Este curso forma parte de Programa especializado: Investment Management with Python and Machine Learning

4.8
estrellas
1,212 calificaciones

94 %

Vijay Vaidyanathan, PhD +1 instructores más

49.740 ya inscrito

ofrecido por

Programa especializado: Investment Management with Python and Machine LearningEscuela de Negocios EDHEC

Acerca de este 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.

Fechas límite flexibles

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100 % en línea

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Curso 1 de 4 en

Programa especializado: Investment Management with Python and Machine Learning

Aprox. 25 horas para completar

Inglés (English)

Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)

Qué aprenderás

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

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 1 de 4 en

Programa especializado: Investment Management with Python and Machine Learning

Aprox. 25 horas para completar

Inglés (English)

Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)

Instructores

Calificación del instructor

4,88/5 (419 calificaciones)

Vijay Vaidyanathan, PhD

Optimal Asset Management Inc.

CEO

53.256 alumnos

2 cursos

Lionel Martellini, PhD

EDHEC-Risk Institute, Director

Finance

56.337 alumnos

3 cursos

ofrecido por

Escuela de Negocios EDHEC

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 - Qué aprenderás en este curso

Calificación del contenido94%(3,917 calificaciones)

Semana

Semana 1

7 horas para completar

Analysing returns

7 horas para completar

14 videos (Total 225 minutos), 6 lecturas, 1 cuestionario

14 videos

Welcome video3m

Installing Anaconda3m

Fundamentals of Returns10m

Lab Session-Basics of returns29m

Measures of Risk and Reward9m

Lab Session-Risk Adjusted returns28m

Measuring Max Drawdown10m

Lab Session-Drawdown30m

Deviations from Normality9m

Lab Session-Building your own modules12m

Downside risk measures8m

Lab Session-Deviations from Normality30m

Estimating VaR10m

Lab Session-Semi Deviation, VAR and CVAR27m

6 lecturas

Material at your disposal5m

Material for the Lab Sessions10m

Module 1- Key points2m

INCORRECT STATEMENT IN “DEVIATION FROM NORMALITY” VIDEO10m

Semi Deviation10m

Before the Quiz10m

1 ejercicio de práctica

Module 1 Graded Quiz1h

Semana

Semana 2

4 horas para completar

An Introduction to Portfolio Optimization

4 horas para completar

10 videos (Total 172 minutos), 1 lectura, 1 cuestionario

10 videos

The only free lunch in Finance11m

Lab Session-Efficient frontier-Part 123m

Markowitz Optimization and the Efficient Frontier9m

Applying quadprog to draw the efficient Frontier11m

Lab Session-Asset Efficient Frontier-Part 220m

Lab Session-Applying Quadprog to Draw the Efficient Frontier38m

Fund Separation Theorem and the Capital Market Line7m

Lab Session-Locating the Max Sharpe Ratio Portfolio25m

Lack of robustness of Markowitz analysis5m

Lab Session-Plotting EW and GMV on the Efficient Frontier20m

1 lectura

Module 2 - Key points2m

1 ejercicio de práctica

Module 2 Graded Quiz1h

Semana

Semana 3

5 horas para completar

Beyond Diversification

5 horas para completar

15 videos (Total 236 minutos), 4 lecturas, 1 cuestionario

15 videos

Limits of diversification8m

Lab session- Limits of Diversification-Part119m

Lab session-Limits of diversification-Part 222m

An introduction to CPPI - Part 17m

An introduction to CPPI - Part 210m

Lab session-CPPI and Drawdown Constraints-Part129m

Lab session-CPPI and Drawdown Constraints-Part228m

Simulating asset returns with random walks10m

Monte Carlo Simulation6m

Lab Session-Random Walks and Monte Carlo22m

Analyzing CPPI strategies11m

Lab Session-Installing IPYWIDGETS5m

Designing and calibrating CPPI strategies12m

Lab session - interactive plots of monte Carlo Simulations of CPPI and GBM-Part119m

Lab session - interactive plots of monte Carlo Simulations of CPPI and GBM-Part221m

4 lecturas

Module 3 - Key points2m

ipywidgets installation - info5m

gbm function10m

Instruction prior to begin the module 3 graded quizz10m

1 ejercicio de práctica

Module 3 Graded Quiz45m

Semana

Semana 4

9 horas para completar

Introduction to Asset-Liability Management

9 horas para completar

12 videos (Total 327 minutos), 5 lecturas, 1 cuestionario

12 videos

From Asset Management to Asset-Liability Management7m

Lab Session-Present Values,liabilities and funding ratio22m

Liability hedging portfolios12m

Lab Session-CIR Model and cash vs ZC bonds1h 8m

Liability-driven investing (LDI)10m

Lab Session-Liability driven investing51m

Choosing the policy portfolio14m

Lab Session-Monte Carlo simulation of coupon-bearing bonds using CIR33m

Beyond LDI11m

Lab Session-Naive risk budgeting between the PSP & GHP44m

Liability-friendly equity portfolios10m

Lab Session-Dynamic risk budgeting between PSP & LHP40m

5 lecturas

Module 4 - Key points2m

Dynamic Liability-Driven Investing Strategies: The Emergence Of A New Investment Paradigm For Pension Funds?1h 30m

Liability-Driven-Investing1h

Instruction prior to begin module 4 graded quiz2m

To be continued (1)5m

1 ejercicio de práctica

Module 4 Graded Quiz1h

Reseñas

4.8

340 reseña

5 stars
85,33 %
4 stars
12,72 %
3 stars
1,13 %
2 stars
0,40 %
1 star
0,40 %

Principales reseñas sobre INTRODUCTION TO PORTFOLIO CONSTRUCTION AND ANALYSIS WITH PYTHON

por LL11 de dic. de 2019

This is one of the best online courses which combines the knowledge of Finance and Python. In fact, with python as a tool, I can have a better understanding of the theory behind it.

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 DB28 de feb. de 2020

Very insightful. Learnt a lot of new things in finance which can't be learnt in regular undergrad programmes in universities. Thank You to EDHEC RISK INSTITUTE and COURSERA...

por MM7 de ago. de 2020

The course was right paced and assignments were challenging. The moderators were very helpful in their support and feedback. Looking forward to doing more course with the Edhec.

Ver todas las reseñas

Acerca de Programa especializado: 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.

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.

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Introduction to Portfolio Construction and Analysis with Python

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Qué aprenderás