Tidy Finance with R

Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
Preface
Author biographies
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 viaMachine 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. MoreModel 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
A: Cover Design
B: Clean Enhanced TRACE with R
Bibliography
Index