Price loading...

Tidy Finance with Python (Chapman & Hall/CRC The Python Series)

Econometrics

Price data last checked 91 day(s) ago - refreshing...

View at Amazon

Price History & Forecast

No Price Data Available

Price history will appear here once data is collected from Amazon.

Price Distribution

No price data available for histogram

Description

This textbook shows how to bring theoretical concepts from finance and econometrics to the data. Focusing on coding and data analysis with Python, we show how to conduct research in empirical finance from scratch. We start by introducing the concepts of tidy data and coding principles using pandas, numpy, and plotnine. 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. Key Features: Self-contained chapters on the most important applications and methodologies in finance, which can easily be used for the reader’s research or as a reference for courses on empirical finance. Each chapter is reproducible in the sense that the reader can replicate every single figure, table, or number by simply copying and pasting the code we provide. A full-fledged introduction to machine learning with scikit-learn based on tidy principles to show how factor selection and option pricing can benefit from Machine Learning methods. We show how to retrieve and prepare the most important datasets financial economics: CRSP and Compustat, including detailed explanations of the most relevant data characteristics. Each chapter provides exercises based on established lectures and classes which are designed to help students to dig deeper. The exercises can be used for self-studying or as a source of inspiration for teaching exercises.

Key Features

New Store Stock

Product Specifications

Format: paperback
ASIN: 1032676418
Category: Books > Subjects > Business, Finance & Law > Economics > Econometrics
Domain: Amazon UK
Release Date: 22 June 2024
Listed Since: 21 December 2023

Barcode

No barcode data available

Tidy Finance with Python (Chapman & Hall/CRC The Python Series)

Price History & Forecast

No Price Data Available

Price Distribution

Description

Key Features

Product Specifications

Barcode

Similar Products You Might Like

Tidy Finance with Python - Chapman and Hall/CRC Textbook

Tidy Finance with R (Chapman & Hall/CRC The R Series)

Machine Learning for Factor Investing: Python Version (Chapman and Hall/CRC Financial Mathematics Series)

Quantitative Finance with Case Studies in Python: A Practical Guide to Investment Management, Trading and Financial Engineering (Chapman and Hall/CRC Financial Mathematics Series)

Machine Learning in Finance: From Theory to Practice

Machine Learning in Finance: From Theory to Practice

Foundations of Reinforcement Learning with Applications in Finance (Chapman & Hall/CRC Mathematics and Artificial Intelligence Series)

Machine Learning for Factor Investing: R Version (Chapman and Hall/CRC Financial Mathematics Series)

Machine Learning for Factor Investing: R Version: R Version (Chapman and Hall/CRC Financial Mathematics Series)

Data Analytics for Finance Using Python (Advances in Digital Technologies for Smart Applications)

Algorithmic Finance: A Companion To Data Science

Computation and Simulation for Finance: An Introduction with Python (Springer Undergraduate Texts in Mathematics and Technology)

Quant_Py: Python for Quantitative Finance

Financial Data Analytics with Machine Learning, Optimization and Statistics (Wiley Finance)

Machine Learning for Algorithmic Trading - Second Edition: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

Data Science and Machine Learning: Mathematical and Statistical Methods, Second Edition (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python

Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis (Chapman & Hall/CRC The R Series)

Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis (Chapman & Hall/CRC The R Series)

Stochastic Finance with Python: Design Financial Models from Probabilistic Perspective

Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python

A Practical Machine Learning with R: Tutorials and Case Studies

Foundations of Statistics for Data Scientists: With R and Python (Chapman & Hall/CRC Texts in Statistical Science)