Corporate and Project Finance Modeling E-Book

Edward Bodmer

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Wiley Finance Editions
• Sprache: Englisch

Seitenzahl: 974

Veröffentlichungsjahr: 2014

Beschreibung

A clear and comprehensive guide to financial modeling and valuation with extensive case studies and practice exercises Corporate and Project Finance Modeling takes a clear, coherent approach to a complex and technical topic. Written by a globally-recognized financial and economic consultant, this book provides a thorough explanation of financial modeling and analysis while describing the practical application of newly-developed techniques. Theoretical discussion, case studies and step-by-step guides allow readers to master many difficult modeling problems and also explain how to build highly structured models from the ground up. The companion website includes downloadable examples, templates, and hundreds of exercises that allow readers to immediately apply the complex ideas discussed. Financial valuation is an in-depth process, involving both objective and subjective parameters. Precise modeling is critical, and thorough, accurate analysis is what bridges the gap from model to value. This book allows readers to gain a true mastery of the principles underlying financial modeling and valuation by helping them to: * Develop flexible and accurate valuation analysis incorporating cash flow waterfalls, depreciation and retirements, updates for new historic periods, and dynamic presentation of scenario and sensitivity analysis; * Build customized spreadsheet functions that solve circular logic arising in project and corporate valuation without cumbersome copy and paste macros; * Derive accurate measures of normalized cash flow and implied valuation multiples that account for asset life, changing growth, taxes, varying returns and cost of capital; * Incorporate stochastic analysis with alternative time series equations and Monte Carlo simulation without add-ins; * Understand valuation effects of debt sizing, sculpting, project funding, re-financing, holding periods and credit enhancements. Corporate and Project Finance Modeling provides comprehensive guidance and extensive explanation, making it essential reading for anyone in the field.

Leseprobe

CONTENTS

Cover

Title Page

Copyright

Preface

Acknowledgments

Part I: Financial Modeling Structure and Design

Chapter 1: Financial Modeling and Valuation Nightmares

Chapter 2: Becoming a Black Belt Modeler

Chapter 3: General Model Objectives of Structuring Transactions, Risk Analysis, and Valuation

Chapter 4: The Structure of Alternative Financial Models

Structure of a Corporate Model: Incorporating Historyand Deriving Forecasts from Historical Analysis

Use of the INDEX Function in Corporate Models

Easing the Pain of Acquiring PDF Data

Structure of a Project Finance Model That Accounts for Different Risks in Different Phases over the Life of a Project

Reconciliation of Internal Rate of Return in Project Finance with Return on Investment in Corporate Finance

Structure of an Acquisition Model: Alternative Transaction Prices and Financing Terms

Structure of an Integrated Merger Model: Forecasting Earnings per Share

Chapter 5: Avoiding Bad Programming Practices and Creating Effective Auditing Processes

How to Make Financial Models More Efficient and Accurate

Chapter 6: Developing and Efficiently Organizing Assumptions

Assumptions in Demand-Driven Models versus Supply-Driven Models: The Danger of Overcapacity in an Industry

Creating a Flexible Input Structure for Model Assumptions

Alternative Input Structures for Project Finance and Corporate Finance Models

Setting Up Inputs with Code Numbers and the INDEX Function

Chapter 7: Structuring Time Lines

Timing in Corporate Finance Models: Distinguishing the Historical Period, Explicit Period, and Terminal Period

Development to Decommissioning: Phases in the Life of a Project Finance Model

Timing in Acquisition Models: Separating the Transaction Period, the Holding Period, and the Exit Period

Structuring a Time Line to Measure History, Explicit Periods, and Terminal Periods in Corporate Models and Risk Phases in Project Finance Models

Computing Start of Period and End of Period Dates

TRUE and FALSE Switches in Modeling Time Periods

Computing the Age of a Project in Years on a Monthly, Quarterly, or Semiannual Basis

The Magic of a HISTORIC Switch in a Corporate Model

Transferring Data from a Corporate Model to an Acquisition Model Using MATCH and INDEX Functions

Chapter 8: Projecting Revenues, Expenses, and Capital Expenditures to Derive Pretax Cash Flow

Transparent Calculations of Pretax Cash Flow

Inflation and Growth Rates in Calculations of Pretax Cash Flow

Valuation Analysis from Prefinancing, Pretax Cash Flow

Chapter 9: Moving from Pretax Cash Flow to After-Tax Free Cash Flow

Working Capital Analysis

Problems in Computing Depreciation Expense in Corporate Models Involving Asset Retirements

Portfolios of Assets with a Vintage Process

Accounting for Asset Retirements in Corporate Models

Alternative Methods for Deriving Retirements Associated with Existing Assets in Corporate Models

Depreciation Issues in Project Finance Models

Modeling the Change in Deferred Taxes in Corporate Models

Adjusting the Tax Basis in an Acquisition

Chapter 10: Adding Debt to a Corporate or Project Finance Model by Programming Cash Flow Waterfalls

Adding the Debt Schedule to a Financial Model

Modeling Scheduled Debt Repayments

Connecting Debt to Cash Flow in Corporate Models

With a Structured Process, You Can Model Any Cash Flow Waterfall

Defaults on Debt and Measuring the Debt Internal Rate of Return

Assessing Risk and Return Characteristics of Subordinated Debt

Chapter 11: Alternative Calculations of Equity Distributions

Modeling Dividend Distributions

Computing a Target Capital Structure through Simulating New Equity Issues and Buybacks

