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A comprehensive guide to simulation, optimization, and machine learning for finance, covering theoretical foundations, practical applications, and data-driven decision-making. 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.
List of contents
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
About the author
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.
