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Machine Learning for Smart Beta

Youngju Nielsen and Haeram Joo

In this 4 week course, you will learn about Smart Beta products. Smart betas products have the characteristics of both passive investment(having predetermined rules) and active investments(allows for factor investment). We will walk through the creation mechanisms behind different smart beta products and recreate some of them using R programming. Then we will apply machine learning methods. Data processing, overfitting prevention techniques will be covered. Finally we will try to create an improved multi-factor model using CART, bagging, boosting and ensemble methods. Students are expected to have listened to my first and second course 'The Fundamental of Data-Driven Investment' and 'Using R for Regression and Machine Learning in Investment', or having equivalent knowledge in investment concepts and a firm grasp on R programming.

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What's inside

Syllabus

Week 1
Building on the concepts learned in previous courses 'The Fundamental of Data-Driven Investment' and 'Using R for Regression and Machine Learning in Investment', this course will cover 'Smart beta'. Smart betas products have the characteristics of both passive investment(having predetermined rules) and active investments(allows for factor investment). Smart beta products' investment mechanisms are open to the public, so we will recreate a MSCI smart beta product in R. Follow along the step-by-step reconstruction of the MSCI Enhanced Value Index and create your own smart beta portfolio.
Read more
Week 2
In order to effectively utilize machine learning in investment, it is important to understand the various characteristics of data. This module covers how to check the prediction accuracy of a machine learning model and prevent overfitting. Get hands on experience in R to manipulate data into a form suitable for machine learning models from regression models to classification trees.
Week 3
The asset selection method based on a score derived from a benchmark index has the problem that the selected assets do not reliably capture underlying information. To solve this problem, a non-traditional method, namely machine learning is used to create an improved multi-factor approach. Familiarize yourself with CART(Classification and Regression Tree), bagging, boosting and ensemble methods to enhance your smart beta portfolio in R.
Week 4
In this final module, we wrap up the discussion by creating a multifactor model applying all the knowledge we have learned so far. Investors have taken a steady interest in multifactor models that take into account the cyclicality of factors. Further, we expand the discussion into the use of factors in bond investment and a new method of active factor allocation.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Addresses Smart Beta products' construction methods, offering hands-on experience in replicating one
Incorporates machine learning methodologies for factor selection and model enhancement, providing practical experience
Utilizes R programming, catering to those familiar with data analysis and statistical modeling
Enhances understanding of data characteristics and overfitting prevention techniques
Delves into advanced asset selection methods using CART, bagging, boosting, and ensemble approaches
Assumes familiarity with fundamental concepts of data-driven investment and R programming

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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 Machine Learning for Smart Beta with these activities:
Review basic machine learning concepts and R programming
Ensure that students have a solid foundation in related topics, enhancing their ability to engage with Smart Beta concepts.
Browse courses on Machine Learning
Show steps
  • Review key concepts in machine learning, such as supervised and unsupervised learning
  • Practice writing and executing basic R code for data analysis and visualization
Join a Study Group for Reinforcement
Engage in active learning by participating in a study group to discuss concepts, clarify doubts, and enhance retention.
Browse courses on Collaborative Learning
Show steps
  • Find or create a study group with other learners.
  • Meet regularly, either in person or virtually.
  • Take turns facilitating discussions and presenting concepts.
  • Use the group as a resource for support and guidance.
Follow tutorials on 'Smart Beta Investing Using Python'
Give practical experience in implementing smart beta strategies using Python.
Show steps
  • Identify online tutorials or courses on 'Smart Beta Investing Using Python'
  • Follow the tutorials step-by-step, implementing the concepts in your own environment
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Solve practice problems on Smart Beta portfolio construction
Solidify understanding of the principles and techniques behind Smart Beta portfolio construction.
Show steps
  • Find practice problems or datasets related to Smart Beta portfolio construction
  • Work through the problems, testing your knowledge and applying the concepts
  • Review the solutions and compare your results, identifying areas for improvement
Organize and review course materials, assignments, and notes
Support effective learning by encouraging regular review and organization of course materials.
Show steps
  • Gather and organize course notes, assignments, and any supplemental materials
  • Review and consolidate information, highlighting key concepts and insights
Practice Smart Beta Portfolio Optimization
Enhance your understanding of smart beta portfolio optimization by applying your knowledge practically in R.
Browse courses on R Programming
Show steps
  • Gather historical data on relevant financial instruments.
  • Apply different smart beta optimization techniques in R.
  • Evaluate the performance of your optimized portfolios.
Develop a presentation on the benefits and limitations of Smart Beta strategies
Foster critical thinking and communication skills while reinforcing knowledge of Smart Beta strategies.
Show steps
  • Gather information from multiple sources on Smart Beta strategies
  • Analyze the benefits and limitations of Smart Beta strategies
  • Structure and prepare a clear and engaging presentation
  • Deliver the presentation to an audience or share it online
Create a data visualization tool to compare the performance of different Smart Beta indices
Develop technical skills in data visualization and reinforce understanding of Smart Beta performance.
Browse courses on Data Visualization
Show steps
  • Gather data on different Smart Beta indices
  • Choose appropriate data visualization techniques to compare their performance
  • Develop an interactive or static data visualization tool
  • Present your findings and insights on the comparison
Explore Machine Learning Techniques for Factor Selection
Deepen your understanding of machine learning techniques and their application in improving smart beta strategies.
Show steps
  • Identify a suitable dataset for factor selection.
  • Explore different machine learning models, such as CART, bagging, and boosting.
  • Implement the selected models in R.
  • Compare the performance of different models and select the best performing one.
Create a Multi-Factor Smart Beta Model
Challenge yourself by developing a comprehensive multi-factor smart beta model that combines both fundamental and quantitative factors.
Show steps
  • Gather data on relevant factors.
  • Identify and select the most influential factors.
  • Build a multi-factor model using R or other statistical software.

