Efnisyfirlit

• Cover Page
• Half-Title Page
• 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 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
• A Cover Design
• B Clean Enhanced TRACE with R
• Bibliography
• Index