Due to space restrictions at the event location, this event is invitation only. Please request an invitation at https://datascienceinfinance.splashthat.com
Financial firms are taking AI and machine learning seriously to augment traditional investment decision making. Alternative datasets including text analytics, cloud computing, algorithmic trading are game changers for many firms who are adopting technology at a rapid pace. As more and more open-source technologies penetrate enterprises, quants and data scientists have a plethora of choices for building, testing and scaling quantitative models. Even though there are multiple solutions and platforms available to build machine learning solutions, challenges remain in adopting machine learning in the enterprise.In this talk we will illustrate a step-by-step process to enable replicable AI/ML research within the enterprise using QuSandbox.
In Part 1, we will discuss the challenges and best practices of adopting data science and machine learning solutions in financial companies.
In Part 2, we will demonstrate a case study in Python to use Natural Language Processing techniques to analyze EDGAR call earnings transcripts that could be used to generate sentiment analysis scores using the Amazon Comprehend, IBM Watson, Google and Azure APIs. We will compare an contrast scores from various APIs and discuss how these scores can be used to augment traditional quantitative research and for trading decisions. At the end of this talk, participants can see a concrete picture on how to take their machine learning from a research and prototype to a scalable production model deployable in the cloud.