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

4.7
estrellas
465 calificaciones

Lionel Martellini, PhD +1 instructores más

18.897 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. 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

Obtén un certificado al finalizar

100 % en línea

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Laboratorios de Coursera

Incluye proyectos de aprendizaje prácticos.

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Curso 2 de 4 en

Programa especializado: Investment Management with Python and Machine Learning

Nivel intermedio

Aprox. 12 horas para completar

Inglés (English)

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.

Laboratorios de Coursera

Incluye proyectos de aprendizaje prácticos.

Obtén más información sobre los laboratorios de Coursera

Curso 2 de 4 en

Programa especializado: Investment Management with Python and Machine Learning

Nivel intermedio

Aprox. 12 horas para completar

Inglés (English)

Instructores

Calificación del instructor

4,87/5 (108 calificaciones)

Lionel Martellini, PhD

EDHEC-Risk Institute, Director

Finance

61.609 alumnos

3 cursos

Vijay Vaidyanathan, PhD

Optimal Asset Management Inc.

CEO

58.331 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

Semana1

Semana 1

4 horas para completar

Style & Factors

4 horas para completar

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

9 videos

Welcome video3mIntroduction to factor investing12mFactor models and the CAPM9mMulti-Factor models and Fama-French7mFactor benchmarks and Style analysis8mShortcomings of cap-weighted indices11mFrom cap-weighted benchmarks to smart-weighted benchmarks12mIntroduction to Lab sessions6mModule 1 Lab Session - Foundations42m

3 lecturas

Requirements2mMaterial at your disposal5mModule 1- Key points2m

1 ejercicio de práctica

Module 1- Graded Quiz1h

Semana2

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

Semana3

Semana 3

3 horas para completar

Robust estimates for expected returns

3 horas para completar

7 videos (Total 77 minutos), 3 lecturas, 1 cuestionario

7 videos

Lack of Robustness of Expected Return Estimates10mAgnostic Priors on Expected Return Estimates6mUsing Factor Models to Estimate Expected Returns11mExtracting Implied Expected Returns8mIntroducing Active Views6mBlack-Litterman Analysis10mModule 3 Lab Session- Black Litterman23m

3 lecturas

Module 3-Key points2mThe Intuition Behind Black-Litterman Model Portfolios10mImportat message before the quiz!10m

1 ejercicio de práctica

Module 3 - Graded Quiz1h

Semana4

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 Diversification11mScientific Diversification11mMeasuring risk contributions6mSimplified risk parity portfolios7mRisk Parity Portfolios7mComparing Diversification Options8mModule 4 Lab Session - Risk Contribution and Risk Parity15m

4 lecturas

Module 4-Key points2mSurvey: Alternative Equity Beta Investing10mDive into heuristic diversification10mTo be continued (2)10m

1 ejercicio de práctica

Module 4 - Graded quiz1h

Reseñas

4.7

121 reseña

5 stars
81,75 %
4 stars
13,30 %
3 stars
3,64 %
2 stars
0,64 %
1 star
0,64 %

Principales reseñas sobre ADVANCED PORTFOLIO CONSTRUCTION AND ANALYSIS WITH PYTHON

por LL4 de sep. de 2021

This course gives a good understanding of Fama-French, GARCH, Black-Litterman and risk parity models among many others, not only theoretically, but also through hands-on Lab sessions.

por RI1 de abr. de 2020

Another excellent course. One thing I would have liked to have is longer lab session videos like in MOOC 1 to ensure we can re-create the notebooks as we go along.

por CC31 de ene. de 2021

Really a great course, instructors video then are a great resource. I'd have liked more mathematical analysis but I understand it could have gone beyond scope.

por PR29 de jun. de 2020

Fantastic portfolio construction techniques, although black letterman model could have been explained better . Overall great course with real world financial applications

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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Advanced Portfolio Construction and Analysis with Python

Acerca de este Curso

Qué aprenderás