MCFAM Seminar Series: “Iterated and exponentially weighted moving principal component analysis"
Please join us for our last seminar of fall semester either in person or virtually! This seminar will feature Paul Bilokon from Imperial College London who will discuss iterated and exponentially weighted moving principal component analysis.
Note: Refreshments will be served to those who attend in person in Vincent Hall, Room 211.
Paul Bilokon
Lecturer in Mathematics for Computer Science
Imperial College London
About the lecture
The principal component analysis (PCA) is a staple statistical and unsupervised machine learning technique in finance. The application of PCA in a financial setting is associated with several technical difficulties, such as numerical instability and nonstationarity. Bilokon and his colleagues attempt to resolve them by proposing two new variants of PCA: an iterated principal component analysis (IPCA) and an exponentially weighted moving principal component analysis (EWMPCA). Both variants rely on the Ogita–Aishima iteration as a crucial step.
About the speaker
Paul Bilokon is the CEO and Founder of Thalesians Ltd and an academic at Imperial College, where his work focuses on machine learning (ML), high-performance computing, big data, and electronic trading. His career in quantitative finance spans Morgan Stanley, Lehman Brothers, Nomura, Citigroup, Deutsche Bank, and BNP Paribas, and he and his team at Thalesians continue to provide consulting services to numerous financial institutions, both on the buy-side and sell-side. He is one of the e-credit pioneers and has co-authored several books, including Machine Learning in Finance: From Theory to Practice and Big Data and High-Frequency Data with kdb+/q. Paul is fluent in C++, Java, Python, and kdb+/q and enjoys building distributed software systems powered by ML and applied mathematics.