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Modeling and Valuation Mistakes

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Modeling and Valuation Mistakes. Introduction. The financial market volatility caused by the decline in value of housing mortgages in the U.S. should create a different way to think about risk. - PowerPoint PPT Presentation
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Integrated Financial Management Jun 27, 2022 Modeling and Valuation Mistakes
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Page 1: Modeling and Valuation Mistakes

Integrated Financial Management Apr 20, 2023

Modeling and Valuation Mistakes

Page 2: Modeling and Valuation Mistakes

www.edbodmer.com [email protected] Apr 20, 2023 2

Introduction

• The financial market volatility caused by the decline in value of housing mortgages in the U.S. should create a different way to think about risk.

• There has been something fundamentally wrong with the manner in which financial professionals assess risk

• Traditional financial theory taught in business schools (beta, option pricing models and Monte Carlo Simulation) provided little or no guidance in valuation

• While the understanding of any discipline requires knowledge of underlying technical principles, when it comes to valuation, learning from past mistakes is also essential, if not even more important.

Page 3: Modeling and Valuation Mistakes

www.edbodmer.com [email protected] Apr 20, 2023 3

Assumption Development is the Most Important Part of Valuation Modelling

• Assuming that prices and volumes can continue to increase in tandem when there is surplus capacity.

• Relying on experts who do not have a vested interest in investments and without verifying analysis with back of the envelope analysis.

• Using statistical analysis on historic data without realizing the manner in which economic variables can suddenly change in non-linear ways.

• Believing in innovative valuation techniques without understanding the ultimate source of cash flow.

• Not studying long-term marginal cost and fundamental economic principles relative to prices in evaluating cash flows.

• Simplistic assumptions with respect to downside and upside cases rather than recognizing differences in upside potential and downside risk.

• Assumption that contracts will protect investments without delving into the potential for contracts to be broken or mismatched.

Page 4: Modeling and Valuation Mistakes

www.edbodmer.com [email protected] Apr 20, 2023 4

Classification of Valuation Mistakes

• Some Prominent valuation errors – restatements of above include:

Failure to forecast potential price declines that occur when there is over-supply in a market;

Adoption of complicated analysis made by others without adequate independent analysis;

Failure to check the underlying logic of the investment with simple tests or back of the envelop analysis;

Belief in supposedly innovative new valuation techniques without fully testing the underlying logic;

Assuming that historic trends and volatility will continue;

Failure to adequately contrast downside risks with upside opportunities.

Failure to account for the flexibility in investments

Page 5: Modeling and Valuation Mistakes

www.edbodmer.com [email protected] Apr 20, 2023 5

Valuation Nightmares

• All of these things factored into the mother of all valuation nightmares – the financial panic precipitated by declines in the U.S. housing loans known as the sub-prime crisis.

• A very general discussion of the explosive mixture of valuation errors that contributed to the sub-prime mess is presented below before specific valuation mistakes are discussed in more detail.

• The problem is that none of these mistakes is very new – they simply occurred on a bigger scale and had wider implications for the overall market.

Page 6: Modeling and Valuation Mistakes

www.edbodmer.com [email protected] Apr 20, 2023 6

Other Valuation Nightmares

• Telecommunication Meltdown – loss of a trillion dollars in market value from over-leverage, over-supply and belief in unrealistic growth.

• Merchant Electricity – caused and estimated decline in market value (debt and equity) of more than $100 billion from ignoring fundamentals of supply and demand.

• Enron Bankruptcy – Enron had very sophisticated salesmen, but there was little underneath many of the products such as its power plant in Dabhol India began the downfall of Enron.

• LBO Crash of Early 1990’s – Over-optimism in assessment of cash flow forecasts resulted in a very high default rate and brought buyouts to a close.

• LTCM in 1998 belief in the complicated mathematical models and the reputation of others led to a major collapse and intervention of the U.S. Federal Reserve.

• Dot Com Bubble – dramatic increase in valuations that ignored fundamental valuation principles and was driven by easy access to investmet.

• Eurodisney/Eurotunnel – Cost over-runs and low volumes because of concept.

Page 7: Modeling and Valuation Mistakes

Integrated Financial Management Apr 20, 2023

Problem OneIgnoring the most basic of economic

principles in developing assumptions for financial models

Page 8: Modeling and Valuation Mistakes

www.edbodmer.com [email protected] Apr 20, 2023 8

Most investors built their pricing models around the sweet spot, the two-year adjustable mortgage with a three-year prepayment penalty, because it maximized revenue for everyone in the food chain.

Page 9: Modeling and Valuation Mistakes

www.edbodmer.com [email protected] Apr 20, 2023 9

Don’t Assume Prices and Supply Can Increase over Long Periods

• Investors and bankers did not account for the obvious prospective oversupply of homes in their analyses. This surplus of residential homes could be verified by a simple drive around sprawling suburban areas of American cities where it was apparent that supply was increasing much faster than the overall population.

Telecommunications Meltdown.

• Telecommunications companies experienced higher bankruptcies than any other industry in 2000-2002.

• There was an overbuilding of fiber-optic cable systems by a factor of at least 10. Many New Economy companies were built based on the idea that the telecom sector would expand perpetually by 15 to 30% per annum.

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Debt to Income versus Debt to Capital

• Part of the problem was relying on debt to the value of assets rather than on debt to income. The chart below shows that the level of debt relative to aggregate income was dramatically increasing.

• Similar problems from relying on equity buffer on balance sheet without examining future cash flow exposure.

• The second chart shows that the housing as measured by cumulative starts has increased by about 40% more than population.

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Page 11: Modeling and Valuation Mistakes

Integrated Financial Management Apr 20, 2023

Problem TwoIgnoring the Supply Side of Variables Driven

By Marginal Cost

Page 12: Modeling and Valuation Mistakes

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Ignoring Economics and Long-Run Marginal Cost when Evaluating Prices

• Loans were granted on the presumption that housing prices would follow historic trends and continue to increase. The most fundamental of economic principles dictate that prices eventually move to long-run marginal cost, or the cost of building a new home. As a corollary, economics suggests that prices can move to short-run marginal when surplus capacity exists. The graph of median housing prices in the U.S. shown below illustrates how the basic economic principles were ignored.

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AES Drax and UK Merchants

Declines in prices were not predicted in merchant electricity markets after increases in supply. Losses were estimated to be $100 billion. In the U.K. changes in the market structure and increased supply pushed prices to marginal cost.

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Increasing credibility to stories – new era stories – that appear to justify the belief that the boom will continue. People think the world is led by independent minds who invariability act with great intelligence.

Page 13: Modeling and Valuation Mistakes

www.edbodmer.com [email protected] Apr 20, 2023 13

Standard and Poors and Housing Price Assumptions

According to one story an investor called the rating agency Standard & Poor’s and asked what would happen to default rates if real estate prices fell.

The man at S&P couldn’t say; its model for home prices had no ability to accept a negative number. ‘They were just assuming home prices would keep going up…’”

Page 14: Modeling and Valuation Mistakes

www.edbodmer.com [email protected] Apr 20, 2023 14

Other Examples of Believing Implausible Forecasts and Herding Behavior Like Lemmings

Page 15: Modeling and Valuation Mistakes

Integrated Financial Management Apr 20, 2023

Problem ThreeForgetting the Fundamental Rule that Value comes from Earning Returns above the Cost of Capital and the Danger of Assuming High

Returns without Competitive Advantage

Page 16: Modeling and Valuation Mistakes

www.edbodmer.com [email protected] Apr 20, 2023 16

Sub-prime lending jumped from an annual volume of $145 million in 2001 to $625 million in 2005 – more than 20% of total issuances.

