R for Regression & ML in Investment
Master R for regression and machine learning in investment analysis. Enhance your skills in data-driven decision-making.
Master R for regression and machine learning in investment analysis. Enhance your skills in data-driven decision-making.
This course focuses on using R for regression and machine learning in investment analysis. Designed for those with basic knowledge of financial economics and R programming, it covers various regression methodologies, including logistic, Lasso, and Ridge regressions. The course introduces machine learning concepts and their application to investment problems, emphasizing practical skills in data-driven investment decision-making. Students will learn to handle different data frequencies, analyze data using Fama-Macbeth regression, develop predictive models, and solve classification problems using logistic regression. The course aims to prepare students for advanced topics in machine learning and equip them with skills applicable to daily investment management tasks.
Instructors:
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What you'll learn
Understand the basic concepts of machine learning in investment
Master commonly used regression methodologies for investment analysis
Learn to distinguish between in-sample and out-of-sample results
Develop skills to create well-performing models for real-life investment scenarios
Gain proficiency in using R programming for investment management tasks
Understand the algorithm-driven investment decision-making process
Skills you'll gain
This course includes:
2.93 Hours PreRecorded video
1 assignment
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FullTime access
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There are 2 modules in this course
This course provides a comprehensive introduction to using R for regression and machine learning in investment analysis. Students will learn to apply various regression methodologies, including logistic, Lasso, and Ridge regressions, to solve investment problems. The curriculum covers key concepts such as handling data with different frequencies, analyzing data using Fama-Macbeth regression, developing predictive models, and solving classification problems. By the end of the course, participants will have gained practical skills in data-driven investment decision-making and be prepared for more advanced topics in machine learning for investment management.
Understanding the big picture of the algorithm-driven investment decision-making process using machine learning and review of regression methodology
Module 1 · 7 Hours to complete
Regression and beyond
Module 2 · 10 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Associate Professor at Sungkyunkwan University Specializing in Machine Learning and Investment Strategies
Youngju Nielsen is an Associate Professor at Sungkyunkwan University, where she teaches courses such as "Machine Learning for Smart Beta," "The Fundamentals of Data-Driven Investment," and "Using R for Regression and Machine Learning in Investment." With a Ph.D. from the University of Pittsburgh and extensive experience in systematic trading and portfolio management on Wall Street, she brings a wealth of practical knowledge to her teaching. Youngju has managed substantial investment portfolios and has held significant roles in quantitative hedge funds, further enriching her academic contributions. Her research interests include fixed income, tactical allocation, and hedge fund portfolio strategies, making her a valuable resource for students interested in finance and data-driven investment methodologies.
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