Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed by both 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 etc. 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 in finance, where machine learning techniques can be applied. Then we will pick a few use cases, involving both structured and unstructured data sets and examine in detail how machine learning models can be used for predictive analytics.
The main focus of the talk will be on understanding how these models work under the hood and on the interpretability of these models. We’ll look at novel interactive visualizations and diagnostic plots to help us better understand these models.
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 engineering from the Indian Institute of Technology (IIT) Madras, India and an MS in computational finance from Carnegie Mellon University.
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