Page 17: Modeling and Valuation Mistakes

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Assumption that Money Can Be Easily Made with No Work and No Competitive Advantage

• Lenders welcomed “flippers” – people buying houses solely for the purpose of reselling in a year or so. By 2005, 40% of all home purchases were either for investment or for second homes. Experts believe that a large share of the “second homes” actually were speculations for resale.

• A surprising number of number of sub-primes went to affluent people stretching for second homes. If loan originators have no stake in the borrower’s continued solvency, the competition for fees will inevitably degrade the average quality of loans.

• In the example below, an electricity plant was assumed to be able to earn far more than its cost of capital in a competitive business where new entrants can easily enter the market.

• Rather than focusing on the model mechanics or debt structure or even the details of forward price forecasts, the question of why the returns can exceed the cost of capital must be answered

Page 18: Modeling and Valuation Mistakes

www.edbodmer.com [email protected] Apr 20, 2023 18

Not evaluating the underlying logic of the investment with simple tests

• In the sub-prime crisis, loans that were made that ignored fundamental lending practices of evaluating whether cash flow could cover debt service.

• In the extreme, reckless loans named NINJA loans (No Income, No Job, no Assets) were dispersed on the presumption that housing prices would continue to increase. People bought bigger and bigger houses.

• U.S. census – the floor area in one-family houses rose from 1,525 square feet in 1973 to 2,248 square feet in 2006, an almost 50% increase.

LBO Defaults

• Financial projections that underpinned several high-profile LBO bankruptcies in the late 1980s. Many of these transactions were based on assumptions that the companies could achieve levels of performance, revenue growth, operating margins, and capital utilization never before achieved in their industry. The buyers of these companies typically had no concrete plans for executing the financial performance necessary to meet their obligations. In many such transactions, the buyers simply assumed that they could resell pieces of the acquired companies for a higher price to someone else.

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Page 19: Modeling and Valuation Mistakes

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Rating Agencies and Examining Underlying Value

• Moody’s did not have access to the individual loan files, much less did it communicate with the borrowers or try to verify the information they provided in their loan applications.

• “We aren’t loan officers,” Claire Robinson, a 20-year veteran who is in charge of asset-backed finance for Moody’s, told me. “Our expertise is as statisticians on an aggregate basis. We want to know, of 1,000 individuals, based on historical performance, what percent will pay their loans?”

Page 20: Modeling and Valuation Mistakes

Integrated Financial Management Apr 20, 2023

Problem FourBelieving that Innovative Financial Ideas can

Create New Sources of Value

Page 21: Modeling and Valuation Mistakes

www.edbodmer.com [email protected] Apr 20, 2023 21

Mortgages were transferred to a trust and then sliced or tranched horizontally into different segments, with different bonds for each segment. The trick was that the top-tier bonds, which represented say 70 percent of the value sold had first claim on all cash flows. Since it is inconceivable that 30 percent of a normal mortgage portfolio can default, top-tier bonds go triple-A, super safe ratings and paid commensurately low yields.

Page 22: Modeling and Valuation Mistakes

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Page 23: Modeling and Valuation Mistakes

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Beware of analysis that supposedly is innovative – Value always comes from assessment of cash flow

• The structured finance products that packaged loans together and distributed different pieces of the cash flows different investors.

• These investments were somewhat complex and opaque and investors did not perform due diligence, but instead accepted the expertise of rating agencies and marketing professionals.

• The securitized products often had strong credit ratings which were not questioned by many sophisticated investors.

The Dot Com Bubble

• Some economists took to questioning long-held tenets of competitive advantage, and "new economy" analysts asked, with the utmost seriousness, why a three-year-old-money-losing Internet purveyor of pet supplies shouldn't be worth more than a billion dollars.

These are not excuses!!!

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Page 24: Modeling and Valuation Mistakes

www.edbodmer.com [email protected] Apr 20, 2023 24

Rating Agencies and Complex Securities

• The first mortgage-backed bonds were created in the late 1980s… structured finance was a process of pure alchemy: a way of turning myriad messy mortgage loans into standardised, regimented and easy-to-assess bonds.

• "The problem is that these instruments have become so incredibly complex that you need incredibly sophisticated computer models to work out their value - and these are always liable to bugs. Moody's has promised to overhaul its process to stop this happening again, but it may be a case of shutting the gate after the horse has bolted: next time some clever banker comes up with a tricky new financial instrument, who's going to believe the ratings agencies now? Nobody with any sense.“

• The complexity of CDO.’s undermined the process as well. Jamie Dimon: “There was a large failure of common sense” by rating agencies and also by banks like his. “Very complex securities shouldn’t have been rated as if they were easy-to-value bonds.”

Page 25: Modeling and Valuation Mistakes

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Statistical Analysis and Credit Rating Agencies

• The real problem is that the agencies’ mathematical formulas look backward while life is lived forward. That is unlikely to change.

• Rating a new transaction, as an analyst, is a relatively simple procedure – but it can be time-consuming. From an ordinary desktop computer, you start the Moody’s rating software. A window opens in which you set the basic assumptions: duration of bond, payment, collateral details ... and then – click – the simulation is set running. Not once, but a million times, each time with a different outcome. It’s the average outcome from all those simulations that gives you a rating.

• A bug had a big impact on ratings. A single small error in the computer coding that Moody’s used to run its CPDO performance simulation had thrown the results way off. When the error was corrected, the likelihood of CPDO default increased significantly. CPDOs, it turned out, weren’t triple-A products at all. Preliminary results suggested the error could have increased the rating by as many as four notches.

Page 26: Modeling and Valuation Mistakes

Integrated Financial Management Apr 20, 2023

Problem FiveRelying on “Independent” Experts and Non-Transparent Analysis without Checking the

Logic and Using Simple Models

Page 27: Modeling and Valuation Mistakes

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www.edbodmer.com [email protected] Apr 20, 2023 28

Rating Agency Problems

• Moody’s Executive: “We’re in the service business, I don’t apologise for that.”

• The agencies were inundated with a huge volume of new structured finance deals that they were being asked to rate. At Moody’s, the flipside to the huge revenue growth was a high-pressure work environment. One analyst recalls rating a $1bn structured deal in 90 minutes. “People at the rating agencies used to say things like, ‘I can’t believe we got comfortable with that deal,’”

• There were stories of analysts going skydiving with clients; ofstructured finance experts and bankers on weekend getaways together;of golf outings and karaoke nights.

Page 29: Modeling and Valuation Mistakes

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Adoption of complicated analysis made by experts without adequate independent analysis

• Despite the clear oversupply of housing and the bubble in housing prices, economic forecasters projected continued increases in housing prices and housing starts. With hindsight, given the oversupply and the high prices, neither could have been sustainable. The macro economic forecasts along with the rating agencies failed.

