Algorithmic finance : a companion to data science

1. Introduction 2. Cross-Sectional Data Analysis 3. Comparative Data Analysis 4. Prices and Returns 5. Log Return and Random Walk 6. Stock Market Indexes and ETFs 7. Indexes from Derivatives 8. Log Return and Random Walk 9. Linear Regression 10. Event Study 11. A Case Study of Modeling: Pair Trading

This book presents the algorithmic aspects of statistics and how they are applied to finance. It covers topics such as dividend payment adjustments, stock splits, and reproducing stock market indexes. Readers can download Python programs and real-world data from a companion website, and can verify their results against free data resources like Yahoo! finance. The book also provides detailed proofs of propositions, such as unbiased estimators and the derived normal distribution. This see-for-yourself textbook is essential for anyone interested in learning data science, especially in finance. Advanced readers may find the book helpful in its mathematical treatment.