Using R for Regression and Machine Learning in Investment from Coursera

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.

What's inside

Syllabus

Understanding the big picture of the algorithm-driven investment decision-making process using machine learning and review of regression methodology

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.

Regression and beyond

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides a solid foundation in algorithm-driven investment decision-making

Explores regression methodology, extending it to logistic, Lasso, and Ridge regressions

Introduces the core concepts of machine learning methodologies

Suitable for students with a working knowledge of financial economics and R programming

Presumes a foundational understanding of statistics and regression

Assumes familiarity with the instructor's previous course, 'Fundamental of data-driven investment'

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Using R for Regression and Machine Learning in Investment with these activities:

Refresh Knowledge: Regression and Statistical Concepts

Show steps

Review the fundamental concepts of regression analysis, statistics, and predictive modeling to strengthen your foundation for this course.

Browse courses on Regression Analysis

Show steps

Review lecture notes or textbooks from previous courses on regression analysis and statistics.
Solve practice problems or take a refresher quiz to test your understanding.

Guided Tutorials: R Programming Refresher

Show steps

Brush up on your R programming skills with guided tutorials to ensure you have a solid foundation for the programming aspects of the course.

Browse courses on R Programming

Show steps

Complete online tutorials or follow a structured course on R programming.
Practice writing and executing R code to reinforce your understanding.

Mentorship: Seeking External Guidance

Show steps

Identify and connect with professionals in the field to gain insights, expand your network, and enhance your learning experience.

Browse courses on Mentoring

Show steps

Attend industry events or reach out to professionals via email or LinkedIn.
Request guidance on specific topics, career advice, or project feedback.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Practice Drills: Regression Calculations

Show steps

Engage in regular practice drills to strengthen your ability to perform regression calculations and improve your accuracy.

Browse courses on Regression Analysis

Show steps

Solve various types of regression problems, including linear, multiple, and logistic regression.
Use statistical software or online calculators to verify your results and identify areas for improvement.

Study Group: Investment Analysis Discussions

Show steps

Engage in collaborative discussions with peers to explore different perspectives on investment analysis and apply regression techniques effectively.

Browse courses on Investment Analysis

Show steps

Form a study group with classmates and meet regularly.
Discuss complex investment scenarios and share insights on regression model selection and interpretation.

Mentoring: Assisting Fellow Students

Show steps

Contribute to the learning community by offering support and guidance to fellow students to deepen your own understanding and foster a collaborative environment.

Browse courses on Mentoring

Show steps

Volunteer to assist students in online forums or study sessions.
Provide clear explanations, share resources, and encourage active participation.

Project: Stock Return Analysis Using Regression

Show steps

Develop a comprehensive analysis of stock returns using regression techniques to enhance your understanding of investment decision-making.

Browse courses on Investment Analysis

Show steps

Gather historical stock return data and explore descriptive statistics.
Build regression models to identify factors influencing stock returns.
Interpret model results and present insights in a visually appealing report.

Career center

Learners who complete Using R for Regression and Machine Learning in Investment will develop knowledge and skills that may be useful to these careers:

Investment Analyst

Investment Analysts use regression and machine learning methods to build predictive models for various investment analysis purposes, such as risk assessment, portfolio optimization, and performance evaluation. This course provides a strong foundation in these techniques, allowing aspiring Investment Analysts to gain the skills necessary to succeed in this field.

See salaries and explore the career path for Investment Analyst

Quantitative Analyst

Quantitative Analysts leverage regression and machine learning techniques to develop and implement quantitative models for investment decision-making. This course helps build a foundation in these methodologies, providing aspiring Quantitative Analysts with the expertise required to excel in this role.

See salaries and explore the career path for Quantitative Analyst

Data Scientist

Data Scientists apply regression and machine learning methods to extract insights from data, which can be valuable for investment management. This course provides a strong foundation in these techniques, preparing aspiring Data Scientists to leverage their skills in the investment industry.

See salaries and explore the career path for Data Scientist

Portfolio Manager

Portfolio Managers utilize regression and machine learning to enhance portfolio construction, risk management, and performance analysis. This course offers a comprehensive understanding of these techniques, enabling aspiring Portfolio Managers to stay competitive in the industry.

See salaries and explore the career path for Portfolio Manager

Risk Manager

Risk Managers employ regression and machine learning to assess and manage investment risks. This course provides a strong foundation in these techniques, equipping aspiring Risk Managers with the knowledge to succeed in this role.

See salaries and explore the career path for Risk Manager

Investment Banker

Investment Bankers may utilize regression and machine learning for financial modeling and valuation. This course provides a foundation in these techniques, enhancing the skills of aspiring Investment Bankers and improving their competitiveness in the industry.

See salaries and explore the career path for Investment Banker

Financial Analyst

Financial Analysts use regression and machine learning to analyze financial data and make recommendations. This course provides a strong foundation in these techniques, empowering aspiring Financial Analysts to excel in this role.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries leverage regression and machine learning techniques to assess and manage financial risks in various industries, including investment. This course provides a foundation in these methodologies, preparing aspiring Actuaries to apply their skills in the investment field.

See salaries and explore the career path for Actuary

Economist

Economists may utilize regression and machine learning to analyze economic data and develop forecasts. This course offers a foundation in these techniques, enhancing the skills of aspiring Economists and expanding their career opportunities in investment-related fields.

See salaries and explore the career path for Economist

Software Engineer

Software Engineers with expertise in regression and machine learning are in high demand in the investment industry. This course provides a solid foundation in these techniques, enabling aspiring Software Engineers to develop and implement data-driven solutions for investment management.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts can leverage regression and machine learning techniques to analyze investment data and extract meaningful insights. This course provides a strong foundation in these methodologies, empowering aspiring Data Analysts to excel in this field.

See salaries and explore the career path for Data Analyst

Statistician

Statisticians with expertise in regression and machine learning are sought after in the investment industry. This course provides a comprehensive understanding of these techniques, enabling aspiring Statisticians to apply their skills to investment-related problems.

See salaries and explore the career path for Statistician

Financial Planner

Financial Planners may utilize regression and machine learning to develop personalized financial plans and optimize investment strategies for their clients. This course provides a foundation in these techniques, enhancing the skills of aspiring Financial Planners and improving their ability to meet the needs of their clients.

See salaries and explore the career path for Financial Planner

Business Analyst

Business Analysts with a background in regression and machine learning can provide valuable insights for investment decision-making. This course offers a strong foundation in these techniques, equipping aspiring Business Analysts to succeed in the investment industry.

See salaries and explore the career path for Business Analyst

Market Researcher

Market Researchers use regression and machine learning to analyze market trends and consumer behavior. This course provides a foundation in these techniques, enhancing the skills of aspiring Market Researchers and increasing their competitiveness in the investment field.

See salaries and explore the career path for Market Researcher