Youngju Nielsen

Offered By

The Fundamental of Data-Driven Investment

Sungkyunkwan University

About this Course

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In this course, the instructor will discuss the fundamental analysis of investment using R programming. The course will cover investment analysis topics, but at the same time, make you practice it using R programming. This course's focus is to train you to do the elemental analysis for investment management that you might need to do in your job every day. 
Additionally, the study note to do using Python programming will be provided. 

The course is designed with the assumption that most students already have a little bit of knowledge in financial economics. Students are expected to have heard about stocks and bonds and balance sheets, earnings, etc., and know the introductory statistics level, such as mean, median, distribution, regression, etc. 

The instructor will explain the detail of R programming for beginners. It will be an excellent course for you to improve your programming skills. If you are very good at R programming, it will provide you an excellent opportunity to practice again with finance and investment examples. 

Professor Youngju Nielsen creates the course with the assistants of Keonwoo Lim and Jeeun Yuen.
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Coursera Course recommendations before this course for those who are not familiar with basic R programming:
<Getting Started with R>  
https://www.coursera.org/projects/getting-started-with-r
<Introduction to Business Analytics with R> 
https://www.coursera.org/learn/business-analytics-r
<Statistics with Python >
https://www.coursera.org/specializations/statistics-with-python

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Beginner Level

Introductory statistics, introductory financial economics, and R programming

Approx. 19 hours to complete

English

Subtitles: English

What you will learn

Build an investment factor model using regression methodology

Employ optimization algorithm using R standard library

Explain the portfolio performance

Skills you will gain

Build an investment factor model using regression methodology.
Explain the portfolio performance.
Employ optimization algorithm using R standard library.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Beginner Level

Introductory statistics, introductory financial economics, and R programming

Approx. 19 hours to complete

English

Subtitles: English

Instructor

Youngju Nielsen

826 Learners

2 Courses

Offered by

Sungkyunkwan University

Sungkyunkwan University (SKKU) was established in 1398 as the highest national educational institute in the early years of Joseon Dynasty in Korea. At present with the support of the world-renowned global company Samsung, SKKU is leading the development of higher education in Korea. SKKU actively encourages international collaboration through developing cutting-edge research and educational programs with its global partners.

Syllabus - What you will learn from this course

Week

Week 1

6 hours to complete

Analyzing Past Returns and Forecasting Future Returns

You will learn how to read stock price time-series data from CSV file and analyze the past return data.

6 hours to complete

6 videos (Total 79 min), 10 readings, 2 quizzes

6 videos

Course Introduction2m

What is Quantitative Investing?7m

Description of the Stock Price Data15m

How to Analyze Asset Returns19m

What Do Determine Future Investment Returns?17m

Forecasting Investment Returns with Factors17m

10 readings

Python Script for Lecture 230m

[Optional] R Script for Lecture 2

DIS Data

Python Script for Lecture 330m

[Optional] R Script for Lecture 3

Python Script for Lecture 430m

[Optional] R Script for Lecture 4

SP500 Data

Python Script for Lecture 530m

[Optional] R Script for Lecture 5

2 practice exercises

Checking Your Basic R Knowledge1h 30m

Practice Project Week 11h 30m

Week

Week 2

5 hours to complete

Understanding the Risk Using Factors

First of all, you will learn how you can gauge investment strategy using backtesting.

You learned the first component of investment strategy, returns, in the first week. You will expand your study to assessing investment risks. To understand stocks' risks, you will calculate covariance and correlation matrix using historical time-series stock return data. You will extend this to market factor and three-factor models to understand the risk you are facing with your investment. Finally, you will calculate factor exposure using a 3-factor model from week 2 and separate common factor risk and idiosyncratic risk of the stock.

5 hours to complete

5 videos (Total 68 min), 9 readings, 1 quiz

5 videos

How to Evaluate Investment Strategies?13m

How to Assess the Risk?16m

Analyzing Market Risk Using CAPM12m

How to Create a 3 Factor Model with the Tidyverse Package13m

What is Risk Factor Analysis and Idiosyncratic Risk Analysis?11m

9 readings

Python Script for Lecture 630m

[Optional] R Script for Lecture 6

Python Script for Lecture 730m

[Optional] R Script for Lecture 7

Python Script for Lecture 830m

[Optional] R Script for Lecture 8

Python Script for Lecture 9-1030m

[Optional] R Script for Lecture 9

[Optional] R Script for Lecture 10

1 practice exercise

Practice Project Week 21h 30m

Week

Week 3

3 hours to complete

Portfolio Analysis and Optimization

In this week, This week, you will download various global ETFs and make global asset allocation portfolio using mean-variance optimization.

3 hours to complete

3 videos (Total 30 min), 6 readings, 1 quiz

3 videos

How to Get Data to Make a Portfolio of Multiple Assets10m

How to preparing Data for Portfolio Optimization7m

How to Create an Optimized Portfolio using Historical Data12m

6 readings

Python Script for Lecture 1130m

[Optional] R Script for Lecture 11

Python Script for Lecture 1230m

[Optional] R Script for Lecture 12

Python Script for Lecture 1330m

[Optional] R Script for Lecture 13

1 practice exercise

Practice Project Week 31h 30m

Week

Week 4

4 hours to complete

Performance Analysis

You will learn about various portfolios other than a mean-variance optimized portfolio. Additionally, you will add a constraint to your portfolio optimization. In reality, you might need to consider more than volatility measured by return standard deviation. You will grasp the concepts of VaR, maximum drawdowns and CvaR, etc.

4 hours to complete

5 videos (Total 54 min), 10 readings, 1 quiz

5 videos

Drawing and Comparing Multiple Portfolios9m

How to Summarize the Result from Optimization9m

How to Add Constraints to Portfolio Optimization11m

Evaluate Asset Performance Using PerformanceAnalytics Package10m

How to Compare Constrained and Unconstrained Portfolios12m

10 readings

[Optional] R Script for Lecture 14

SP500MonthlyExcel Data

Python Script for Lecture 1530m

[Optional] R Script for Lecture 15

Python Script for Lecture 1630m

[Optional] R Script for Lecture 16

Python Script for Lecture 1730m

[Optional] R Script for Lecture 17

Python Script for Lecture 1830m

[Optional] R Script for Lecture 18

1 practice exercise

Final Project1h 30m

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The Fundamental of Data-Driven Investment

The Fundamental of Data-Driven Investment