On the public citation graph
Spatiotemporal Signals in Residuals: A Two-Stage Boosting Graph Neural Network for Stock Return Prediction
0 signed · 26 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/0304-405x(93)90023-5openalex-snapshot
Common risk factors in the returns on stocks and bonds
inferredcites →https://doi.org/10.1016/j.asoc.2010.10.015openalex-snapshot
A novel hybridization of artificial neural networks and ARIMA models for time series forecasting
inferredcites →https://doi.org/10.1016/j.asoc.2022.109132openalex-snapshot
Dynamic forecasting of the Shanghai Stock Exchange index movement using multiple types of investor sentiment
inferredcites →https://doi.org/10.1016/j.asoc.2023.110595openalex-snapshot
A knowledge graph–GCN–community detection integrated model for large-scale stock price prediction
inferredcites →https://doi.org/10.1016/j.engappai.2022.105626openalex-snapshot
Forecasting stock market for an efficient portfolio by combining XGBoost and Hilbert–Huang transform
inferredcites →https://doi.org/10.1016/j.eswa.2019.01.083openalex-snapshot
Combining Principal Component Analysis, Discrete Wavelet Transform and XGBoost to trade in the financial markets
inferredcites →https://doi.org/10.1016/j.eswa.2023.122502openalex-snapshot
Forecasting carbon price trends based on an interpretable light gradient boosting machine and Bayesian optimization
inferredcites →https://doi.org/10.1016/j.ijforecast.2019.02.011openalex-snapshot
FFORMA: Feature-based forecast model averaging
inferredcites →https://doi.org/10.1016/j.ijforecast.2019.03.017openalex-snapshot
A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
inferredcites →https://doi.org/10.1016/j.ijforecast.2021.03.012openalex-snapshot
Temporal Fusion Transformers for interpretable multi-horizon time series forecasting
inferredcites →https://doi.org/10.1016/j.ins.2020.12.068openalex-snapshot
A novel graph convolutional feature based convolutional neural network for stock trend prediction
inferredcites →https://doi.org/10.1016/j.ins.2023.119236openalex-snapshot
Stock ranking prediction using a graph aggregation network based on stock price and stock relationship information
inferredcites →https://doi.org/10.1016/j.ins.2024.121824openalex-snapshot
Stock complex networks based on the GA-LightGBM model: The prediction of firm performance
inferredcites →https://doi.org/10.1016/j.jfineco.2014.10.010openalex-snapshot
A five-factor asset pricing model
inferredcites →https://doi.org/10.1016/j.jfineco.2021.08.017openalex-snapshot
Machine learning in the Chinese stock market
inferredcites →https://doi.org/10.1016/s0925-2312(01)00702-0openalex-snapshot
Time series forecasting using a hybrid ARIMA and neural network model
inferredcites →https://doi.org/10.1017/s0022109022001120openalex-snapshot
Machine Learning and the Stock Market
inferredcites →https://doi.org/10.1109/tassp.1978.1163055openalex-snapshot
Dynamic programming algorithm optimization for spoken word recognition
inferredcites →https://doi.org/10.1109/tnnls.2020.2978386openalex-snapshot
A Comprehensive Survey on Graph Neural Networks
inferredcites →https://doi.org/10.1111/jofi.13477openalex-snapshot
Forest through the Trees: Building Cross‐Sections of Stock Returns
inferredcites →https://doi.org/10.1214/aos/1013203451openalex-snapshot
Greedy function approximation: A gradient boosting machine.
inferredcites →https://doi.org/10.1609/aaai.v35i1.16077openalex-snapshot
Modeling the Momentum Spillover Effect for Stock Prediction via Attribute-Driven Graph Attention Networks
inferredcites →https://doi.org/10.1609/aaai.v35i1.16127openalex-snapshot
Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach
inferredcites →https://doi.org/10.1609/aaai.v38i13.29381openalex-snapshot
MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction
inferredcites →https://doi.org/10.1609/aaai.v38i8.28770openalex-snapshot
CI-STHPAN: Pre-trained Attention Network for Stock Selection with Channel-Independent Spatio-Temporal Hypergraph
inferredcites →https://doi.org/10.1609/aaai.v39i1.31993openalex-snapshot
FactorGCL: A Hypergraph-Based Factor Model with Temporal Residual Contrastive Learning for Stock Returns Prediction
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.