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Chapter 4 Getting Real about Real Options

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Chapter 4 Getting Real about Real Options Powered By Docstoc
					Real Options
  Mehmet Bozbay
   Hoksung Yau
  Laura Goadrich
  Ping Fong Hsieh
  Sirisha Sumanth

   March 8, 2004
Chapter 4: Getting Real About
                 Real Options
Objectives

Overview
Terminology
Real options in the Real world
  Overvaluing options (Agouron)
  Exploring options (Cisco)
  Case study (Nole)
Summary
Issues in Real Options
 Advantages:
  Successfully explains valuation of multiple
   companies believed to have substantial real options
  Explain some of the difference in markets not
   accounted by traditional techniques
 Disadvantage:
  Real Options can be miscaluculated/misused and
   misvalue a company
 Provides a method to exemplify market
  outcomes using nontraditional techniques
Overview: Real Options
 Helps investors determine whether a
  company’s stock is over- or undervalued
 Real options considers impact of:
  Risk
  New technology
  New market
  …
 Real-World Examples: Agouron
  Pharmaceuticals, Cisco, Nole
Terminology
 Scope-up options
    Opportunities to increase variability in product lines
    Eg. IBM expanding to create graphic cards
 Scale-up options
    Opportunities to expand capacity
    Eg. Power Packaging took over one plant of General Mills
 Learning options
    Opportunities to acquire companies with the goal of entering
     into new businesses
    Eg. GE taking over Datex-Ohmeda
 Equity Stakes
    Purchasing equity in start-up companies
Real-World Example:
Agouron Pharmaceuticals

 Kellogg & Charnes (2000) Financial Analysts
  Journal

 Illustrates the problem of valuing a company with
  real options and how that valuation can differ
  from the market’s valuation

 Background: biotechnology companies known for
  having high values when their products are in
  development (no positive cash flow)
Real-World Example:
Agouron Pharmaceuticals

  Types of real options available
   Growth option
       Expand production if favorable in the market
   Abandonment option (why choose?)
       Viable reasons for abandonment
         • inhibit share holder loss
         • scrap failing projects
         • …
Real-World Example:
Agouron Pharmaceuticals
Real World Example:
Agouron Pharmaceuticals
 Valuations of Agouron based on real options differed from
  the actual market values of the company’s stock as a
  particular drug progressed through the development
  process
      Discovery
      Preclinical
      Clinical trial- phase I
      Clinical trial- phase II
      Clinical trial- phase III
      FDA filing and review
 Once the drug hit the market, the drug can vary in quality
  from
   Breakthrough - Above Average - Average - Below Average - Dog
Real World Example:
Agouron Pharmaceuticals
 Root cause of differences:
  The abandonment of a drug is rarely announced
      Only one drug made it to phase II and III
      Potential projects were included in the valuation when
       they were not part of the product pipeline
  Investors were making different assumptions
      Political pressure for FDA to approve drugs for HIV-
       positive patients
      Assumed need less than eight years from phase II to
       launch, but only took two years
  Sales were four times expectations in the first year
 Lesson: real options were not overlooked by the
  market, but may have been overvalued
Real-World Example:
Cisco Basics

 Sell: Networking supplies
 Scale-up options
  Supply network equipment for Internet connectivity
 Scope-up options
   Supplies businesses and individuals
 Learning options
  Integrating voice, video and data in their network
Real-World Example:
Cisco Example
 Traditional discounted cash flow example
 Market Value (FY 2000): $445.1 billion

 Assumptions:
  Earnings will grow at a rate of 10% annually after 2005
  The risk-free rate is 5%
  The market risk premium is 6%
  There is no adjustment for earnings after 5 years
Real-World Example:
Cisco Evaluation




 CostOfCapital = riskFreeRateOfInterest + riskPremiumOfMarket * Volatility
              = 5% + (6% * 1.45) = 13.7%

                         $15 .093 billion (1  0.10 )
      TermValue 2005                                  448 .800 billion
                             (0.137  0.10 )
Real-World Example:
Cisco Sensitivity Analysis

  DCF = $266.565billion vs. Market Value= $445.1billion
     Difference: $178.535billion

  Sensitivity Analysis
     Vary constant growth rate of 10% to 11%
     DCF – MarketValue = $91.061million

     Vary cost of capital from 13.7% to 16%
     Difference: $284.873million

  Lesson: Not considering options poorly represents the
   actual market value.
 Real-World Example:
 Nole Background

