A substantially updated new edition of the essential text on financial modeling, with revised material, new data, and implementations shown in Excel, R, and Python. Financial Modeling has become the gold-standard text in its field, an essential guide for students, researchers, and practitioners that provides the computational tools needed for modeling finance fundamentals. This fifth edition has been substantially updated but maintains the straightforward, hands-on approach, with an optimal mix of explanation and implementation, that made the previous editions so popular. Using detailed Excel spreadsheets, it explains basic and advanced models in the areas of corporate finance, portfolio management, options, and bonds. This new edition offers revised material on valuation, second-order and third-order Greeks for options, value at risk (VaR), Monte Carlo methods, and implementation in R. The examples and implementation use up-to-date and relevant data. Parts I to V cover corporate finance topics, bond and yield curve models, portfolio theory, options and derivatives, and Monte Carlo methods and their implementation in finance. Parts VI and VII treat technical topics, with part VI covering Excel and R issues and part VII (now on the book’s auxiliary website) covering Excel’s programming language, Visual Basic for Applications (VBA), and Python implementations. Knowledge of technical chapters on VBA and R is not necessary for understanding the material in the first five parts. The book is suitable for use in advanced finance classes that emphasize the need to combine modeling skills with a deeper knowledge of the underlying financial models.

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Too often, finance courses stop short of making a connection between textbook finance and the problems of real-world business. "Financial Modeling" bridges this gap between theory and practice by providing a nuts-and-bolts guide to solving common financial problems with spreadsheets. The CD-ROM contains Excel* worksheets and solutions to end-of-chapter exercises. 634 illustrations.

The Oxford Guide to Financial Modeling is accompanied by a companion web site that serves as an interactive workbook designed specifically for the book. This site is simple to use yet exceedingly robust with regard to its technological efficiency and purposeful usability. It is designed to further enhance understanding of the use and applications of the models referred to in the book and it is accessible free of charge at www.thomasho.com. This on-line workbook and resource tool contains more than 95 downloadable Excel models. The models provide clear expositions of the mathematical formulations and can be used along with the book. The companion web site is rich with a plethora of research and analytic tools designed for "doing finance" on-line.

This book contains several innovative models for the prices of financial assets. First published in 1986, it is a classic text in the area of financial econometrics. It presents ARCH and stochastic volatility models that are often used and cited in academic research and are applied by quantitative analysts in many banks. Another often-cited contribution of the first edition is the documentation of statistical characteristics of financial returns, which are referred to as stylized facts. This second edition takes into account the remarkable progress made by empirical researchers during the past two decades from 1986 to 2006. In the new Preface, the author summarizes this progress in two key areas: firstly, measuring, modelling and forecasting volatility; and secondly, detecting and exploiting price trends. Sample Chapter(s). Chapter 1: Introduction (1,134 KB). Contents: Features of Financial Returns; Modelling Price Volatility; Forecasting Standard Deviations; The Accuracy of Autocorrelation Estimates; Testing the Random Walk Hypothesis; Forecasting Trends in Prices; Evidence Against the Efficiency of Futures Markets; Valuing Options; Appendix: A Computer Program for Modelling Financial Time Series. Readership: Academic researchers in finance & economics; quantitative analysts.

Financial Modeling for Business Owners and Entrepreneurs: Developing Excel Models to Raise Capital, Increase Cash Flow, Improve Operations, Plan Projects, and Make Decisions may be one of the most important books any entrepreneur or manager in a small or medium-sized enterprise will read. It combines logical business principles and strategies with a step-by-step methodology for planning and modeling a company and solving specific business problems. You’ll learn to create operational and financial models in Excel that describe the workings of your company in quantitative terms and that make it far more likely you will avoid the traps and dead ends many businesses fall into. Serial entrepreneur and financial expert Tom Y. Sawyer shows how to break your company down into basic functional and operational components that can be modeled. The result is a financial model that, for example, you can literally take to the bank or bring to local angel investors to receive the funding you need to launch your business or a new product. Or it might be a model that shows with startling clarity that your new product development effort is a likely winner—or loser. Even better, you’ll learn to create models that will serve as guideposts for ongoing operations. You’ll always know just where you are financially, and where you need to be. The models you will learn to build in Financial Modeling for Business Owners and Entrepreneurs can be used to: Raise capital for startup or any stage of growth Plan projects and new initiatives Make astute business decisions, including go/no-go assessments Analyze ROI on your product development and marketing expenditures Streamline operations, manage budgets, improve efficiency, and reduce costs Value the business when it is time to cash out or merge In addition to many valuable exercises and tips for using Excel to model your business, this book contains a combination of practical advice born of hard-won lessons, advanced strategic thought, and the insightful use of hard skills. With a basic knowledge of Excel assumed, it will help you learn to think like an experienced business person who expects to make money on the products or services offered to the public. You’ll discover that the financial model is a key management tool that, if built correctly, provides invaluable assistance every step of the entrepreneurial journey. Tom Y. Sawyer has used the principles this book contains to create financial models of numerous startup and early-stage companies, assisting them in planning for and raising the capital that they needed to grow their businesses and ultimately exit with multiples of their initial investment. Financial Modeling for Business Owners and Entrepreneurs, a mini-MBA in entrepreneurship and finance, will show you how you can do the same. Note: This book is an updated version of Sawyer's 2009 title, Pro Excel Financial Modeling.

In this book, Pascal Costantini gives a lively and wonderfully readable account of ten years of efforts by a small group of investment analysts to find a reliable, practical and implementable method for valuing and selecting shares. The result of their effort is an original investment methodology called CROCI (Cash Return on Capital Invested), best described as a variation of the economic profit model. For over a decade now, Costantinis group at Deutsche Bank has been using this valuation tool every time it has had to take a view on the pricing of an equity asset, be it a market, a sector or an individual sharein other words, every single working day, since it is this groups job to advise institutional investors on equity valuation. Costantini describes in detail, accompanied by concrete examples in the form of charts and graphs, the precise investment results of the actual implementation of the CROCI approach in the global equity markets since 1996. Readers will enjoy taking this journey with Costantini to see how and why the model was developed, assess the results of ten years of actual implementation and measure the successes of using this model in stock picking and portfolio construction. This book will also make it easy for them to see how the CROCI approach can be used successfully by others now and in the future.The book is divided into four parts. The first part is a review and discussion of the fundamentals of investment analysis. The second part is dedicated to the construction of economic data, with the sole objective of calculating an economically meaningful asset multiple and relative return, the combination of which gives an economic PE ratio, the authors main stock selection tool. While the economic profit model is not exactly new, it is still largely ignored by the investment community. In essence, it does three things: it calculates the real amount of cash, or value created by a business; it compares the market value of an asset to an approximation of its replacement value; and it assumes that the former will converge to the latter through the arbitrage of investors and capital providers. The third part is dedicated to the analysis of economic data, and the last part deals with the actual implementation of the CROCI economic profit model, including real life examples. This final part also discusses how to use the output of the CROCI model with individual stocks, and then with investment portfolios.*Techniques are based on the authors performance record at Deutsche Bank since 1996 *Based on almost ten years of proprietary knowledge and implementation of these techniques*Factual illustrations of the results of the valuation techniques are provided at each step*Techniques are based on the author's performance record at Deutsche Bank since 1996 *Based on almost ten years of proprietary knowledge and implementation of these techniques*Factual illustrations of the results of the valuation techniques are provided at each step

In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment.