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Financial Modeling

Author: Simon Benninga
Publisher: MIT Press
ISBN: 0262027283
Size: 26.24 MB
Format: PDF
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A substantially revised edition of a bestselling text combining explanation and implementation using Excel; for classroom use or as a reference for finance practitioners.

Exct Worksheets And Solutions To Exercises To Accompany Financial Modelling 4e Access Card

Author: Simon Benninga
Publisher:
ISBN: 9780262322607
Size: 40.59 MB
Format: PDF, ePub, Docs
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Downloadable Excel worksheets and solutions to end-of-chapter exercises accompany Financial Modeling, Fourth Edition, by Simon Benninga. Access codes are required to download the supplemental material. New print copies of this book include a card affixed to the inside back cover with a unique access code. If you purchased a used copy of this book, this is a separately purchased printed access card.

Financial Modeling

Author: Joachim Häcker
Publisher: Springer
ISBN: 1137426586
Size: 55.84 MB
Format: PDF, Docs
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This book provides a comprehensive introduction to modern financial modeling using Excel, VBA, standards of financial modeling and model review. It offers guidance on essential modeling concepts around the four core financial activities in the modern financial industry today: financial management; corporate finance; portfolio management and financial derivatives. Written in a highly practical, market focused manner, it gives step-by-step guidance on modeling practical problems in a structured manner. Quick and interactive learning is assured due to the structure as a training course which includes applied examples that are easy to follow. All applied examples contained in the book can be reproduced step by step with the help of the Excel files. The content of this book serves as the foundation for the training course Certified Financial Modeler. In an industry that is becoming increasingly complex, financial modeling is a key skill for practitioners across all key sectors of finance and banking, where complicated problems often need to be solved quickly and clearly. This book will equip readers with the basic modeling skills required across the industry today.

The Handbook Of Post Crisis Financial Modelling

Author: Emmanuel Haven
Publisher: Springer
ISBN: 1137494492
Size: 31.95 MB
Format: PDF, ePub, Mobi
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The 2008 financial crisis was a watershed moment which clearly influenced the public's perception of the role of 'finance' in society. Since 2008, a plethora of books and newspaper articles have been produced accusing the academic community of being unable to produce valid models which can accommodate those extreme events. This unique Handbook brings together leading practitioners and academics in the areas of banking, mathematics, and law to present original research on the key issues affecting financial modelling since the 2008 financial crisis. As well as exploring themes of distributional assumptions and efficiency the Handbook also explores how financial modelling can possibly be re-interpreted in light of the 2008 crisis.

The Oxford Guide To Financial Modeling

Author: Thomas S. Y. Ho
Publisher: Oxford University Press
ISBN: 9780199727704
Size: 57.46 MB
Format: PDF, ePub
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The essential premise of this book is that theory and practice are equally important in describing financial modeling. In it the authors try to strike a balance in their discussions between theories that provide foundations for financial models and the institutional details that provide the context for applications of the models. The book presents the financial models of stock and bond options, exotic options, investment grade and high-yield bonds, convertible bonds, mortgage-backed securities, liabilities of financial institutions--the business model and the corporate model. It also describes the applications of the models to corporate finance. Furthermore, it relates the models to financial statements, risk management for an enterprise, and asset/liability management with illiquid instruments. The financial models are progressively presented from option pricing in the securities markets to firm valuation in corporate finance, following a format to emphasize the three aspects of a model: the set of assumptions, the model specification, and the model applications. Generally, financial modeling books segment the world of finance as "investments," "financial institutions," "corporate finance," and "securities analysis," and in so doing they rarely emphasize the relationships between the subjects. This unique book successfully ties the thought processes and applications of the financial models together and describes them as one process that provides business solutions. Created as a companion website to the book readers can visit www.thomasho.com to gain deeper understanding of the book's financial models. Interested readers can build and test the models described in the book using Excel, and they can submit their models to the site. Readers can also use the site's forum to discuss the models and can browse server based models to gain insights into the applications of the models. For those using the book in meetings or class settings the site provides Power Point descriptions of the chapters. Students can use available question banks on the chapters for studying.

