- 14 Videos
- 57 Exercises
- 5 hours
- 10,298 Participants
- 4450 XP

**Instructor(s):**

Kris Boudt is a Professor of Finance and Econometrics at Vrije Universiteit Brussel and Amsterdam. Since 2011, he is the research partner at Finvex, a leading financial investment designer. Kris Boudt is an expert in portfolio analysis and has contributed to the development of several smart beta equity indices and has published his research in the Journal of Portfolio Management, Journal of Financial Econometrics and the Review of Finance, among others. He has a passion for developing financial econometrics tools in R and is a coauthor of the highfrequency, PeerPerformance and PortfolioAnalytics R packages.

Lore Dirick

Josiah Parry

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.

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.

- Welcome To The Course 50 xp
- Getting a grasp of the basics 50 xp
- Get a feel for the data 100 xp
- The Portfolio Weights 50 xp
- Calculating portfolio weights when component values are given 100 xp
- The weights of an equally weighted portfolio 50 xp
- The weights of a market capitalization weighted portfolio 100 xp
- The Portfolio Return 50 xp
- Calculation of portfolio returns 100 xp
- From simple to gross and multi-period returns 50 xp
- The asymmetric impact of gains and losses 50 xp
- PerformanceAnalytics 50 xp
- Buy-and-hold versus (daily) rebalancing 50 xp
- The time series of asset returns 100 xp
- The time series of portfolio returns 100 xp
- The time series of weights 100 xp

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 statisical 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.

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

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.