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R for Regression and Machine Learning in Investment

Learn how to use R to apply machine learning and regression methodology to investing and improve your decision-making strategies.

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Boost your R programming skills and learn the core concepts of machine learning

Data is an essential tool for every sector, including investments. With data analytics, investment strategies, portfolio management, and decision-making are algorithm and data-driven, reducing the risk factors associated with investment.

This two-week course from Sungkyunkwan University (SKKU) will introduce you to fundamental machine learning and regression methodologies and help you improve your R programming skills to use data analysis in solving investment problems.

Understand how to use regression for data analysis of investments

This course will guide you through using regression methodology for various investment analysis purposes by using Ridge, Lasso, and logistic regression.

You’ll begin by gauging investment strategy using backtesting and learn about regression methodology, how to solve classification problems with logistic regression, and analyse data using the Fama-Macbeth Regression method.

Create a machine learning model to predict the movement of the stock market

On this course, you’ll take the first step toward using machine learning methodologies in solving investment problems.

Not only will you develop a firm grasp on the core concepts of machine learning, but you’ll also learn about machine learning models that are used to predict the movement of the stock market and create your own macro factor model using R programming.

Learn with the experts at Sungkyunkwan University

The instructor of this course has more than 15 years of experience with algorithmic trading and investment portfolio management experience in the G10 markets at Wall Street major firms.

With her expertise and guidance, you’ll be well-equipped to apply regression and machine learning methods to real data and improve your investment strategies.

Syllabus

  • Week 1

    Understanding algorithm-driven investment decision-making.

    • Welcome to the course!

      Welcome to the 'R for Regression and Machine Learning in Investment' course. Read on to learn more about this course.

    • Brief History on Investing, Machine Learning and Alternative Data.

      The evolution of the investment industry, the uses of machine learning and alternative data.

    • Ingredients for Maching Learning Based Investment

      The ingredients of machine learning based investment - market data, fundamental data and alternative data

    • Big Picture of Algorithm-Driven Investment.

      Overview of the algorithmic trading structure and the difference between supervised, unsupervised machine learning.

    • Understanding the Characteristics of Factors.

      Short recap of CAPM and the Fama-French 3 Factor model. Downloading and cleaning data for the Fama-French 3-Factor model.

    • Understanding Machine Learning Concepts.

      Introduction and application of the Fama-French 5 factor model to FAANG stock data.

    • Summary of WEEK 1

      Summary of what we learned in week 1

  • Week 2

    Regression and beyond.

    • Handling Data with Different Frequencies.

      Factor analysis using macroeconomic factors. Solve common problems you will face when dealing with financial data.

    • Analyzing Data Using Fama-Macbeth Regression.

      Use time-varying data and the Fama-Macbeth regression to determine how the beta of each assets' factor relates to the assets' risk premium.

    • Predictive Models.

      Create a predictive model that predicts future profits using a regression model.

    • Making a Model that Performs Well in Real Life.

      Explore the various ways you can improve a model so that it will perform well when applied to real life data.

    • Logistic Regression - Solving Classification Problems.

      Modify the data we used in regression to data that can be used for logistic regression.

    • Summary of WEEK 2

      Summary of what we learned in week 2

When would you like to start?

Start straight away and join a global classroom of learners. If the course hasn’t started yet you’ll see the future date listed below.

  • Available now

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

  • Explain core concepts of machine learning
  • Apply regression models to stock market data
  • Evaluate in-sample and out-of-sample results to create a model that performs well in real-life
  • Create a macro factor model using R programming

Who is the course for?

This course is designed for anyone with financial economics and R programming knowledge, who is interested in learning how to apply advanced regression methods to real data and concepts of machine learning.

You should already be familiar with basic R programming to benefit from this course.

What software or tools do you need?

Download R and R Studio or use RStudio Cloud

Who will you learn with?

Professor Youngju Nielsen worked 15 years at major finance corporations on Wall Street as a trader/portfolio manager. She now teaches at SKK GSB.
https://www.linkedin.com/in/youngjunielsen/

Who developed the course?

Sungkyunkwan University (SKKU)

Sungkyunkwan University, founded in 1398 as the highest national educational institute in the early years of the Joseon Dynasty in Korea, has fostered leaders of Korean society for over 600 years.

  • Established

    1398
  • Location

    Seoul, South Korea
  • World ranking

    Top 100Source: QS World University Rankings 2021
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