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Eurotunnel Traffic Studies

• Expert Traffic Studies were dramatically wrong

• Traffic study did not anticipate response of ferries, surplus capacity, stable growth, price elasticity

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Page 30: Modeling and Valuation Mistakes

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Difficulty in Making Forecasts of Economic Variables

• The problem with making forecasts of economic variables versus physical variables is illustrated by oil price forecasts made by the famous Energy Information Agency of the U.S. which hires the most respected consultants

Page 31: Modeling and Valuation Mistakes

Integrated Financial Management Apr 20, 2023

Problem SixUsing Statistical Models that Assume Stable

Systems for Economic Variables

Page 32: Modeling and Valuation Mistakes

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Statistical Analysis of Historic Data Ignoring Structural Changes

• Analysts often used databases that computed historic default statistics to value securities. Statistical analysis of historic data can go badly wrong when applied to economic variables. Because of increasing leverage, declining home prices and a slowing economy, historic default rates turned out to be irrelevant in predicting bad loans.

Growth Estimates in Philippines

• Forecasts of growth rate using historic trends and statistical analysis have created many problems. Forecasts of growth rates caused major economic problems in the Philippines because of over-capacity and high capacity charges.

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Moody’s estimated that this C.D.O. could potentially incur losses of 2 percent. It has since revised its estimate to 27 percent. The bonds it rated have been decimated, their market value having plunged by half or more. A triple-A layer of bonds has been downgraded 16 notches, all the way to B.

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Default Rates – Problems with History

• Moody’s used statistical models to assess C.D.O.’s; it relied on historical patterns of default. This assumed that the past would remain relevant in an era in which the mortgage industry was morphing into a wildly speculative business.

• When the sub-prime CDO market first took off in 2005, sub-prime mortgage defaults were only 3%. A 20% cushion of equity and subordinated debt seemed like ample protection, so rating agencies generally assigned triple A to the top 80 percent of bonds in the CDO.

• Default rates then trended to 10 percent and rising.

Page 34: Modeling and Valuation Mistakes

Integrated Financial Management Apr 20, 2023

Problem SevenAssuming that Variables Follow Smooth and

Linear Trends

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Moody’s estimated that this C.D.O. could potentially incur losses of 2 percent. It has since revised its estimate to 27 percent. The bonds it rated have been decimated, their market value having plunged by half or more. A triple-A layer of bonds has been downgraded 16 notches, all the way to B.

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Economic Variables are Non-Linear and Difficult to Evaluate with Statistical Analysis of Historic Data

• It is apparent that investors did not appropriately consider changes in the probability of default when different loans and economic conditions occurred.

• The problem is that investors focus on expected returns without paying enough attention to the skweness of the upside and downside returns. The upside return on underlying loans was a credit and a higher margin when the loans were re-financed.

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California Market Prices

Prices before the California electricity crisis were relatively low. But most of the forces that lead to the extremely high prices such as high electricity demand, no new capacity and low levels of water in damns could have been predicted.

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Statistical Problems and Rating Agencies

• To add to the confusion, by the autumn of 2007 it seemed that events in some US neighbourhoods were throwing the ratings agencies’ models off even further. As house prices fell, defaults were rising to such a degree that they were blighting entire areas. That was pushing house prices lower still, sparking yet more defaults. This vicious circle had never been witnessed in the world of corporate loan defaults; nor did it fit the traditional “bell curve” central to the statistical risk assessment systems that were ubiquitous inside banks and ratings agencies.

• The “class of 2005 and 2006” borrowers were defaulting much faster than households which had taken out mortgages before those dates.

• A particularly pernicious aspect of the defaults was that when this new breed of subprime borrowers walked away from their homes, they often left them in such a bad state that it was hard for lenders to realise any value from the repossessed properties. Until the autumn of 2007, Moody’s had assumed, on the basis of past housing cycles, that lenders could recoup 70 per cent of their loans in case of default. By October 2007, it had slashed that projection to just 40 per cent.

Page 38: Modeling and Valuation Mistakes

Integrated Financial Management Apr 20, 2023

Problem EightIgnoring Incentives

Page 39: Modeling and Valuation Mistakes

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Incentives of Rating Agencies and Bankers

• Bankers, who are anxious to earn fees, convince themselves to believe forecasts that are not sensible. Further, risk assessment mistakes are compounded because after one major bank accepts the risk of a loan, if analysts at a second bank question the efficacy of the analysis, they are scoffed at.

Suppose you are a credit analyst at a relatively small bank – ABC bank – and you believe there is too much risk for the suggested level of debt. A typical conversation may be that if Citibank and HSBC determined that a loan is an acceptable risk, who are you to say that you do a better analysis than such a very sophisticated bank.

• In structured finance, a handful of banks return again and again, paying much bigger fees. A deal the size of XYZ can bring Moody’s $200,000 and more for complicated deals. And the banks pay only if Moody’s delivers the desired rating.

• “You start with a rating and build a deal around a rating”

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Belief in analysis of others when they do not have the same incentives

• Many of the problems from the sub-prime crisis came from assuming that brokers, initial lenders, financial institutions and rating agencies had similar incentives.

• The brokers, lenders and rating agencies did not appropriately analyse the risk.

• Enron and the Dabhol Plant

• Part of Enron’s downfall began with problems from the high cost Dabohl plant in India.

• A World Bank analysis questioned the project's economic viability and the contract price allowed Enron to earn an equity IRR of above 26%.

• A New government was elected and the Plant did not begin operations.

• .

Governmentof Maharashtra

Lending Banks

DabholPowerCorp

MaharashtraState

Electricity Board

FullGuarantee

PPA

LoanAgreement

FX Availability

ReserveBank of

India

Ministryof Power

ComfortLetter

Governmentof India

Limitedguarantee

Domestic(Indian)

Financial Institutions

(Banks)

US Exim

Political RiskGuarantee

StateSupport

Agreement

Within a few years of the advent of the CMO, however, the industry decomposed into highly focused sub-sectors. Mortgage brokers solicited and screened applicants. Thinly capitalized mortgage banks bid for loans and held them until they had enough support of a CMO. Investment banks designed and marketed CMO bonds. Servicing specialists managed collections and defaults.

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Re-thinking Risk Assessment and Finance

• The sub-prime crisis and other valuation mistakes should prompt re-thinking about what finance theory has to say about a variety of issues including the manner in which risk affects investment decisions and the cost of capital.

• Established finance theory did very little to either explain the decision making process or to assist professionals in making investment decisions.

Even if beta could be measured, the manner in which CAPM is applied does not suggest that there is much of a difference in risk for alternative investments. The typical risk premium used in investment analysis is about 4% and betas vary from about .5 to 1.5.

• Using debt capacity along with sound thinking about the fundamental economics of projects and mathematical simulation provides an alternative to risk assessment that provides more guidance to decision makers. If lenders would rigorously establish the debt capacity of an investment (as did not happen in the sub prime experience), investors then would have a much more objective basis than the CAPM to assess risk as part of investment decisions.

