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

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 (2023). Attention Based Dynamic Graph Neural Network for Asset Pricing. Global Finance Journal, 58(November 2023),

Gu, Jingyi , & Shukla, Sarvesh , & Ye, Junyi, & Uddiin, Ajim, & Wang, Guiling (2023). Deep Learning Model with VADER Sentiment Score in Stock Price Prediction. SN Business & Economics, 3(119),

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),

Fang, Ming, & Taylor, Stephen, & Uddiin, Ajim (2022). The network structure of overnight index swap rates. Finance Research Letters, 46,

Uddiin, Ajim, & Tao, Xinyuan, & Chou, Chia, & Yu, Dantong (2022). Are missing values important for earnings forecasts? A machine learning perspective. Quantitative Finance, 22(6), 1113-1132.

Chowdhury, Mohammad, & Meo, Muhammad, & Uddiin, Ajim, & Haque, Md (2021). Asymmetric effect of energy price on commodity price: New evidence from NARDL and time frequency wavelet approaches. Energy, 231,

Uddiin, Ajim, & Yu, Dantong (2020). Latent factor model for asset pricing. Journal of Behavioral and Experimental Finance, 27,

Core Matrix Regression and Prediction with Regularization
November 2022

Machine Learning for Earnings Prediction: A Nonlinear Tensor Approach for Data Integration and Completion
November 2022

Temporal Bipartite Graph Neural Networks for Bond Prediction
November 2022

Attention Based Dynamic Graph Learning Framework for Asset Pricing
October (4th Quarter/Autumn) 2021

Nonlinear Tensor Completion Using Domain Knowledge: An Application in Analysts' Earnings Forecast
November 2020