Duration
4 weeksWeekly study
4 hours100% online
How it works
Introduction to Data Analytics for Investment
Learn how to use data analytics skills and regression to forecast returns
We live in an era where “data is the new oil”. No matter your area of expertise, having strong data analytical skills is becoming increasingly important. This four-week course from Sungkyunkwan University (SKKU) will help you use R or Python programming to apply data analysis to finance and investing.
During the first week of this course, you’ll learn to analyse and understand past return data and make a future return forecasting model using regression.
Discover how to assess risk and gauge and test investment strategies
The second week will guide you through how to gauge your investment strategy using backtesting. You’ll utilise the knowledge gained from the first week’s content, as well as your forecasting model, to determine the validity of your investment strategy.
You’ll also expand your knowledge and understanding of assessing investment risks by using probability and statistics to analyse and calculate investment risk.
Create an investment portfolio with global ETFs and optimise it using R
To give you a hands-on learning experience, you’ll create your own investment portfolio using global Exchange-Traded Funds (ETFs).
Once you’ve created your portfolio, your educators will instruct you in managing and optimising it by employing an optimization algorithm using the R standard library.
Analyse the performance of your portfolio with Sungkyunkwan University
For the final week, you’ll learn about various types of portfolio and how to assess the performance of your portfolio with help from the experts at Sungkyunkwan University.
Once you’ve successfully completed this course, you will be well-equipped to employ real data and programming skills to strengthen your investment portfolio and investment strategies.
Syllabus
Week 1
Analyzing Past Returns and Forecasting Future Returns
Welcome to the course!
Welcome to the 'Introduction to Data Analytics for Investment' course. Read on to learn more about this course.
What is Quantitative Investing?
Explanation of quantitative investing.
Description of the Stock Price Data.
Examine the characteristics of stock price data using Disney's daily stock price data.
How to Analyze Asset Returns.
Using R programming to calculate the asset returns of our Disney stock data.
What Determines Future Investment Returns?
Graphing data and examining which factor is a good predictor of future investment returns.
Forecasting Investment Returns with Factors.
Using regression to determine the relationship between S&P dividend rates and annual return.
Practice Project Week 1.
Programming project.
Week 2
Understanding Risk Using Factors
How to Evaluate Investment Strategies?
Introduction to the basic concepts of backtesting.
How to Assess Risk.
Use the 'quantmod' package to call API data. Apply this package to do basic analysis of data such as calculating daily returns, standard deviation and covariance.
Analyzing Market Risk Using CAPM.
Create a CAPM model using R to find market beta.
How to Create a 3 Factor Model with the Tidyverse Package.
Applying the 'tidyverse' package to prepare data for the 3 Factor Model.
What is Risk Factor Analysis and Idiosyncratic Risk Analysis?
Combine two data sets and examine the relationship between stock risk premiums and factors. Identify the idiosyncratic risk, risk that is unexplained by factors.
Practice Project Week 2.
Programming project
Week 3
Portfolio Analysis and Optimization
Downloading Data to Make a Portfolio of Multiple Assets.
Explore the various types of data you can download using the 'quantmod' package.
Preparing Data for Portfolio Optimization.
Explanation of the various functions used to prepare data for portfolio optimzation.
How to Create an Optimized Portfolio using Historical Data.
Creating an optimal portfolio based on return and risk.
Practice Project Week 3.
Programming project.
Week 4
Performance Analysis
Graphing and Comparing Multiple Portfolios.
Compare the graphs of multiple portfolios to select the best portfolio.
How to Summarize the Result from Optimization.
Visualizing the portfolio optimization results.
How to Add Constraints to Portfolio Optimization.
Create a portfolio that achieves maximum return under a given risk level.
Evaluate Asset Performance Using PerformanceAnalytics Package.
Measure the performance level of your investment portfolio or strategy.
How to Compare Constrained and Unconstrained Portfolios.
Comparing constrained and unconstrained porfolios with the 'PerformanceAnalytics' package.
Final project.
Final programming project.
Congratulations on finishing the whole course!
Congratulations on coming to the end of this course. We hope to see you in the next class!
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...
- Describe investment models such as CAPM, 3 Factor Model
- Create an investment factor model using regression
- Code an optimization algorithm using R standard library
- Assess portfolio performance levels
Who is the course for?
This course is designed for students with financial economics knowledge who are interested in learning how to design, analyse, and test investment strategies and portfolio management systems through R or Python programming.
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?
Established
1398Location
Seoul, South KoreaWorld ranking
Top 100Source: QS World University Rankings 2021
Ways to learn | Buy this course | Subscribe & save | Limited access |
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Choose the best way to learn for you! | $134/one-off payment | $244.99 for a whole year Automatically renews | Free |
Fulfill your current learning need | Develop skills to further your career | Sample the course materials | |
Access to this course | tick | tick | Access expires 11 Nov 2024 |
Access to 1,000+ courses | cross | tick | cross |
Learn at your own pace | tick | tick | cross |
Discuss your learning in comments | tick | tick | tick |
Certificate when you're eligible | Printed and digital | Digital only | cross |
Cancel for free anytime |
Ways to learn
Choose the best way to learn for you!
Subscribe & save
$244.99 for a whole year
Automatically renews
Develop skills to further your career
- Access to this course
- Access to 1,000+ courses
- Learn at your own pace
- Discuss your learning in comments
- Digital certificate when you're eligible
Cancel for free anytime
Buy this course
$134/one-off payment
Fulfill your current learning need
- Access to this course
- Learn at your own pace
- Discuss your learning in comments
- Printed and digital certificate when you’re eligible
Limited access
Free
Sample the course materials
- Access expires 11 Nov 2024
Find out more about certificates, Unlimited or buying a course (Upgrades) Sale price available until 31 October 2024 at 23:59 (UTC). T&Cs apply. |
Find out more about certificates, Unlimited or buying a course (Upgrades)
Sale price available until 31 October 2024 at 23:59 (UTC). T&Cs apply.
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