Traders at Berkeley

Introduction to Quantitative Finance

STAT 198 at UC Berkeley: Thursday 6-8PM PT

Lecturer

Alex Kwon

akwon [at] berkeley.edu

Annie Ouyang

ouyang.annie [at] berkeley.edu

TA

Jerry Li

jerryli0273 [at] berkeley.edu

Aryan Shah

aryanshah [at] berkeley.edu

Karthik Sreedhar

karthiksreedhar [at] berkeley.edu

Sandeep Mukherjee

sandeep.m [at] berkeley.edu

Course Description

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.

Syllabus

Schedule

Date Lecture HW
2/4 Introduction to Quantitative Finance (Open to all) HW1
2/11 Asset Valuation and Macroeconomics HW2
2/18 Invited Speaker - Options Trading HW3
2/25 Market Microstructure HW4
3/4 Multi-factor Alpha Model HW5
3/11 Quantitative Strategies HW6
3/18 Portfolio Optimization HW7
3/25 Spring Break -
4/1 Trading Competition P
4/8 Invited Speaker - High Frequency Trading P
4/15 Careers in Quantitative Finance P
4/22 Final Presentations P
4/29 Final Presentations -

Prerequisite

A strong interest in the intersection of Finance and Technology and basic coding ability in Python.

Overview

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.

Desired Outcome

By the end of the term, students who have taken Introduction to Quantitative Finance are expected to be able to:

  1. Understand the basic concepts of market dynamics
  2. Understand basic data processing skills to scrape, extract, and process data
  3. Be aware of wide-ranging applications of modeling techniques
  4. Be able to use prototype software and packages to develop their own projects

Methods of Instruction

The course will meet once a week for two hours. One hour of office hours is designated after each lecture to assist the student’s understanding of the material.


Grading

This course will be graded on attendance, participation, weekly assignments, and a final project.

Participation and Attendance: 30%

Students will have one excused absence for the semester. Attendance is extremely important to understand the course material and to stay on track. Every unexcused absence will result as a 10% reduction in the final grade.

Weekly assignments: 30%

There will be weekly assignments to test on the practical applications of the course material for the week. These will be helpful in implementing the student’s final project. Assignments will be a mix of weekly reading summaries and applying theoretical concepts in Jupyter notebook.

Assignments will be assigned during each class, and due the following week (Thursday) before class to be emailed to the instructors. Students will receive feedback on their assignments by Sunday midnight.

Final Project: 40%

This will be a cumulative final project that the student will work on for the entire semester. It will test the student’s knowledge and understanding by applying the course material to a real-live trading environment.

Pass/No Pass

In order to pass the course, you need at least 70%.


Policy

This course will be graded on attendance, participation, weekly assignments, and a final project.

Late Assignments

Assignments are due in class with a 50% penalty per day until the score reaches 0%.

Academic Dishonesty

Students must cite any source used in their work. Failure to do so will be considered academic dishonesty.


Professor

Thank you Professor Peng Ding for sponsoring us!

Peng Ding

pengdingpku [at] berkeley.edu