Chapter 12: Putting Together Financial Statements and Calculating Income Taxes

Computation of Taxes Paid and Taxes Deferred

Cash Flow Statement and Balance Sheet

Part II: Analyzing Risks with Financial Models

Chapter 13: Risk Assessment

Six Alternative Ways to Assess the Risk of a Company, a Project, or a Contract

Using Direct Risk Assessment to Measure Cash Flow and Financial Ratios

Chapter 14: Defining, Describing, and Assessing Risk in a Risk Allocation Matrix

Chapter 15: Presentation of Risk Analysis through Adding Sensitivity Analysis to Financial Models

Setting Up Data for Making Graphs by Converting Periodic Data into Annual, Semiannual, or Quarterly Data

Using the INDIRECT Function to Automate Conversion to Time Period Data

Making Flexible Graphs for Sensitivity Analysis

Chapter 16: Using Financial Models to Establish Break-Even Points for Key Input Variables with Data Tables

Establishing Break-Even Criteria When Analyzing Financial Models

Mechanics of Using Data Tables to Compute Break-Even Points Automatically

Creating Data Tables Using VBA Instead of the Data Table Tool

Summary of Break-Even Analysis

Chapter 17: Constructing Flexible Scenario Analysis for Risk Assessment

Mechanics of Scenario Analysis

Using VBA Code to Create a Scenario Analysis

Getting the Best of Both Worlds: Creating a Special Custom Scenario That Allows Use of Spinner Buttons and Drop-Down Boxes

Chapter 18: Generating Tornado Diagrams, Spider Charts, and Waterfall Graphs

Tornado Diagrams That Display Which Variables Have the Largest Effect on Value and Which Variables Have the Least Effect on an Output Variable

Creating a Tornado Diagram by Extending Scenario Analysis

Creating a Tornado Diagram Using a Two-Way Data Table

Spider Diagrams That Illustrate How Each Range in Input Variables Affects an Output Variable

How to Create a Spider Diagram Using a Two-Way Data Table

Presenting Sensitivity Analysis with a Waterfall Chart

Chapter 19: Adding Probabilistic Risk Analysis and Time Series Equations to Financial Models

Definition of Some Terms for Adding Stochastic Analysis to Your Financial Models

Using Probability Distributions with Spreadsheet Functions Rather Than Equations with Greek Letters

Chapter 20: Taking the Mystery out of Applying Time Series Analysis and Monte Carlo Simulation in Financial Models

Step-by-Step Procedure to Incorporate a Monte Carlo Simulation into Your Models

Chapter 21: Constructing Probability Distributions with Trends, Mean Reversion, Price Boundaries, and Correlations among Variables

Starting Point for Developing Time Series Equations—Brownian Motion and Normal Distributions

Testing the Assumption That Input Variables Are Normally Distributed

Price Boundaries and Short-Run Marginal Cost

Mean Reversion and Long-Run Equilibrium Analysis

Modeling Correlations among Variables in Time Series Equations

Chapter 22: The Difficult Problem of Estimating Volatility, Mean Reversion, Time Trends, Correlations, and Price Boundaries from Historical Data or Market Data

Calculation of Volatility from a Random Walk Process

Attempting to Measure the Presence of Mean Reversion in Historical Data

Attempting to Measure the Presence of Mean Reversion by Evaluating Changes in Periodic Volatility

Risk Analysis Summary

Part III: Advanced Corporate Modeling: Modeling Terminal Value with Stable Ratios in the Discounted Cash Flow Model, Deriving Implied Multiples, and Computing the Bridge between Equity Value and Enterprise Value

Chapter 23: Overview of Issues When Computing Normalized Cash Flow and Terminal Value

Chapter 24: Computing the Return on Invested Capital for Historical and Projected Periods in Corporate Models

Working with a Free Cash Flow Perspective, an Equity Cash Flow Perspective, or Both in Computing Financial Ratios

Presenting Return on Invested Capital in Financial Models

Chapter 25: Calculation of Invested Capital

Dissecting the Financial Structure of a Corporation to Understand the Bridge from Enterprise Value to Equity Value

Drawing an Imaginary Line underneath EBIT to Understand the Financial Structure of a Corporation

Constructing a Long-Term Model to Create Proof of Corporate Finance Concepts

Chapter 26: Complex Items in Balance Sheet Analysis

Treatment of Accumulated Deferred Taxes Arising from Depreciation

Classification of Operating Cash That Produces Interest Income below the EBITDA Line

Treatment of Derivative Assets and Liabilities Depending on How Derivatives Affect EBITDA

Chapter 27: Four General Terminal Value Methods

Method 1: Stable Growth Using the (1 + g)/(WACC – g) Formula

Method 2: Value Driver Method—Incorporating the Return Relative to Cost of Capital in Terminal Value

Method 3: Use of Multiples from Comparative Analysis

Method 4: Derived Multiple Formula

Chapter 28: Terminal Value and Philosophy

Computing Transition Periods Using Compound Growth Rates and Switch Variables

Computing Explicit Period Cash Flow and Terminal Value with Different Starting and Ending Points

