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Introduction to Portfolio Analysis in R

via Datacamp

Overview

Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.

A golden rule in investing is to always test the portfolio strategy on historical data, and, once you are trading the strategy, to constantly monitor its performance. In this course, you will learn this by critically analyzing portfolio returns using the package PerformanceAnalytics. The course also shows how to estimate the portfolio weights that optimally balance risk and return. This is a data-driven course that combines portfolio theory with the practice in R, illustrated on real-life examples of equity portfolios and asset allocation problems. If you'd like to continue exploring the data after you've finished this course, the data used in the first three chapters can be obtained using the tseries-package. The code to get them can be found here. The data used in chapter 4 can be downloaded here.

Syllabus

The building blocks
-Asset returns and portfolio weights; those are the building blocks of a portfolio return. This chapter is about computing those portfolio weights and returns in R.

Analyzing performance
-The history of portfolio returns reveals valuable information about how much the investor can expect to gain or lose. This chapter introduces the R functionality to analyze the investment performance based on a statistical analysis of the portfolio returns. It includes graphical analysis and the calculation of performance statistics expressing average return, risk, and risk-adjusted return over rolling estimation samples.

Performance drivers
-In addition to studying portfolio performance based on the observed portfolio return series, it is relevant to determine how individual (expected) returns, volatilities, and correlations interact to determine the total portfolio performance.

Optimizing the portfolio
-We have up to now considered the portfolio weights as given. In this chapter, you learn how to determine in R the portfolio weights that are optimal in terms of achieving a target return with minimum variance, while satisfying constraints on the portfolio weights.

Taught by

Kris Boudt

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