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Valuation and the Financial Crisis: The Case of Constellation Energy

• Instead of making generalizations about financial crisis, study one company

• Was the company a victim or a villain in the financial crisis

• What has happened to multiples in the financial crisis

• How much real value really is created by trading and buying other businesses

• What does it really mean to not be transparent from the perspective of valuation

• What method should be used to compute the value of different segments of the company

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Constellation Stock Price History

0

1

2

3

4

5

6

01 0

9

02 0

1

02 0

5

02 0

9

03 0

1

03 0

5

03 0

9

04 0

1

04 0

5

04 0

9

05 0

1

05 0

5

05 0

9

06 0

1

06 0

5

06 0

9

07 0

1

07 0

5

07 0

9

08 0

1

08 0

5

08 0

9

Constellation (CEG) and Other Stock Indicies

SP500

Utility Index

PJM Index

CEG

• Constellation stated it was “laser focused” on increasing its stock price, it ventured into businesses that could produce growth in earnings per share.

Compute cost of capital and arbitrage pricing model

Why was valuation so bad

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Background and Problem

2001 2002 2003 2004 2005 2006 2007 2008Exelon 17.20% 20.10% 18.80% 19.50% 23.60% 23.70% 26.90% 24.60%PSEG 18.60% 18.60% 18.60% 18.60% 18.60% 18.60% 18.60% 18.60%PPL 20.80% 18.10% 20.20% 16.10% 16.50% 17.30% 18.20% 18.20%Constellation Energy 9.20% 9.30% 11.10% 11.70% 12.30% 14.80% 14.70% 2.60%

Return on Equity Reported by Value Line

• Increasing stock price was difficult for the Company because Constellation purchased three nuclear plants at premium prices in New York that came along with fixed price power contracts (named “below market hedges” by the company).

• Peers were earning high returns from the transition to deregulated rates as shown in the table below.

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Increasing Earnings

Constellation Historic EPS and ROE

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Ret

run

on

Eq

uit

y

-

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

Ear

nin

gs

per

Sh

are

EPS

Return on Equity

• The company was able to double earnings which resulted in the increased stock prices.

• It also projected strong future growth in earnings

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Business Components

2006 2007 2008Gross margin:

Generation 1,490 1,700 1,956 Customer Supply 764 889 765 Global Commodities 656 654 260

Total 2,910 3,243 2,981

Generation Percent 51% 52% 66%

Constellation Merchant Segments Reported in 2009

• The peer companies primarily in the business of selling electricity from merchant generating plants and operating regulated distribution companies.

• As shown in the table (which was not published until 2009), Constellation was earning almost fifty percent of its non-utility earnings from businesses other than generation in 2006 and 2007.

bought ships that transported coal and named it “freight intermediation”

purchased oil and gas producing properties and named them “energy related assets”

(the company purchased almost $1 billion of natural gas producing properties as natural gas prices were increasing, justifying the purchases by the bizarre logic that: “As a merchant supplier, we are able to identify opportunities to serve customers, which provides the insight to acquire assets and deploy risk capital at the right time.”)

“deploys risk capital in traded energy markets” that investors finally found out that meant taking speculative positions on energy prices.

How would you value the different components

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Solution – Trading and Non-transparent Reporting

• Mao Shattuck’s solution was to expand speculative trading, purchase companies that could produce near term earnings and attempt to minimize the risks of the new business ventures through no-transparent reporting.

The lack of transparency was not limited to reporting financial results, but also included use of confusing terms and distortions of investor presentations involving what was the true nature of its business activities.

• Entry into the businesses along with increasing electricity prices did produce increased cash flow.

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Meaning of Being Non-Transparent: Financial Reporting

• One aspect of transparency involves presentation of financial statements.

The problem was not that assets were hidden in special purpose vehicles, but that cash flows from different businesses were mixed together.

Before 2008, investors had no way to differentiate between the safe and stable profits made from selling power from one of its nuclear plants under fixed price purchased power contracts and the profits made by speculating on the direction of energy prices.

The volatility of cash flows, cash flow drivers and trends in future cash flow were different for each business segment and the historic data was useless in making valuations.

Constellation was hoping that the aggregate cash flows would be valued at the price to earnings and other multiples of peer companies that had safer businesses.

• We continue to hear from you regarding the transparency of our business and our overall disclosure…[In] improving transparency … we will be working towards discrete reporting on each business unit to provide more detailed information on segments currently reported. As you are aware, in 2008, we refined our reporting to show gross margin by activity…

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Meaning of Being Transparent: Creating Confusion

• Lack of transparency for Constellation was not limited to its financial presentation. The second aspect of opaque presentation was the manner in which Constellation explained its businesses to investors.

• Language used by Constellation is a good example of the way finance professionals attempt to create confusion though showing how smart they are.

• In earnings conference calls and other presentations, Constellation would use phrases such as

“asymmetric collateral requirements”,

“deployment of risk capital”,

“leveraging business platforms”,

“as priced margins”,

“transitional liquidity”,

“right-sizing of strategic footprints”

• The general idea of the presentations seemed to be that investors should trust the superior qualities of the company and not worry about risks in the business

• Mao Schattuck: “the realignment of all our merchant businesses allows us to leverage our world class capabilities in risk management and portfolio management across our industry-leading platform.”

• When listening to Mao Schattuk and other Constellation managers, they seemed to want to leave an impression of being very smart. It was easy to feel quite inferior to their superior intellect.

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Non-transparent Reporting: Distorting Business Activities

• A third component of non-transparency was the way in which Constellation minimized its exposure to potential losses from its trading activities by emphasizing that most of its collateral requirements were for hedging activities related to its merchant plants, hedging for customers and hedging its coal business.

• Constellation never directly admitted that the company had been engaging in speculation until he discussed the transaction with EDF in December, 2008 when he admitted “taking positions.”

Assertions that the company was not betting on energy price movements were contrary to other statements made by Constellation. For example, management stated that it was bullish on energy prices in its second quarter conference call. One of the company executives reported: “As Mayo stated, we entered the second quarter bullish on energy commodities …”

• In fact, Constellation was profiting from the long bubble in energy prices similar to the way many companies and people were profiting from the housing price bubble. While energy prices were increasing, it was easy to be confident in the trading strategies that were producing profits. After all, when crude oil prices reached $147 per barrel in the summer of 2008, almost everybody seemed to believe that oil prices would soon reach $200 per barrel.

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Meaning of Being Transparent: Not Presenting Risk

• A fourth way in which Constellation was non-transparent was in the way it presented risk.

• The company regularly reports “value at risk,”

a complicated statistic that supposedly measures the maximum loss that could be realized in one day with a one percent probability.

In the third quarter of 2008, the value at risk number was about $30 million. This compares to the actual decline in earnings for the commodities business of $634 million.

Dividing 634 by 30 implies there were as many as 21 days of one percent likelihood events.

• With hindsight, the value at risk statistic was meaningless for risk assessment and it would have been for more useful to simply show what happens to cash flow and earnings at different levels of commodity price.

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Suggested Valuation of Components by Constellation

• Constellation’s valuation just before the price collapse. Comment on the use of multiples and the sample of comparable companies.

• .