Computing Value with Changing Weighted Average Cost of Capital and a Midyear Convention

Chapter 29: Normalizing Terminal Year Cash Flows for Stable Working Capital Investment

Effect of Changes in Growth on Working Capital Investment, Capital Expenditures, Depreciation, and Deferred Taxes

Developing a Simple Equation for Normalizing Working Capital

Incorporating Terminal Period Normalized Cash Flow in a Corporate Model

Chapter 30: Relationship of Growth, Capital Expenditures, Depreciation, and Return on Investment

The Long-Term Stable Ratio of Capital Expenditures to Depreciation and the Ratio of Depreciation Expense to Net Plant

Computing the Ratio of Capital Expenditures to Depreciation When Historical Growth Differs from Prospective Growth

Computing the Ratio of Capital Expenditures to Depreciation

Implementing the Stable Ratio of Capital Expenditures to Depreciation in Valuation Analysis

Chapter 31: Computing Normalized Deferred Tax Changes

Stable Ratio of Deferred Tax to Capital Expenditure without Change in Growth Rate

Normalized Deferred Tax with Change in Growth Rate

Chapter 32: Terminal Value and the Ability of a Company to Earn Returns above the Cost of Capital

The Myth of Convergence of Return on Capital to Cost of Capital

Chapter 33: Errors and Distortions in Applying the Value Driver Formula

Deriving the Value Driver Formula for the Price/EarningsRatio and Equity Value

Deriving Implicit Assumptions about the Progression of the Incremental Return on Equity in the Equity-Based Value Driver Formula

Deriving the Value Driver Formula Using the Return on Invested Capital and the Weighted Average Cost of Capital

Biases in the Value Driver Formula in a Case with Only Working Capital

Problems of the Value Driver Formula When Invested Capital Includes Net Plant

Chapter 34: Computing Implied Price/Earnings Ratios for Use in Terminal Value Calculations

Model for Deriving the P/E Ratio from Value Drivers

Chapter 35: Computing an Implied EV/EBITDA Ratio in Terminal Value Calculations

Simulation Model to Derive Implied EV/EBITDA Ratio from Invested Capital with Constant Growth

Function to Derive Implied EV/EBITDA Ratio

Comprehensive Analysis to Derive Implied EV/EBITDARatio with Changing Growth, Deferred Taxes, and Working Capital

Chapter 36: Developing Value Drivers for P/E and EV/EBITDA Ratios with Benchmarking and Regression

Benchmarking Multiples to Derive Cost of Capital

Downloading Data for a Sample of Companies from the Internet into a Spreadsheet

Running Regression Analysis on Financial Data

Advanced Corporate Modeling Summary

Part IV: Complex Issues: Circular References and Other Complex Issues from Financial Structuring in Project Finance and Corporate Finance Models

Chapter 37: Resolving Circular References in Acquisition Models

Circular References and Use of Opening Balances in Annual Models

Alternative Techniques for Solving Circular Reference Logic Problems in Financial Models

Resolution of Circular References from a Cash Flow Sweep Using the Iteration Button

Solving Circular References from Cash Sweeps with Goal Seek and Solver

Solving Basic Circular References from Cash Sweeps with a Horrible Copy and Paste Macro

Solving Circular References Related to a Cash Sweep Using Algebra

Solving Circular References with Functions That Iterate around Equations That Cause the Problem

Chapter 38: Creating a Structured Cash Flow Process in a Corporate Model to Resolve Circular References

Structuring a Corporate Model with a Cash Flow Waterfall

Resolving Circular References in a Corporate Model Using an Iterative User-Defined Function

Chapter 39: Overview of Complex Project Finance Modeling Structuring Issues

Difficult Project Finance Problems: Structuring versus Risk Analysis Elements of a Model

Items in Project Finance Models That Cause Circularity

Chapter 40: Funding Techniques in Project Finance and the Associated Circular Reference Problems

Case 1: No Circular Reference—Pro-Rata Funding, Interest Paid during Construction, and Debt Size from Cash Flow

Case 2: Circular Reference from Pro-Rata Funding with Capitalized Interest or Debt Ratio Input

Case 3: Pro-Rata Funding with Capitalized Fees

Case 4: Cascade with Equity Funded before Debt That Can Be Solved with Backward Induction

Case 5: Bond Financing in a Single Period

Chapter 41: Debt Sculpting in a Project Finance Model

Sculpting Method 1: Use of Solver

Sculpting Method 2: Goal Seek and Algebra

Sculpting Method 3: Net Present Value of Target Debt Service

Sculpting Method 4: Backward Induction

Sculpting Approaches in Complex Cases with Taxes, Debt Service Reserve Accounts, and Interest Income

Solving Difficult Sculpting Problems with User-Defined Functions

Chapter 42: Automating the Goal Seek Process for Annuity and Equal Installment Repayments

Debt Sizing with Level Repayments or Annuity Repayments Using a Goal Seek Macro

Computing Debt Size for Equal Installment Structuring with a User-Defined Function

Computing Debt Size for Annuity Structure with User-Defined Function

Chapter 43: Modeling Debt Service Reserve Accounts

Structuring the Debt Service Reserve Account in a Project Finance Model

Avoiding Circular References in Funding Debt Service Reserve Accounts through Separating Construction Debt from Permanent Debt

