Ana içeriğe atla

Ücretsiz Kurs

R ile Portföy Analizine Giriş

TemelBeceri Seviyesi

Güncel 11.2023

Finans ve R becerilerinizi kullanarak finansal portföyleri geriye dönük test edin, analiz edin ve optimize edin.

Ücretsiz Kursa Başlayın

Ücretsiz Olarak Dahildir

RApplied Finance

5 sa

14 video

57 Egzersiz

4,400 XP

35,278

Başarı Belgesi

Binlerce şirketten öğrencinin sevgisini kazandı

Training a Team?

Try for Business

Kurs Açıklaması

Yatırımda altın kural: Portföy stratejini her zaman tarihsel veriler üzerinde test etmek ve stratejiyi uygular hale geldiğinde performansını sürekli izlemek. Bu derste, PerformanceAnalytics paketiyle portföy getirilerini eleştirel biçimde analiz ederek bunu öğreneceksin. Ayrıca, risk ve getiriyi en iyi dengeleyecek portföy ağırlıklarını nasıl tahmin edeceğini de göreceksin. Bu, portföy teorisini R uygulamalarıyla birleştiren, hisse senedi portföyleri ve varlık dağılımı sorunlarına dair gerçek örneklerle desteklenen, veriye dayalı bir ders. Dersi bitirdikten sonra verileri daha fazla keşfetmek istersen, ilk üç bölümde kullanılan veriler tseries paketinden elde edilebilir.

Önkoşullar

Intermediate R for Finance

1

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.

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

Bölümü Başlat

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.

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

Bölümü Başlat

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.

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?

Bölümü Başlat

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.

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

Bölümü Başlat

R ile Portföy Analizine Giriş

Kurs
Tamamlandı

Başarı Belgesi Kazanın

Bu kimlik bilgisini LinkedIn profilinize, özgeçmişinize veya CV'nize ekleyin
Sosyal medyada ve performans incelemenizde paylaşınŞimdi Kaydolun

Bugün 19 milyondan fazla öğrenciye katılın ve R ile Portföy Analizine Giriş eğitimine başlayın!

DataCamp for Mobile ile veri becerilerinizi geliştirin

Mobil kurslarımız ve günde 5 dakikalık kodlama görevlerimizle hareket halindeyken ilerleme kaydedin.