EBITDA

Generation at Market Prices $3,142.00 7.00 8.00 21,994.00 25,136.00 PV of PPA Contracts ($4,800.00) (4,800.00) (4,800.00) Net Value 17,194.00 20,336.00

Customer Supply $456.00 4.50 6.00 2,052.00 2,736.00

Global Commodities $567.00 3.50 5.00 1,984.50 2,835.00

Merchant Debt ($3,647.00) (3,647.00) (3,647.00)

Equity Value 17,583.50 22,260.00

BG&E $127.00 14.00 16.00 1,778.00 2,032.00

Total Equity Value 19,361.50 24,292.00

Shares 179.00 179.00

Value per Share $108.16 $135.71

Multiple Range Total Value

Constellation Value in Second Quarter Earnings Presentation

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Valuation of Generation Component

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Alternative Valuation (Two Months Later)

Low Range High Range

Fair value of businesses other than global commodities $12,000.00 $13,000.00 Less repayment of outstanding indebtedness ($6,000.00) ($6,000.00) Less net loss (negative value) of global commodities business ($2,000.00) ($2,000.00) Net equity value (before costs and expenses) $4,000.00 $5,000.00

Shares 179.00 179.00

Net equity value per share (before costs and expenses) $22.35 $27.93

Valuation by Morgan Stanley

• When MidAmerican proposed a merger, the investment bank made a dramatically different valuation.

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Alternative Valuation of Components

• Issues:

What method should be used

How should valuation be presented

What adjustments should be made for fixed contract payments

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Constellation’s Demise

• The specific reason for Constellation’s ultimate financial demise was panic in the financial community that the company could not raise enough cash from lenders provide back-up loans so that it could continue its trading activities. Management defined the finance collapse as a “liquidity crisis” and attributed it to events that were beyond its control -- on “unprecedented turmoil in financial markets,” “volatile energy commodity prices” and the actions of rating agencies who were worried about trading partners losing confidence. The table below illustrates that credit risk was even higher for Constellation than for Lehman Brothers. The downfall began when rating agencies finally recognized that Constellation had more risks than its peers – the downgrade occurred after energy prices had began to fall. The rating agency Fitch, noted Constellation’s exposure to energy prices, implying that it was taking positions in its trading: “Constellation … is exposed to risks surrounding market price, volumes, counterparty credit, and liquidity for collateral.” When the company places blame on volatile financial markets for its problems, it is like investors in sub-prime mortgages blaming the fall in housing prices. It is not appropriate to term Constellation as a victim of the financial crisis.

1 Year Prior 6 Months Prior 3 Months Prior 1 Month Prior 17-Sep-08 18-Sep-08Exelon Generation 0.638% 1.702% 1.138% 1.353% 2.292% 2.392%Exelon Corp. 0.608% 1.512% 1.002% 1.235% 1.925% 2.162%PPL ES 0.595% 1.888% 1.152% 1.491% 2.817% 2.617%PSEG Power 0.492% 1.664% 0.999% 1.282% 2.383% 2.471%IG10 Index 0.610% 1.850% 1.120% 1.344% 2.034% 1.787%Lehman 0.925% 4.433% 2.528% 3.043% 7.067% 7.067%Constellation 0.528% 1.793% 1.017% 1.818% 7.650% 2.808%

Credit Default Swap Spreads

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Payments in Merger with Warren Buffet’s Energy Company

• The accompanying table shows that the cost of the failed MidAmerican Merger was significant.

$ Millions

Discout on Preferred Stock $39.00Put Option Premium $15.00Preferred Stock Conversion Premium $943.00Merger Termination Fee $175.00

Total Direct Costs $1,172.00

Total Shares 178.00Share Value $55.00Value of Total Shares $9,790.00

Percent Owned by MidAmerican 9.90%Value of Shares $969.21

Value Realized by MidAmerican $2,141.21

Percent of Total Constellation Value 21.9%

MidAmerican Offer $26.50Total Merger Consideration $4,717.00

Value Realized as Percent of Offer 45.39%

Value Received by MidAmerican

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Finance and Confusion

• A medical doctor, an engineer and an investment banker are at a cocktail party.

The medical doctor pompously asserts that the medical profession is the oldest profession. He cites a passage from the Bible, in Genesis where God creates man and woman. “Surely,” he says, “this was the first medical act.”

The engineer jumps in and says, “I remember a passage prior to that, which says, out of the chaos and confusion, God created the earth. Surely, this was the first act of engineering and predates the first medical act.”

“Aha!” says the investment banker, “who created the confusion?!”

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Forecast Error Examples

Not considering the tendency for humans to be over-optimistic in complex endeavors such as new technology, mergers and war

Not checking highly complex, un-transparent models with a back of the envelope analysis

Forgetting fundamental principles of supply, demand and price elasticity in establishing model assumptions

Assuming that things are really different, believing fast talking analysts and assuming that history will not repeat

Believing that other large reputable institutions have some new kind of analysis and/or that they have done their homework

Example

• Airbus, Chrysler, Euro-disney

• FPL purchase of CMP assets

• Argentina Electricity Market Over-build, ENRON Dabhol

• LBO multiples

• Iridum

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Forecast Error Examples

Applying historic trend lines in growth that cannot be sustained

Accepting models that contain high returns far above the cost of capital in competitive industries

Pretending that real life is linear and follows a normal distribution where the upside and downside can be derived from historic statistical analysis

Assuming that variables such as operating expenses are variable when they are partially fixed

Incorrectly accounting for the relationship between capital expenditures and sales without considering the cost of new capital equipment

Example

• U.S. Electricity Industry

• Internet Bubble

• Natural Gas Prices

• Manufacturing Administrative Expenses

• Freight Airline

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Forgetting To Look At Returns To See If They Make Sense

Models must reflect the economic reality of a company. If the company has a sustainable competitive advantage, the return on invested capital may be greater than the weighted average cost of capital. Economic profit – ROIC above WACC -- comes from things like barriers to entry, pricing power and cost structure efficiency which should be explicitly modeled.

If you model all of the details of the company and then the ROIC is not at a plausible level given the competitive position of the company, go back and re-visit the forecast.

If you plan to forecast large returns on capital and high growth, make sure you can explicitly point to the source of the competitive advantage

Look at the rate of return earned on investment in the terminal period and evaluate if it is really sustainable instead of simply assuming growth from the terminal period.

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Assuming That Your Investment Will Earn High Returns and Nobody Else Will Figure it Out

• In the example below, an electricity plant was assumed to be able to earn far more than its cost of capital in a competitive business where new entrants can easily enter the market.

• Rather than focusing on the model mechanics or debt structure or even the details of forward price forecasts, the question of why the returns can exceed the cost of capital must be answered

Can we really earn 24% when anybody can build a similar plant

100 becomes 1,000 in 10 years with 24% return

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Beware of Investment Strategies That Depend on High Growth That Cannot Be Sustained

• Long-term growth rates are required directly or indirectly in various parts of a financial forecast. Long-term growth rates that are high can imply that the company becomes larger than the entire economy. Growth rates should be sustainable and reflect the earning power of the enterprise.

• Examples of errors in growth rate estimation:

Use a long-period for growth estimation rather than recent data.

Do not account for saturation in demand and price elasticity of demand as is often the case in forecasts of growth in electricity consumption.

A better strategy would have been to make flexible investments that could respond to growth rather than locking into long-term contracts with long lead times.

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Overestimation of Growth – Value of Flexibility to Adjust to Growth Rate Changes

• The electric power industry has provided many examples of valuation mistakes when extrapolating historic growth from historic trends rather than accounting for possible changes in future growth. In the US, electric power demand grew at a compound growth rate of 7.9% from 1949 to 1974 and then, in years since it has grown at a compound growth rate of 2.5%. The change in growth rates occurred because of increased energy prices, saturation of appliance use, improvements in energy efficiency and other factors.