Avoiding Circular References Due to Cash Flow Sweeps and the Debt Service Reserve Account

Chapter 44: Modeling Maintenance Reserve Accounts

MRA Case 1: Constant Maintenance Time Period Increments and Level Expenditures

MRA Case 2: Constant Time Period Increments and Changing Expenditures

MRA Case 3: Varying Time Period Increments and Changing Expenditures Using the MATCH Function

Chapter 45: Refinancing and Valuing a Project Given Risk Changes over the Life of a Project

Computed Internal Rate of Return with Changes in Discount Rate over Project Life

Effects of Refinancing on the Value of a Project

Mechanics of Implementing Refinancing into a Project Finance Model

Chapter 46: Covenants and Cash Flow Sweeps in Project Finance Models

Mechanics of Modeling Covenants and Cash Flow Sweeps

Chapter 47: Asset Portfolios, Progress Payments, and Lease Rolls in Real Estate Models

Modeling a Single Real Estate Project

Modeling Multiple Projects That Are Part of a Combined Portfolio with Percent of Time Function

Modeling a Portfolio with the INDEX Function and Data Table Tools

About the Author

About the Website

Index

End User License Agreement

List of Tables

Table 4.1

Table 13.1

Table 13.2

Table 14.1

Table 17.1

Table 17.2

Table 22.1

Table 22.2

Table 24.1

Table 37.1

List of Illustrations

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Guide

Cover

Table of Contents

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Part 1

Chapter 1

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Corporate and Project Finance Modeling

Theory and Practice

Edward Bodmer

Cover image: ©iStock.com/Kamil Krawczyk

Cover design: Wiley

Copyright © 2015 by Edward Bodmer. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:

Bodmer, E. (Edward)

Corporate and project finance modeling/Edward Bodmer.

pages cm

ISBN 978-1-118-85436-5 (cloth); ISBN 978-1-118-85446-4 (ePDF); ISBN 978-1-118-85445-7 (ePub); ISBN 978-1-118-95739-4 (oBook)

1. Valuation—Mathematical models. 2. Finance—Mathematical models. 3. Financial risk—Mathematical models. I. Title.

HG4028.V3B52 2014

658.1501′1–dc23

2014016731

Preface

Corporate and Project Finance Modeling: Theory and Practice is intended to be a comprehensive guidebook for anyone who is interested in creating and/or interpreting and/or understanding financial models. Through compiling many years of experience in creating, reviewing, and teaching corporate finance, project finance, acquisition, and real estate modeling, I describe in this book how you can master many difficult modeling problems and how you can build highly structured models from the ground up. Flexible, efficient, and stable model structures are explained along with describing unique solutions that address complex issues. In explaining model design as well as detailed programming techniques, Corporate and Project Finance Modeling can help you to become a much better modeler, whether you are just beginning or are very experienced and want to take your skills to a really high level. By covering how to build, analyze, and present results from a variety of alternative financial models, the book will provide an understanding of why particular modeling features are generally included in one kind of model but not in others. It is hoped that you will be able to find creative ways to borrow subtle concepts from issues addressed in different modeling applications and apply them to your own models.

Corporate and Project Finance Modeling explains how you can build flexible, transparent, and accurate financial analyses, but it does not simply document techniques that are commonly applied in modern models, which have become ever more elaborate and artistic over the past few years. Instead, the book also introduces unique modeling techniques that address many complex issues that are not typically used by even the most experienced modelers. For example, you can learn how to build user-defined functions to solve circular logic and avoid cumbersome copy and paste macros or how to write a function that derives the ratio of enterprise value to earnings before interest, taxes, depreciation, and amortization (EV/EBITDA) that accounts for asset life, historical growth, taxes, return on investment, and cost of capital. Distinctive modeling techniques introduced in Corporate and Project Finance Modeling include accounting for retirements when computing depreciation, automating models to incorporate additional periods of historical data, combining and presenting scenario and sensitivity analysis in a flexible manner, accurately computing net operating loss carryforwards and deferred taxes, adding time series equations and Monte Carlo simulations into financial models without any Microsoft Excel add-ins, normalizing terminal period capital expenditures and deferred taxes, sculpting debt repayments to after-tax cash flow, computing debt service and maintenance reserve accounts, modeling portfolios of assets with different starting and ending periods, and establishing long-term models to prove the treatment of items in the bridge between equity value and enterprise value. Some of the topics address things that you may not even have realized are issues, such as automatically computing the stable level of capital expenditures to depreciation as a function of historical and prospective growth when normalizing cash flow in a corporate model. Many of the unique ways to address risk analysis, circular logic, normalized cash flow, depreciation expense, and modeling multiple assets with different start and end dates are solved by programming user-defined Visual Basic for Applications (VBA) functions.

The goal of Corporate and Project Finance Modeling is not only to show you how to solve a modeling problem but also to explain the finance theory underlying why you should construct your models in a particular way. To address modeling issues ranging from the fundamental structure of different types of financial models to the creation of user-defined VBA functions that resolve circular references, each topic is introduced with theoretical discussion clarifying why the issue is relevant from a financial perspective. Theoretical topics that precede explanation of step-by-step modeling mechanics cover different corporate structures; valuation in the context of project finance, corporate finance, and acquisition finance; risk allocation resulting from cash flow waterfalls; credit analysis in corporate and project finance; debt structuring in the context of risk analysis; determination of items that should be in the bridge between enterprise value and equity value; and many other subjects. The modeling subjects are also described in the context of patterns of valuation mistakes and whether a particular aspect of constructing model assumptions could have avoided some of the common and recurring financial blunders. After the theoretical discussion of each modeling concept, modeling problems are explained on a step-by-step basis with many diagrams and screen shots from actual models.

