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DataCamp

Intermediate Portfolio Analysis in R

via DataCamp

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Overview

Advance you R finance skills to backtest, analyze, and optimize financial portfolios.

This course builds on the fundamental concepts from Introduction to Portfolio Analysis in R and explores advanced concepts in the portfolio optimization process. It is critical for an analyst or portfolio manager to understand all aspects of the portfolio optimization problem to make informed decisions. In this course, you will learn a quantitative approach to apply the principles of modern portfolio theory to specify a portfolio, define constraints and objectives, solve the problem, and analyze the results. This course will use the R package PortfolioAnalytics to solve portfolio optimization problems with complex constraints and objectives that mirror real world problems.

Syllabus

Introduction and Portfolio Theory
-This chapter will give you a brief review of Modern Portfolio Theory and introduce you to the PortfolioAnalytics package by solving a couple portfolio optimization problems.

Portfolio Optimization Workflow
-The focus of this chapter is a detailed overview of the recommended workflow for solving portfolio optimization problems with PortfolioAnalytics. You will learn how to create a portfolio specification, add constraints, objectives, run the optimization, and analyze the results of the optimization output.

Objective Functions and Moment Estimation
-In this chapter, you will learn about estimating moments, characteristics of the distribution of asset returns, as well as custom objective functions.

Application
-In the final chapter of the course, you will solve a portfolio optimization problem that mimics a real world real world example of constructing a portfolio of hedge fund strategy with different style definitions.

Taught by

Ross Bennett

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