Advances in Financial Machine Learning

□ I. THÔNG TIN SẢN PHẨM
□ Mã sản phẩm : STT330
□ Nhà xuất bản : Wiley
□ Tác giả : Marcos Lopez de Prado
□ Ngôn ngữ : Tiếng Anh
□ ISBN : 9781119482086
□ Số trang : 400 trang
□ Hình thức : Bìa Mềm, RUỘT IN ĐEN TRẮNG, BÌA IN MẪU LASER GIẤY C300 CÓ CÁN
□ Loại : Sách gia công đóng gáy keo chắc chắn chất lượng cao
□ Giấy in : Giấy ngoại định lượng 70msg, viết vẽ và highlight thoải mái.
□ Chất lượng : Bản in rõ nét, giá rất tốt cho mọi người.

□ II. MÔ TẢ SẢN PHẨM
□ 1.Mô tả sản phẩm đầy đủ
Advances in Financial Machine Learning by Marcos Lopez de Prado represents a paradigm shift in the field of quantitative finance. While traditional econometric methods often rely on linear assumptions and simplified models, Lopez de Prado argues that the complexity and non-stationarity of modern financial markets demand the sophisticated tools of machine learning (ML). However, he also cautions that applying standard ML algorithms directly to financial datasets is a recipe for failure due to unique challenges like low signal-to-noise ratios and temporal dependencies. The book is structured to guide the reader through the entire pipeline of a professional quantitative research project. It begins with the fundamental problem of data representation, introducing financial bars (volume, tick, and dollar bars) as superior alternatives to standard time-sampled data, which often suffer from heteroscedasticity and non-normality. From there, the author delves into advanced labeling techniques, such as the triple-barrier method, and feature engineering strategies like fractional differentiation, which allows researchers to achieve stationarity without losing the statistical memory essential for predictive power. One of the most critical contributions of the book is its focus on backtesting and validation. Lopez de Prado introduces the concept of the Probability of Backtest Overfitting and provides statistical frameworks to determine whether a strategy's success is due to genuine alpha or merely the result of multiple testing. He details techniques like Purged and Embargoed Cross-Validation to prevent data leakage, ensuring that the performance metrics reflect real-world potential. Beyond strategy development, the text covers portfolio construction—introducing Hierarchical Risk Parity (HRP) as a robust alternative to traditional mean-variance optimization—and discusses the practicalities of high-performance computing and the organizational structure of a successful quantitative lab. Written by one of the most respected practitioners in the field, this book serves as an essential manual for investment professionals and data scientists who seek to move beyond black-box trading and build scientifically grounded, resilient investment processes. By bridging the gap between academic theory and institutional practice, Lopez de Prado provides a roadmap for the future of asset management.

□ 2. Tác giả
Dr. Marcos Lopez de Prado is a principal at AQR Capital Management and its head of machine learning. He is also a research fellow at Lawrence Berkeley National Laboratory. He has published dozens of scientific articles on machine learning and supercomputing in leading academic journals and has received multiple awards for his work in quantitative finance. He holds PhDs in financial economics and mathematical finance from Universidad Complutense de Madrid and has taught at Cornell University’s School of Engineering.