Quantitative Finance has a high barrier of entry with expertise in quantitative subjects required for a career. We hope to bridge the gap between industry expectations and the student’s possible career choices by exposing them to a basic understanding of quantitative finance. Through units in economics, machine learning, and quantitative investing, students will learn the necessary skills to be familiarized with the industry, and we hope that this is an opportunity for students to develop their own quantitative intuition about the market.
The course will start by introducing the fundamentals of the Capital Markets, including Market Microstructure and Securities Pricing. Then, we will look at basic data processing skills to scrape, extract, and process open data from the web to build predictive models using modern Machine Learning techniques. We will tie the course together by introducing basic portfolio optimization and the popular methods used by various quantitative firms. Finally, we will test the student’s understanding of the material by having each student build their own trading strategy from selected tickers, analyze, and present the results.
We are part of Traders at Berkeley:
No day(s) left until application deadline!
|Lecture||Alex Kwon, Annie Ouyang||25||Zoom||[Th] 6:00PM-8:00PM||09/10/2020||Full||--||--|