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Introduction to Portfolio Analysis in R

Débutant

Updated 12/2024

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

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RFinance appliquée5 heures14 vidéos57 exercices4,400 XP33,730Déclaration de réalisation

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Description du cours

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.

Conditions préalables

Intermediate R for Finance

1

The Building Blocks

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Welcome to the course

Getting a grasp of the basics

Get a feel for the data

The portfolio weights

Calculating portfolio weights when component values are given

The weights of an equally weighted portfolio

The weights of a market capitalization-weighted portfolio

The portfolio return

Calculation of portfolio returns

From simple to gross and multi-period returns

The asymmetric impact of gains and losses

PerformanceAnalytics

Buy-and-hold versus (daily) rebalancing

The time series of asset returns

The time series of portfolio returns

The time series of weights

2

Analyzing Performance

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Dimensions of portfolio performance

Exploring the monthly S&P 500 returns

The monthly mean and volatility

The (annualized) Sharpe ratio

Excess returns and the portfolio's Sharpe ratio

Annualized mean and volatility

Time-variation in portfolio performance

Effect of window length choice

Rolling annualized mean and volatility

Subperiod performance analysis and the function window

Non-normality of the return distribution

Balancing risk and reward

Detecting non-normality using skewness and kurtosis

Downside risk measures

Drawdowns due to buying high, selling low

3

Performance Drivers

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Drivers in the case of two assets

Driver 1: The assets' individual performance

Driver 2: The choice of portfolio weights

Driver 3: The correlation between the asset returns

Interpreting correlation

Using matrix notation

Making a risk-reward scatter diagram

The covariance matrix

Matrix-based calculation of portfolio mean and variance

Portfolio risk budget

Who did it?

4

Optimizing the Portfolio

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Modern portfolio theory of Harry Markowitz

Mean-variance based investing in DJIA stocks

Exploring monthly returns of the 30 DJIA stocks

Finding the mean-variance efficient portfolio

Effect of the return target

Imposing weight constraints

The efficient frontier

Computing the efficient frontier using a grid of target returns

Interpreting the efficient frontier

Properties of the efficient frontier

The minimum variance and maximum Sharpe ratio portfolio

In-sample vs. out-of-sample evaluation

Split-sample evaluation

Out of sample performance evaluation

It ain't over

Introduction to Portfolio Analysis in R

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