onlincourse

Introduction to Data Analytics for Investment

Discover how to use data analysis and programming for investment management and strategies with this online course from Sungkyunkwan University.

Duration

4 weeks

Weekly study

4 hours

100% online

How it works

Unlimited subscription

Learn more

Established

1398

Location

Seoul, South Korea

World ranking

Source: QS World University Rankings 2021

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.

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!