Este curso faz parte do Programa de cursos integrados Investment Management with Python and Machine Learning

4.8
estrelas
1,103 classificações
•
310 avaliações

Vijay Vaidyanathan, PhD +1 mais instrutores

41.052 já se inscreveram

oferecido por

Programa de cursos integrados Investment Management with Python and Machine LearningEDHEC Business School

Informações sobre o curso

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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.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Certificados compartilháveis

Tenha o certificado após a conclusão

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Curso 1 de 4 no

Programa de cursos integrados Investment Management with Python and Machine Learning

Aprox. 24 horas para completar

Inglês

Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Alemão, Russo, Inglês, Espanhol

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

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Certificados compartilháveis

Tenha o certificado após a conclusão

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Curso 1 de 4 no

Programa de cursos integrados Investment Management with Python and Machine Learning

Aprox. 24 horas para completar

Inglês

Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Alemão, Russo, Inglês, Espanhol

Instrutores

Classificação do instrutor

4,88/5 (375 classificações)

Vijay Vaidyanathan, PhD

Optimal Asset Management Inc.

CEO

43.974 aprendizes

2 Cursos

Lionel Martellini, PhD

EDHEC-Risk Institute, Director

Finance

46.598 aprendizes

3 Cursos

oferecido por

EDHEC Business School

Founded in 1906, EDHEC is now one of Europe’s top 15 business schools .

Based in Lille, Nice, Paris, London and Singapore, and counting over 90 nationalities on its campuses, EDHEC is a fully international school directly connected to the business world. With over 40,000 graduates in 120 countries, it trains committed managers capable of dealing with the challenges of a fast-evolving world. Harnessing its core values of excellence, innovation and entrepreneurial spirit, EDHEC has developed a strategic model founded on research of true practical use to society, businesses and students, and which is particularly evident in the work of EDHEC-Risk Institute and Scientific Beta. The School functions as a genuine laboratory of ideas and plays a pioneering role in the field of digital education via EDHEC Online, the first fully online degree-level training platform. These various components make EDHEC a centre of knowledge, experience and diversity, geared to preparing new generations of managers to excel in a world subject to transformational change. EDHEC in figures: 8,600 students in academic education, 19 degree programmes ranging from bachelor to PhD level, 184 professors and researchers, 11 specialist research centres.

Programa - O que você aprenderá com este curso

Classificação do conteúdo94%(3,672 classificações)

Semana

Semana 1

6 horas para concluir

Analysing returns

6 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

Semana

Semana 2

4 horas para concluir

An Introduction to Portfolio Optimization

4 horas para concluir

10 vídeos (Total 172 mín.), 1 leitura, 1 teste

10 videos

The only free lunch in Finance11min

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

Semana

Semana 3

5 horas para concluir

Beyond Diversification

5 horas para concluir

15 vídeos (Total 236 mín.), 4 leituras, 1 teste

15 videos

Limits of diversification8min

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

Semana

Semana 4

9 horas para concluir

Introduction to Asset-Liability Management

9 horas para concluir

12 vídeos (Total 327 mín.), 5 leituras, 1 teste

12 videos

From Asset Management to Asset-Liability Management7min

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 quiz2min

To be continued (1)5min

1 exercício prático

Module 4 Graded Quiz1h

Avaliações

4.8

310 avaliações

5 stars
85,40%
4 stars
12,62%
3 stars
1,16%
2 stars
0,44%
1 star
0,35%

Principais avaliações do INTRODUCTION TO PORTFOLIO CONSTRUCTION AND ANALYSIS WITH PYTHON

por SW5 de Ago de 2020

I am self taught in Python and have an industry background in Finance. This course was a good connector/provided additional insight on using Python to process portfolio performance and data analysis.

por MA29 de Nov de 2020

Awesome Course, Great instructors that give a nice balance of theory and practice, the practice is very hands-on and will help you understand the theory and give you useful tools from the first week.

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 TK26 de Set de 2020

The course is particularly useful for people with a finance background to learn how to model a complex process using python. Lecturers are very knowledgeable and step-by-step guide in teaching.

Visualizar todas as avaliações

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.

Perguntas Frequentes – FAQ

Quando terei acesso às palestras e às tarefas?
O acesso a palestras e tarefas depende do tipo de inscrição. Se você participar de um curso como ouvinte, você poderá ver quase todo o conteúdo do curso gratuitamente. Para acessar tarefas valendo nota e obter um Certificado, você precisará adquirir a experiência do Certificado, durante ou após a participação como ouvinte. Se você não vir a opção de participar como ouvinte:
o curso pode não oferecer essa opção. Você pode experimentar um teste gratuito ou solicitar o auxílio financeiro.
Em vez disso, o curso pode oferecer 'Curso completo, sem Certificado'. Com esta opção, é possível ver todo o conteúdo do curso, enviar as avaliações necessárias e obter uma nota final. Isso também significa que você não poderá comprar uma experiência de Certificado.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
Existe algum auxílio financeiro disponível?
Sim, a Coursera oferece auxílio financeiro ao aluno que não possa pagar a taxa. Faça a solicitação clicando no link Auxílio Financeiro, abaixo do botão "Inscreva-se" à esquerda. Preencha uma solicitação e será notificado caso seja aprovado. Você terá que completar esta etapa para cada curso na Especialização, incluindo o Trabalho de Conclusão de Curso. Saiba mais .

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Introduction to Portfolio Construction and Analysis with Python