Tidy Finance with R First Edition by Christoph Scheuch 1032389338 9781032389332

Tidy Finance with R First Edition Christoph Scheuch – Ebook Instant Download/Delivery ISBN(s): 1032389338 ,9781032389332

Product details:

• ISBN 10: 1032389338
• ISBN 13:9781032389332
• Author:  Christoph Scheuch 

This textbook shows how to bring theoretical concepts from finance and econometrics to the data. Focusing on coding and data analysis with R, we show how to conduct research in empirical finance from scratch. We start by introducing the concepts of tidy data and coding principles using the tidyverse family of R packages. Code is provided to prepare common open-source and proprietary financial data sources (CRSP, Compustat, Mergent FISD, TRACE) and organize them in a database. We reuse these data in all the subsequent chapters, which we keep as self-contained as possible. The empirical applications range from key concepts of empirical asset pricing (beta estimation, portfolio sorts, performance analysis, Fama-French factors) to modeling and machine learning applications (fixed effects estimation, clustering standard errors, difference-in-difference estimators, ridge regression, Lasso, Elastic net, random forests, neural networks) and portfolio optimization techniques.

Table contents:

I Getting Started
1 Introduction to Tidy Finance
1.1 Working with Stock Market Data
1.2 Scaling Up the Analysis
1.3 Other Forms of Data Aggregation
1.4 Portfolio Choice Problems
1.5 The Efficient Frontier
1.6 Exercises
II Financial Data
2 Accessing & Managing Financial Data
2.1 Fama-French Data
2.2 q-Factors
2.3 Macroeconomic Predictors
2.4 Other Macroeconomic Data
2.5 Setting Up a Database
2.6 Managing SQLite Databases
2.7 Exercises
3 WRDS, CRSP, and Compustat
3.1 Accessing WRDS
3.2 Downloading and Preparing CRSP
3.3 First Glimpse of the CRSP Sample
3.4 Daily CRSP Data
3.5 Preparing Compustat Data
3.6 Merging CRSP with Compustat
3.7 Some Tricks for PostgreSQL Databases
3.8 Exercises
4 TRACE and FISD
4.1 Bond Data from WRDS
4.2 Mergent FISD
4.3 TRACE
4.4 Insights into Corporate Bonds
4.5 Exercises
5 Other Data Providers
5.1 Exercises
III Asset Pricing
6 Beta Estimation
6.1 Estimating Beta using Monthly Returns
6.2 Rolling-Window Estimation
6.3 Parallelized Rolling-Window Estimation
6.4 Estimating Beta using Daily Returns
6.5 Comparing Beta Estimates
6.6 Exercises
7 Univariate Portfolio Sorts
7.1 Data Preparation
7.2 Sorting by Market Beta
7.3 Performance Evaluation
7.4 Functional Programming for Portfolio Sorts
7.5 More Performance Evaluation
7.6 The Security Market Line and Beta Portfolios
7.7 Exercises
8 Size Sorts and p-Hacking
8.1 Data Preparation
8.2 Size Distribution
8.3 Univariate Size Portfolios with Flexible Breakpoints
8.4 Weighting Schemes for Portfolios
8.5 P-hacking and Non-standard Errors
8.6 The Size-Premium Variation
8.7 Exercises
9 Value and Bivariate Sorts
9.1 Data Preparation
9.2 Book-to-Market Ratio
9.3 Independent Sorts
9.4 Dependent Sorts
9.5 Exercises
10 Replicating Fama and French Factors
10.1 Data Preparation
10.2 Portfolio Sorts
10.3 Fama and French Factor Returns
10.4 Replication Evaluation
10.5 Exercises
11 Fama-MacBeth Regressions
11.1 Data Preparation
11.2 Cross-sectional Regression
11.3 Time-Series Aggregation
11.4 Exercises
IV Modeling & Machine Learning
12 Fixed Effects and Clustered Standard Errors
12.1 Data Preparation
12.2 Fixed Effects
12.3 Clustering Standard Errors
12.4 Exercises
13 Difference in Differences
13.1 Data Preparation
13.2 Panel Regressions
13.3 Visualizing Parallel Trends
13.4 Exercises
14 Factor Selection via Machine Learning
14.1 Brief  Theoretical Background
14.1.1 Ridge regression
14.1.2 Lasso
14.1.3 Elastic Net
14.2 Data Preparation
14.3 The Tidymodels Workflow
14.3.1 Pre-process data
14.3.2 Build a model
14.3.3 Fit a model
14.3.4 Tune a model
14.3.5 Parallelized workflow
14.4 Exercises
15 Option Pricing via Machine Learning
15.1 Regression Trees and Random Forests
15.2 Neural Networks
15.3 Option Pricing
15.4 Learning Black-Scholes
15.4.1 Data simulation
15.4.2 Single layer networks and random forests
15.4.3 Deep neural networks
15.4.4 Universal approximation
15.5 Prediction Evaluation
15.6 Exercises
V Portfolio Optimization
16 Parametric Portfolio Policies
16.1 Data Preparation
16.2 Parametric Portfolio Policies
16.3 Computing Portfolio Weights
16.4 Portfolio Performance
16.5 Optimal Parameter Choice
16.6 More Model Specifications
16.7 Exercises
17 Constrained Optimization and Backtesting
17.1 Data Preparation
17.2 Recap of Portfolio Choice
17.3 Estimation Uncertainty and Transaction Costs
17.4 Optimal Portfolio Choice
17.5 Constrained Optimization
17.6 Out-of-Sample Backtesting
17.7 Exercises

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