Video description
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
Product information
- Title: Applied machine learning in finance - 2019 Artificial Intelligence Conference, New York
- Author(s):
- Release date: October 2019
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 0636920339731
You might also like
video
Applied machine learning in finance - 2019 O'Reilly Strata Data Conference, San Francisco
Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed …
book
Machine Learning for Finance
A guide to advances in machine learning for financial professionals, with working Python code Key Features …
video
Machine Learning for Finance
Machine Learning for Finance is a perfect course for financial professionals entering the fintech domain. It …
book
Big Data and Machine Learning in Quantitative Investment
Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment …
