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Introduction to Portfolio Analysis in R

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

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Course Description

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.
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In the following Tracks

Finance Fundamentals in R

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Quantitative Analyst in R

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  1. 1

    The Building Blocks

    Free

    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.

    Play Chapter Now
    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
  2. 2

    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.

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    Dimensions of portfolio performance
    50 xp
    Exploring the monthly S&P 500 returns
    100 xp
    The monthly mean and volatility
    100 xp
    The (annualized) Sharpe ratio
    50 xp
    Excess returns and the portfolio's Sharpe ratio
    100 xp
    Annualized mean and volatility
    100 xp
    Time-variation in portfolio performance
    50 xp
    Effect of window length choice
    50 xp
    Rolling annualized mean and volatility
    100 xp
    Subperiod performance analysis and the function window
    100 xp
    Non-normality of the return distribution
    50 xp
    Balancing risk and reward
    50 xp
    Detecting non-normality using skewness and kurtosis
    100 xp
    Downside risk measures
    100 xp
    Drawdowns due to buying high, selling low
    100 xp
  3. 3

    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.

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    Drivers in the case of two assets
    50 xp
    Driver 1: The assets' individual performance
    50 xp
    Driver 2: The choice of portfolio weights
    100 xp
    Driver 3: The correlation between the asset returns
    50 xp
    Interpreting correlation
    100 xp
    Using matrix notation
    50 xp
    Making a risk-reward scatter diagram
    100 xp
    The covariance matrix
    100 xp
    Matrix-based calculation of portfolio mean and variance
    100 xp
    Portfolio risk budget
    50 xp
    Who did it?
    100 xp
  4. 4

    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.

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    Modern portfolio theory of Harry Markowitz
    50 xp
    Mean-variance based investing in DJIA stocks
    50 xp
    Exploring monthly returns of the 30 DJIA stocks
    100 xp
    Finding the mean-variance efficient portfolio
    100 xp
    Effect of the return target
    100 xp
    Imposing weight constraints
    100 xp
    The efficient frontier
    50 xp
    Computing the efficient frontier using a grid of target returns
    100 xp
    Interpreting the efficient frontier
    50 xp
    Properties of the efficient frontier
    50 xp
    The minimum variance and maximum Sharpe ratio portfolio
    100 xp
    In-sample vs. out-of-sample evaluation
    50 xp
    Split-sample evaluation
    100 xp
    Out of sample performance evaluation
    100 xp
    It ain't over
    100 xp
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

In the following Tracks

Finance Fundamentals in R

Go To Track

Quantitative Analyst in R

Go To Track

datasets

Stock prices for Apple and MicrosoftBonds pricesCommodities pricesEquities pricesStock prices for DIJAReal estate pricesDaily prices in S&P 500

collaborators

Collaborator's avatar
Lore Dirick

prerequisites

Intermediate R for Finance
Kris Boudt HeadshotKris Boudt

Professor of Finance and Econometrics at VUB and VUA

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