Investment Portfolio Analysis with R
4.1 (129 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
841 students enrolled

Investment Portfolio Analysis with R

Learn investment portfolio analysis from basic to expert level through a practical course with R statistical software.
4.1 (129 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
841 students enrolled
Created by Diego Fernandez
Last updated 1/2018
English
English [Auto]
Current price: $14.99 Original price: $24.99 Discount: 40% off
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This course includes
  • 6.5 hours on-demand video
  • 8 articles
  • 8 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Read or download main asset classes benchmark indexes replicating funds data to perform investment portfolio analysis operations by installing related packages and running script code on RStudio IDE.
  • Compare main asset classes benchmark indexes replicating funds returns and risks tradeoffs for cash, bonds, stocks, commodities, real estate and currencies.
  • Estimate portfolio expected returns, historical and market participants implied volatility.
  • Approximate portfolio expected excess returns using capital asset pricing model (CAPM), Fama-French-Carhart factors model and arbitrage pricing theory model (APT).
  • Hedge portfolio systematic risk through options trading strategies benchmark indexes replicating funds.
  • Evaluate hedge fund index performance and assess portfolio returns and risks amplification through leverage.
  • Calculate portfolio performance metrics such as Sharpe, Treynor, Sortino, and Kelly ratios.
  • Estimate benchmark global portfolios returns from periodically rebalanced equal weighted asset allocations and those from well-known investment managers.
  • Optimize global portfolios asset allocation weights for mean maximization, standard deviation minimization, mean maximization and standard deviation minimization, mean maximization and value at risk minimization objectives within training range based on Markowitz portfolio theory.
  • Approximate global portfolios returns from periodically rebalanced optimized asset allocations within testing range and compare them with equal weighted and well-known investment managers benchmark portfolios.
  • Evaluate global portfolios performance through global risk factors model and estimate their expected return, expected excess return and expected return contribution from global risk factors exposure while assessing investment costs impact on portfolio performance.
Requirements
  • R statistical software is required. Downloading instructions included.
  • RStudio Integrated Development Environment (IDE) is recommended. Downloading instructions included.
  • Practical example data and R script code files provided with the course.
  • Prior basic R statistical software knowledge is useful but not required.
Description

Full Course Content Last Update 01/2018

Learn investment portfolio analysis through a practical course with R statistical software using index replicating ETFs and Mutual Funds historical data for back-testing. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced investor. All of this while exploring the wisdom of Nobel Prize winners and best practitioners in the field.

Become an Investment Portfolio Analysis Expert in this Practical Course with R

  • Read or download main asset classes benchmark indexes replicating funds data to perform investment portfolio analysis operations by installing related packages and running script code on RStudio IDE.
  • Compare main asset classes benchmark indexes replicating funds returns and risks tradeoffs for cash, bonds, stocks, commodities, real estate and currencies.
  • Estimate portfolio expected returns, historical and market participants implied volatility.  
  • Approximate portfolio expected excess returns using capital asset pricing model (CAPM), Fama-French-Carhart factors model and arbitrage pricing theory model (APT).
  • Hedge portfolio systematic risk through options trading strategies benchmark indexes replicating funds.
  • Evaluate hedge fund index performance and assess portfolio returns and risks amplification through leverage.
  • Calculate portfolio performance metrics such as Sharpe, Treynor, Sortino, and Kelly ratios.
  • Estimate benchmark global portfolios returns from periodically rebalanced equal weighted asset allocations and those from well-known investment managers.
  • Optimize global portfolios asset allocation weights for mean maximization, standard deviation minimization, mean maximization and standard deviation minimization, mean maximization and value at risk minimization objectives within training range based on Markowitz portfolio theory.
  • Approximate global portfolios returns from periodically rebalanced optimized asset allocations within testing range and compare them with equal weighted and well-known investment managers benchmark portfolios.
  • Evaluate global portfolios performance through global risk factors model and estimate their expected return, expected excess return and expected return contribution from global risk factors exposure while assessing investment costs impact on portfolio performance.

Become an Investment Portfolio Analysis Expert and Put Your Knowledge in Practice

Learning investment portfolio analysis is indispensable for finance careers in areas such as asset management, private wealth management, and risk management within institutional investors represented by banks, insurance companies, pension funds, hedge funds, investment advisors, endowments and mutual funds. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors optimized asset allocation strategies research and development.