 Example illustrating valuing an option.
 Initial start-up costs
  capital expenditures $500million
  investment in working capital $50million
 Depreciation & capital expenditures
     $100million/year
 Option 1: Not expand
  Revenues
     Y1 $1billion       Y2 $1.2billion     Y3 $1.44billion
     Y4 $1.526billion   Y5 $1.617billion   Y6 $1.715billion
Real-World Example:
Nole Choices
 Option 2: Expand in year 3 with $2billion
  Annual depreciation $200million
  Expenditures
     Annual capital expenditures year 4, 5, 6 each
      $100million
     No additional capital expenditures
  Revenues
     Y1 $1billion   Y2 $1.2billion    Y3 $1.44billion
     Y4 $1.9billion Y5 $2.47billion   Y6 $3.211billion
Real-World Example:
Nole Expanding in Y3, init Y0
Real-World Example:
Nole Without Expanding in Y3, init Y0
Real-World Example:
Nole Value of Expanding Option
Real-World Example:
Nole Strategic Value
Real-World Example:
Nole Expanding Black-Scholes


  P   presentValueOfCashFlow  termValue

 Value of option=    P x N(d1) –       X    x e –r x t x N(d2)

                = $1,231 x 0.4678 – $2,300 x e-6% x 3 x 0.1718

                = $245million

 Value of Expansion Option using DCF = -$31million
Real-World Example:
Nole Volitility
        Real-World Example:
        Nole Variability of Volatility


Increasing volatility

   increases cost of capital

  decreases value of underlying

  decreases value of option
 Summary
 Agouron showed a large difference between real
  world valuations and traditional methods.
 Cisco illustrated the positive impact of using
  options.
 Nole compared the valuation traditional verses
  options and clearly expressed the need for
  options to describe the marketplace.
 Chapter 5: Pitfalls and
Pratfalls in Real Option
               Valuation
Complication from Internal and External
 Interactions
 Interaction between option holders and underlying
 asset’s value can complicate the analysis of real option.
Inability to Explain Absurd Valuation
 The options a company has are usually not independent
 with each other. Their values are not additive. It’s
 questionable whether the presence of real options can
 explain the absurd price that were witnessed in recent
 years for many Internet stock.
 Model Risk
  The risk associated with the use of an incorrect model
  or incorrect inputs

Example :
      American put option on a stock priced $100
      The exercise price is $100
      Risk-free is 5 %
      One year to expiration
      Volatility is 32 %

   The correct model (binomial model) gives the price value $16.41
   Incorrect model ( Black-Scholes) gives the price value   $15.48
   Error 5.7%
Failure to meet Assumption
  Major Assumptions
  Lognormality
  Randomness
  Known and constant volatility


  Minor Assumptions
  Known and constant risk-free rate
  No taxes and transaction costs
  American-style option
Major Assumptions

Lognormality
    The rate of return on the underlying asset is lognormally distribution.
Example:
    A non-dividend –paying stock sells for $100 and moves up to $110 after one
    year. The logarithmic return is ln(1.10) = 9.53%

    The model typically assume the logarithmic return follows a normal distribution,
    which means the return itself follows a lognormality distribution

Randomness
    Prices are randomness to assure that markets are competitiveness that allows
    pricing models to work. No one participant can dominate all the others.

Known and constant volatility
    The volatility in standard option-pricing models is not directly observe and easy
    to obtain.
    Also, the models are sensitive to the volatility.
Minor Assumptions
1. Known and constant risk-free rate
   Option-pricing models generally assume a known and
   constant risk-free rate.
2. No taxes and transaction costs
   It facilitates the capture of most essential elements of
   the economic process being modeled.
3. American-style option
   The option is the one that can be exercised before
   expiration. It offers more flexibility.
Difficulty of Estimating Inputs
1. Market Value of the Underlying Asset
  Sometimes, the estimating for appropriate discount rate, the life of a
  project may be difficult.
2. Exercise Price
  The amount of money can be received or paid in the future are difficult
  to determine.
3. Time to Expiration
  A company can’t know how long it can keep a project before
  abandoning it to claim a salvage value.
4. Volatility
  The option prices are very sensitive to the estimate of volatility. But it is
  very difficult to observe in financial option-pricing application.
5. Risk-Free Rate
    The value of an option is not so sensitive to estimate of the risk-free
     rate.
    It is acceptable to obtain an estimate of the risk-free rate by
     estimating the rate on a default-free zero-coupon security.

