# A New Book For Portfolio Analysis Using R

Later this month I’ll be publishing my third book: Quantitative Investment Portfolio Analytics In R: An Introduction To R For Modeling Portfolio Risk and Return. Although there are already many R books on the market, this one serves a particular niche: a short guide for recovering Excel addicts running relatively sophisticated analytics on investment portfolios and bumping up against the limits of spreadsheets.

I’ll be writing more about the book in the coming days and weeks. Meantime, here’s a preview of the chapters:

1 A Gentle Entry Into The World Of R
1.1 Getting Started
1.2 A Short Introduction To Basic Calculations

2 Portfolio Analysis: A Primer
2.1 Reviewing Market History
2.2 Plotting Multivariate Datasets
2.3 Risk Metrics
2.4 Volatility & Sharpe Ratio
2.5 Sortino Ratio
2.6 Beta
2.7 Value at Risk
2.8 Short-Fall Risk
2.9 Drawdown

3 Rebalancing And Backtesting
3.1 Backtesting A 60/40 Strategy
3.2 Backtesting A Simple Tactical
Asset Allocation Strategy

4 Estimating “Optimal” Portfolios
4.1 Optimization, Part I (The Hard Way)
4.2 Optimization, Part II (The Easy Way)

5 Factor Analysis
5.1 Decomposing S&P 500 Risk With
The Fama-French 3-Factor Model
5.2 Exploratory Factor Analysis

6 Monte Carlo Simulations
6.1 Simulating Data With Sampling
6.2 Parametric Simulations
6.3 Simulating Returns With Non-Normal Distributions
6.4 Nonparametric Bootstrapping

7 Modeling Tail Risk
7.1 Using VaR And ES To Estimate Tail Risk
7.2 Extreme Value Theory

8 Risk Contribution and Risk Parity
8.1 Risk Contribution
8.2 Risk Parity
8.3 Volatility Targeting

9 Style Analysis And Replicating Indexes
9.1 One-Period Style Analysis
9.2 Rolling-Period Style Analysis
9.3 Index Replication Using Style Weights

10 Estimating Shocks On Asset Prices
10.1 Simulating A Response In Equities
To An Oil-Price Shock

11 Pretty Graphs
11.1 Customizing a Basic Line Chart
11.2 Creating Graphs With ggplot2