Applied Machine Learning in Finance
2:30 - 3:00 | Friday, May 24
Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed by both the sell-side and buy-side institutions. Techniques like time-series analysis, stochastic calculus, multivariate statistics and numerical optimization are often used by “quants” for modeling asset prices, portfolio construction/optimization, building automated trading strategies. My talk will focus on the how machine learning and deep learning techniques are being used in this field.
In the first part of the talk, we will look at use cases involving both structured and unstructured data sets in finance, where machine learning techniques can be applied. Then we will pick a few case studies and examine in detail how machine learning models can be applied for predictive analytics.
We’ll look at interactive plots running in Jupyter notebooks. The main focus of the talk will be on reproducible research and model interpretability.
Sr. Quantitative Researcher, Bloomberg
Chakri Cherukuri is a senior researcher in the Quantitative Financial Research group at Bloomberg LP in NYC. His research interests include quantitative portfolio management, algorithmic trading strategies and applied machine learning. He has extensive experience in scientific computing and software development. Previously, he built analytical tools for the trading desks at Goldman Sachs and Lehman Brothers. He holds an undergraduate degree in mechanical engineering from the Indian Institute of Technology (IIT) Madras, India and an MS in computational finance from Carnegie Mellon University.