MN20211: Corporate Finance 2009/10:
1. Revision: Investment Appraisal, Portfolio, CAPM (AB).
2. Investment flexibility, Decision trees, Real Options (RF).
3. Funding (AB)
4. Capital Structure and Value of the Firm (RF).
5. Optimal Capital Structure - Agency Costs, Signalling (RF).
6. Dividend policy/repurchases (RF)
7. Mergers and Acquisitions (AB).
8. Venture Capital and Private Equity (RF).
9. Intro to Behavioural Finance (RF).
10. Revision.
1
RF’s Lectures
•
•
•
•
•
•
Week 3: Investment flexibility/Real Options
Week 5: Capital Structure/Dividends
Week 6: Capital Structure/Dividends
Week 9: Venture Capital and Private Equity
Week 10: Introduction to BF/BCF.
Week 11 Revision
2
Lecture 3: Investment Flexibility/
Real options.
• Reminder of Corporation’s Objective :
Take projects that increase shareholder
wealth (Value-adding projects).
• Investment Appraisal Techniques: NPV,
IRR, Payback, ARR
• Decision trees
• Monte Carlo.
• Real Options
3
Lecture 3: Investment Flexibility, Decision Trees, and
Real Options
Decision Trees and Sensitivity Analysis.
•Example: From Ross, Westerfield and Jaffe: “Corporate Finance”.
•New Project: Test and Development Phase: Investment
$100m.
•0.75 chance of success.
•If successful, Company can invest in full scale
production, Investment $1500m.
•Production will occur over next 5 years with the
following cashflows.
4
Production Stage: Base Case
$000
Year 1
Year 2 - 6
Revenues
Variable Costs
Fixed Costs
Depreciation
6000
-3000
-1791
-300
Pretax Profit
Tax (34%)
909
-309
Net Profit
Cashflow
600
900
Initial Investment
-1500
6
900
Date 1 NPV = -1500 + 
t
(
1
.
15
)
t 2
= 1517
5
Decision Tree.
Date 1: -1500
Date 0: -$100
P=0.75
Success
Test
Invest
NPV = 1517
Do not Invest
NPV = 0
Do not Invest
Failure
P=0.25
Do Not Test
Invest
NPV = -3611
Solve backwards: If the tests are successful, SEC should invest,
since 1517 > 0.
If tests are unsuccessful, SEC should not invest, since 0 > -3611.
6
Now move back to Stage 1.
Invest $100m now to get 75% chance of $1517m one year later?
Expected Payoff = 0.75 *1517 +0.25 *0 = 1138.
NPV of testing at date 0 = -100 + 1138 = $890
1.15
Therefore, the firm should test the project.
Sensitivity Analysis (What-if analysis or Bop analysis)
Examines sensitivity of NPV to changes in underlying
assumptions (on revenue, costs and cashflows).
7
Sensitivity Analysis.
- NPV Calculation for all 3 possibilities of a single variable +
expected forecast for all other variables.
NPV
Market Size
Market Share
Price
Variable Cost
Fixed Cost
Investment
Pessimistic
-1802
-696
853
189
1295
1208
Expected
or Best
1517
1517
1517
1517
1517
1517
Optimistic
8154
5942
2844
2844
1628
1903
Limitation in just changing one variable at a time.
Scenario Analysis- Change several variables together.
Break - even analysis examines variability in forecasts.
It determines the number of sales required to break even.
8
Real Options.
A digression: Financial Options
A call option gives the holder the right (but not the obligation) to
buy shares at some time in the future at an exercise price agreed
now.
A put option gives the holder the right (but not the obligation) to
sell shares at some time in the future at an exercise price agreed
now.
European Option – Exercised only at maturity date.
American Option – Can be exercised at any time up to maturity.
For simplicity, we focus on European Options.
9
Example:
• Today, you buy a call option on Marks and
Spencer’s shares. The call option gives you the
right (but not the obligation) to buy MS shares at
exercise date (say 31/12/10) at an exercise price
given now (say £10).
• At 31/12/10: MS share price becomes £12. Buy at
£10: immediately sell at £12: profit £2.
• Or: MS shares become £8 at 31/12/10: rip option
up!
10
Factors Affecting Price of European Option (=c).
-Underlying Stock Price S.
-Exercise Price X.
-Variance of of the returns of the underlying asset ,

2
-Time to maturity, T.
c
c
c
c
 0,
 0, 2  0,
 0.
S
X

T
The riskier the underlying returns, the greater the probability that
the stock price will exceed the exercise price.
The longer to maturity, the greater the probability that the stock
price will exceed the exercise price.
11
Options: Payoff Profiles.
Selling a put option.
Buying a Call Option.
W
S
Selling a Call Option.