• Growth rates of above 7% in the 1950’s and 1960’s led managers to make projections of similar growth rates for subsequent periods. This demand growth implied a need for requirements to construct large amounts of new capacity.

Total US Total Retail Sales Sales Actual from 1949 to 1974 Trended at 7.9% After 1974

0

2,000,000,000

4,000,000,000

6,000,000,000

8,000,000,000

10,000,000,000

12,000,000,000

14,000,000,000

16,000,000,000

18,000,000,000

1949

1951

1953

1955

1957

1959

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1996

1998

2000

2002

2004

P

Actual Sales

Trended Sales

4% Volatility 0% Volatility

Difference - 4% vs 0% Volatility

5 Year Construction 105,443.00 86,436.00 19,007.00

1 Year Construction 101,138.00 90,758.00 10,380.00

Difference in Cost (4,305.00) 4,322.00 (8,627.00)

Percent Difference -4.3% 4.8%

Assumptions: 6% Load GrowthProjected Growth from Exponential Smoothing

Cost of Investment with and without Demand Volatility

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Evaluation of Alternative Growth Rates and Classic Risk Analysis

• Real world risk analysis that accounts for tradeoffs between cost and uncertainty can range from relatively simple judgmental considerations to complex mathematical approaches. Alternative risk analysis techniques include:

Adjusting the discount so that there is a greater risk premium on alternatives with less flexibility with respect to growth rates.

Computing sensitivity cases to understand how the variation on demand affects different scenarios.

Determining the break-even point in terms of changes in growth rates to determine the year in which growth rate changes produce different investment optimal investment strategies.

Computing alternative scenarios with different growth rates and assigning probability to those alternative cases in order to gauge the expected value and the distribution in value of different alternatives.

Using regression and other statistical techniques to compute the volatility of sales growth and then using Monte Carlo simulation to compute alternative probability distributions. In gauging the un

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Convincing Yourself That Unrealistic Optimistic Forecasts Will Occur – Firehouse Effect

• It seems obvious that the base case should represent likely comments rather than optimistic or best case estimates. However, models often represent optimistic rather than likely outcomes. The Firehouse effect – Fireman with too much time agree on many things that an outside, impartial observer would find ludicrous.

• Examples:

LBO’s: Some transactions were based on assumptions that companies could achieve levels of performance – revenue growth, operating margins, capital utilization – never before achieved. Buyers had no concrete plans.

Eurotunnel: The capital cost estimates of highly technical projects often are over budget and do not reflect the history of cost estimates for similar projects. In the case of US nuclear plants, estimates from statistical analysis of other plants was far better than engineering estimates.

• S&P

Financial projections …are probably inherently skewed toward successful results...hiding the true technical and operating risks inherent in many projects..."

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Example of Buying into Unrealistic Assumptions: Euro tunnel Case – How Did All The Banks Buy Into This

• 31 Mile tunnel between England and France

• Similar project in Japan had 100% cost over-run

• Financed by 225 banks

• Construction expected to be completed in May 1993; actual was in December 1994

• Original construction budget was £4.9 billion; actual £12 billion

• Bankers egos and ties got in the way, bankers were forced to take exposure in order to be in other UK transactions

• Problems with rail links, low cost airlines

• Serious aspect of risk misjudged

Completion

Traffic (.6B vs 1.2B)

Infrastructure

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Eurotunnel Mistakes

• Eurotunnel comments

"We were predicting that on Eurostar there would be 21 million passengers (annually)," admits David Freud of Warburg, the investment house which sold Eurotunnel shares to the public.

The actual figure was less than a third of that.

• "So the traffic forecasts were not just out by a little bit. They were completely potty; they were nowhere."

• Those who drafted Eurotunnel's prospectus failed completely to foresee a robust response from the ferries.

• When the world's most successful investor, Warren Buffett, said, "if you overpay for an asset, there ain't no cure", he might have had Eurotunnel in mind.

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Eurotunnel – Price and Traffic Estimates

• Not only did P&O not fade away, as Eurotunnel had thought likely, it fought back with better ships and lower prices, retaining the loyalty of passengers who had been forecast to switch to the tunnel.

• Another blow to Eurotunnel was the unexpected emergence of no-frills airlines, offering rock-bottom prices on short-haul trips to a wide range of European destinations.

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Relying on Complicated Models without Doing a Back of the Envelope Check

• Many models are highly complex these days. One example is traffic studies that measure the number of trips on every single road in an area and then attempt to project the number of people who will use a toll road. Another example is electricity market studies that project the hourly production of every plant in a country for twenty or thirty years. These forecasts should be checked as illustrated by the errors made in traffic forecasts:

• Simple Checks:

-What is the total revenues people are supposed to pay for the toll road – in one study, people were expected to pay $7,000 per year.

-What is the market share of the toll road. In one study, a road that was unrealistically expected to capture 60% of airport traffic.

-What is the price elasticity of toll road users. In one study, traffic growth was projected even though real toll rates double.

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LBO Bubble – Make Yourself Believe Crazy Assumptions So You Can Earn The Fees

• In 1981, 99 LBO deals took place in the US; by 1988, the number was 381.Early on, LBO players grounded their deal activity in solid analysis and realistic economics.

• Yet as the number of participants in the hot market increased, discipline declined. The swelling ranks of LBO firms bid up prices for takeover prospects encouraged by investment bankers, who stood to reap large advisory fees, as well as with the help of commercial bankers, who were willing to support aggressive financing plans.

Default Rate from unrealistic financing of LBO’s. This caused LBO activity to dry up for decades

Private Equity Transactions

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LBO Bubble – Will History be Repeated

• Financial projections that underpinned several high-profile LBO bankruptcies in the late 1980s. Many of these transactions were based on assumptions that the companies could achieve levels of performance, revenue growth, operating margins, and capital utilization never before achieved in their industry. The buyers of these companies typically had no concrete plans for executing the financial performance necessary to meet their obligations. In many such transactions, the buyers simply assumed that they could resell pieces of the acquired companies for a higher price to someone else.

• Why wouldn't investors see through such shoddy analyses?

• In many of these transactions, bankers and loan committees felt great pressure to keep up with their peers and generate high up-front fees, so they approved highly questionable loans. In other cases, each participant assumed someone else had carefully done the homework.

Buyers assumed that if they could get financing, the deal must be good.

High-yield bond investors figured that the commercial bankers providing the senior debt must surely have worked their numbers properly. After all, the bankers selling the bonds had their reputations at stake, and the buyers had some capital in the game as well.

• Whatever the assumption, however, the immutable laws of economics and value creation prevailed. Many deals went under.

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Assuming That Upside Potential Equals Downside Exposure -- Neglecting Skewed Distributions

• Examples

Prices before the California electricity crisis were relatively low. But most of the forces that lead to the extremely high prices such as high electricity demand, no new capacity and low levels of water in damns could have been predicted.

The real estate crash that occurred in many cities in the late 1980’s related to the S&L crisis could have been anticipated by oversupply.

California Orgeon Border Prices

18.5 19.328.0 31.6

140.8 139.0

26.1

-

20

40

60

80

100

120

140

160

1996 1997 1998 1999 2000 2001 2002$/

MW

H

More potential for price increase than potential for price decrease – should be accounted for in forecasts

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Illustration of Considering the Skewness in Cash Flows with Tornado Diagram

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Assuming That Growth Can Occur Without Making Capital Expenditures

• Forecasts often are computed from revenue growth, operating margins and turnover of capital. While these may be reasonable forecasts, the capital expenditures can be inconsistent with other aspects of the forecast.