But as learning curve can become steep as complexity grows, this course helps by leading you step by step using index replicating funds historical data for back-testing and to achieve greater effectiveness. 

Content and Overview

This practical course contains 44 lectures and 6.5 hours of content. It’s designed for all investment portfolio analysis knowledge levels and a basic understanding of R statistical software is useful but not required.

At first, you’ll learn how to read or download index replicating funds historical data to perform investment portfolio analysis operations by installing related packages and running script code on RStudio IDE.

Then, you’ll define main asset classes by comparing their benchmark indexes replicating funds returns and risks tradeoffs. After that, you’ll segment main asset classes into traditional and alternative ones. For traditional asset classes, you’ll define cash and cash equivalents, fixed income or bonds and equities or stocks. Regarding cash and cash equivalents traditional asset class, you’ll use U.S. total money market benchmark index replicating fund. Regarding cash and cash equivalents traditional asset class, you’ll use U.S. total money market benchmark index replicating fund is used. Regarding fixed income or bonds traditional asset class, U.S. total bond market, U.S. short term bond market, U.S. long term bond market and international total bond market benchmark indexes replicating funds. Regarding equities or stocks traditional asset class, you’ll use U.S. total stock market, U.S. large cap stock market, U.S. small cap stock market, U.S. small cap growth stock market, U.S. small cap value stock market, international total stock market, international developed stock market and international emerging stock market benchmark indexes replicating funds. For alternative asset classes, you’ll define commodities, real estate and currencies or foreign exchange. Regarding commodities alternative asset class, you’ll use oil and gold prices benchmark indexes replicating funds. Regarding real estate alternative asset class, you’ll use U.S. real estate investment trust market benchmark index replicating fund. Regarding currencies or foreign exchange alternative asset class, you’ll use U.S. dollar major currencies benchmark index replicating fund.

Next, you’ll define returns and risks using U.S. large cap stocks market benchmark index replicating fund. After that, you’ll calculate expected returns through historical returns mean and media. Then, you’ll estimate risks through historical returns standard deviation, mean absolute deviation and market participants implied volatility. Later, you’ll approximate portfolio expected excess returns through capital asset pricing model (CAPM), Fama-French-Carhart factors model and arbitrage pricing theory model (APT). Next, you’ll hedge portfolio systematic risk through options trading strategies and evaluate hedge fund index performance together with the assessment of returns and risks amplification through portfolio leverage. 

After that, you’ll define portfolio optimization through global assets allocation. Next, you’ll calculate Sharpe ratio, Treynor ratio, Sortino ratio and Kelly ratio portfolio performance metrics. Then, you’ll estimate benchmark global portfolios returns from periodically rebalanced equal weighted asset allocations and those from well-known investment managers. Later, you’ll optimize global asset allocation weights within training range for mean maximization, standard deviation minimization, mean maximization and standard deviation minimization, mean maximization and value at risk minimization objectives based on Markowitz portfolio theory. After that, you’ll calculate global portfolio returns within testing range using previously optimized periodically rebalance asset allocation weights and compared with equal weighted and well-known investment managers benchmark portfolios.

Later, you’ll evaluate optimized portfolios performance through global risk factors model. After that, you’ll estimate optimized portfolios expected returns, expected excess returns and global risk factors exposure returns contribution. Finally, you’ll assess investment costs impact on portfolio performance.

Who this course is for:
  • Undergraduates or postgraduates who want to learn about investment portfolio analysis using R statistical software.
  • Finance professionals or academic researchers who wish to deepen their knowledge in quantitative finance.
  • Experienced investors who desire to research optimized asset allocation strategies.
  • This course is NOT about “get rich quick” trading systems or magic formulas.
Course content
Expand all 44 lectures 06:40:32
+ Course Overview
8 lectures 36:06

In this lecture you will view course disclaimer and learn which are its objectives, how you will benefit from it, its previous requirements and my profile as instructor.

Preview 06:03

In this lecture you will learn that it is recommended to view course in an ascendant manner as each section builds on last one and also does its complexity. You will also study course structure and main sections (course overview, asset classes, returns and risks, portfolio optimization and portfolio performance).

Preview 05:00

In this lecture you will learn investment portfolio analysis definition, R statistical software and RStudio Integrated Development Environment (IDE) downloading websites.

