# MASTER: Market-Guided Stock Transformer for Stock Price Forecasting

arXiv: https://arxiv.org/abs/2312.15235 · HTML: https://mlsanity.com/paper/2312.15235/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2023.

## Built on by (most cited first, 30 of 32)

- [R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization](https://mlsanity.com/paper/2505.15155/) (2025)
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- [RegimeFolio: A Regime Aware ML System for Sectoral Portfolio Optimization in Dynamic Markets](https://mlsanity.com/paper/2510.14986/) (2025)
- [A Survey of Financial AI: Architectures, Advances and Open Challenges](https://mlsanity.com/paper/2411.12747/) (2024)
- [FinTexTS: Financial Text-Paired Time-Series Dataset via Semantic-Based and Multi-Level Pairing](https://mlsanity.com/paper/2603.02702/) (2026)
- [MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRU](https://mlsanity.com/paper/2410.20679/) (2024)
- [CSPO: Cross-Market Synergistic Stock Price Movement Forecasting with Pseudo-volatility Optimization](https://mlsanity.com/paper/2503.22740/) (2025)
- [On Evaluating Loss Functions for Stock Ranking: An Empirical Analysis With Transformer Model](https://mlsanity.com/paper/2510.14156/) (2025)
- [RefineBridge: Generative Bridge Models Improve Financial Forecasting by Foundation Models](https://mlsanity.com/paper/2512.21572/) (2025)
- [FactorEngine: A Program-level Knowledge-Infused Factor Mining Framework for Quantitative Investment](https://mlsanity.com/paper/2603.16365/) (2026)
- [Beyond Fixed Patches: Enhancing GPTs for Financial Prediction with Adaptive Segmentation and Learnable Wavelets](https://mlsanity.com/paper/2505.02880/) (2025)
- [Mitigating Distribution Shift in Stock Price Data via Return-Volatility Normalization for Accurate Prediction](https://mlsanity.com/paper/2508.20108/) (2025)
- [Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting](https://mlsanity.com/paper/2509.23668/) (2025)
- [Integrating Inductive Biases in Transformers via Distillation for Financial Time Series Forecasting](https://mlsanity.com/paper/2603.16985/) (2026)
- [Vector-Quantized Discrete Latent Factors Meet Financial Priors: Dynamic Cross-Sectional Stock Ranking Prediction for Portfolio Construction](https://mlsanity.com/paper/2605.13407/) (2026)
- [Adapting to the Unknown: Robust Meta-Learning for Zero-Shot Financial Time Series Forecasting](https://mlsanity.com/paper/2504.09664/) (2025)
- [Why Regression? Binary Encoding Classification Brings Confidence to Stock Market Index Price Prediction](https://mlsanity.com/paper/2506.03153/) (2025)
- [From Patterns to Predictions: A Shapelet-Based Framework for Directional Forecasting in Noisy Financial Markets](https://mlsanity.com/paper/2509.15040/) (2025)
- [Stock Prediction via a Dual Relation Fusion Network incorporating Static and Dynamic Relations](https://mlsanity.com/paper/2510.10695/) (2025)
- [Generalized Stock Price Prediction for Multiple Stocks Combined with News Fusion](https://mlsanity.com/paper/2603.19286/) (2026)
- [RAVEN: A Regime-Aware Variable-context Expert Network for Financial Time Series Forecasting](https://mlsanity.com/paper/2606.24062/) (2026)
- [Emergent Latent-State Computation under Stochastic Volatility](https://mlsanity.com/paper/2607.25459/) (2026)
- [Nystr"om Attention Matches Full Attention for Cross-Sectional Stock Prediction](https://mlsanity.com/paper/2609.08106/) (2026)
- [AlphaDiverse: Post-Training Local Quantitative Research Agents for Diverse Exploration in Alpha Factor Mining](https://mlsanity.com/paper/2609.29014/) (2026)
- [RICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting](https://mlsanity.com/paper/2609.34004/) (2026)
- [Volatility-Clustering Adaptation for Financial Time Series](https://mlsanity.com/paper/2609.37715/) (2026)
- [DualCast: A Dual-Path Language Model for Bimodal Financial Time-Series Forecasting](https://mlsanity.com/paper/2609.38197/) (2026)
- [DIVINE: Simple Cross-Market Stock Pretraining via Diverse Indicator Reconstruction](https://mlsanity.com/paper/2610.02866/) (2026)
- [STOCK-JEPA: Prior-Anchored Latent Revision Representation Learning in Equity Markets](https://mlsanity.com/paper/2610.07006/) (2026)

## Abstract (arXiv)

Stock price forecasting has remained an extremely challenging problem for many decades due to the high volatility of the stock market. Recent efforts have been devoted to modeling complex stock correlations toward joint stock price forecasting. Existing works share a common neural architecture that learns temporal patterns from individual stock series and then mixes up temporal representations to establish stock correlations. However, they only consider time-aligned stock correlations stemming from all the input stock features, which suffer from two limitations. First, stock correlations often occur momentarily and in a cross-time manner. Second, the feature effectiveness is dynamic with market variation, which affects both the stock sequential patterns and their correlations. To address the limitations, this paper introduces MASTER, a MArkert-Guided Stock TransformER, which models the momentary and cross-time stock correlation and leverages market information for automatic feature selection. MASTER elegantly tackles the complex stock correlation by alternatively engaging in intra-stock and inter-stock information aggregation. Experiments show the superiority of MASTER compared with previous works and visualize the captured realistic stock correlation to provide valuable insights.