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The Fundamental of Data-Driven Investment

Sungkyunkwan University via Coursera


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.
Coursera Course recommendations before this course for those who are not familiar with basic R programming:


  • 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.
      After you understand the past return data, you will determine what impacts stocks' return and make a future return forecasting model using regression.
  • 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.
  • 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.
  • 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.

Taught by

Youngju Nielsen

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