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

4.8
estrellas
407 calificaciones

Lionel Martellini, PhD +1 instructores más

14.629 ya inscrito

ofrecido por

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

Acerca de este Curso

19.468 vistas recientes

The practice of investment management has been transformed in recent years by computational methods. 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. In this course, we cover the estimation, of risk and return parameters for meaningful portfolio decisions, and also introduce a variety of state-of-the-art portfolio construction techniques that have proven popular in investment management and portfolio construction due to their enhanced robustness.
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. If you follow along and implement all the lab exercises, you will complete the course with a powerful toolkit that you will be able to use to perform your own analysis and build your own implementations and perhaps even use your newly acquired knowledge to improve on current methods.

Fechas límite flexibles

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Certificado para compartir

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100 % en línea

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Curso 2 de 4 en

Programa Especializado - Investment Management with Python and Machine Learning

Nivel intermedio

Aprox. 11 horas para completar

Inglés (English)

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

Qué aprenderás

Analyze style and factor exposures of portfolios

Implement robust estimates for the covariance matrix

Implement Black-Litterman portfolio construction analysis

Implement a variety of robust portfolio construction models

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 2 de 4 en

Programa Especializado - Investment Management with Python and Machine Learning

Nivel intermedio

Aprox. 11 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

4,86/5 (93 calificaciones)

Lionel Martellini, PhD

EDHEC-Risk Institute, Director

Finance

46.353 alumnos

3 cursos

Vijay Vaidyanathan, PhD

Optimal Asset Management Inc.

CEO

43.739 alumnos

2 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

Semana

Semana 1

3 horas para completar

Style & Factors

3 horas para completar

9 videos (Total 114 minutos), 3 lecturas, 1 cuestionario

9 videos

Welcome video3m

Introduction to factor investing12m

Factor models and the CAPM9m

Multi-Factor models and Fama-French7m

Factor benchmarks and Style analysis8m

Shortcomings of cap-weighted indices11m

From cap-weighted benchmarks to smart-weighted benchmarks12m

Introduction to Lab sessions6m

Module 1 Lab Session - Foundations42m

3 lecturas

Requirements2m

Material at your disposal5m

Module 1- Key points2m

1 ejercicio de práctica

Module 1- Graded Quiz1h

Semana

Semana 2

2 horas para completar

Robust estimates for the covariance matrix

2 horas para completar

7 videos (Total 70 minutos), 1 lectura, 1 cuestionario

Semana

Semana 3

3 horas para completar

Robust estimates for expected returns

3 horas para completar

7 videos (Total 77 minutos), 2 lecturas, 1 cuestionario

7 videos

Lack of Robustness of Expected Return Estimates10m

Agnostic Priors on Expected Return Estimates6m

Using Factor Models to Estimate Expected Returns11m

Extracting Implied Expected Returns8m

Introducing Active Views6m

Black-Litterman Analysis10m

Module 3 Lab Session- Black Litterman23m

2 lecturas

Module 3-Key points2m

The Intuition Behind Black-Litterman Model Portfolios10m

1 ejercicio de práctica

Module 3 - Graded Quiz1h

Semana

Semana 4

3 horas para completar

Portfolio Optimization in Practice

3 horas para completar

7 videos (Total 67 minutos), 4 lecturas, 1 cuestionario

7 videos

Naive Diversification11m

Scientific Diversification11m

Measuring risk contributions6m

Simplified risk parity portfolios7m

Risk Parity Portfolios7m

Comparing Diversification Options8m

Module 4 Lab Session - Risk Contribution and Risk Parity15m

4 lecturas

Module 4-Key points2m

Survey: Alternative Equity Beta Investing10m

Dive into heuristic diversification10m

To be continued (2)10m

1 ejercicio de práctica

Module 4 - Graded quiz1h

Reseñas

4.8

112 reseña

5 stars
81,99 %
4 stars
13,13 %
3 stars
3,64 %
2 stars
0,72 %
1 star
0,48 %

Principales reseñas sobre ADVANCED PORTFOLIO CONSTRUCTION AND ANALYSIS WITH PYTHON

por MM13 de abr. de 2020

Loved how this course was presented. It built well off of the first course and provided labs that let me explore the content. I really enjoyed how Lionel and Vijay presented the material.

por LT17 de feb. de 2021

Good overview on portfolio theory with some of the latest trends (multi-factor models) and Python Lab sessions follow the same logic than the first course, with good tips and good timing.

por DB4 de may. de 2020

This course teaches a great approach to portfolio construction. Crisp course content makes learning even more convenient. My sincere regards to the team behind this noble task,

por Y1 de ago. de 2021

very good quality, covers very in depth knowledge in portfolio construction, but I find the python part is a bit challenging unless you have good command of python programming.

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, Coursera ofrece ayuda económica a los estudiantes que no pueden pagar la tarifa. Solicítala haciendo clic en el enlace de Ayuda económica que está debajo del botón “Inscribirse” a la izquierda. Se te pedirá que completes una solicitud. Recibirás una notificación en caso de que se apruebe. Deberás completar este paso para cada uno de los cursos que forman parte del Programa especializado, incluido el proyecto final. Obtén más información.

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Advanced Portfolio Construction and Analysis with Python