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Artículo: AMZ-B0GDXP2RZ4
LINEAR ALGEBRA IN FINANCIAL DATA ANALYSIS: Correlation Structures for Yield and Growth in Stocks, Commodities, and ETFs
Format:
Hardcover
Detalles del producto
Disponibilidad
En stock
En stock
Peso con empaque
0.40 kg
0.40 kg
Devolución
Sí
Sí
Condición
Nuevo
Nuevo
Producto de
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Master the Mathematical Language of Modern FinanceWhat if you could see the hidden geometric structure underlying market movements, portfolio risk, and asset correlations? Linear Algebra in Financial Data Analysis reveals how the elegant mathematics of vectors, matrices, and eigendecomposition provides the essential toolkit for understanding today's complex financial markets.This isn't just another quantitative finance textbook. Written by a seasoned practitioner with over 15 years navigating global markets, this book bridges rigorous mathematical foundations with real-world application—showing you not just how to compute a covariance matrix, but why it matters when correlations spike during a crisis, and what to do about it.What You'll Discover:From the geometry of portfolio diversification to the spectral decomposition of yield curves, this comprehensive guide takes you on a journey through the mathematical structures that drive modern quantitative finance:Transform raw financial data into actionable insight using vector spaces, matrix operations, and eigendecomposition—the same tools used by hedge funds and institutional investors worldwideDecode correlation regimes across stocks, bonds, commodities, and ETFs—understanding why diversification works in calm markets and fails precisely when you need it mostMaster factor models that decompose returns into yield-driven and growth-driven components, revealing the hidden forces moving your portfolioBuild robust portfolios using mean-variance optimization, network analysis, and regularization techniques that account for estimation errorNavigate real market events through detailed case studies of the 2008 financial crisis, the 2013 taper tantrum, the COVID crash, and the 2022 rate shockWho This Book Is For:Whether you're a quantitative analyst seeking deeper mathematical foundations, a portfolio manager wanting to understand the models driving your risk systems, a graduate student preparing for a career in finance, or a sophisticated investor curious about the mathematics behind modern markets—this book provides the rigorous yet accessible treatment you need.Theory Meets Practice:Every chapter connects abstract mathematics to concrete financial applications. You'll learn why eigenvalues reveal how many independent risk factors truly exist in your portfolio, how singular value decomposition filters noise from signal in correlation estimates, and what graph Laplacians tell us about systemic risk and market connectivity.The extensive appendix provides a complete matrix algebra reference, while the comprehensive bibliography points you toward the foundational literature in quantitative finance, from Markowitz to machine learning.The market is a high-dimensional space. Navigate it with the right coordinates.
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$80,60
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