Career center

Learners who complete Machine Learning for Smart Beta will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine learning engineers develop and implement machine learning models. They work with data scientists to identify the best models for a given problem, and they build the software to implement these models. This course can help you develop the skills you need to be a successful machine learning engineer. You will learn how to use machine learning to create investment models, and you will gain experience in using R programming to analyze financial data. This course can help you build a foundation for a career as a machine learning engineer.
See salaries and explore the career path for Machine Learning Engineer
Portfolio Manager
Portfolio managers are responsible for managing the investments of their clients. They make decisions about which stocks, bonds, and other investments to buy and sell. This course can help you develop the skills you need to be a successful portfolio manager. You will learn how to use machine learning to create investment models, and you will gain experience in using R programming to analyze financial data. This course can help you build a foundation for a career as a portfolio manager.
See salaries and explore the career path for Portfolio Manager
Risk Manager
Risk managers are responsible for identifying and managing the risks associated with investments. They develop strategies to mitigate these risks and protect the value of their clients' portfolios. This course can help you develop the skills you need to be a successful risk manager. You will learn how to use machine learning to create investment models, and you will gain experience in using R programming to analyze financial data. This course can help you build a foundation for a career as a risk manager.
See salaries and explore the career path for Risk Manager
Financial Analyst
Financial analysts use their knowledge of the financial markets to help companies make investment decisions. They may also provide advice to individuals on how to invest their money. This course can help you develop the skills you need to be a successful financial analyst. You will learn how to use machine learning to create investment models, and you will gain experience in using R programming to analyze financial data. This course can help you build a foundation for a career as a financial analyst.
See salaries and explore the career path for Financial Analyst
Quantitative Analyst
Quantitative analysts use mathematical and statistical techniques to analyze financial data. They develop models to predict the performance of stocks, bonds, and other investments. This course can help you develop the skills you need to be a successful quantitative analyst. You will learn how to use machine learning to create investment models, and you will gain experience in using R programming to analyze financial data. This course can help you build a foundation for a career as a quantitative analyst.
See salaries and explore the career path for Quantitative Analyst
Investment Analyst
Investment analysts provide research and analysis on companies and industries to help investors make informed investment decisions. This course can help you develop the skills you need to be a successful investment analyst. You will learn how to use machine learning to create investment models, and you will gain experience in using R programming to analyze financial data. This course can help you build a foundation for a career as an investment analyst.
See salaries and explore the career path for Investment Analyst
Data Scientist
Data scientists use their knowledge of mathematics, statistics, and computer science to solve business problems. They develop algorithms to analyze data and extract insights. This course can help you develop the skills you need to be a successful data scientist. You will learn how to use machine learning to create investment models, and you will gain experience in using R programming to analyze financial data. This course can help you build a foundation for a career as a data scientist.
See salaries and explore the career path for Data Scientist
Software Engineer
Software engineers design, develop, and maintain software systems. They work with users to understand their needs and develop software that meets those needs. This course can help you develop the skills you need to be a successful software engineer. You will learn how to use R programming to analyze financial data, and you will gain experience in using machine learning to create investment models. This course can help you build a foundation for a career as a software engineer.
See salaries and explore the career path for Software Engineer
Trader
Traders buy and sell stocks, bonds, and other financial instruments for their own account or for the account of their clients. This course can help you develop the skills you need to be a successful trader. You will learn how to use machine learning to create investment models, and you will gain experience in using R programming to analyze financial data. This course can help you build a foundation for a career as a trader.
See salaries and explore the career path for Trader
Statistician
Statisticians use their knowledge of mathematics and statistics to collect, analyze, and interpret data. They develop models to predict future events and make informed decisions. This course can help you develop the skills you need to be a successful statistician. You will learn how to use machine learning to create investment models, and you will gain experience in using R programming to analyze financial data. This course can help you build a foundation for a career as a statistician.
See salaries and explore the career path for Statistician
Data Analyst
Data analysts use their knowledge of mathematics, statistics, and computer science to analyze data and extract insights. They develop models to predict future events and make informed decisions. This course can help you develop the skills you need to be a successful data analyst. You will learn how to use machine learning to create investment models, and you will gain experience in using R programming to analyze financial data. This course can help you build a foundation for a career as a data analyst.
See salaries and explore the career path for Data Analyst
Actuary
Actuaries use their knowledge of mathematics and statistics to assess risk and uncertainty. They develop models to predict the likelihood of future events, such as death, disability, and retirement. This course can help you develop the skills you need to be a successful actuary. You will learn how to use machine learning to create investment models, and you will gain experience in using R programming to analyze financial data. This course can help you build a foundation for a career as an actuary.
See salaries and explore the career path for Actuary
Investment Banker
Investment bankers help companies raise money by issuing stocks and bonds. They also advise companies on mergers and acquisitions. This course can help you develop the skills you need to be a successful investment banker. You will learn how to use machine learning to create investment models, and you will gain experience in using R programming to analyze financial data. This course can help you build a foundation for a career as an investment banker.
See salaries and explore the career path for Investment Banker
Economist
Economists study the production, distribution, and consumption of goods and services. They develop models to explain economic behavior and predict economic outcomes. This course can help you develop the skills you need to be a successful economist. You will learn how to use machine learning to create investment models, and you will gain experience in using R programming to analyze financial data. This course can help you build a foundation for a career as an economist.
See salaries and explore the career path for Economist
Financial Advisor
Financial advisors provide financial advice to individuals and families. They help their clients make informed decisions about their investments, retirement planning, and other financial matters. This course can help you develop the skills you need to be a successful financial advisor. You will learn how to use machine learning to create investment models, and you will gain experience in using R programming to analyze financial data. This course can help you build a foundation for a career as a financial advisor.
See salaries and explore the career path for Financial Advisor

