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) 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.
Table of contents
- Title: Applied machine learning in finance - 2019 O'Reilly Strata Data Conference, San Francisco
- Release date: October 2019
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 0636920332480
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