The website, www.wiley.com/go/internationalvaluation, accompanies Corporate and Project Finance Modeling and includes exercises, model examples, video explanations, and case studies and is integral to the book. This website contains hundreds of customized exercises; many template project finance, corporate finance, and acquisition models; and a number of featured completed models of business enterprises in a wide variety of different industries. In addition, you can download special utilities to read PDF files into Excel, to make automated waterfall charts, to automatically color cells linked to different sheets, to create a table of contents with hyperlinks, and many other things.

Through using information on the website, there are various ways you can read the book and work with the exercises, models, and videos on the website. One way is to read Corporate and Project Finance Modeling from cover to cover without touching an Excel workbook. This would be like reading a cookbook without ever trying out the recipes. Alternatively, if you are relatively new to modeling and you want to become a top-notch modeler, you could work through accompanying models on the website as you read each chapter. A third way to use the book if you already have a lot of experience in modeling is to treat it as a reference manual where you can selectively look up new ways to tackle difficult modeling issues.

In working through the theory and practice of financial modeling, the book is divided into four parts. Part I explains the structure of alternative types of financial models and how to build a financial model from A to Z. Part II describes how to add risk analysis to your financial model. Part III addresses complex issues in corporate models related to computing normalized cash flow, deriving implicit valuation multiples, and evaluating the bridge between enterprise value and equity value. Part IV introduces unique approaches to solving complex problems in project finance and corporate models arising from circular logic related to cash flow sweeps, project funding prior to operation, debt sculpting, and reserve accounts.

The first part of the book describes how to build different types of financial models that can ultimately be effective in performing risk analysis, structuring transactions, and assessing value from a debt and equity perspective. Philosophical differences between a project finance investment with definite begin and end dates relative to a corporation with an indefinite life are discussed from the perspective of designing the architecture of a financial model. These model structures are then used to guide an explanation of how an efficient model should be put together, beginning with a discussion of programming practices that should be avoided. Explanations of how to construct audit checks that quickly identify errors in a model are introduced before working through the logical flow of a model. In describing the modeling process on a step-by-step basis, the manner in which assumptions should be laid out in a model, along with the economic theory underlying the construction of different assumptions is explained first. Next, techniques to creatively build time lines that solve the pesky problems of adding new historical periods in corporate models, evaluating construction delays in project finance models, and simulating alternative transaction dates in acquisition models are described. Once time lines are established, the central structuring idea of beginning with pretax cash flow, moving to after-tax cash flow, and then incorporating financing is covered. In computing taxes and depreciation, techniques that create user-defined functions that dynamically solve for historical growth rates and asset retirements are explained. Adding debt financing to alternative types of models is discussed in the context of simulating cash flow distributions to alternative investors and efficiently modeling cash flow waterfalls.

The second part of Corporate and Project Finance Modeling explains how you can use the financial models you build to analyze risks of the investment. The theory of risk analysis is introduced by discussing different possible ways to evaluate risk ranging from a qualitative risk matrix to stochastic time series equations. Next, a host of risk analysis techniques that can be used in different financial models, including sensitivity analysis, break-even analysis, scenario analysis, and Monte Carlo simulation are addressed. Each technique is explained on a step-by-step basis that includes how to make effective presentations using dynamic graphs, drop-down boxes, data tables, and selected macros. A form of scenario analysis that sets up a cockpit master scenario page to drive the model and at the same time allows management to play around with a model using sensitivity tools is described in detail. After discussing scenario analysis, techniques are presented to quickly build tornado diagrams and spider graphs into the financial models. Next, Monte Carlo simulation is explained. Using a bit of VBA programming, you will see how sophisticated Monte Carlo simulation can easily be added to just about any financial model without any add-in programs. In describing how to incorporate stochastic approaches to modeling, Corporate and Project Finance Modeling explains how you can use various tools to realistically measure risk rather than just presenting elegant distribution charts. In building simulation models, you will understand the importance of estimating mean reversion, correlation among variables, and boundary conditions for time series equations.

The third part of Corporate and Project Finance Modeling addresses challenging issues in corporate models associated with computing terminal value, normalizing cash flow, interpreting and deriving valuation multiples, and computing the bridge between enterprise value and equity value. This part of the book begins by discussing how you can often verify the reasonableness of your assumptions by making an effective presentation of the historical and prospective return on invested capital. Advanced corporate modeling issues are introduced by working through how to decipher which balance sheet items should be classified as financing related and which items should be classified as EBITDA related. To resolve thorny issues of how to treat alternative items in valuation analysis, the efficacy of building a theoretical model that simulates accounting and cash flow elements over a long-term period is demonstrated. The theoretical modeling approach is used to derive simple formulas for applying the half-year convention with varying costs of capital in discounted cash flow models and to explain how valuation errors occur from things like incorrectly assuming that accumulated deferred tax should be treated like debt. A central idea in this part of the book is showing you how to develop unique solutions to the challenging problem of computing normalized EBITDA, capital expenditures, and deferred taxes through writing user-defined VBA functions. After demonstrating how you can master calculation of normalized cash flow techniques, the value driver formula (1 − g/ROIC)/(WACC − g) is shown to be highly flawed for purposes of computing terminal value. As the value driver formula should not be used in valuations, Corporate and Project Finance Modeling shows you how to construct a more accurate user-defined function to compute the EV/EBITDA and price/earnings ratios automatically in the context of changing growth rates, costs of capital, returns, and other factors.

