In this talk we will discuss the evolution of the machine learning landscape from the perspective of the global financial industry. We will describe the development route of several Bloomberg machine learning projects, such as sentiment analysis, prediction of market impact, social media monitoring and question answering. We will show that these interdisciplinary problems lie at the intersection of linguistics, finance, computer science and mathematics, requiring input from signal processing, machine vision and other fields. We will talk about the methods, problem formulation, and throughout, talk about practicalities of delivering machine learning solutions to problems of finance, emphasizing issues such as appropriate problem decomposition, validation and interpretability. We will also summarize the current state and discuss possible future directions for the applications of natural language processing and machine learning methods in finance.
Karan Uppal is a Software Engineer in Bloomberg's ML – Text Analysis group.