Investment Portfolio Analysis
05:30

In this lecture you will learn investment portfolio analysis data downloading into RStudio Integrated Development Environment (IDE), data sources for historical back-testing, R script in .TXT files and investment portfolio analysis computation instructions with R script files (tseries, quantmod, Quandl, PortfolioAnalytics, PerformanceAnalytics and DEoptim packages).

Investment Portfolio Analysis Data
19:20

Before starting course please download .TXT data file in .CSV format as additional resources.

Course Data File
00:03

Before starting course please download .TXT R script code file as additional resources.

Course Script Code File
00:03

You can download .PDF section slides file as additional resources.

Course Overview Slides
00:02

You can download .PDF course bibliography slides file as additional resources.

Course Bibliography
00:03
+ Asset Classes
9 lectures 01:29:09

You can download .PDF section slides file as additional resources.

Asset Classes Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to asset classes (cash and cash equivalents, fixed-income or bonds, equities or stocks, commodities, real estate and currencies or foreign exchange).

Preview 12:04

In this lecture you will learn cash and cash equivalents definition and main calculations (getSymbols(), monthlyReturn(), colnames() table.AnnualizedReturns() and charts.PerformanceSummary() functions).

Cash and Cash Equivalents
06:56

In this lecture you will learn fixed-income or bonds definition and main calculations (getSymbols(), monthlyReturn(), colnames() table.AnnualizedReturns(), charts.PerformanceSummary() and cbind() functions).

Fixed-Income or Bonds
13:59

In this lecture you will learn U.S. equities or stocks definition and main calculations (getSymbols(), monthlyReturn(), colnames() table.AnnualizedReturns(), charts.PerformanceSummary() and cbind() functions). 

U.S. Equities or Stocks
17:08

In this lecture you will learn international equities or stocks definition and main calculations (getSymbols(), monthlyReturn(), colnames() table.AnnualizedReturns(), charts.PerformanceSummary() and cbind() functions).

International Equities or Stocks
11:49

In this lecture you will learn commodities definition and main calculations (getSymbols(), monthlyReturn(), colnames() table.AnnualizedReturns(), charts.PerformanceSummary() and cbind() functions). 

Commodities
08:04

In this lecture you will learn real estate definition and main calculations (getSymbols(), monthlyReturn(), colnames() table.AnnualizedReturns() and charts.PerformanceSummary() functions). 

Real Estate
06:15

In this lecture you will learn currencies or foreign exchange definition and main calculations (getSymbols(), monthlyReturn(), colnames() table.AnnualizedReturns() and charts.PerformanceSummary() functions). 

Currencies or Foreign Exchange
12:52
+ Returns and Risks
12 lectures 01:56:17

You can download .PDF section slides file as additional resources.

Returns and Risks Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to returns and risks (expected returns, risks, returns normality, returns and risks relationship, capital asset pricing model CAPM, Fama-French-Carhart factors model, arbitrage pricing theory model APT, portfolio hedge, hedge funds and portfolio leverage).

Returns and Risks Overview
07:31

In this lecture you will learn expected returns definition and main calculations (mean(), median(), cbind() functions).

Expected Returns
07:51

In this lecture you will learn risks definition and main calculations (sd(), MeanAbsoluteDeviation(), cbind(), getSymbols(), to.monthly(), plot(), sqrt(), mean(), exp() and log() functions).

Risks
14:01

In this lecture you will learn returns normality definition and main calculations (skewness(), kurtosis(), jarque.bera.test(), VaR() and colnames() functions).

Returns Normality
08:12

In this lecture you will learn returns and risks relationship definition and main calculations (data.frame(), cor(), cov() functions).

Returns and Risks Relationship
08:39

In this lecture you will learn capital asset pricing model definition and main calculations (lm(), CAPM.beta(), CAPM.alpha(), mean(), summary()$sigma functions).

Capital Asset Pricing Model
14:19

In this lecture you will learn Fama-French-Carhart factors model definition and main calculations (lm(), summary(), summary()$coefficients, cbind(), mean() and summary()$sigma functions).

Fama-French-Carhart Factors Model
13:15

In this lecture you will learn arbitrage pricing theory model definition and main calculations (Quandl(), lm(), summary(), summary()$coefficients, cbind(), mean(), summary()$sigma functions).

Arbitrage Pricing Theory Model
12:00

In this lecture you will learn portfolio hedge definition and main calculations (getSymbols(), monthlyReturn(), colnames(), cbind(), table.AnnualizedReturns() and charts.PerformanceSummary() functions).

