{"product_id":"simulation-optimization-and-machine-learning-for-finance-second-edition-isbn-9780262049801","title":"Simulation, Optimization, and Machine Learning for Finance, second edition","description":"\u003cb\u003eA comprehensive guide to simulation, optimization, and machine learning for finance, covering theoretical foundations, practical applications, and data-driven decision-making.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003ci\u003eSimulation, Optimization, and Machine Learning for Finance\u003c\/i\u003e 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 \u003ci\u003eSimulation and Optimization in Finance\u003c\/i\u003e, 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.\u003cbr\u003e\u003cbr\u003eBeginning 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.\u003cbr\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eProvides a structured introduction to probability, inferential statistics, and data science\u003c\/li\u003e\n\u003cli\u003eExplores cutting-edge techniques in simulation modeling, optimization, and machine learning\u003c\/li\u003e\n\u003cli\u003eDemonstrates real-world asset allocation strategies, advanced portfolio risk measures, and fixed-income portfolio management using quantitative tools\u003c\/li\u003e\n\u003cli\u003eCovers factor models and stochastic processes in asset pricing\u003c\/li\u003e\n\u003cli\u003eIntegrates capital budgeting and real options analysis, emphasizing the role of uncertainty and quantitative modeling in long-term financial decision-making\u003c\/li\u003e\n\u003cli\u003eIs suitable for practitioners, students, and self-learners\u003c\/li\u003e\n\u003c\/ul\u003ePreface\u003cbr\u003eAcknowledgements\u003cbr\u003eChapter 1: Introduction\u003cbr\u003ePart One: Background Topics\u003cbr\u003eChapter 2: Important Finance Concepts\u003cbr\u003eChapter 3: Random Variables and Probability Distributions\u003cbr\u003eChapter 4: Inferential Statistics\u003cbr\u003ePART TWO: FUNDAMENTALS OF SIMULATION, OPTIMIZATION, AND MACHINE LEARNING\u003cbr\u003eChapter 5: Simulation Modeling\u003cbr\u003eChapter 6: Optimization Modeling\u003cbr\u003eChapter 7: Optimization under Uncertainty\u003cbr\u003eChapter 8: Data and Data Science\u003cbr\u003eChapter 9: Regression Models\u003cbr\u003eChapter 10: Machine Learning\u003cbr\u003eChapter 11: Natural Language Processing\u003cbr\u003ePART THREE: Applications to Asset Management\u003cbr\u003eChapter 12: Asset Allocation Models\u003cbr\u003eChapter 13: Advanced Portfolio Risk Measures\u003cbr\u003eChapter 14: Equity Portfolio Selection in Practice\u003cbr\u003eChapter 15: Fixed Income Portfolio Management in Practice\u003cbr\u003ePART FOUR: ASSET PRICING MODELS\u003cbr\u003eChapter 16: Factor Models\u003cbr\u003eChapter 17: Modeling Asset Price Dynamics\u003cbr\u003ePART FIVE: FINANCIAL DERIVATIVES AND MORTGAGE-BACKED SECURITIES\u003cbr\u003eChapter 18: Introduction to Derivatives\u003cbr\u003eChapter 19: Pricing Derivatives with Simulation\u003cbr\u003eChapter 20: Using Derivatives in Portfolio Management\u003cbr\u003eChapter 21: Structuring and Pricing Residential Mortgage-Backed Securities\u003cbr\u003ePART SIX: CAPITAL BUDGETING DECISIONS\u003cbr\u003eChapter 22: Capital Budgeting Under Uncertainty\u003cbr\u003eChapter 23: Application of Real Options to Capital Budgeting\u003cbr\u003eReference List\u003cb\u003eDessislava A. Pachamanova\u003c\/b\u003e is Professor and Zwerling Family Endowed Term Chair at Babson College and Research Affiliate at the Massachusetts Institute of Technology. She is coauthor of \u003ci\u003eRobust Portfolio Optimization and Management\u003c\/i\u003e and \u003ci\u003ePortfolio Construction and Analytics\u003c\/i\u003e.\u003cbr\u003e \u003cbr\u003e\u003cb\u003eFrank J. Fabozzi \u003c\/b\u003eis Professor of Practice in Finance at Johns Hopkins’ Carey Business School, author of \u003ci\u003eIntroduction to Fixed-Income Analysis and Portfolio Management; Capital Markets, sixth edition\u003c\/i\u003e; and \u003ci\u003eEntrepreneurial Finance and Accounting for High-Tech Companies, \u003c\/i\u003eand coauthor of\u003ci\u003e Bond Markets, Analysis, and Strategies, tenth edition\u003c\/i\u003e; \u003ci\u003eFoundations of Global Financial Markets and Institutions\u003c\/i\u003e; and \u003ci\u003eThe Economics of FinTech,\u003c\/i\u003e all published by the MIT Press.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eFrancesco A. Fabozzi\u003c\/b\u003e is Research Director at Yale School of Management's International Center for Finance. He serves as the Managing Editor of \u003ci\u003eThe Journal of Financial Data Science\u003c\/i\u003e 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.","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":48233551593701,"sku":"NP9780262049801","price":150.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262049801.jpg?v=1767736659","url":"https:\/\/k12savings.com\/products\/simulation-optimization-and-machine-learning-for-finance-second-edition-isbn-9780262049801","provider":"K12savings","version":"1.0","type":"link"}