Revenues Revenue Growth

Operating Expense Revenue x Margin

Total Assets Revenue x (Assets/Revenue)

Capital Expenditure Assetst – Assetst-1

• As with revenue forecasts from price and quantity, it is better to compute capital expenditures from the quantity of production required (reserves, manufacturing capacity, electric capacity, number of planes, number of subscribers etc.) and multiply the cost of new capacity by the quantity. The capital expenditure forecast should include inflation in the cost of procuring capacity and include sustaining capital expenditure.

• Example

Revenue forecast for air freight company without consideration of maintenance capital expenditures and without explicit modelling of the cost of new planes.

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Growth In Quantity Sold With Increasing Prices And Declining Cost/Unit

• In simple forecasts of revenue growth and margins, management would like to grow both the level of sales and the margin on sales. There are many cases when innovative products have been developed where this can be the case. However, an increase in margin generally implies higher prices which can cause volumes to decline because of price elasticity.

• Examples of not reflecting price elasticity in forecasts

Toll roads in developing countries – the value of added time in using roads.

Direct subway systems that connect airports – there have been many failures because traffic has not been realized.

Purchase of Distribution Companies in South America by US Firms (AES, Aliant Energy, CMS). US firms assumed prices could be increased without properly considering political pressures.

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Ignoring Economic Cycles

• If possible, forecasts should have enough history so that economic cycles are reflected. Cycles include economic recessions and the behavior of commodity prices. The worst error is creating hockey stick forecasts from the top of the cycle. Other errors involve assuming no economic downturns in forecasts.

• Forecasts should account for the long-run marginal cost of the product being produced --- prices that are always above or below marginal cost cannot be sustained.

• Examples of Valuation Mistakes and Economic Cycles

Florida power and light purchased assets from Central Maine Power for much more than replacement cost. In the long-run prices converge to marginal cost and the purchase price above replacement cost was not logical. In addition to the logic error of assuming that prices would remain above long-rum marginal cost, there was a mechanical error in the spreadsheet.

Oil price forecasts throughout the 1980’s and into the 1990’s assumed by banks projected significant increases and did not account for mean reversion in oil price.

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Merchant Plant Activity

• “…in the US, private companies that own merchant plants have lost of more than $100 billion in market capitalization.”

• Banks are “now highly reluctant to take merchant risk of any kind… and they are skeptical about long-term purchase or tolling contracts that in any way are considered to be out of the money.”

• “Merchants will have to redesign their business models. Those players that have 80-90 percent of their capital in the form of debt won't survive. The ratings agencies have said that such debt-to-capital ratios must be in the 50-50 range to earn investment grade status so that the cost of borrowing is reasonable.”

• The merchant plant activity has been very high.

New Merchant Capacity in Database

2,075 1,335 1,229

3,5804,869

2,494

5,136

9,783

13,924

29,513

23,942

-

5,000.00

10,000.00

15,000.00

20,000.00

25,000.00

30,000.00

35,000.00

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

MW

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Argentina Example of Merchant Problems

• In Argentina, plant efficiency, over-capacity and increased hydro generation caused financial problems with projects.

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Mistakes by Ignoring the Fundamentals

• By the time the Internet frenzy peaked at the end of the 1990s, even staunch traditionalists like Warren Buffett pondered whether the economy had entered a new era of prosperity unbounded by traditional constraints. Some economists took to questioning long-held tenets of competitive advantage, and "new economy" analysts asked, with the utmost seriousness, why a three-year-old-money-losing Internet purveyor of pet supplies shouldn't be worth more than a billion dollars.

Priceline.com

0

200

400

600

800

1000

1200

3/31

/199

9

6/30

/199

9

9/30

/199

9

12/3

1/19

99

3/31

/200

0

6/30

/200

0

9/30

/200

0

12/3

1/20

00

3/31

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1

6/30

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1

9/30

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1

12/3

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3/31

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9/30

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3/31

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6/30

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12/3

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9/30

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4

12/3

1/20

04

3/31

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5

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Internet Notion – Increasing Returns with Size and First Mover

• The basic idea is this: In certain situations, as companies get bigger, they can earn higher margins and return on capital because their product becomes more valuable with each customer who purchases it. In most industries, competition forces returns back to reasonable levels. But in so called increasing-return industries, returns become high and stay there.

• Take Microsoft's Office software, which provides word processing, spreadsheets, and graphics. It is important for customers to be able to share their work with others, so they are unwilling to purchase and use competing products. As the installed base gets bigger and bigger, it becomes even more attractive for customers to use Office for these tasks.

• Because of this advantage, Microsoft earns 75 percent margins and operating profits of $7 billion on this product, one of the most profitable products of all time.

• As the Microsoft example illustrates, the concept of increasing returns to scale is sound economics.

• What was unsound during the Internet era was its application to almost every product and service related to the Internet.

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Intellectual Short-cuts in the Internet Bubble

• In the case of Microsoft Office, a key driver is the desire for compatibility to share documents. But during the Internet bubble, the concept was misinterpreted to mean that merely getting big faster than your competitors in a given market would result in enormous profits. Some analysts applied the idea to mobile-phone service providers, even though customers can and do easily switch from provider to provider, forcing these providers to compete largely on price. The same logic seemed to apply to Internet grocery delivery services, even though the result of attracting more customers is that these services need more drivers, trucks, warehouses, and inventory.

• The Internet bubble years were full of such intellectual shortcuts to justify absurd share prices for technology companies. The history of innovation has shown how difficult it is to earn monopolized sized rents except in very limited circumstances. But that was no matter to the commentators who ignored those lessons. Those who questioned the new economics were handed as people who simply "didn't get it."

• When the laws of economics prevailed, as they always do, competition reined in returns in most product areas. The Internet has revolutionized the economy, as have other innovations, but it could not render obsolete the rules of economics and competition.

• The Internet bubble shows what happens when managers, investors, and bankers ignore the fundamental principles of economics and the underlying history of value creation. It was also a classic example of herding behavior, as investors, managers, and commentators followed—the crowd instead of relying on their own independent analysis. For example, many equity analysts could not justify the values of companies based on fundamentals, so they resorted to commenting only on relative values—how one company was valued relative to another—instead of dealing in absolute terms.

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Evaluate the ROIC and Market to Book Ratio in Forecasts

ELoan Stock Price

0102030405060708090

100

6/29

/199

9

9/29

/199

9

12/2

9/19

99

3/29

/200

0

6/29

/200

0

9/29

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0

12/2

9/20

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1

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1

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4

12/2

9/20

04

3/29

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5

• If a forecast includes a return on invested capital that is greater than the WACC, the question should be asked as to what forces allow the firm to maintain a sustainable competitive advantage and economic rents.

• For example, in the late 1990’s it was considered old fashioned to use traditional cash flow and financial ratios in evaluating the value of new economy stocks. “Why a three-year old money losing Internet purveyor of pet supplies should not be worth more than $3 billion.” Yet the implicit assumption in valuations was that the companies could grow economic rents. For some companies such as Microsoft and Cisco this may have been reasonable. For others such as priceline.com this was unreasonable because there were no barriers to entry that prevented other firms from entering the market.

