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
You will learn how to read stock price time-series data from CSV file and analyze the past return data.
After you understand the past return data, you will determine what impacts stocks' return and make a future return forecasting model using regression.
What's included
6 videos10 readings2 assignments
Show info about module content
6 videos•Total 79 minutes
Course Introduction•3 minutes
What is Quantitative Investing?•7 minutes
Description of the Stock Price Data•15 minutes
How to Analyze Asset Returns•19 minutes
What Do Determine Future Investment Returns?•17 minutes
Forecasting Investment Returns with Factors•17 minutes
10 readings•Total 120 minutes
Python Script for Lecture 2•30 minutes
[Optional] R Script for Lecture 2•0 minutes
DIS Data•0 minutes
Python Script for Lecture 3•30 minutes
[Optional] R Script for Lecture 3•0 minutes
Python Script for Lecture 4•30 minutes
[Optional] R Script for Lecture 4•0 minutes
SP500 Data•0 minutes
Python Script for Lecture 5•30 minutes
[Optional] R Script for Lecture 5•0 minutes
2 assignments•Total 180 minutes
Checking Your Basic R Knowledge•90 minutes
Practice Project Week 1•90 minutes
Understanding the Risk Using Factors
Module 2•5 hours to complete
Module details
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.
What's included
5 videos9 readings1 assignment
Show info about module content
5 videos•Total 68 minutes
How to Evaluate Investment Strategies?•14 minutes
How to Assess the Risk?•17 minutes
Analyzing Market Risk Using CAPM•12 minutes
How to Create a 3 Factor Model with the Tidyverse Package•14 minutes
What is Risk Factor Analysis and Idiosyncratic Risk Analysis?•11 minutes
9 readings•Total 120 minutes
Python Script for Lecture 6•30 minutes
[Optional] R Script for Lecture 6•0 minutes
Python Script for Lecture 7•30 minutes
[Optional] R Script for Lecture 7•0 minutes
Python Script for Lecture 8•30 minutes
[Optional] R Script for Lecture 8•0 minutes
Python Script for Lecture 9-10•30 minutes
[Optional] R Script for Lecture 9•0 minutes
[Optional] R Script for Lecture 10•0 minutes
1 assignment•Total 90 minutes
Practice Project Week 2•90 minutes
Portfolio Analysis and Optimization
Module 3•3 hours to complete
Module details
In this week, This week, you will download various global ETFs and make global asset allocation portfolio using mean-variance optimization.
What's included
3 videos6 readings1 assignment
Show info about module content
3 videos•Total 30 minutes
How to Get Data to Make a Portfolio of Multiple Assets•11 minutes
How to preparing Data for Portfolio Optimization•7 minutes
How to Create an Optimized Portfolio using Historical Data•12 minutes
6 readings•Total 90 minutes
Python Script for Lecture 11•30 minutes
[Optional] R Script for Lecture 11•0 minutes
Python Script for Lecture 12•30 minutes
[Optional] R Script for Lecture 12•0 minutes
Python Script for Lecture 13•30 minutes
[Optional] R Script for Lecture 13•0 minutes
1 assignment•Total 90 minutes
Practice Project Week 3•90 minutes
Performance Analysis
Module 4•4 hours to complete
Module details
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.
What's included
5 videos10 readings1 assignment
Show info about module content
5 videos•Total 54 minutes
Drawing and Comparing Multiple Portfolios•10 minutes
How to Summarize the Result from Optimization•9 minutes
How to Add Constraints to Portfolio Optimization•12 minutes
Evaluate Asset Performance Using PerformanceAnalytics Package•10 minutes
How to Compare Constrained and Unconstrained Portfolios•13 minutes
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.
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Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.