Example
   A real option expires in 275 days
   Let the bid and ask discount rates on US government zero-coupon
    bonds (Treasury Bills) for maturity be 4.52% and 4.54%
   We spilt the difference and assume a rate of 4.53%
Example

    The price of one year bill
                                      Days to maturity 
    Price  Face value Discount Rate                  
                                            360        
                  275 
     $100  4.53     
                  360 
     $96.54

    If the T-bill price is $96.54 per $100 par, the annual rate is
                                      ( Daysto maturity / 365)
            Face value 
    Rate                                                      1
              Price 
                  ( 365 / 275)
       100 
                              1
       96.54 
     0.0478
    The continuous compounded rate is (in order to use in Black-Schole Model)
     ln(1.0478)  0.0467  4.67%
Nontradability of the Underlying Asset


 Assumption in the area of real options analysis:
  underlying asset can be bought and sold in a
  liquid market.

 When using binomial approach, the ability to
  trade the asset and the option in such a manner
  that no arbitrage opportunity exists is the glue
  that binds the models together.
Assumptions of Hedging, Tradability, and
Risk Neutral Valuation
      1 r  d   1.05  0.50
   p                          0.55
       ud        1. 5  0 . 5

  r: Risk-free rate (5%)
  u: Holding period return on the stock if it goes
   up ($150)
  d: Holding period return on the stock if it goes
   down ($50)
  Stock price: $100
Option price calculation




       0.55($50)  0.45($0)
    c                       $26.19
              1.05
Risk-adjusted discount rate and
probability of outcomes

           q($150)  (1  q)($50)
    $100 
                   1 k

If the probability of up move is 0.6, then
 Risk-adjusted discount rate k = 0.1.
If k = 0.12, then q=0.62.
Risk-adjusted discount rate and
probability of outcomes (cont.)
 If k is risk-free rate, then

              q ($150)  (1  q )($50)
      $100 
                       1.05
      q  0.55
      q p

 q plays the same role as p in the option-
  valuation problem. Option-pricing models are
  often said to use risk neutral valuation.
Consistency of All Approaches
 No one assume investors are risk neutral.
  Rather, risk neutral valuation is simple and
  imposes only light demands.

 Risk neutral valuation is not a different approach
  that obtains different numbers from a standard
  risk-adjusted approach.(Feinstein 1999)
Example

 Invest $9 in a project
 If the outcome is good, invest $18 and begin to
  generate $10 a year forever.
 If the outcome is bad, invest $18 and begin to
  generate $3 a year forever.
 Probability of good outcome is 0.6 and bad
  outcome is 0.4
 Discount rate is 25%
Example (cont.)


  The market value of the project is:
               $10
      V 1
         G
                    $40 or
               0.25
                $3
      V1B           $12
               0.25
  The market value of the project at time 1 is:
      X 1  $40  $18  $22 or
        G


      X 1B  $12  $18  $6
Example (cont.)
 The value of the project at time 0 is:
           [0.6($22)  0.4($0.6)]
      V                            $8.64
        0            1.25
 NPV is $8.64  $9  $0.36

                 $22
 Up factor: u        2.5463
                 8.64
                      $6
   Down factor: d         0.6944
                     $8.64
 The risk neutral probability is

              1.05( 0.6944)
        p                       0.5383
            ( 2.5463( 0.6944)
  Example (cont.)
 Option value  0.5383($22)  0.4617($0)  $11.28
                                    1.05
 According to Feinstein’s approach, the overall discount rate
  is a blend of 25% and 5%. So the weighted discounted rate
  is:
                 [qX 1  (1  q ) X 1 ](1  r )
                   G          B
          kw                                   -1
                     pX 1  (1  p ) X 1
                        G                B


               [0.6($22)  0.4($0)]( .05)
                                   1
                                         1
               0.5383($22)  0.4617($0)
              0.1704

 The correct project value is
           0.6($22)  0.4($0)
                               $11.28
               1  0.1704
 Summary
 One source of difficulty in applying real options
  valuation is the assumption may or may not be
  appropriate in the case of real options (lognormality
  distribution of the value of the underlying asset,
  randomness of prices)

 The estimation of inputs, such as the volatility of the
  value of the underlying asset, the exercise price, the
  time to expiration, is more challenging for real
  options than for fincial options.
Chapter 6: Empirical Evidence
  on the Use and Accuracy of
       Real Options Valuation
Paddock, Siegel, and Smith (1988) – Option
Valuation of Claims on Real Assets: The Case of
Offshore Petroleum Leases

 Real options model for valuing offshore oil and gas
  leases in a federal sale of 21 tracts in the Gulf of
  Mexico.
 Real options were not able to explain the bids as
  well as one might have hoped.
   Real options theory was not very well-known in 1988.
   Data provided by the government were not too good to
    carry out analysis.
 Winner’s curse – tendency for the highest bidder
  to pay more than fair
Quigg (1993) – Empirical Testing of Real Option-Pricing
Models



  Market prices of 2,700 land transactions in
   Seattle during 1976-1979.
    Market prices reflect a premium for the option to wait
     to invest (optimal development) that has a mean
     value of 6% of the land value.
    Supports the belief that investors either use real
     options models or trade in such a manner that their
     valuations are consistent with those of real options
     models.
Berger, Ofek, and Swary (1996) – Investor
Valuation of the Abandonment Option
 Whether investors price the option to abandon a firm
  at its exit value.

