Ajim Uddin

Orcid: 0000-0002-3745-5194

According to our database1, Ajim Uddin authored at least 11 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
DySTAGE: Dynamic Graph Representation Learning for Asset Pricing via Spatio-Temporal Attention and Graph Encodings.
Proceedings of the 5th ACM International Conference on AI in Finance, 2024

2023
HODL: The Hold of Reddit Over the Stock Market.
Proceedings of the 39th IEEE International Conference on Data Engineering, ICDE 2023, 2023

A Fast Non-Linear Coupled Tensor Completion Algorithm for Financial Data Integration and Imputation.
Proceedings of the 4th ACM International Conference on AI in Finance, 2023

NMTucker: Non-linear Matryoshka Tucker Decomposition for Financial Time Series Imputation.
Proceedings of the 4th ACM International Conference on AI in Finance, 2023

The Network of Mutual Funds: A Dynamic Heterogeneous Graph Neural Network for Estimating Mutual Funds Performance.
Proceedings of the 4th ACM International Conference on AI in Finance, 2023

2022
Temporal Bipartite Graph Neural Networks for Bond Prediction.
Proceedings of the 3rd ACM International Conference on AI in Finance, 2022

Core Matrix Regression and Prediction with Regularization.
Proceedings of the 3rd ACM International Conference on AI in Finance, 2022

Machine Learning for Earnings Prediction: A Nonlinear Tensor Approach for Data Integration and Completion.
Proceedings of the 3rd ACM International Conference on AI in Finance, 2022

2021
MLCTR: A Fast Scalable Coupled Tensor Completion Based on Multi-Layer Non-Linear Matrix Factorization.
CoRR, 2021

Attention Based Dynamic Graph Learning Framework for Asset Pricing.
Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

2020
Nonlinear Tensor Completion Using Domain Knowledge: An Application in Analysts' Earnings Forecast.
Proceedings of the 20th International Conference on Data Mining Workshops, 2020


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