Dr. Ali Habibnia
Ph.D. in Statistics (Time Series and Statistical Learning) at London School of Economics
In this talk, we consider forecasting in big data environments. We develop a category of high-dimensional nonlinear forecasting to benefit from many potential predictors while accounting for any possible nonlinear dynamics within the environment. Combining these two elements is a developing area of research. To overcome the curse of dimensionality and to manage data and model complexity, we suggest a shrinkage estimation of a backpropagation algorithm of a neural network with skip-layer connections including both linear and nonlinear structure. We apply this approach to forecast equity returns, and show that capturing nonlinear dynamics between equities significantly improves the quality of forecasts over current univariate and multivariate factor models.
All students and enthusiasts are invited to attend this lecture
Ali Habibnia is a Ph.D. in Statistics (Time Series and Statistical Learning) at London School of Economics. He received his MSc in Quantitative Finance from Cass Business School, and he also holds an MSc and a BA in Economics from the University of Tehran. His research focuses on the intersection of statistics, machine learning, and big data analytics, with a particular interest in the high-dimensional nonlinear time series analysis and their applications in financial forecasting and identifying risk in highly interconnected financial networks