 This option is priced as an American put, whose
  value increases with exit value.
   Significant relationship between a company’s market
    value and its estimated exit value, suggesting that
    investors take the option to exit into account when
    valuing companies.
   The more likely the option will be exercised, the more
    valuable is the option.
Hayn (1995) – The Information Content of Losses



 Hypothesizes that because shareholders have a
  liquidation option, losses are not expected to
  perpetuate. They are thus less informative than
  profits about the firm’s future prospects.
   The results are consistent with the hypothesis.
   Investors do not respond to losses to the same
    magnitude that they do to profits.
   Option to liquidate is valued by investors.
Moel and Tufano (2002) – When Are Real Options
Exercised? An Empirical Study of Mine Closings

 The flexibility that mining firms have to open and close
  mines.
   The overall pattern of closures is well predicted by real
    option theory.
   Closures are influenced by the price and volatility of gold,
    firm’s operating costs, proxies for closing costs, and the
    size of reserves.
   Fail to capture aspects of firm-level decision making.
 Divisions within a firm share a common destiny and
  decision about particular units are influenced by the
  performance of the other parts of the firm.
Clayton and Yermack (1999) – Major League Baseball
Player Contracts: An Investigation of the Empirical
Properties of Real Options


 Contracts negotiated between professional baseball
  players and teams to investigate the use of real
  options in a commercial setting.
 Baseball contracts feature options in diverse forms,
  and they found that these options have significant
  effects on player compensation.
    As predicted by theory, players receive higher guaranteed
     compensation when they allow teams to take options on
     their future services, and lower salaries when they bargain
     for options to extend their own contracts.
    The apparent value of options decreases as a function of the
     "spread" between option exercise price and annual salary
     and increases as a function of the time until exercise.
Howell and Jagle (1997) – Laboratory Evidence on How
Managers Intuitively Value Real Growth Options


 Asked managers series of questions on growth options from
  some investment case studies, asked other questions related to
  their personal situations and the kinds of investment decisions
  they make in their work.
    Skilled managerial decision makers agree only approximately with
     real option theory.
    They tend on average to value growth options in an erratic way.
    Overvaluation seems to be a function of “Industry”, being lowest in
     the oil industry, and it is also a function of (Business) “Experience”
     and “Position” being highest for more senior people.
 The result can be interpreted in two ways:
    This limited sample of managers is not sufficiently knowledgeable
     about real options models.
    Real options models are simply not used in practice.
    Small sample size is a major limitation of this study (82 managers)
Busby and Pitts (1997) - Real options in practice: an
exploratory survey of how finance officers deal with
flexibility in capital appraisal

 Dissatisfaction with discounted cash flow techniques has lead to a
  growing literature focusing on the value of managerial flexibility in
  handling real asset investments, a subject area known as real options.

 An exploratory survey of senior finance officers in industrial firms,
  examining the significance that real options assumed in their
  investment decisions, whether their firms had established procedures
  for assessing real options, and whether their intuitions were consistent
  with what theory prescribes.
     There was wide variation between individual decision-makers in their
      perception of real options.
     Few firms have procedures to assess options in advance.
     Very few decision-makers seemed to be aware of real option research but,
      mostly, their intuitions agreed with the qualitative prescriptions of such work.
Chapter 7: Summary and
            Conclusions
 Companies are often highly misvalued in the market
 Corporate investment decisions are typically made
  using standard discounted cash flow (DCF)
  techniques, which are not equipped to accommodate
  real options
 Discounted cash flow techniques that attempt to
  capture flexibility are not adequate
 The valuation of financial options has benefited from
  years of study, evolving from the binomial and Black–
  Scholes models.
 A number of limitations and difficulties arise in
  applying real options
 Real options models oftentimes do not meet the
  assumptions inherent in the models
 The estimation of inputs in real options models is
  particularly challenging
 The models are based on the idea that one can trade
  the underlying asset and the option to form a risk-free
  hedge or trade a combination of the underlying asset
  and risk free bonds to replicate the payoffs of the
  option
 Empirical research has provided some, but very
  limited, support for the real-world applicability of real
  options models
Thank you

				
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