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Course Description
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.
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Introduction and Portfolio Theory
FreeThis 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.
Welcome to the course!50 xpLoad the PortfolioAnalytics package100 xpSolve a simple portfolio optimization problem100 xpVisualize results100 xpModern Portfolio Theory objective50 xpDefining risk50 xpChallenges of portfolio optimization50 xpQuadratic utility50 xpMaximize quadratic utility function100 xpIntroduction to PortfolioAnalytics50 xpKey design goals50 xp - 2
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.
Portfolio specification, constraints, and objectives50 xpCreate a portfolio specification100 xpAdd constraints100 xpAdd objectives100 xpRunning optimizations50 xpSingle-Period optimization100 xpOptimization with periodic rebalancing100 xpGlobal solvers50 xpAnalyzing optimization results50 xpObjective measure values100 xpOptimal weights100 xp - 3
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.
Introduction to moments50 xpSample moment estimates100 xpAdvanced moment estimates100 xpMethod for estimating moments50 xpCustom moment functions50 xpDefine a custom moment function100 xpOptimization with custom moment function100 xpObjective functions50 xpCustom objective function100 xpOptimization with custom objective function100 xp - 4
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.
Application50 xpCompute benchmark returns100 xpDefine the portfolio optimization problem100 xpBenchmark50 xpOptimization backtest50 xpBacktest with periodic rebalancing100 xpRefine constraints and objectives100 xpDo improved estimates lead to improved performance?100 xpAnalyze results and compare to benchmark100 xpCongratulations!50 xp
In the following tracks
Associate Data AnalystAssociate Data EngineerData ScientistData ScientistFinance FundamentalsQuantitative AnalystSQL FundamentalsDatasets
Portfolio specifications object IPortfolio specifications object IISet of random portfolios ISet of random portfolios IICollaborators
Prerequisites
Introduction to Portfolio Analysis in RRoss Bennett
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Co-author of PortfolioAnalytics R package
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