Verfügbarkeit: Hands-On Data Analysis in R for Finance

Hands-On Data Analysis in R for Finance:

The textbook is an introduction to data science/data analysis applied to finance, using R and its most recent and freely available extension libraries. The target audience are undergrad data science, finance and graduate students, and practitioners or professionals who need a desk reference

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Bibliographische Detailangaben
1. Verfasser:	Collard, Jean-Francois (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Milton CRC Press LLC 2022
Schlagworte:	Electronic books
Online-Zugang:	HWR01
Zusammenfassung:	The textbook is an introduction to data science/data analysis applied to finance, using R and its most recent and freely available extension libraries. The target audience are undergrad data science, finance and graduate students, and practitioners or professionals who need a desk reference
Beschreibung:	1 Online-Ressource (414 Seiten)
ISBN:	9781000787375

Internformat

MARC


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505	8		\|a 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
505	8		\|a 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
505	8		\|a 11. Modeling Returns &amp -- 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.
505	8		\|a 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
520	3		\|a The textbook is an introduction to data science/data analysis applied to finance, using R and its most recent and freely available extension libraries. The target audience are undergrad data science, finance and graduate students, and practitioners or professionals who need a desk reference
653		6	\|a Electronic books
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contents	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 &amp -- 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
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discipline	Informatik Wirtschaftswissenschaften
discipline_str_mv	Informatik Wirtschaftswissenschaften
format	Electronic eBook
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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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">11. Modeling Returns &amp -- 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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">The textbook is an introduction to data science/data analysis applied to finance, using R and its most recent and freely available extension libraries. 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illustrated	Not Illustrated
index_date	2024-07-03T20:50:26Z
indexdate	2024-07-10T09:40:31Z
institution	BVB
isbn	9781000787375
language	English
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physical	1 Online-Ressource (414 Seiten)
psigel	ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE
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spelling	Collard, Jean-Francois Verfasser aut Hands-On Data Analysis in R for Finance Milton CRC Press LLC 2022 ©2023 1 Online-Ressource (414 Seiten) txt rdacontent c rdamedia cr rdacarrier 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 &amp -- 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 The textbook is an introduction to data science/data analysis applied to finance, using R and its most recent and freely available extension libraries. The target audience are undergrad data science, finance and graduate students, and practitioners or professionals who need a desk reference Electronic books Erscheint auch als Druck-Ausgabe Collard, Jean-Francois Hands-On Data Analysis in R for Finance Milton : CRC Press LLC,c2022 9781032340975
spellingShingle	Collard, Jean-Francois Hands-On Data Analysis in R for Finance 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 &amp -- 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
title	Hands-On Data Analysis in R for Finance
title_auth	Hands-On Data Analysis in R for Finance
title_exact_search	Hands-On Data Analysis in R for Finance
title_exact_search_txtP	Hands-On Data Analysis in R for Finance
title_full	Hands-On Data Analysis in R for Finance
title_fullStr	Hands-On Data Analysis in R for Finance
title_full_unstemmed	Hands-On Data Analysis in R for Finance
title_short	Hands-On Data Analysis in R for Finance
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work_keys_str_mv	AT collardjeanfrancois handsondataanalysisinrforfinance

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