The final part of Corporate and Project Finance Modeling explains how to solve complex financial modeling issues using methods that are not traditionally applied in elaborate models built by experienced modelers. The user-defined functions that are developed to work out difficult financial modeling problems do not require you to have a lot of programming knowledge or to write lengthy VBA code. Much of the last part of the book deals with resolving circular logic that arises in corporate models, acquisition models, and especially in project finance models. A central theme of this part of the book is that all circularity references can be resolved without copy and paste macros that arguably ruin a model. After discussing the philosophy of circular reference logic, a simple problem that occurs in acquisition models for cash sweeps is used to introduce the issue. Five different techniques for addressing the circular reference problem, including the clumsy copy and paste macro approach, are discussed, and a method of isolating equations in a user-defined function is suggested as a much better solution. A user-defined function with isolated equations is used to resolve the famous circular problem of interest expense and interest income. Difficult modeling issues in project finance models are covered, including: (1) funding, capitalized interest, and fees; (2) debt repayments and sculpting with taxes; (3) debt service reserve accounts; (4) maintenance reserve accounts; (5) cash sweeps and dividend lockup covenants; (6) debt refinancing and valuation of projects in different stages; and (7) incorporating portfolios of assets in real estate models.

Acknowledgments

I owe a great debt to my many students, from Moscow to Munich, to Singapore, to Bangkok, to Lagos, to Copenhagen, to Lima, to Pretoria, to Paris, to Prague, to Zurich, and so many other cities, who have inspired me to write this book and given me so many ideas, suggestions, modeling techniques, and practical insight.

Part IFinancial Modeling Structure and DesignStructure and Mechanics of Developing Financial Models for Corporate Finance and Project Finance Analysis

Chapter 1Financial Modeling and Valuation NightmaresProblems That Financial Models Cannot Solve

An inevitable step in just about any financial analysis these days is making some kind of explicit or implicit projection of cash flow and/or earnings and/or financial ratios that measure profitability, credit quality, or other key performance indicators. Since valuation of debt or equity is all about making forecasts, you could go to a fortune-teller or read the astrology section of your newspaper to make a prediction about the future. These days, however, forecasts used in valuation are more often founded on fancy financial models built using elaborate spreadsheets. After the East Asian crisis of 1997, the bursting of the dot-com bubble in 2000, the global financial crisis of 2008, the European debt crisis in 2010, and innumerable other less famous valuation disasters or missed investment opportunities where debt and equity valuation failures had relied on sophisticated financial models, it could be argued that going to astrologers and fortune-tellers would have been a better strategy.

Notwithstanding serious questions about the general efficacy of making financial projections and the dangerous ways in which people make forecasts, the fact is that financial models are becoming more and more complex and they are also being used more than ever before in all types of investment analysis. Seemingly sophisticated financial models using elaborate programming functions can appear impressive and even artistic. But these beautiful models are also often almost impossible to use in assessing risk and value. Given the prominence of modeling in financial analysis, the first part of this book describes how to build flexible, accurate, structured, and transparent financial models that can be used to assess various different valuation problems.

When studying many valuation mistakes made in the past decades, it becomes clear very quickly that the most important pitfall in modeling is the development of economic assumptions for prices, volumes, capital expenditures, and operating expenses that are put into the models. The problems did not happen because of making a spreadsheet that did not follow some bureaucratic best practice defined by some IT staff. If you take a step back and think about all sorts of past financial failures ranging from the global financial crisis to bankruptcies of small business enterprises to industry-specific failures such as solar panel manufacturers, there are a few patterns of mistakes that are repeated and that seem obvious after the fact. Before delving into sophisticated mathematical equations, spreadsheet techniques, and model structure issues that deal with methods to resolve difficult project and corporate finance modeling challenges, you should think about why the outcomes of financial analysis using financial models sometimes fail so miserably. You can then leave these ideas somewhere in the back of your brain while you create the ornate models that follow all of the rules about flexibility, accuracy, structuring, and transparency.

Some recurring valuation mistakes related to financial modeling that continue to be made despite more and more sophistication in financial analysis include the following nine errors:

1. Making assumptions in financial models that business entities earning a rate of return substantially higher than their cost of capital and growing quickly can continue this financial performance for a long time even when they do not have some kind of sustained competitive advantage.