• .

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Under or Over Use of History and Back of the Envelope Checks

• History should be used in developing trends and relationships in forecasts. You should calibrate items such and administrative expenses, working capital ratios, depreciation rates and tax rates to historic data. However, use of historic trends without an understanding of the underlying economics can lead to problems.

• Real-life business events can produce results that are anything but linear and knowledge of supply and demand factors can predict market crashes and price spikes.

• Dismissing the past is as unwise as blindly projecting past performance into the future.

• If the firm has consistently met targets, it would be unwise to dismiss this input

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Separation of Fixed and Variable Costs – Is The CEO’s Salary A Variable Cost

• One of the basic elements in a financial model is operating leverage and the sensitivity of financial position to fixed costs. Assuming that costs such as salaries can easily be varied when they are in fact fixed can lead to optimistic forecasts.

• Examples

Cutting salaries and pension costs in the Airline industry has caused the failures of many large airline companies. When passenger volumes declined, the cost per passenger increased leading to the bankruptcy of UAL.

Capacity Factor - Passenger Miles/Available Seats

0

0.5

1

1.5

2

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Salaries per Mile

30

35

40

45

50

55

60

65

1993 1994 1995 1996 1997 1998 1999 2000 2001

UALCost

SWA Cost

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Too Complex and Non-Transparent Analysis Techniques

• A medical doctor, an engineer, and a finance professor are at a cocktail party. The medical doctor pompously asserts that the medical profession is the oldest profession. He cites a passage from the Bible, in Genesis where god creates man and woman. “Surely,” he says, “this was the first medical act.”

• The engineer jumps in and says, “I remember a passage prior to that, which says, out of the chaos and confusion, God created the earth. Surely, this was the first act of engineering and predates the first medical act.”

• “Aha!” says the finance professor, “who created the confusion?!”

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Cost Structure – Understand Fixed Costs and Exposure to Sales Declines

Total Price and Operating Cost per Passenger Mile

10.00

10.5011.00

11.5012.00

12.5013.00

13.5014.00

14.50

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

UAL Cost

UAL Price

Total Price and Operating Cost per Passenger Mile

10

10.511

11.5

1212.5

13

13.514

14.5

1997 1998 1999 2000 2001 2002

SWA Cost

SWA Price

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The Iridium '‘Team’'

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Forgetting Fundamentals of Supply and Demand and Back of the Envelope Analysis -- Telecommunications

• At the end of the day you should evaluate whether forecasts make sense in light of fundamental economic principles.

In the 1990’s telecoms seemingly limitless upside potential

Venture capitalists and stock investors fell over each other to invest insane amounts of money in many companies

Success of internet companies premised oh high growth continuing

When companies failed to generate cash flow, defaulted companies skyrocketed

Barriers to telecommunications companies came down

Telecommunications network became overbuilt due to lack of demand

Defaluts by Industry

0

2

4

6

8

10

12

14

16

18

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19

81

19

82

19

83

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19

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00

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01

20

02

Pe

rce

nt

of

Lo

an

s O

uts

tan

din

g

Energy

Financial

Real Estate

Telecoms

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Default Rates by Industry

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Telecommunications Meltdown

• In 2001, 77 telecommunications companies sought bankruptcy

• In 2000, 20 declared bankrupcy.

• Two Trillion in Market Value Lost

• Large bankruptcies included:

WorldCom’s -- the single largest bankruptcy in U.S. history.

The fiber optic network operator, Global Crossing, 4th largest

Other leaders -- Williams Communications Group and Network Plus

• Reasons

Long distance price competition in pursuit of retaining market share.

Entry into local markets blocked.

• It's fallout from a telecoms industry in which supply has dwarfed demand.

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Surplus Supply in Telecommunications

• .

•There was an overbuilding of telecom capacity based on the fantasized vision of the objectives of the New Economy, which will never be realized. For example, there was an overbuilding of fiber-optic cable systems by a factor of at least 10. Many New Economy companies were built based on the idea that the telecom sector would expand perpetually by 15 to 30% per annum.

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Demand versus Supply

• The result is overcapacity: 39 million miles of cable were laid underneath railroad beds, natural gas lines, corn fields, and roads—enough to encircle the Earth more than 1,500 times. Today less than 5% of the cable is "lit"; the rest remains dark, and most is not likely to be "lit."

• But reality has further asserted itself, causing additional problems in the physical economy and revenues of the telecom sector, and ripping apart that sector's two fundamental assumptions. The sector's CEOs thought that increased volumes of data traffic, as opposed to voice calls, would be the savior of the telecom industry. But data users, mostly corporations, instead of paying on the more expensive per-minute basis, are paying for the data in bulk. On this basis, data transmission is not even as profitable as old-fashioned voice calls.

• Belief that voice-call traffic would rise. But alarmed industry executives report that people are sending millions of e-mails per day, instead of spending money for telephone calls. Some industry sources now predict that, in the future, the volume of voice calls will fall each year.

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Financial and business problems

• The telecom sector collapse is driven by two intertwined forces.

First, it is over-leveraged: Its companies borrowed enormous sums of money during the 1990s, to finance a wave of mergers and some expansion. Telecoms' total outstanding debt—still estimated at $650 billion or more—requires debt service far larger than that portion of the sector's revenue stream available to service it; it is sucking the telecom sector dry.

Every company that could get its hands on the stuff proclaimed that it was going to build a national, or super-regional fiber-optic network. In some cases, four to six companies built fiber-optic cable networks between or within the same major cities, far beyond prospective levels of voice or data transmission.

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The Iridium Concept

• International Coverage

66-LEO Satellites

Launched 72; got 67; 5-year life each

12 Ground Stations

• Handset Cost = US$3,000

A ‘brick’

• Call Cost = US$3.00-US$7.50 per min.

• US$800 million Loan

LIBOR + 4%; 2-year Bullet

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The Iridium Concept

• Coverage

Does not work in your car

Does not work in a city

interference from buildings

Does not work as you exit an airport

Satellite crosses the sky in ca. 14 minutes

handover software aboard the satellites

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The Iridium Concept

• Market Share Assumptions

Iridium

500,000 in 1st year of service

6,000,000 in 6th year

Existing Intelsat system

Laptop sized phone

140,000 subscribers

20-year history

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Failure to Account for Options and Distributions

• Models often assume a more narrow band in variables than really exists and assume that the upside case and the downside cases have an equal magnitude and an equal probability.

• In the case of Iridium, a satellite venture developed by Motorola, there was a relatively narrow band around the market penetration assumption even though the technology was very expensive and the product had never been tried.

• In another case, a company retired a nuclear plant when market prices were very low. The decision would have been reasonable had prices remained low, but the company did not account for the high upside potential and limited downside risk that would have occurred from making the decision to keep the plant operating.

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Resources and Contacts

• My contacts

Ed Bodmer

Phone: +001-630-886-2754

E-mail: [email protected]

• Other Sources

www.sec.us.gov -- financial documents

www.finance.yahoo.com; www.googlefinance.com; www.valueline.com -- stock prices and financial ratios

www.standardandpoors.com; www.moodys.com – credit rating and other information

www.bondsonline.com – credit spreads

http://pages.stern.nyu.edu/~adamodar


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