Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
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Statistical quantitative methods are vital for
financial valuation models and benchmarking machine learning models in
finance.
This book 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 book 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 book 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
Who This Book Is For
Data scientists, machine learning engineers, finance professionals, and software engineers
Categories:Business & Economics - Mathematical Economics
Content Type:Books
Year:2025
Edition:1
Language:english
Pages:301
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