Semyon Malamud Lectures

Malamud Lectures

Foundations of Big Data and Machine Learning
in Finance, Statistics, and Beyond

Mon - Thu, Sept 22-25
Mon - Wed, Sept 29 - Oct 1

Registration is not required but is appreciated.

Semyon Malamud
Senior Chair, Swiss Finance Institute
Associate Professor, EPFL
Research Fellow, CEPR
semyon.malamud@epfl.ch

Time: 4:00-6:00 p.m.
Location: McNair Hall 318

Overview

Semyon Malamud is a distinguished contributor to the literature on machine learning applications for investment management, and, together with Bryan Kelly (Tanner Professor of Finance, Yale and Head of Machine Learning, AQR), he has done path-breaking theoretical and empirical work on the virtues of complex models for understanding asset returns. We are delighted that he will share his knowledge with us in a series of lectures this September that are geared to appeal to students and faculty in Finance, Statistics, Computer Science, Economics, and related disciplines.

Learning Outcomes

Modeling with High-Dimensional Predictors
Understand modern methods for large-scale data and factor models in finance
Overfitting and Complexity
Recognize the role of over-parameterized models in predictive performance, portfolio choice, and decision making
Neural Networks
Interpret deep vs. shallow learning architectures and their application to financial data. Deep learning and feature learning
Factor Models
Construct and analyze high-dimensional factor models for the cross-section of returns
Equilibrium
Assess how complexity corrections change asset pricing fundamentals and how complexity leads to tractable equilibria with non-linear parameter learning