mlsanityData to 14 Oct 2026

Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data

arXiv 2107.10941 ↗First posted 22 Jul 2021cs.CL q-fin.STcited by 8

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Abstract (arXiv)

Predicting stock prices from textual information is a challenging task due to the uncertainty of the market and the difficulty understanding the natural language from a machine's perspective. Previous researches focus mostly on sentiment extraction based on single news. However, the stocks on the financial market can be highly correlated, one news regarding one stock can quickly impact the prices of other stocks. To take this effect into account, we propose a new stock movement prediction framework: Multi-Graph Recurrent Network for Stock Forecasting (MGRN). This architecture allows to combine the textual sentiment from financial news and multiple relational information extracted from other financial data. Through an accuracy test and a trading simulation on the stocks in the STOXX Europe 600 index, we demonstrate a better performance from our model than other benchmarks.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 20 of its 83 references that are in the map. First posted on arXiv 22 Jul 2021.

Builds on

20 of 83 references.

References · 20

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8 more
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Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 3; 2024: 2.

8 papers.

  1. 2024MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Predictioncited by 18
  2. 2022DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutionscited by 6
  3. 2023Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networkscited by 4
  4. 2024Stock Movement Prediction with Multimodal Stable Fusion via Gated Cross-Attention Mechanismcited by 3
  5. 2022On the universality of the volatility formation process: when machine learning and rough volatility agreecited by 1
  6. 2023Exploiting Supply Chain Interdependencies for Stock Return Prediction: A Full-State Graph Convolutional LSTMcited by 1
  7. 2022Towards mapping the contemporary art world with ArtLM: an art-specific NLP modelcited by 0
  8. 2023Learning to Predict Short-Term Volatility with Order Flow Image Representationcited by 0