This course develops a framework to use quantitative methods to build and analyze investment strategies. We will take advantage of recent innovations in AI models and extensively use models such as GPT-4 (and related tools). You will get an in-depth understanding and hands-on experience how these methods are incredibly useful in the asset management industry and how they can transform the industry in the future.
We will use the AI models to develop code to analyze big data (such as stock prices and returns, firm fundamentals, text data, portfolio holdings and flows) and to then predict returns, measure risk, estimate the valuation of firms, and ultimately build investment strategies. The final project requires you to develop and pitch a new investment strategy using this framework as well. The course will use Python as a coding language, but no prior knowledge of Python is required for this course.
The course starts from a brief review of the traditional portfolio choice framework introduced in the Investments course and then covers much of the recent research on quantitative methods to build and critically assess investment strategies.
Key topics covered in the course are:
- An overview of the developments in the asset management industry related to active vs passive investing, institutional vs retail investors, and sustainable investing.
- Recent innovations in quantitative investing, such as factor investing, and industry applications via fundamental indexing and smart-beta products. We will also discuss how macroeconomic conditions (e.g., inflation and monetary policy) impact the success of these strategies.
- Market frictions and the capacity of investment strategies, incentives of asset managers, and evaluating the performance of actively-managed strategies, with applications to ETFs, hedge funds, and mutual funds.
- AI/ML methods and big data in the asset management industry: Applications and insights from big data (text, holdings, and flows).
- Using quantitative methods for firm valuation, and how to connect and integrate different approaches to investing, such as fundamental/value investing and quantitative investing.
The lecture material is built around recent academic research, problem sets, case studies, and significant time is spent on current events.
