Using R for Regression and Machine Learning in Investment

Using R for Regression and Machine Learning in Investment

Instructor: Youngju Nielsen

Access provided by SGCSRC

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Understanding the basic common concept of machine learning
Familiarizing with most commonly used methodology, regression
Distinguishing in-sample and out-of-sample results and leading to well-performing models in a real-life

Skills you'll gain

Tools you'll learn

R Programming

Details to know

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Assessments

1 assignment

Taught in English

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There are 2 modules in this course

In this course, the instructor will discuss various uses of regression in investment problems, and she will extend the discussion to logistic, Lasso, and Ridge regressions. At the same time, the instructor will introduce various concepts of machine learning. You can consider this course as the first step toward using machine learning methodologies in solving investment problems. 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 use various regression methodologies for investment management that you might need to do in your job every day and make you ready for more advanced topics in machine learning.

The course is designed with the assumption that most students already have a little bit of knowledge in financial economics and R programming. 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. Students are also expected to know of the instructors' 1st course, 'Fundamental of data-driven investment.' The instructor will explain the detail of R programming. It will be an excellent course for you to improve your programming skills but you must have basic knowledge in R. If you are very good at R programming, it will provide you with an excellent opportunity to practice again with finance and investment examples.

Understand the characteristics of predictive models and various data in investment The instructor will give you the big picture of the algorithm-driven investment decision-making process. After you understand that, we will review the regression concept and connect it with the core concepts of machine learning methodologies.

What's included

5 videos9 readings

5 videos Total 82 minutes

L1. Brief History on Investing, Machine Learning and Alternative Data 14 minutes
L2. Ingredients for Maching Learning Based Investment 15 minutes
L3. Big Picture of Alorithm-Driven Investment 18 minutes
L4. Understanding the Characteristics of Factors 20 minutes
L5. Understanding Machine Learning Concepts 16 minutes

9 readings Total 340 minutes

Learning Content 60 minutes
References 10 minutes
Learning Content 60 minutes
References 10 minutes
Learning Content 60 minutes
Learning Content 60 minutes
References 10 minutes
Learning Content 60 minutes
References 10 minutes

Use regression methodology for various investment analysis purpose and improve models by using ridge, lasso, and logistic regression. 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.