Buying a Put Option.
12
Pricing Call Options – Binomial Approach.
Cu = 3
uS=24.00
q
q
c
S=20
1- q
dS=13.40
1- q
Cd=0
S = £20. q=0.5. u=1.2. d=.67. X = £21.
1 + rf = 1.1.
Risk free hedge Portfolio: Buy One Share of Stock and write m
call options.
uS - mCu = dS – mCd => 24 – 3m = 13.40.
M = 3.53.
By holding one share of stock, and selling 3.53 call options, your
13
payoffs are the same in both states of nature (13.40): Risk free.
Since hedge portfolio is riskless:
(1  rf )( S  mc)  uS  mcu .
1.1 ( 20 – 3.53C) = 13.40.
Therefore, C = 2.21.
This is the current price per call option. The total present value of
investment = £12 .19, and the rate of return on investment is
13.40 / 12.19 = 1.1.
14
Alternative option-pricing method
• Black-Scholes
• Continuous Distribution of share returns
(not binomial)
• Continuous time (rather than discrete time).
15
Real Options
• Just as financial options give the investor the right
(but not obligation) to future share investment
(flexibility)
• Researchers recognised that investing in projects
can be considered as ‘options’ (flexibility).
• “Real Options”: Option to delay, option to
expand, option to abandon.
• Real options: dynamic approach (in contrast to
static NPV).
16
Real Options
• Based on the insights, methods and
valuation of financial options which give
you the right to invest in shares at a later
date
• RO: development of NPV to recognise
corporation’s flexibility in investing in
PROJECTS.
17
Real Options.
• Real Options recognise flexibility in
investment appraisal decision.
• Standard NPV: static; “now or never”.
• Real Option Approach: “Now or Later”.
• -Option to delay, option to expand, option
to abandon.
• Analogy with financial options.
18
Types of Real Option
• Option to Delay (Timing Option).
• Option to Expand (eg R and D).
• Option to Abandon.
19
Option to Delay (= call option)
•
Investment in
waiting:
Valuecreation
Project
value
(sunk)
20
Option to expand (= call option)
Value creation
•
Investment in
initial project:
eg R and D
(sunk)
Project
value
21
Option to Abandon ( = put option)
•
Project goes
badly: abandon
for liquidation
value.
Project
value
22
Valuation of Real Options
• Binomial Pricing Model
• Black-Scholes formula
23
Value of a Real Option
• A Project’s Value-added = Standard NPV
plus the Real Option Value.
• For given cashflows, standard NPV
decreases with risk (why?).
• But Real Option Value increases with risk.
• R and D very risky: => Real Option element
may be high.
24
Simplified Examples
• Option to Expand (page 241 of RWJ)
If Successful
Expand
Build First Ice
Hotel
Do not Expand
If unsuccessful
25
Option to Expand (Continued)
•
•
•
•
•
•
•
NPV of single ice hotel
NPV = - 12,000,000 + 2,000,000/0.20 =-2m
Reject?
Optimistic forecast: NPV = - 12M + 3M/0.2
= 3M.
Pessimistic: NPV = -12M + 1M/0.2 = - 7m
Still reject?
26
Option to expand (continued)
• Given success, the E will expand to 10
hotels
• =>
• NPV = 50% x 10 x 3m + 50% x (-7m) =
11.5 m.
• Therefore, invest.
27
Option to abandon.
•
•
•
•
•
•
•
NPV(opt) = - 12m + 6m/0.2 = 18m.
NPV (pess) = -12m – 2m/0.2 = -22m.
=> NPV = - 2m. Reject?
But abandon if failure =>
NPV = 50% x 18m + 50% x -12m/1.20
= 2.17m
Accept.
28
Option to delay and Competition (Smit and Ankum).
•-Smit and Ankum present a binomial real option model:
•Option to delay increases value (wait to observe market demand)
•But delay invites product market competition: reduces value (lost
monopoly advantage).
•cost: Lost cash flows
•Trade-off: when to exercise real option (ie when to delay and
when to invest in project).
•Protecting Economic Rent: Innovation, barriers to entry,
product differentiation, patents.
•Firm needs too identify extent of competitive advantage.
29
Option to delay versus competition:
Game-theoretic approach
Firm 1\Firm 2
Invest early
Delay
Invest early
NPV = 500,NPV = 500
NPV = 700, NPV = 300
Delay
NPV = 300, NPV = 700
NPV = 600,NPV = 600
30
Option to delay versus competition:
effects of legal system
Firm 1\ Firm 2
Invest early
Delay
Invest early
NPV = 500,NPV = 500
NPV = 700- 300, NPV =
300+300
Delay
NPV = 300+300, NPV =
700-300
NPV = 600,NPV = 600
31
Monte Carlo methods
• BBQ grills example in RWJ.
• Application to Qinetiq (article by Tony
Bishop).
32
Use of Real Options in Practice
•
33
Lecture 5 and 6: Capital Structure and Dividends.
Positive NPV project immediately increases current equity
value (share price immediately goes up!)
Pre-project announcement
V  Bo  Eo
I
New capital (all equity)
New project:
Value of Debt
Original equity holders
New equity
New Firm Value
NPV  Vn  I .
Bo
E0  Vn  I
I
V  Vn
34
Example:
V  Bo  Eo
I
=500+500=1000.
20
NPV  Vn  I 
60 -20 = 40.
Bo
Value of Debt
Original Equity
E0  Vn  I
New Equity
I
= 20
V  Vn
=1000+60=1060.
Total Firm Value
= 500.
= 500+40 = 540
35
Positive NPV: Effect on share price.
Assume all equity.
£K
Current
Market
Value
No of
Shares
1000
New Project
Project Income
60
Required Investment
20
NPV
40
1000
Price per
Share
1
Market
Value
No of
Shares
Price per
Share
1040
1000
1.04
20
19
1.04
1060
1019
1.04
36
Value of the Firm and Capital Structure
Value of the Firm = Value of Debt + Value of Equity = discounted
value of future cashflows available to the providers of capital.
(where values refer to market values).
Capital Structure is the amount of debt and equity: It is the way a firm
finances its investments.
Unlevered firm = all-equity.
Levered firm = Debt plus equity.
Miller-Modigliani said that it does not matter how you split the cake
between debt and equity, the value of the firm is unchanged (Irrelevance
Theorem).
37
Value of the Firm = discounted value of future cashflows available to
the providers of capital.
-Assume Incomes are perpetuities.
Miller- Modigliani Theorem:
VU 
NCF (1  T )

