.07999/) (2019)
- [Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis](https://mlsanity.com/paper/1909.10660/) (2019)
- [Can Online Emotions Predict the Stock Market in China?](https://mlsanity.com/paper/1604.07529/) (2016)

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

- [UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting](https://mlsanity.com/paper/2310.09751/) (2023)
- [HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information](https://mlsanity.com/paper/2110.13716/) (2021)
- [R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization](https://mlsanity.com/paper/2505.15155/) (2025)
- [Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning](https://mlsanity.com/paper/2306.12964/) (2023)
- [Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction](https://mlsanity.com/paper/2305.08740/) (2023)
- [AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors](https://mlsanity.com/paper/2406.18394/) (2024)
- [Automate Strategy Finding with LLM in Quant Investment](https://mlsanity.com/paper/2409.06289/) (2024)
- [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)
- [Navigating the Alpha Jungle: An LLM-Powered MCTS Framework for Formulaic Factor Mining](https://mlsanity.com/paper/2505.11122/) (2025)
- [Cognitive Alpha Mining via LLM-Driven Code-Based Evolution](https://mlsanity.com/paper/2511.18850/) (2025)
- [LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU](https://mlsanity.com/paper/2409.08282/) (2024)
- [From Deep Learning to LLMs: A survey of AI in Quantitative Investment](https://mlsanity.com/paper/2503.21422/) (2025)
- [LLM-Based Routing in Mixture of Experts: A Novel Framework for Trading](https://mlsanity.com/paper/2501.09636/) (2025)
- [Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence](https://mlsanity.com/paper/2301.04020/) (2022)
- [Modeling News Interactions and Influence for Financial Market Prediction](https://mlsanity.com/paper/2410.10614/) (2024)
- [E2EAI: End-to-End Deep Learning Framework for Active Investing](https://mlsanity.com/paper/2305.16364/) (2023)
- [MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRU](https://mlsanity.com/paper/2410.20679/) (2024)
- [Learning Universal Multi-level Market Irrationality Factors to Improve Stock Return Forecasting](https://mlsanity.com/paper/2502.04737/) (2025)
- [CSPO: Cross-Market Synergistic Stock Price Movement Forecasting with Pseudo-volatility Optimization](https://mlsanity.com/paper/2503.22740/) (2025)
- [SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting](https://mlsanity.com/paper/2602.16220/) (2026)
- [Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey](https://mlsanity.com/paper/2308.04947/) (2023)
- [FactorEngine: A Program-level Knowledge-Infused Factor Mining Framework for Quantitative Investment](https://mlsanity.com/paper/2603.16365/) (2026)
- [Factor Investing with a Deep Multi-Factor Model](https://mlsanity.com/paper/2210.12462/) (2022)
- [MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading](https://mlsanity.com/paper/2407.01577/) (2024)
- [EventTSF: Event-Aware Non-Stationary Time Series Forecasting](https://mlsanity.com/paper/2508.13434/) (2025)
- [SVTime: Small Time Series Forecasting Models Informed by "Physics" of Large Vision Model Forecasters](https://mlsanity.com/paper/2510.09780/) (2025)
- [KAIROS: Unified Training for Universal Non-Autoregressive Time Series Forecasting](https://mlsanity.com/paper/2510.02084/) (2025)
- [KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning](https://mlsanity.com/paper/2602.20494/) (2026)
- [AlphaG-OPD: Reliability-Gated Sibling Counterfactuals for On-Policy Distillation in Symbolic Alpha Factor Discovery](https://mlsanity.com/paper/2608.01303/) (2026)

## Abstract (arXiv)

Stock trend forecasting, aiming at predicting the stock future trends, is crucial for investors to seek maximized profits from the stock market. Many event-driven methods utilized the events extracted from news, social media, and discussion board to forecast the stock trend in recent years. However, existing event-driven methods have two main shortcomings: 1) overlooking the influence of event information differentiated by the stock-dependent properties; 2) neglecting the effect of event information from other related stocks. In this paper, we propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts. To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks. The experimental studies on the real-world data demonstrate the efficiency of our REST framework. The results of investment simulation show that our framework can achieve a higher return of investment than baselines.