Earning a higher return than the cost of capital and growing quickly seems to put a company in the famous powerhouse square shown on management consultant PowerPoint slides, which is supposedly the best place to be for valuation. But when returns and growth are high, valuations are also high. More important, other companies from all over the world will attempt to enter the industry no matter how unique managers of the company claim to be. New capital expenditures from other companies entering the market then lead to industrywide overcapacity, followed by reduced prices and sudden dramatic declines in returns. If demand growth is slower than expected, which happens more often than not, the overcapacity and depressed prices can last for many years and the company suddenly finds itself in the worst box on those management consulting slides. Examples of high growth and returns leading to industry expansion followed by surplus capacity and price crashes include the famous telecom industry meltdown in the late 1990s, in which more than 50 percent of loans defaulted; the merchant electric power crash of 2000–2001 in the United Kingdom, where virtually every electricity plant without a fixed price contract defaulted on its debt; the real estate industry during many periods, most notably before the U.S. crash of 2008; very high returns earned by solar manufacturing companies, followed by massive new entry and dramatic price declines after Chinese manufacturers entered the industry; high returns earned by bulk cargo vessels before 2008, followed by overcapacity and depressed prices that have continued long after commodity prices and other industries recovered; and depressed occupancy rates and room rates for hotels in Iquitos, Peru, following a period of overbuilding that was initiated when the region received UNESCO heritage site status.

2. Entering projected prices in financial models that remain above the long-run cost of production even when capacity is increasing in an industry.

You can define a bubble as a situation in which prices are above long-run marginal cost and/or asset values are not consistent with levels that provide investors with a reasonable return on their investment. Assuming that prices can be sustained above marginal cost is an error that has happened before the U.S. real estate crash, when people believed they could profit by buying and selling (or flipping) a product. It occurred during the famous tulip bubble in Holland in the seventeenth century, and it may be happening in U.S. natural gas prices above the marginal cost of producing shale gas. The assumption that prices could remain above marginal cost was behind the valuation mistakes just discussed in comparing returns to the cost of capital, ranging from the telecom industry crash to overproduction of container ships.

3. Using information in financial models that relies on so-called independent experts, whether these people or institutions are credit rating agencies, large and reputable corporations, consulting companies that create very fancy models, experts speaking on CNBC or Bloomberg, famous finance professors, or former politicians.

Many valuation nightmares have demonstrated after the fact that it is more important to put your feet on the ground by visiting countries, meeting with real consumers, trying out products and services, and having a thorough independent understanding of the business idea than to trust on so-called experts when developing financial projections. Reliance on entities like rating agencies not only was a cause of the global financial crisis of 2008, but has also occurred with traffic studies made for project financings such as the Eurotunnel; toll roads and toll bridges all over the world; theme parks; and the Iridium disaster, in which Motorola promoted its satellite phones; and countless other cases. The famous Panama Canal catastrophe in which French investors lost so much money in the nineteenth century resulted from trusting the opinion of a famous engineer who had visited Panama only once. Relying on the reputations of companies that were thought to be the most innovative in their industry—such as Enron, WorldCom, and Lehman Brothers—without thinking through the fundamental competitive advantages and product quality has turned out to be very dangerous.

4. Trusting financial model results where increasing returns are projected by management, but not recognizing that the projected returns come about only because the company is taking on increased risks.

Companies with declining returns or lower margins than their peers often desperately try to increase or maintain equity returns. But these companies (or individuals) can generally meet their return objectives only by incurring increased risks and then trying to hide those risks using the latest business jargon and/or creative accounting. When taking on new ventures or deploying capital that involves taking greater risk, it is tempting for management to directly or indirectly cover up the risks through not fully disclosing things or worse, by using very sophisticated and confusing financial terms along with financial models that are impossible to understand. Examples of valuation errors caused by presenting confusing information include Constellation Energy in 2006–2008, Enron's impossible to understand financial statements, and innumerable financial institutions that made risky loans or engaged in risky trading behavior to boost their returns before the financial crisis of 2008.

5. Ignoring shifts in the cost structure and demand changes that can quickly render existing assets obsolete when developing risk analysis using financial models.

Sudden shifts in demand and/or price is a particular problem in modeling oligopolistic industries where seemingly stable returns and cash flows can suddenly change on the whim of competitor actions and/or changes in consumer taste and/or global events. Think about the sequence of Hewlett-Packard (HP), Nokia, Research in Motion (RIM, now BlackBerry), and Apple. A few years ago Nokia was all the rage with investors and the company was assumed to have unique products that would yield a sustainable competitive advantage and strong returns over an indefinite period. Then Nokia lost its luster and Research in Motion was the poster child for investors. A couple of years later RIM lost its popularity and Apple became the most valuable company in the world as it somehow made people even more addicted to their cell phones. In the case of automobile companies and airlines, sudden changes in industry demand could not be absorbed by companies with cost structures that contained high proportions of fixed cost from labor contracts, such as General Motors and United Airlines. Commodity industries may be very volatile and not offer extraordinary returns, but at least you can apply basic economic principles when thinking about prices, volumes, industry capacity, and market demand. Oligopolistic industries can be more challenging to evaluate in financial models because seemingly stable cash flows are subject to sudden changes that can occur that result in returns falling to levels below those of companies in competitive industries.

6. Putting faith in fancy, complicated, and innovative new financial paradigms when creating financial models.

At the turn of the twenty-first century the so-called new economy was supposed to replace traditional financial analysis that relied on cash flow and rate of return relative to cost of capital. New economy principles could explain why dot-com companies did not need cash flow or profit to generate value; real option models were used to justify new electricity peaking power plants that did not make economic sense using traditional discounted cash flow analysis; collateralized debt obligations supposedly could somehow reduce risk by putting together a bunch of shady loans that had been granted to people who could not repay them. When such new models cannot be explained in simple terms and when the seemingly sophisticated financial models cannot explain why one can somehow earn high returns without having a sustained competitive advantage, they almost always turn out to be rubbish. It is much better to study fixed and variable costs together when evaluating different possibilities of demand growth.

