دانلود کتاب Tidy Finance with R

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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