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Machine Learning for Smart Beta.
Cover image
Causal Factor Investing
Save
Provides a practical guide to using machine learning in asset management. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for investors who want to learn more about this emerging field.
Causal Factor Investing (Elements in Quantitative...
Paperback
$$
Causal Factor Investing: Can Factor Investing...
Kindle Edition
$$
Cover image
Asset Management: Tools And Issues
Save
Provides a comprehensive overview of machine learning for investment management. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for investors who want to learn more about how to use machine learning to improve their investment performance.
Asset Management: Tools and Issues
Hardcover
$$$$
Cover image
The Elements of Statistical Learning
Save
Classic textbook on statistical learning. It covers a wide range of topics, from linear regression to deep learning. It valuable resource for investors who want to learn more about the mathematical foundations of machine learning.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
Modern Britain
Save
Provides a practical guide to using R for finance. It covers a wide range of topics, from data analysis to financial modeling. It valuable resource for investors who want to learn more about how to use R for their own financial research.
Modern Britain: An Introduction
Hardcover
$$$$
Modern Britain: An Economic and Social History
Kindle Edition
$$
Cover image
Python for Data Analysis
Save
Practical guide to using Python for data analysis. It covers a wide range of topics, from data cleaning to data visualization. It valuable resource for investors who want to learn more about how to use Python for their own research.
Python for Data Analysis
Paperback
Check
Python for Data Analysis
Kindle Edition
Check
Cover image
R for Data Science
Save
Practical guide to using R for data science. It covers a wide range of topics, from data cleaning to data visualization. It valuable resource for investors who want to learn more about how to use R for their own research.
R for Data Science: Import, Tidy, Transform,...
Paperback
$$
(Deutsch) R für Data Science: Daten importieren, bereinigen,...
Kindle Edition
$$$
Financial Theory with Python
Save
Provides a practical guide to using Python for finance. It covers a wide range of topics, from data analysis to financial modeling. It valuable resource for investors who want to learn more about how to use Python for their own financial research.
Financial Theory with Python: A Gentle Introduction
Paperback
$$
Cover image
Drawing Codes
Save
Gentle introduction to machine learning. It covers a wide range of topics, from supervised learning to unsupervised learning. It valuable resource for investors who want to learn more about the basics of machine learning.
Drawing Codes: Experimental Protocols of...
Hardcover
$$
Cover image
Deep Learning
Save
Gentle introduction to deep learning. It covers a wide range of topics, from convolutional neural networks to recurrent neural networks. It valuable resource for investors who want to learn more about the basics of deep learning.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$

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Course image
Effort
4 weeks
Level
Intermediate
Via
Coursera
Institution
Sungkyunkwan University
Instructors
Youngju Nielsen
Haeram Joo
Language
English
Good to know
Addresses Smart Beta products' construction methods, offering hands-on experience in replicating one
Incorporates machine learning methodologies for factor selection and model enhancement, providing practical experience
Utilizes R programming, catering to those familiar with data analysis and statistical modeling
Enhances understanding of data characteristics and overfitting prevention techniques
Delves into advanced asset selection methods using CART, bagging, boosting, and ensemble approaches
Assumes familiarity with fundamental concepts of data-driven investment and R programming
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