使用Python的整洁财务（真正的EPUB）

English | 2024 | ISBN: 9781032684307 | 362 pages | True EPUB | 2.82 MB

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.

英文| 2024 |国际标准图书编号：9781032684307 | 362页|真正的EPUB | 2.82 MB 这本教科书展示了如何将金融和计量经济学的理论概念转化为数据。我们专注于使用Python进行编码和数据分析，展示了如何从头开始进行实证金融研究。我们首先介绍整洁数据的概念和使用pandas、numpy和plotnine的编码原则。提供代码来准备常见的开源和专有财务数据源（CRSP、Compustat、Mergent FISD、TRACE），并将其组织在数据库中。我们在所有后续章节中重复使用这些数据，并尽可能保持其自包含性。实证应用范围从实证资产定价的关键概念（贝塔估计、投资组合分类、绩效分析、Fama-French因子）到建模和机器学习应用（固定效应估计、聚类标准误差、差分估计差、岭回归、拉索、弹性网、随机森林、神经网络）和投资组合优化技术。 主要特点 关于金融领域最重要的应用和方法的独立章节，可以很容易地用于读者的研究或作为实证金融课程的参考。每一章都是可复制的，因为读者可以通过简单地复制和粘贴我们提供的代码来复制每一个数字、表格或数字。基于整洁原则的scikit-learn对机器学习的全面介绍，展示了因子选择和期权定价如何从机器学习方法中受益。我们展示了如何检索和准备最重要的金融经济学数据集：CRSP和Compustat，包括对最相关数据特征的详细解释。每一章都提供了基于既定讲座和课程的练习，旨在帮助学生更深入地挖掘。这些练习可以用于自学，也可以作为教学练习的灵感来源。

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