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Simulation, Optimization, and Machine Learning for Finance, second edition
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- Nombre de pages672
- FormatePub
- ISBN978-0-262-38377-6
- EAN9780262383776
- Date de parution09/09/2025
- Protection num.Adobe DRM
- Taille22 Mo
- Infos supplémentairesepub
- ÉditeurThe MIT Press
Résumé
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
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