Simulation, Optimization, and Machine Learning for Finance, second edition
Published on 9. September 2025
Book
Hardback
720 pages
978-0-262-04980-1 (ISBN)
Description
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.
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
More details
Language
English
Place of publication
Cambridge (Massachusetts)
United States
Publishing group
MIT Press Ltd
Illustrations
71 GRAPHS
Dimensions
Height: 259 mm
Width: 205 mm
Thickness: 29 mm
Weight
1385 gr
ISBN-13
978-0-262-04980-1 (9780262049801)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Dessislava A. Pachamanova | Frank J. Fabozzi | Francesco A. Fabozzi
Simulation, Optimization, and Machine Learning for Finance, second edition
E-Book
09/2025
MIT Press
€147.99
Available for download
Persons
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 Portfolio Construction and Analytics.
Frank J. Fabozzi is Professor of Practice in Finance at Johns Hopkins’ Carey Business School, author of Introduction to Fixed-Income Analysis and Portfolio Management; Capital Markets, sixth edition; and Entrepreneurial Finance and Accounting for High-Tech Companies, and coauthor of Bond Markets, Analysis, and Strategies, tenth edition; Foundations of Global Financial Markets and Institutions; and The Economics of FinTech, all published by the MIT Press.
Francesco A. Fabozzi is Research Director at Yale School of Management's International Center for Finance. He serves as the Managing Editor of The Journal of Financial Data Science and the Director of Data Science at the CFA Institute Research Foundation and is the coauthor of six books in asset management and corporate finance.
Frank J. Fabozzi is Professor of Practice in Finance at Johns Hopkins’ Carey Business School, author of Introduction to Fixed-Income Analysis and Portfolio Management; Capital Markets, sixth edition; and Entrepreneurial Finance and Accounting for High-Tech Companies, and coauthor of Bond Markets, Analysis, and Strategies, tenth edition; Foundations of Global Financial Markets and Institutions; and The Economics of FinTech, all published by the MIT Press.
Francesco A. Fabozzi is Research Director at Yale School of Management's International Center for Finance. He serves as the Managing Editor of The Journal of Financial Data Science and the Director of Data Science at the CFA Institute Research Foundation and is the coauthor of six books in asset management and corporate finance.
Content
Preface
Acknowledgements
Chapter 1: Introduction
Part One: Background Topics
Chapter 2: Important Finance Concepts
Chapter 3: Random Variables and Probability Distributions
Chapter 4: Inferential Statistics
PART TWO: FUNDAMENTALS OF SIMULATION, OPTIMIZATION, AND MACHINE LEARNING
Chapter 5: Simulation Modeling
Chapter 6: Optimization Modeling
Chapter 7: Optimization under Uncertainty
Chapter 8: Data and Data Science
Chapter 9: Regression Models
Chapter 10: Machine Learning
Chapter 11: Natural Language Processing
PART THREE: Applications to Asset Management
Chapter 12: Asset Allocation Models
Chapter 13: Advanced Portfolio Risk Measures
Chapter 14: Equity Portfolio Selection in Practice
Chapter 15: Fixed Income Portfolio Management in Practice
PART FOUR: ASSET PRICING MODELS
Chapter 16: Factor Models
Chapter 17: Modeling Asset Price Dynamics
PART FIVE: FINANCIAL DERIVATIVES AND MORTGAGE-BACKED SECURITIES
Chapter 18: Introduction to Derivatives
Chapter 19: Pricing Derivatives with Simulation
Chapter 20: Using Derivatives in Portfolio Management
Chapter 21: Structuring and Pricing Residential Mortgage-Backed Securities
PART SIX: CAPITAL BUDGETING DECISIONS
Chapter 22: Capital Budgeting Under Uncertainty
Chapter 23: Application of Real Options to Capital Budgeting
Reference List
Acknowledgements
Chapter 1: Introduction
Part One: Background Topics
Chapter 2: Important Finance Concepts
Chapter 3: Random Variables and Probability Distributions
Chapter 4: Inferential Statistics
PART TWO: FUNDAMENTALS OF SIMULATION, OPTIMIZATION, AND MACHINE LEARNING
Chapter 5: Simulation Modeling
Chapter 6: Optimization Modeling
Chapter 7: Optimization under Uncertainty
Chapter 8: Data and Data Science
Chapter 9: Regression Models
Chapter 10: Machine Learning
Chapter 11: Natural Language Processing
PART THREE: Applications to Asset Management
Chapter 12: Asset Allocation Models
Chapter 13: Advanced Portfolio Risk Measures
Chapter 14: Equity Portfolio Selection in Practice
Chapter 15: Fixed Income Portfolio Management in Practice
PART FOUR: ASSET PRICING MODELS
Chapter 16: Factor Models
Chapter 17: Modeling Asset Price Dynamics
PART FIVE: FINANCIAL DERIVATIVES AND MORTGAGE-BACKED SECURITIES
Chapter 18: Introduction to Derivatives
Chapter 19: Pricing Derivatives with Simulation
Chapter 20: Using Derivatives in Portfolio Management
Chapter 21: Structuring and Pricing Residential Mortgage-Backed Securities
PART SIX: CAPITAL BUDGETING DECISIONS
Chapter 22: Capital Budgeting Under Uncertainty
Chapter 23: Application of Real Options to Capital Budgeting
Reference List