Applied Machine Learning in Finance

Recorded at Rev 2 | May 23-24, 2019 | New York Day 2 - Practitioner - Bloomberg Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed by both the 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. My talk will focus on 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 involving both structured and unstructured data sets in finance, where machine learning techniques can be applied. Then we will pick a few case studies and examine in detail how machine learning models can be applied for predictive analytics. We’ll look at interactive plots running in Jupyter notebooks. The main focus of the talk will be on reproducible research and model interpretability.