ISBN: 9780262049801 - simulation, optimization, and machine learning for finance, second edition (21 Ergebnisse)

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Zustand: New. Dessislava A. Pachamanova is Professor and Zwerling Family Endowed Term Chair at Babson College and Research Affiliate at the Massachusetts Institute of Technology. She is coauthor of Robust Portfolio Optimization and Management and Portfol.

Buch. Zustand: Neu. Simulation, Optimization, and Machine Learning for Finance, second edition | Dessislava A. Pachamanova (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2025 | MIT Press Ltd | EAN 9780262049801 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.…

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Hardcover. Zustand: new. Hardcover. A comprehensive guide to simulation, optimization, and machine learning for finance, covering theoretical foundations, practical applications, and data-driven decision-making.A comprehensive guide to simulation, optimization, and machine learning for finance, covering theoretical foundations, practical applications, and data-driven decision-making.Simulation, Optimization, and Machine Learning for Finance offers a comprehensive introduction to the quantitative tools essential for asset management and corporate finance. This extensively revised and expanded edition builds upon the foundation of the textbook Simulation and Optimization in Finance, integrating the latest advancements in quantitative tools. Designed for undergraduates, graduate students, and professionals seeking to enhance their analytical expertise in finance, the book bridges theory with practical application, making complex financial concepts more accessible.Beginning with a review of foundational finance principles, the text progresses to advanced topics in simulation, optimization, and machine learning, demonstrating their relevance in financial decision-making. Readers gain hands-on experience developing financial risk models using these techniques, fostering conceptual understanding and practical implementation.Provides a structured introduction to probability, inferential statistics, and data scienceExplores cutting-edge techniques in simulation modeling, optimization, and machine learningDemonstrates real-world asset allocation strategies, advanced portfolio risk measures, and fixed-income portfolio management using quantitative toolsCovers factor models and stochastic processes in asset pricingIntegrates capital budgeting and real options analysis, emphasizing the role of uncertainty and quantitative modeling in long-term financial decision-makingIs suitable for practitioners, students, and self-learners "A textbook for developing financial risk models using optimization and simulation, with instructions for programming in various languages"-- Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. …

Buch. Zustand: Neu. Neuware - A comprehensive guide to simulation, optimization, and machine learning for finance, covering theoretical foundations, practical applications, and data-driven decision-making.Simulation, Optimization, and Machine Learning for Finance offers a comprehensive introduction to the quantitative tools essential for asset management and corporate finance. This extensively revised and expanded edition builds upon the foundation of the textbook Simulation and Optimization in Finance, integrating the latest advancements in quantitative tools. Designed for undergraduates, graduate students, and professionals seeking to enhance their analytical expertise in finance, the book bridges theory with practical application, making complex financial concepts more accessible.Beginning with a review of foundational finance principles, the text progresses to advanced topics in simulation, optimization, and machine learning, demonstrating their relevance in financial decision-making. Readers gain hands-on experience developing financial risk models using these techniques, fostering conceptual understanding and practical implementation.- Provides a structured introduction to probability, inferential statistics, and data science- Explores cutting-edge techniques in simulation modeling, optimization, and machine learning- Demonstrates real-world asset allocation strategies, advanced portfolio risk measures, and fixed-income portfolio management using quantitative tools- Covers factor models and stochastic processes in asset pricing- Integrates capital budgeting and real options analysis, emphasizing the role of uncertainty and quantitative modeling in long-term financial decision-making- Is suitable for practitioners, students, and self-learners.…