Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Simulation, Optimization, and Machine Learning for Finance, Second Edition

Simulation, Optimization, and Machine Learning for Finance, Second Edition

Pachamanova, Dessislava A.
Fabozzi, Frank J.
Fabozzi, Francesco A.

Hardcover

Business GeneralInvesting & FinanceGeneral Computers

ISBN10: 0262049805
ISBN13: 9780262049801
Publisher: MIT Press
Published: Sep 9 2025
Pages: 672
Weight: 3.45
Height: 1.18 Width: 8.11 Depth: 10.16
Language: English
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

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