# Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks

arXiv: https://arxiv.org/abs/2201.04965 · HTML: https://mlsanity.com/paper/2201.04965/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 63 references that are in the map.
- First posted on arXiv 11 Jan 2022.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [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)
- [CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets](https://mlsanity.com/paper/2104.04041/) (2021)
- [Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models](https://mlsanity.com/paper/1702.01978/) (2017)
- [Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction](https://mlsanity.com/paper/2005.04955/) (2020)
- [Improving Stock Market Prediction via Heterogeneous Information Fusion](https://mlsanity.com/paper/1801.00588/) (2018)
- [Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization](https://mlsanity.com/paper/1406.7330/) (2014)
- [Event-Driven Learning of Systematic Behaviours in Stock Markets](https://mlsanity.com/paper/2010.15586/) (2020)
- [Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes](https://mlsanity.com/paper/2006.14209/) (2020)

## Built on by (most cited first, 13 of 13)

- [A Survey of Financial AI: Architectures, Advances and Open Challenges](https://mlsanity.com/paper/2411.12747/) (2024)
- [Graph Learning and Its Advancements on Large Language Models: A Holistic Survey](https://mlsanity.com/paper/2212.08966/) (2022)
- [MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRU](https://mlsanity.com/paper/2410.20679/) (2024)
- [Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey](https://mlsanity.com/paper/2308.04947/) (2023)
- [Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations](https://mlsanity.com/paper/2412.04034/) (2024)
- [TimeCNN: Refining Cross-Variable Interaction on Time Point for Time Series Forecasting](https://mlsanity.com/paper/2410.04853/) (2024)
- [STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Trading](https://mlsanity.com/paper/2412.09468/) (2024)
- [MEME: Modeling the Evolutionary Modes of Financial Markets](https://mlsanity.com/paper/2602.11918/) (2026)
- [Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning](https://mlsanity.com/paper/2211.01346/) (2022)
- [ESIE-BERT: Enriching Sub-words Information Explicitly with BERT for Joint Intent Classification and SlotFilling](https://mlsanity.com/paper/2211.14829/) (2022)
- [Probabilistic Learning of Multivariate Time Series with Temporal Irregularity](https://mlsanity.com/paper/2306.09147/) (2023)
- [Transferable Graph Condensation from the Causal Perspective](https://mlsanity.com/paper/2601.21309/) (2026)
- [Financial Language Models as Applied Artificial Intelligence Systems for News-Based Trading under Market Frictions](https://mlsanity.com/paper/2609.23703/) (2026)

## Abstract (arXiv)

Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically only learn the simple connection information among related companies, which inevitably fail to model complex relations of listed companies in the real financial market. To address this issue, we first construct a more comprehensive Market Knowledge Graph (MKG) which contains bi-typed entities including listed companies and their associated executives, and hybrid-relations including the explicit relations and implicit relations. Afterward, we propose DanSmp, a novel Dual Attention Networks to learn the momentum spillover signals based upon the constructed MKG for stock prediction. The empirical experiments on our constructed datasets against nine SOTA baselines demonstrate that the proposed DanSmp is capable of improving stock prediction with the constructed MKG.