Hands-On Data Analysis in R for Finance

Description

The subject of this textbook is to act as an introduction to data science / data analysis applied to finance, using R and its most recent and freely available extension libraries. The targeted academic level is undergrad students with a major in data science and/or finance and graduate students, and of course practitioners or professionals who need a desk reference. • Assumes no prior knowledge of R; • The content has been tested in actual university classes; • Makes the reader proficient in advanced methods such as machine learning, time series analysis, principal component analysis and more; • Gives comprehensive and detailed explanations on how to use the most recent and free resources, such as financial and statistics libraries or open database on the internet. Cover Half Title Title Page Copyright Page Dedication Contents List of Figures Preface 1. Your Working Environment 1.1. RStudio 1.2. R Notebooks 1.3. Packages 1.4. Specialized Packages for Finance 2. Reading Data in R 2.1. Reading Input (Data) Files 2.2. Reading Excel Files 2.3. Reading Tables 2.4. Packages Come With Datasets 2.5. Reading XML Data 2.6. JSON 2.7. Chapter-End Summary 3. Financial Data 3.1. Yahoo! Finance 3.2. Federal Reserve Economic Data (FRED) 3.3. Nasdaq 3.4. Other Data Sources 4. Introduction to R 4.1. Expressions 4.2. Creating New Variables 4.3. Data Types and Type Conversion 4.4. Vectors 4.5. Matrices 4.6. Lists 4.7. Data Frames 4.8. Time Series 4.9. Data Wrangling 4.10. Exercises 4.10.1. Formatting 4.10.2. Format Conversion 4.10.3. Wrangling Using pivot__longer 4.10.4. Computing Daily Returns From Daily Prices 4.10.5. Histogram of Apple’s Daily Returns 5. Functions 5.1. Calling Existing Functions 5.2. Creating New Functions 5.3. Function Composition (a.k.a Piping) 5.4. Optimization 5.5. Manipulating Character Strings 5.6. Key Statistics Functions 5.7. Empirical Distributions 5.8. Chapter-End Summary 5.9. Exercises 5.9.1. Histogram of Oil Returns 5.9.2. ECDF of Oil Prices 5.9.3. Peak of Oil Prices 5.9.4. Qnorm 5.9.5. Returns vs Log Returns 5.9.6. Skew and Kurtosis 5.9.7. Function to Calculate Returns 5.9.8. Risk Limit 5.9.9. Probability of Reaching a Profit Target 5.9.10. Finding Most Significant Outlier 6. Data Transformation 6.1. Selecting Rows: Slicing 6.2. Group__by 6.3. Filter 6.4. Arrange 6.5. Rename 6.6. Mutate 6.7. Summarize 6.8. Contingency Tables 6.9. Aggregate 6.10. Chapter-End Summary 6.11. Exercises 6.11.1. Filtering on Either of Two Conditions 6.11.2. Performance by Sector 6.11.3. Ordering and Plotting Returns 6.11.4. Removing NAs 6.11.5. Removing Outliers 6.11.6. Deutsche Bank’s Long-Term Debt 7. Merging Data Sets 7.1. Inner Join 7.2. Left Join 7.3. Right Join 7.4. Full Join (a.k.a. Outer Join) 7.5. Merging Nasdaq Datasets 7.6. Chapter-End Summary 7.7. Exercises 7.7.1. The Zacks EE Dataset 7.7.2. Merging Dividend and Split Data 8. Graphing Using Ggplot 8.1. The Grammar of Ggplot Commands 8.2. Geometric Objects 8.3. Separating by Color 8.4. Separating by Size 8.5. Separating by Shape 8.6. Curves of Best Fit 8.7. Case Study: The House Price Dataset 8.8. Case Study: The Ocean Portfolio 8.9. Exercises 8.9.1. Change the Marker Shape by Region 8.9.2. Change the Marker Color by Price 8.9.3. Market Cap by Countries 9. Returns and Returns-based Statistics 9.1. Single-Period Returns 9.2. Multiple Periods 9.3. Prices and Adjusted Prices 9.4. Returns 9.5. Volatility 9.6. Sharpe 9.7. Drawdowns 9.8. Benchmark-Relative Performance and Risk 9.9. Rolling Correlations 9.10. Normality of Return Distributions 9.11. Fitting A Distribution 9.12. Are Differences in Returns Significant? 