Signature transform has recently gained significant attention in the communities of finance and stochastic analysis. In this talk, I will discuss how the signature transform can be exploited to address several long-standing challenges in financial time series data analysis.

Financial data are typically non-stationary, nonlinear, and often fragmented. At the same time, modern deep learning models often suffer from limited interpretability and in principle require large volumes of training data.

In this talk, we propose a simple signature-based adaptive Lasso approach that has been successfully developed and implemented in industry. This method addresses many of the challenges mentioned above while demonstrating strong potential for a wide range of applications. We begin with a brief introduction to the signature transform, which has its origins in topology and has been extensively developed within rough path theory. We will then review the key properties of the signature transform that are most relevant to our statistical methodology. The talk is intended to be self-contained.

Bio: Professor Xin Guo is the Coleman Fung Chair Professor in Financial Modeling in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley, where she also serves as Department Chair. An internationally recognized leader with fundamental contributions to financial mathematics, stochastic processes, stochastic control, game theory, and machine learning, her work bridges mathematics, engineering, and data science to address real-world problems in finance, medicine, and engineering. Professor Guo is a frequent plenary and keynote speaker at major conferences in financial and applied mathematics. She is the co-founder of Women in Financial Mathematics and serves on the editorial boards of a number of leading journals, including Mathematics of Operations Research, SIAM Control and Optimization, and Mathematical Finance.