Portfolio Hedge
13:50

In this lecture you will learn hedge funds definition and main calculations (Quandl, monthlyReturn(), colnames(), cbind(), table.AnnualizedReturns() and charts.PerformanceSummary() functions).

Hedge Funds
07:21

In this lecture you will learn portfolio leverage definition and main calculations (yearlyReturn(), abs(), min(), max(), getSymbols(), colnames(), abs(), max(), min(), table.AnnualizedReturns() and charts.PerformanceSummary() functions).

Portfolio Leverage
09:16
+ Portfolio Optimization
8 lectures 01:26:58

You can download .PDF section slides file as additional resources.

Portfolio Optimization Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to portfolio optimization (portfolio performance metrics, portfolio benchmarks, mean maximization portfolio optimization, standard deviation minimization portfolio optimization, Markowitz portfolio optimization, mean maximization and VaR minimization portfolio optimization).

Portfolio Optimization Overview
07:46

In this lecture you will learn portfolio performance metrics definition and main calculation (SharpeRatio (), TreynorRatio(), SortinoRatio() and KellyRatio() functions).

Portfolio Performance Metrics
08:42

In this lecture you will learn portfolio benchmarks definition and main calculations (cbind(), as.numeric(), names(), Return.portfolio(), colnames(), table.AnnualizedReturns() and charts.PerformanceSummary() functions ).

Portfolio Benchmarks
14:47

In this lecture you will learn mean maximization portfolio optimization definition and main calculations (portfolio.spec(), add.constraint(), add.objective(), optimize.portfolio(), chart.Weights(), Return.portfolio(), extractWeights() colnames(), cbind(), table.AnnualizedReturns() and charts.PerformanceSummary() functions).

Mean Maximization Portfolio Optimization
14:50

In this lecture you will learn standard deviation minimization portfolio optimization definition and main calculations (portfolio.spec(), add.constraint(), add.objective(), optimize.portfolio(), chart.Weights(), Return.portfolio(), extractWeights(), colnames(), cbind(), table.AnnualizedReturns() and charts.PerformanceSummary() functions).

Standard Deviation Minimization Portfolio Optimization
11:56

In this lecture you will learn Markowitz portfolio optimization definition and main calculations (portfolio.spec(), add.constraint(), add.objective(), optimize.portfolio(), chart.Weights(), chart.Weights(), chart.EfficientFrontier(), Return.portfolio(), extractWeights(), colnames(), cbind(), table.AnnualizedReturns() and charts.PerformanceSummary() functions).

Markowitz Portfolio Optimization
12:54

In this lecture you will learn mean maximization and VaR minimization portfolio optimization definition and main calculations (portfolio.spec(), add.constraint(), add.objective(), optimize.portfolio(), chart.Weights(), Return.portfolio(), extractWeights(), colnames(), cbind(), table.AnnualizedReturns() and charts.PerformanceSummary() functions).

Mean Maximization and VaR Minimization Portfolio Optimization
16:01
+ Portfolio Performance
7 lectures 01:11:59

You can download .PDF section slides file as additional resources.

Portfolio Performance Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to portfolio performance (global factors model and investment costs).

Portfolio Performance Overview
08:07

In this lecture you will learn mean maximization portfolio performance definition and main calculations (cbind(), colnames(), extractWeights(), lm(), summary(), summary()$coefficients, mean(), barplot(), text() and round() functions).

Mean Maximization Portfolio Performance
16:32

In this lecture you will learn standard deviation minimization portfolio performance definition and main calculations (cbind(), colnames(), extractWeights(), lm(), summary(), summary()$coefficients, mean(), barplot(), text() and round() functions).

Standard Deviation Minimization Portfolio Performance
12:34

In this lecture you will learn Markowitz portfolio performance definition and main calculations (cbind(), colnames(), extractWeights(), lm(), summary(), summary()$coefficients, mean(), barplot(), text() and round() functions).

Markowitz Portfolio Performance
12:26

In this lecture you will learn mean maximization and VaR minimization portfolio performance definition and main calculations (cbind(), colnames(), extractWeights(), lm(), summary(), summary()$coefficients, mean(), barplot(), text() and round() functions).

Mean Maximization and VaR Minimization Portfolio Performance
13:41

In this lecture you will learn investment costs definition and main calculations (Return.annualized() and cbind() functions).

Investment Costs
08:37