Introduction to Portfolio Construction and Analysis with Python
The practice of investment management has been transformed in recent years by computational methods. This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. However, instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language.
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- Level Foundation
- المدة 25 ساعات hours
- الطبع بواسطة EDHEC Business School
-
Offered by
عن
The practice of investment management has been transformed in recent years by computational methods. This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. However, instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language. This course is the first in a four course specialization in Data Science and Machine Learning in Asset Management but can be taken independently. In this course, we cover the basics of Investment Science, and we'll build practical implementations of each of the concepts along the way. We'll start with the very basics of risk and return and quickly progress to cover a range of topics including several Nobel Prize winning concepts. We'll cover some of the most popular practical techniques in modern, state of the art investment management and portfolio construction. As we cover the theory and math in lecture videos, we'll also implement the concepts in Python, and you'll be able to code along with us so that you have a deep and practical understanding of how those methods work. By the time you are done, not only will you have a foundational understanding of modern computational methods in investment management, you'll have practical mastery in the implementation of those methods.الوحدات
Section 1- Fundamentals of risk and returns
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8
Videos
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3
Readings
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1
Lab
1
Labs
- Code and Data
8
Videos
- Welcome video
- Installing Anaconda
- Fundamentals of Returns
- Lab Session-Basics of returns
- Measures of Risk and Reward
- Lab Session-Risk Adjusted returns
- Measuring Max Drawdown
- Lab Session-Drawdown
3
Readings
- Material at your disposal
- Material for the Lab Sessions
- Module 1- Key points
Section 2- Beyond the Gaussian case:Extreme risk estimates
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6
Videos
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3
Readings
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1
Discussion
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1
Assignment
1
Assignment
- Module 1 Graded Quiz
1
Discussions
- Evidence of non-normality in asset returns
6
Videos
- Deviations from Normality
- Lab Session-Building your own modules
- Downside risk measures
- Lab Session-Deviations from Normality
- Estimating VaR
- Lab Session-Semi Deviation, VAR and CVAR
3
Readings
- INCORRECT STATEMENT IN “DEVIATION FROM NORMALITY” VIDEO
- Semi Deviation
- Before the Quiz
Section 1-Introduction to Optimization and The Efficient Frontier
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6
Videos
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1
Readings
6
Videos
- The only free lunch in Finance
- Lab Session-Efficient frontier-Part 1
- Markowitz Optimization and the Efficient Frontier
- Applying quadprog to draw the efficient Frontier
- Lab Session-Asset Efficient Frontier-Part 2
- Lab Session-Applying Quadprog to Draw the Efficient Frontier
1
Readings
- Module 2 - Key points
Section 2-Implementing Markowitz
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4
Videos
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1
Discussion
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1
Assignment
1
Assignment
- Module 2 Graded Quiz
1
Discussions
- Merits and limits of portfolio optimization methods
4
Videos
- Fund Separation Theorem and the Capital Market Line
- Lab Session-Locating the Max Sharpe Ratio Portfolio
- Lack of robustness of Markowitz analysis
- Lab Session-Plotting EW and GMV on the Efficient Frontier
Section 1
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8
Videos
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1
Readings
8
Videos
- Limits of diversification
- Lab session- Limits of Diversification-Part1
- Lab session-Limits of diversification-Part 2
- An introduction to CPPI - Part 1
- An introduction to CPPI - Part 2
- Lab session-CPPI and Drawdown Constraints-Part1
- Lab session-CPPI and Drawdown Constraints-Part2
- Simulating asset returns with random walks
1
Readings
- Module 3 - Key points
Section 2
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7
Videos
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3
Readings
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1
Discussion
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1
Assignment
1
Assignment
- Module 3 Graded Quiz
1
Discussions
- Merits and limits of portfolio insurance strategies
7
Videos
- Monte Carlo Simulation
- Lab Session-Random Walks and Monte Carlo
- Analyzing CPPI strategies
- Lab Session-Installing IPYWIDGETS
- Designing and calibrating CPPI strategies
- Lab session - interactive plots of monte Carlo Simulations of CPPI and GBM-Part1
- Lab session - interactive plots of monte Carlo Simulations of CPPI and GBM-Part2
3
Readings
- ipywidgets installation - info
- gbm function
- Instruction prior to begin the module 3 graded quizz
Section 1
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6
Videos
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3
Readings
6
Videos
- From Asset Management to Asset-Liability Management
- Lab Session-Present Values,liabilities and funding ratio
- Liability hedging portfolios
- Lab Session-CIR Model and cash vs ZC bonds
- Liability-driven investing (LDI)
- Lab Session-Liability driven investing
3
Readings
- Module 4 - Key points
- Dynamic Liability-Driven Investing Strategies: The Emergence Of A New Investment Paradigm For Pension Funds?
- Liability-Driven-Investing
Section 2
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6
Videos
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2
Readings
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1
Discussion
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1
Assignment
1
Assignment
- Module 4 Graded Quiz
1
Discussions
- Merits and limits of asset-liability management
6
Videos
- Choosing the policy portfolio
- Lab Session-Monte Carlo simulation of coupon-bearing bonds using CIR
- Beyond LDI
- Lab Session-Naive risk budgeting between the PSP & GHP
- Liability-friendly equity portfolios
- Lab Session-Dynamic risk budgeting between PSP & LHP
2
Readings
- Instruction prior to begin module 4 graded quiz
- To be continued (1)
Auto Summary
Unlock the world of modern investment management with "Introduction to Portfolio Construction and Analysis with Python." This course, designed for the Business & Management domain, dives into the computational methods that have revolutionized investment practices. Led by Coursera, the course offers a robust foundation in Investment Science while emphasizing practical skills through hands-on Python programming. You'll begin with basic concepts of risk and return, then quickly advance to cover a range of topics, including several Nobel Prize-winning ideas. Each theoretical lesson is paired with practical coding exercises, ensuring you gain not only theoretical knowledge but also practical mastery of state-of-the-art investment management techniques. Spanning approximately 1500 minutes, this foundational course is the first in a four-part specialization in Data Science and Machine Learning in Asset Management, though it stands strong on its own. Ideal for beginners looking to break into the field, it is available through Coursera’s Starter subscription package. Whether you're a budding investment manager or a professional looking to enhance your computational skills, this course provides the tools and knowledge to excel in the evolving landscape of portfolio construction and analysis.
Vijay Vaidyanathan, PhD
Martellini