9.13. Exercises 9.13.1. Verifying GM’s and Ford’s Returns 9.13.2. Computing Monthly Percentage Changes of Oil Prices 9.13.3. Comparing Returns and Log-Returns 9.13.4. Worst and Best Days for Bitcoin 9.13.5. Bull Beta 10. Portfolios 10.1. Building Portfolios Using Tidyquant 10.2. Building Portfolios Using PerformanceAnalytics 10.3. Portfolio Optimization 10.4. Exercises 10.4.1. Correlation Matrix 10.4.2. Improving the Portfolio Growth Graph 10.4.3. Portfolio of Hedge Funds 10.4.4. Larger Search Space 11. Modeling Returns & Simulations 11.1. Normal and Log-normal Models 11.2. Log-normal Model – Multi-period Return 11.3. Random Walk 11.4. Geometric Random Walk 11.5. Toward Simulations 11.6. The Multiple Questions Simulations Can Answer 11.7. Exercises 11.7.1. Probability of a Loss 12. Linear and Polynomial Regression 12.1. The House Price Dataset 12.2. Multi-linear Regression 12.3. Collinearity 12.4. Variance Inflation Factor 12.5. ANOVA 12.6. Response Transformation 12.7. Linear Regression with Categorical Variables 12.8. Polynomial Regression 12.9. Exercises 12.9.1. Collinearity 12.9.2. Order of Independent Variables in Multi-linear Regressions 13. Fixed Income 13.1. Present Value 13.2. Present Value of Coupon Bonds 13.3. Exercises 13.3.1. Alternative Formula for the Present Value of a Coupon Bond 13.3.2. Modified Duration 13.3.3. Yield to Maturity 14. Principal Component Analysis 14.1. Directions of Most Variance 14.2. Application to a Full Example 14.3. How Much Variance is Explained by Each Principal Component? 14.4. Chapter-End Summary 14.5. Exercises 14.5.1. PCA on Rates 14.5.2. PCA on ACWI 15. Options 15.1. European Options 15.2. American Options 15.3. Embedded Optionality in Callable Bonds 15.4. Exercises 15.4.1. Black-Scholes 15.4.2. Plot d1 as a Function of Time 16. Value at Risk 16.1. Parametric VaR 16.2. Nonparametric VaR 16.3. Calculating VaR Using the Covariance Matrix 16.4. Conditional Value at Risk 16.5. Calculating VaR Using PerformanceAnalytics 16.6. Calculating VaR Using Tidyquant 16.7. Chapter-End Summary 16.8. Exercises 16.8.1. How Sensitive is VaR to α, Revisited 16.8.2. Comparing VaR Methods 16.8.3. Comparing CVaR Methods 16.8.4. Rolling VaR 16.8.5. Non-parametric VaR 17. Time Series Analysis 17.1. ACFs and PACFs 17.2. But What Are These Autoregressive (AR) and Moving Average (MA) Models? 17.3. Fitting a Model 17.4. Forecasting 17.5. First Differencing, or Integrated Model? 17.6. A Digression: The Intuition of the ACF Values 18. Machine Learning 18.1. Supervised Algorithms 18.2. KNN 18.3. Logistic Regression 18.4. Decision Tree 18.5. Regression Trees (Supervised) 18.6. K-Means Clustering 18.7. Hierarchical Clustering 18.8. Chapter-End Summary 18.9. Exercises 18.9.1. K-means Clustering on GICS Industries 18.9.2. Hierarchical Clustering on P/CF and ROE 19. Presenting the Results of Your Analyses 19.1. Markdown Documents 19.2. Shiny 20. Appendix: Main Packages Seen in this Book Index