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Modeling and Valuation Mistakes

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  1. Modeling and Valuation Mistakes

  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.

  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.

  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

  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.

  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.

  7. Problem OneIgnoring the most basic of economic principles in developing assumptions for financial models

  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.

  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. • .

  10. 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.

  11. Problem TwoIgnoring the Supply Side of Variables Driven By Marginal Cost

  12. 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. • . • . 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. 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.

  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…’”

  14. Other Examples of Believing Implausible Forecasts and Herding Behavior Like Lemmings

  15. 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

  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.

  17. 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

  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. • .

  19. 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?”

  20. Problem FourBelieving that Innovative Financial Ideas can Create New Sources of Value

  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.

  22. 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!!!

  23. 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.”

  24. 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.

  25. Problem FiveRelying on “Independent” Experts and Non-Transparent Analysis without Checking the Logic and Using Simple Models

  26. As one former Moody’s staffer recalls: “The change wasjust precipitous. There was suddenly a concentration on profits.Management got stock options. It’s true there was a big personalityshift in the company – lots of cozying up to clients went on.”

  27. 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.

  28. 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. • . Eurotunnel Traffic Studies • Expert Traffic Studies were dramatically wrong • Traffic study did not anticipate response of ferries, surplus capacity, stable growth, price elasticity ₤600 vs ₤1,600

  29. 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

  30. Problem SixUsing Statistical Models that Assume Stable Systems for Economic Variables

  31. 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. • . 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.

  32. 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.

  33. Problem SevenAssuming that Variables Follow Smooth and Linear Trends

  34. 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.

  35. 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. • . • . 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.

  36. 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.

  37. Problem EightIgnoring Incentives

  38. 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”

  39. 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. • . 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.

  40. 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.

  41. 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

  42. Constellation stated it was “laser focused” on increasing its stock price, it ventured into businesses that could produce growth in earnings per share. Constellation Stock Price History Why was valuation so bad Compute cost of capital and arbitrage pricing model

  43. 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. Background and Problem

  44. The company was able to double earnings which resulted in the increased stock prices. It also projected strong future growth in earnings Increasing Earnings

  45. 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. Business Components How would you value the different components

  46. 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.

  47. 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…

  48. 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.