Corporate Finance

Author: Pierre Vernimmen
Publisher: John Wiley & Sons
ISBN: 1118849299
Size: 20.70 MB
Format: PDF, Docs
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Merging theory and practice into a comprehensive,highly-anticipated text Corporate Finance continues its legacy as one of the mostpopular financial textbooks, with well-established content from adiverse and highly respected author team. Unique in its features,this valuable text blends theory and practice with a direct,succinct style and commonsense presentation. Readers will beintroduced to concepts in a situational framework, followed by adetailed discussion of techniques and tools. This latest editionincludes new information on venture finance and debt structuring,and has been updated throughout with the most recent statisticaltables. The companion website provides statistics, graphs, charts,articles, computer models, and classroom tools, and the freemonthly newsletter keeps readers up to date on the latesthappenings in the field. The authors have generously madethemselves available for questions, promising an answer inseventy-two hours. Emphasizing how key concepts relate to real-world situations iswhat makes Corporate Finance a valuable reference with realrelevance to the professional and student alike. Readers will gaininsight into the methods and tools that shape the industry,allowing them to: Analyze investments with regard to hurdle rates, cash flows,side costs, and more Delve into the financing process and learn the tools andtechniques of valuation Understand cash dividends and buybacks, spinoffs, anddivestitures Explore the link between valuation and corporate finance As the global economy begins to recover, access to the mostcurrent information and statistics will be required. To remainrelevant in the evolving financial environment, practitioners willneed a deep understanding of the mechanisms at work. CorporateFinance provides the expert guidance and detailed explanationsfor those requiring a strong foundational knowledge, as well asmore advanced corporate finance professionals.

The Art And Science Of Financial Modeling

Author: Anurag Singal
Publisher:
ISBN: 9781948976947
Size: 11.30 MB
Format: PDF, Mobi
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The importance of sound financial modelling skills, deep understanding of valuation methods and the assessment of outputs of valuations for finance professionals cannot be overemphasized The book aims to help a user deep dive into the art of financial modelling and valuation. The reader will be able to prepare/use existing models more competently, interpret the results and have greater comfort over the integrity and accuracy of the model's calculations. It seeks to disseminate the skill-set to prepare financial models for business cases, be it Mergers & Acquisitions, Venture Capital/ Private Equity or long-term Financial Forecasts of companies It is suited for an aspirant who seeks to learn the art of preparing financial models in a logical structured and disciplined manner. In turn, the user can go for correct valuation analyses, which in turn, fuels well-informed and appropriate strategic organizational decisions.

Foundations Of Real Estate Financial Modelling

Author: Roger Staiger
Publisher: Routledge
ISBN: 1317687094
Size: 34.73 MB
Format: PDF, ePub, Docs
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Foundations of Real Estate Financial Modelling is specifically designed to provide an overview of pro forma modelling for real estate projects. The book introduces students and professionals to the basics of real estate finance theory before providing a step-by-step guide for financial model construction using Excel. The idea that real estate is an asset with unique characteristics which can be transformed, both physically and financially, forms the basis of discussion. Individual chapters are separated by functional unit and build upon themselves to include information on: Amortization Single-Family Unit Multi-Family Unit Development/Construction Addition(s) Waterfall (Equity Bifurcation) Accounting Statements Additional Asset Classes Further chapters are dedicated to risk quantification and include scenario, stochastic and Monte Carlo simulations, waterfalls and securitized products. This book is the ideal companion to core real estate finance textbooks and will boost students Excel modelling skills before they enter the workplace. The book provides individuals with a step-by-step instruction on how to construct a real estate financial model that is both scalable and modular. A companion website provides the pro forma models to give readers a basic financial model for each asset class as well as methods to quantify performance and understand how and why each model is constructed and the best practices for repositioning these assets.

Modeling Techniques In Predictive Analytics

Author: Thomas W. Miller
Publisher: FT Press
ISBN: 0133886190
Size: 11.47 MB
Format: PDF, ePub, Docs
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To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Modeling Techniques In Predictive Analytics With Python And R

Author: Thomas W. Miller
Publisher: FT Press
ISBN: 013389214X
Size: 20.30 MB
Format: PDF, ePub, Mobi
View: 5595
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Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more