Machine Learning in Asset Pricing

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• Author: Stefan Nagel
• Publisher: Princeton University Press
• Publication Year: 2021
• Difficulty: ⭐⭐⭐⭐⭐
• Recommendation Index: ⭐⭐⭐⭐⭐
• PDF Download: [Click to download](https://asset.quant-wiki.com/pdf/Machine Learning in Asset Pricing-Princeton Univ Pr (2021).pdf)

Introduction

This book systematically introduces the application of machine learning in asset pricing, covering from traditional methods to the latest deep learning technology, providing a new methodological framework for asset pricing research.

Core Chapter

1. Basics of machine learning
2. Feature engineering and selection
3. Nonlinear asset pricing model
4. Deep learning applications
5. Risk premium prediction

Main features

• Strong theoretical depth
• Algorithm implementation details
• Rich in empirical research
• Cutting-edge technology coverage

Suitable for the crowd

• Quantitative researcher
• Asset pricing researcher
• Machine Learning Engineer
• PhD student in finance

Supporting resources

• Python code implementation

• Research datasets

• Model examples 本书籍由TheQuantHackers社区整理, 并提供PDF下载, 只供学习交流使用, 版权归原作者所有。

• 作者: Stefan Nagel

• 出版社: Princeton University Press

• 出版年份: 2021

• 难度: ⭐⭐⭐⭐⭐

• 推荐指数: ⭐⭐⭐⭐⭐

• PDF下载: [点击下载](https://asset.quant-wiki.com/pdf/Machine Learning in Asset Pricing-Princeton Univ Pr (2021).pdf)

内容简介

本书系统地介绍了机器学习在资产定价中的应用，涵盖了从传统方法到最新的深度学习技术，为资产定价研究提供了全新的方法论框架。

核心章节

1. 机器学习基础
2. 特征工程与选择
3. 非线性资产定价模型
4. 深度学习应用
5. 风险溢价预测

主要特点

• 理论深度强
• 算法实现详细
• 实证研究丰富
• 前沿技术覆盖

适合人群

• 量化研究员
• 资产定价研究者
• 机器学习工程师
• 金融博士生

配套资源

• Python代码实现
• 研究数据集
• 模型示例