7. Having confidence in contracts that may be well drafted by sophisticated lawyers but that do not make economic sense, and incorporating those contracts into financial models.

Financial contracts that have turned out to be unsustainable included subprime loans issued before the financial crisis of 2008; electricity purchase contracts called power purchase agreements in Senegal, India, Indonesia, the United States, the Philippines, and many other places; construction contracts for large, complex projects such as the Eurotunnel and Euro Disney that chronically underestimated the actual cost; oil projects where ownership structures resulted in extreme economic profit for private investors; and financial subsidies from governments in Spain and the Czech Republic that led to very high returns for project developers. In each of these cases, financial projections made by analysts assumed contracts that would remain in place even though the contracts allocated risks in crazy ways and led to prices and returns that were far away from returns that could be realized on other projects with comparable risk. When contracts lead to returns that seem too good to be true, they probably are.

8. Inputting symmetrical upside case and downside assumptions into models when developing risk analysis without adequately considering differences in upward limits and downward exposures that create skewed returns.

Not properly accounting for deviations between upside and downside variation led to the California crisis in electricity prices in 2000–2001; it also leads to underestimating exposure to risk of nationalization when oil prices are low, and to retiring large plants when prices are low and have much more potential movement to the upside than to the downside.

9. Ignoring long-term trends in historic data and not understanding the value of long-term historic returns when evaluating financial projections.

In making financial forecasts you should carefully study the past and test your projections in light of any historic data that you can get your hands on. If results of your model do not make sense in the context of history, then something is probably wrong with the assumptions in your model. Similarly, investments for which you have good quality historic data are better than investments that rely on some kind of business plan or consulting study, all else being equal. Valuation mistakes that arise from not looking at history are illustrated by the stock price of General Electric in 2007–2009. In 2007 GE's stock price reached a high of $42 while in March 2009 the stock price fell to a level of $5. The valuation mistake in this case did not concern making a bad investment that went down, but rather failing to capitalize on an investment opportunity. To justify a stock price of $5 you would have had to make a series of pretty unrealistic assumptions about GE's rate of return in light of a long series of historic data. The return would have to reach levels far below those ever experienced in history and it would have to stay at those low levels for a very long time. With hindsight, it is clear that not accounting for historical data when investing in GE and realizing upside was a big mistake.

Chapter 2Becoming a Black Belt Modeler

The four parts of this book explain how to: (1) build and interpret corporate finance, project finance, and acquisition financial models; (2) perform risk analysis using all different kinds of financial models; (3) analyze multiples, terminal values, the bridge between equity value and enterprise value, and normalized cash flow in deriving value from corporate models; and (4) use mathematical programming techniques to resolve circular logic problems related to financing, sculpting, and credit enhancements in corporate and project finance models. While the mechanical descriptions along with practical exercises of these subjects will make your life easier, explaining on a step-by-step basis how to construct the best financial models in the world has little direct effect on the recurring human mistakes discussed in Chapter 1. Because of the importance of recurring valuation mistakes that are a backdrop to the description of modeling techniques, introduction to various subjects in the four parts of the book will periodically return to these chronic errors.

In describing model structure, risk analysis, corporate valuation, and circular logic, this book discusses different model types, including corporate finance models, project finance models, and acquisition models. You may wonder whether the subject is too broad for a single book and if some of the intricate issues that arise in different modeling contexts can be adequately all addressed in one place. The philosophy of dealing with a variety of different types of model types and valuation analyses is that you can discover creative modeling techniques by contrasting different kinds of models. You can also understand why certain model structures are used in particular analyses and others are used in different models through contrasting the different genres of financial models. This will reinforce your ability to set up analyses that address financial structuring, credit analysis, valuation, and risk analysis in your models. Further, while one can make generalizations about the different modeling categories, many actual transactions and investment analyses have overlapping aspects of project finance, corporate finance, and acquisition finance. An investment may be initially structured using project finance concepts; it may then gain characteristics of a corporate finance analysis as it develops a history and expands into other activities. After the corporation has existed for a few years, it may consider acquiring new companies or be the target of an acquisition, requiring acquisition analysis.

As much of this book is designed to be a practical reference guide on how to structure and build models, there are a number of ways to read the book. One way is to read through different chapters without touching a spreadsheet. This may not be very exciting and would be something akin to reading a cookbook without trying out the recipes. A second way to read the book is to work through one of the many accompanying models while you tackle the various issues. More than 200 customized exercises with instructions along with project finance, corporate model, and acquisition model templates are included on the associated website, www.wiley.com/go/internationalvaluation. There are also many carefully designed featured example models that may be the most helpful tools for learning how to become a truly top-notch modeler. These exercises and template models, and the completed model examples on the website, are an integral part of this book. A third way to use this book if you already have experience in modeling is to treat it as a reference manual. You can selectively look up difficult modeling issues, such as constructing a debt service reserve account in a project finance model without any circularity, or writing a function to deal with retirements of assets and accelerated depreciation in a corporate model.