Buchumschlag

SGAT‐SP: Sparse Graph Attention Network for Stock Prediction

Fuente: Wiley Open Access

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Gautam Gupta, Priyanshi Goel, Divyanshi Verma, Amarjit Malhotra
Format:	Artículo Open Access
Veröffentlicht:	Wiley 2026
Schlagworte:	Journal of Forecasting
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Stock Price Limit and Its Predictability in the Chinese Stock Market
von: Haohui Liang, et al.
Veröffentlicht: (2024)

Economic Conditions and Predictability of US Stock Returns Volatility: Local Factor Versus National Factor in a GARCH‐MIDAS Model
von: Afees A. S alisu, et al.
Veröffentlicht: (2025)

Stock Return Prediction Based on a Functional Capital Asset Pricing Model
von: Ufuk Beyaztas, et al.
Veröffentlicht: (2025)

Image‐Based Deep Learning Models for Stock Predictions: Combining Line, Candlestick, and Bar Charts
von: Wei‐Chao Lin, et al.
Veröffentlicht: (2026)

Decomposing, Learning, and Predicting Realized Volatilities: A Comparison Analysis From the Global Stock Markets
von: Wei Zhou, et al.
Veröffentlicht: (2025)

On Capturing Multi‐Scale Market Dynamics for High‐Frequency Stock Price Forecasting Using a Hybrid Attention‐Based Deep Learning Model
von: Runze Jiang, et al.
Veröffentlicht: (2026)

Stock Portfolio Management Based on AI Technology
von: Alejandro Moreno Alonso, et al.
Veröffentlicht: (2025)

Stock Return Forecasting: A Supervised PCA With Selecting and Scaling
von: Ting Zhang, et al.
Veröffentlicht: (2025)

Forecasting Chinese Stock Market Volatility With Volatilities in Bond Markets
von: Likun Lei, et al.
Veröffentlicht: (2024)

Stock movement prediction: A multi‐input LSTM approach
von: Pan Tang, et al.
Veröffentlicht: (2024)

Information Illusion: Different Amounts of Information and Stock Price Estimates
von: Andreas Oehler, et al.
Veröffentlicht: (2025)

Forecasting the Realized Volatility of Stock Markets: The Roles of Jumps and Asymmetric Spillovers
von: Abdel Razzaq Al Rababaa, et al.
Veröffentlicht: (2024)

Adaptive Model Integration for Stock Price Forecasting With CNN‐ATTN‐BiLSTM
von: Yi Xiao, et al.
Veröffentlicht: (2026)

Forecasting Volatility of Australian Stock Market Applying WTC‐DCA‐Informer Framework
von: Hongjun Zeng, et al.
Veröffentlicht: (2025)

Predicting Enterprise Bankruptcy With HBA‐DGNN: An Innovative Approach by Hypergraph and Bidirectional Attention‐Based Dual GNNs
von: Yuhao Zhu, et al.
Veröffentlicht: (2026)

The Information Content of Overnight Information for Volatility Forecasting: Evidence From China's Stock Market
von: Yi Zhang, et al.
Veröffentlicht: (2025)

Sectoral Corporate Profits and Long‐Run Stock Return Volatility in the United States: A GARCH‐MIDAS Approach
von: Afees Salisu, et al.
Veröffentlicht: (2024)

Dual‐Branch Spectral‐Trend Attention Network With Gated Flux–Momentum Decomposition for Multiscale Financial Time‐Series Forecasting
von: Pradeep Singh, et al.
Veröffentlicht: (2026)

Exploring the Nexus Between Sustainability Index and Central European Stock Markets Competitiveness: Evidence Through Time–Frequency Analysis and SHAP
von: Zuzana Janková, et al.
Veröffentlicht: (2026)

Matrix Autoregressive Time Series With Reduced‐Rank and Sparse Structural Constraints
von: Xiaohang Wang, et al.
Veröffentlicht: (2025)

A Quantification Approach of Changes in Firms' Financial Situation Using Neural Networks for Predicting Bankruptcy
von: Philippe du Jardin
Veröffentlicht: (2024)

Novel Aligned Correlation Method to Estimate Lead–Lag Relationship Between Time Series
von: Kartikay Gupta, et al.
Veröffentlicht: (2025)

Can Attention Mechanisms Improve Carbon Price Forecasting Accuracy?
von: Ting Yao, et al.
Veröffentlicht: (2025)

Ensemble Multitask Prediction of Air Pollutants Time Series: Based on Variational Inference, Data Projection, and Generative Adversarial Network
von: Kang Wang, et al.
Veröffentlicht: (2024)

Forecasting the realized volatility of agricultural commodity prices: Does sentiment matter?
von: Matteo Bonato, et al.
Veröffentlicht: (2024)

Media and Business Cycle Predictability
von: Salim Baz, et al.
Veröffentlicht: (2026)

Leveraging an Integrated First and Second Moments Modeling Approach for Optimal Trading Strategies: Evidence From the Indian Pharma Sector in the Pre‐ and Post‐COVID‐19 Era
von: Himanshu Kautkar, et al.
Veröffentlicht: (2025)

Fire Prediction and Risk Identification With Interpretable Machine Learning
von: Shan Dai, et al.
Veröffentlicht: (2025)

Default Prediction Framework With Optimal Feature Set and Matching Ratio
von: Guotai Chi, et al.
Veröffentlicht: (2025)

A Novel Interpretable Deep Learning‐Based Wind Speed and Power Generation Forecasting Using Multiscale Attention and Post Hoc Feature Importance Mechanism
von: Haoyu Fang, et al.
Veröffentlicht: (2025)

Spread Option Pricing Method Based on Nonparametric Predictive Inference Copula
von: Ting He
Veröffentlicht: (2025)

Robust Prediction Intervals for Time Series Forecasting: A Bootstrap and Bayesian Approach
von: Betty X.Y. Chu, et al.
Veröffentlicht: (2026)

Enhancing Volatility Prediction: A Wavelet‐Based Hierarchical Forecast Reconciliation Approach
von: Adam Clements, et al.
Veröffentlicht: (2026)

A Two‐Stage Interpretable Model to Explain Classifier in Credit Risk Prediction
von: Lu Wang, et al.
Veröffentlicht: (2025)

Global Insights Into Term Spreads: Unveiling Their Predictive Power During Unconventional Monetary Policy
von: Petri Kuosmanen, et al.
Veröffentlicht: (2026)

Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models
von: Saptarshi Ganguly, et al.
Veröffentlicht: (2026)

Predicting tail risks by a Markov switching MGARCH model with varying copula regimes
von: Markus J. Fülle, et al.
Veröffentlicht: (2024)

New forecasting methods for an old problem: Predicting 147 years of systemic financial crises
von: Emile du Plessis, et al.
Veröffentlicht: (2024)

Deep Dive Into Churn Prediction in the Banking Sector: The Challenge of Hyperparameter Selection and Imbalanced Learning
von: Vasileios Gkonis, et al.
Veröffentlicht: (2024)

A Frailty Cumulative Link Model for Enhanced Prediction of Loss Given Default Distribution
von: Ruey‐Ching Hwang, et al.
Veröffentlicht: (2025)