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.
Programa de cursos integrados Investment Management with Python and Machine LearningEDHEC Business SchoolInformações sobre o curso
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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Aprox. 24 horas para completar
Inglês
Legendas: Inglês
O que você vai aprender
Gain an intuitive understanding for the underlying theory behind Modern Portfolio Construction Techniques
Write custom Python code to estimate risk and return parameters
Utilize powerful Python optimization libraries to build scientifically and systematically diversified portfolios
Build custom utilities in Python to test and compare portfolio strategies
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Aprox. 24 horas para completar
Inglês
Legendas: Inglês
oferecido por
EDHEC Business School
Founded in 1906, EDHEC is now one of Europe’s top 15 business schools .
Programa - O que você aprenderá com este curso
5 horas para concluir
Analysing returns
5 horas para concluir
14 vídeos (Total 225 mín.), 5 leituras, 1 teste
14 videos
Welcome video3min
Installing Anaconda3min
Fundamentals of Returns10min
Lab Session-Basics of returns29min
Measures of Risk and Reward9min
Lab Session-Risk Adjusted returns28min
Measuring Max Drawdown10min
Lab Session-Drawdown30min
Deviations from Normality9min
Lab Session-Building your own modules12min
Downside risk measures8min
Lab Session-Deviations from Normality30min
Estimating VaR10min
Lab Session-Semi Deviation, VAR and CVAR27min
5 leituras
Material at your disposal5min
Material for the Lab Sessions10min
Module 1- Key points2min
INCORRECT STATEMENT IN “DEVIATION FROM NORMALITY” VIDEO10min
Before the Quiz10min
1 exercício prático
Module 1 Graded Quiz1h
4 horas para concluir
An Introduction to Portfolio Optimization
4 horas para concluir
10 vídeos (Total 172 mín.), 1 leitura, 1 teste
10 videos
Lab Session-Efficient frontier-Part 123min
Markowitz Optimization and the Efficient Frontier9min
Applying quadprog to draw the efficient Frontier11min
Lab Session-Asset Efficient Frontier-Part 220min
Lab Session-Applying Quadprog to Draw the Efficient Frontier38min
Fund Separation Theorem and the Capital Market Line7min
Lab Session-Locating the Max Sharpe Ratio Portfolio25min
Lack of robustness of Markowitz analysis5min
Lab Session-Plotting EW and GMV on the Efficient Frontier20min
1 leituras
Module 2 - Key points2min
1 exercício prático
Module 2 Graded Quiz1h
5 horas para concluir
Beyond Diversification
5 horas para concluir
15 vídeos (Total 236 mín.), 4 leituras, 1 teste
15 videos
Lab session- Limits of Diversification-Part119min
Lab session-Limits of diversification-Part 222min
An introduction to CPPI - Part 17min
An introduction to CPPI - Part 210min
Lab session-CPPI and Drawdown Constraints-Part129min
Lab session-CPPI and Drawdown Constraints-Part228min
Simulating asset returns with random walks10min
Monte Carlo Simulation6min
Lab Session-Random Walks and Monte Carlo22min
Analyzing CPPI strategies11min
Lab Session-Installing IPYWIDGETS5min
Designing and calibrating CPPI strategies12min
Lab session - interactive plots of monte Carlo Simulations of CPPI and GBM-Part119min
Lab session - interactive plots of monte Carlo Simulations of CPPI and GBM-Part221min
4 leituras
Module 3 - Key points2min
ipywidgets installation - info5min
gbm function10min
Instruction prior to begin the module 3 graded quizz10min
1 exercício prático
Module 3 Graded Quiz45min
9 horas para concluir
Introduction to Asset-Liability Management
9 horas para concluir
12 vídeos (Total 327 mín.), 5 leituras, 1 teste
12 videos
Lab Session-Present Values,liabilities and funding ratio22min
Liability hedging portfolios12min
Lab Session-CIR Model and cash vs ZC bonds1h 8min
Liability-driven investing (LDI)10min
Lab Session-Liability driven investing51min
Choosing the policy portfolio14min
Lab Session-Monte Carlo simulation of coupon-bearing bonds using CIR33min
Beyond LDI11min
Lab Session-Naive risk budgeting between the PSP & GHP44min
Liability-friendly equity portfolios10min
Lab Session-Dynamic risk budgeting between PSP & LHP40min
5 leituras
Module 4 - Key points2min
Dynamic Liability-Driven Investing Strategies: The Emergence Of A New Investment Paradigm For Pension Funds?1h 30min
Liability-Driven-Investing1h
Instruction prior to begin module 4 graded quizz2min
To be continued (1)5min
1 exercício prático
Module 4 Graded Quiz1h
Avaliações
Principais avaliações do INTRODUCTION TO PORTFOLIO CONSTRUCTION AND ANALYSIS WITH PYTHON
por DH26 de Mai de 2020
Enjoyable course. One has to be conversant with basic Phyton to follow this course. What I learnt the most is the ability to use Phyton coding to demonstrate the concept of portfolio investment.
por SK8 de Dez de 2019
The course is great, I enjoyed very detailed Python labs, they taught me a lot. But almost no support from the staff in the forums. If you don't understand something, you are on your own.
por TT26 de Dez de 2019
Very useful course that covered some simple investment strategies and taught you to implement it using python. Recommended to those who want to enter the quantitative investment world.
por XX7 de Mar de 2020
One of the most hardcore, challenging but knowledge-harvesting courses that I ever had. Highly recommend, and you will be better off if you type in all codes VJ did simultaneously.
Sobre Programa de cursos integrados Investment Management with Python and Machine Learning
The Data Science and Machine Learning for Asset Management Specialization has been designed to deliver a broad and comprehensive introduction to modern methods in Investment Management, with a particular emphasis on the use of data science and machine learning techniques to improve investment decisions.By the end of this specialization, you will have acquired the tools required for making sound investment decisions, with an emphasis not only on the foundational theory and underlying concepts, but also on practical applications and implementation. 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 through a series of dedicated lab sessions.
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The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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