都是2025年的最新资料！

Data Analytics in Finance.pdf
大小:(2.13 MB)

只需: RMB 11元  马上下载

（金融数据分析）

Statistical Quantitative Methods in Finance From Theory to Quantitative Portfoli.pdf
大小:(3.52 MB)

只需: RMB 11元  马上下载

（金融中的统计量化方法：从理论到量化投资组合管理）

都是300多页的大型资料，内容非常丰富，欢迎下载学习！

资料1：

The resource covers the methods and application of data analytics in all major areas of finance. It introduces statistical inference of big data, financial modeling, machine learning, database querying, data engineering, data visualization, and risk analysis.
资料2：
Statistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance.
This resource explores the theoretical foundations of statistical models, from ordinary least squares (OLS) to the generalized method of moments (GMM) used in econometrics. It enriches your understanding through practical examples drawn from applied finance, demonstrating the real-world applications of these concepts. Additionally, the resource delves into non-linear methods and Bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. By mastering these topics, you will be equipped to build foundational models crucial for applied data science, a skill highly sought after by software engineering and asset management firms. The resource also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. These enhancements are illustrated through real-world examples from finance and econometrics, accompanied by Python code. This practical approach ensures that you can apply what you learn, gaining proficiency in the statsmodels library and becoming adept at designing, implementing, and calibrating your models.

What You Will Learn
• Understand the fundamentals of linear regression and its applications in financial data analysis and prediction
• Apply generalized linear models for handling various types of data distributions and enhancing model flexibility
• Gain insights into regime switching models to capture different market conditions and improve financial forecasting
• Benchmark machine learning models against traditional statistical methods to ensure robustness and reliability in financial applications