The Fundamental of Data-Driven Investment
Learn R programming for investment analysis, covering quantitative investing, risk assessment, and portfolio optimization.
Learn R programming for investment analysis, covering quantitative investing, risk assessment, and portfolio optimization.
This course teaches fundamental analysis of investment using R programming, focusing on practical skills for everyday investment management tasks. Covering topics like quantitative investing, risk assessment using factor models, and portfolio optimization, the course combines investment theory with hands-on R programming practice. Designed for those with basic knowledge in financial economics and introductory statistics, it's ideal for improving both investment analysis and programming skills. Python alternatives are also provided for each R script.
Instructors:
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What you'll learn
Build an investment factor model using regression methodology
Employ optimization algorithms using R standard library
Explain portfolio performance using various metrics
Analyze past returns and forecast future returns
Assess investment risks using factor models
Create and optimize portfolios using mean-variance analysis
Skills you'll gain
This course includes:
4 Hours PreRecorded video
5 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course offers a comprehensive introduction to data-driven investment analysis using R programming. Students will learn to analyze past returns, forecast future returns using regression models, and assess investment risks through factor analysis. The curriculum covers quantitative investing techniques, portfolio optimization using mean-variance analysis, and performance evaluation. Participants will gain hands-on experience in manipulating financial data, building investment models, and optimizing portfolios using both R and Python programming languages.
Analyzing Past Returns and Forecasting Future Returns
Module 1 · 6 Hours to complete
Understanding the Risk Using Factors
Module 2 · 4 Hours to complete
Portfolio Analysis and Optimization
Module 3 · 3 Hours to complete
Performance Analysis
Module 4 · 4 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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