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 and optimization, and building automated trading strategies.
Chakri Cherukuri explains how machine learning and deep learning techniques are being used in quantitative finance. Chakri outlines use cases for machine learning in finance and dives into a few examples, involving both structured and unstructured datasets, to examine in detail how machine learning models can be used for predictive analytics. Chakri details how these models work under the hood and explores the interpretability of these models. Along the way, you’ll look at novel interactive visualizations and diagnostic plots that will help you 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 mechanical engineering from the Indian Institute of Technology (IIT) Madras, India, and an MS in computational finance from Carnegie Mellon University.
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