BMSO 758F

For the past few decades, several industries have experienced a proliferation of data, both structured and unstructured, affecting business operations and business decisions in many ways. With the financial world bringing together several industries and aspects of the real economy, learning how to collect, process, and analyze this data for financial professionals can lead to insights that would have been otherwise impossible a few decades ago. Many financial institutions, from banks, investment firms, think tanks, to financial regulators, seek candidates with familiarity in financial analytics. This course serves as an introduction to financial analytics, as applied to asset pricing and credit risk modeling, to prepare students for this new and evolving field tying together modern advances in machine learning and financial economics. In this course, students can expect to learn how to extract relevant information for policy makers, market participants, or other stakeholders/investors, using financial data as applied to asset pricing and credit risk. Second, students will learn about the tradeoffs of using various machine learning models depending on the question at hand. Lastly, students can expect to gain familiarity for applying machine learning models in one of the leading programming languages for financial analytics, R, using real-world financial data.