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 (Bloomberg LP) demonstrates how to apply machine learning techniques in quantitative finance, covering use cases involving both structured and alternative datasets. The focus of the talk will be on promoting reproducible research (through Jupyter notebooks and interactive plots) and interpretable models.
This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
Table of contents
- Title: Applied machine learning in finance - 2019 Artificial Intelligence Conference, New York
- Release date: October 2019
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 0636920339731
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