Ajim Uddin

Ajim Uddin recently completed a Ph.D. in Business Data Science at the New Jersey Institute of Technology. His research area is FinTech and the application of machine learning to finance, with a special focus on financial networks. He has applied his research toward the development of a dynamic graph learning framework for asset pricing and incorporating signed networks, i.e., positive and negative connections, in equity pricing models. He is also working on understanding the influence of network connections among decision-makers in the financial decision-making process. This includes designing a network projection model to examine how being connected to more managers or connected to prominent managers influences institutional investors' herding behavior and portfolio performance. His work has been published in peer-reviewed journals such as Quantitative Finance, Global Finance Journal and Finance Research Letters. He also received the "Best Contribution to Theory" and "Overall Best Conference Paper" awards at the 51st Annual Conference of the Northeast Decision Sciences Institute.

Ph.D.; New Jersey Institute of Technology; Business Data Science; 2022

M.S.; Southern Illinois University Edwardsville; Economics and Finance; 2017

M.B.A.; Shahjalal University of Science & Technology; Finance and Banking; 2016

B.B.A.; Shahjalal University of Science & Technology; Finance and Banking; 2013

https://web.njit.edu/~au76/

FIN 315 - FUND OF CORPORATE FINANCE

FIN 611 - INTRO TO TOPICS IN FIN TECH

BDS 792B - PRE-DOCTORAL RESEARCH

Dr. Uddin’s teaching interests include Financial Technology (FinTech), Data Mining and Machine Learning for Finance, and Financial Data Analytics.

FinTech, Application of Machine Learning in Asset Pricing, Graph Neural Network

Uddiin, Ajim, & Tao, Xinyuan, & Yu, Dantong (2024). The network factor of equity pricing: a signed graph laplacian approach. Journal of Financial Econometrics ,

Uddiin, Ajim, & Tao, Xinyuan, & Yu, Dantong (2024). The network factor of equity pricing: a signed graph laplacian approach. Journal of Financial Econometrics ,

Uddiin, Ajim, & Tao, Xinyuan, & Yu, Dantong (2023). Attention based dynamic graph neural network for asset pricing. Global Finance Journal, 58,

Abdullah, Mohammad, & Chowdhury, Mohammad Ashraful Ferdous , & Uddiin, Ajim, & Moudud-Ul-Huq, Syed (2023). Forecasting Non-Performing Loans Using Machine Learning. Journal of Forecasting, 42(7),

Uddiin, Ajim, & Tao, Xinyuan, & Yu, Dantong (2023). Attention Based Dynamic Graph Neural Network for Asset Pricing. Global Finance Journal, 58(November 2023),

A Fast Non-Linear Coupled Tensor Completion Algorithm for Financial Data Integration and Imputation
4th ACM International Conference on AI in Finance: ICAIF'23, November 2023

A Fast Non-Linear Coupled Tensor Completion Algorithm for Financial Data Integration and Imputation
4th ACM International Conference on AI in Finance: ICAIF'23, November 2023

NMTucker: Non-linear Matryoshka Tucker Decomposition for Financial Time Series Imputation
ACM, November 2023

The Network of Mutual Funds: A Dynamic Heterogeneous Graph Neural Network for Estimating Mutual Funds Performance
ACM, November 2023

Core Matrix Regression and Prediction with Regularization
November 2022

South Asian Journal of Business Studies
Editor, Associate Editor, 2024