 VE
NCF (1  T )
VL  VU  T .B 
 VE  VD
WACC

NI kd .B

.
K
Kd
e
Irrelevance Theorem: Without Tax, Firm Value is
independent of the Capital Structure.
Note that
WACC  %debt * K d (1  t )  %equity * K e
38
K
K
Without Taxes
D/E
With Taxes
D/E
V
V
D/E
39
D/E
Examples
• Firm X
• Henderson Case study
40
MM main assumptions:
- Symmetric information.
-Managers unselfish- maximise shareholders wealth.
-Risk Free Debt.
MM assumed that investment and financing decisions
were separate. Firm first chooses its investment projects
(NPV rule), then decides on its capital structure.
Pie Model of the Firm:
D
E
E
41
MM irrelevance theorem- firm can use any mix of
debt and equity – this is unsatisfactory as a policy tool.
Searching for the Optimal Capital Structure.
-Tax benefits of debt.
-Asymmetric information- Signalling.
-Agency Costs (selfish managers).
-Debt Capacity and Risky Debt.
Optimal Capital Structure maximises firm value.
42
Combining Tax Relief and Debt Capacity (Traditional View).
K
V
D/E
43
D/E
Section 4: Optimal Capital Structure,
Agency Costs, and Signalling.
Agency costs - manager’s self interested actions.
Signalling - related to managerial type.
Debt and Equity can affect Firm Value because:
- Debt increases managers’ share of equity.
-Debt has threat of bankruptcy if manager shirks.
- Debt can reduce free cashflow.
But- Debt - excessive risk taking.
44
AGENCY COST MODELS.
Jensen and Meckling (1976).
- self-interested manager - monetary rewards V private
benefits.
- issues debt and equity.
Issuing equity => lower share of firm’s profits for
manager => he takes more perks => firm value
Issuing debt => he owns more equity => he takes less
perks => firm value
45
Jensen and Meckling (1976)
V
V*
Slope = -1
A
V1
B1
B
If manager owns all of the equity, equilibrium point A.
46
Jensen and Meckling (1976)
V
V*
Slope = -1
A
B
V1
Slope = -1/2
B1
B
If manager owns all of the equity, equilibrium point A.
If manager owns half of the equity, he will got to point B if he
can.
47
Jensen and Meckling (1976)
V
V*
Slope = -1
A
B
V1
Slope = -1/2
V2
C
B1
B2
B
If manager owns all of the equity, equilibrium point A.
If manager owns half of the equity, he will got to point B if he
can.
Final equilibrium, point C: value V2, and private benefits B1.48
Jensen and Meckling - Numerical Example.
PROJECT
A
EXPECTED INCOME
500
MANAGER'S SHARE:
100%
VALUE OF PRIVATE
BENEFITS
TOTAL WEALTH
MANAGER'S SHARE:
50%
VALUE OF PRIVATE
BENEFITS
TOTAL WEALTH
PROJECT
B
1000
500
1000
800
500
1300
1500
250
500
800
500
1050
1000
Manager issues
100% Debt.
Chooses Project B.
Manager issues
some Debt and
Equity.
Chooses Project A.
Optimal Solution: Issue Debt?
49
Issuing debt increases the manager’s fractional
ownership => Firm value rises.
-But:
Debt and risk-shifting.
State 1
100
0
0.5
State 2
100
170
0.5
100
85
Debt
50
25
Equity
50
60
Values:
50
OPTIMAL CAPITAL STRUCTURE.
Trade-off: Increasing equity => excess perks.
Increasing debt => potential risk shifting.
Optimal Capital Structure => max firm value.
V
V*
D/E*
D/E
51
Other Agency Cost Reasons for Optimal Capital
structure.
Debt - bankruptcy threat - manager increases effort level.
(eg Hart, Dewatripont and Tirole).
Debt reduces free cashflow problem (eg Jensen 1986).
52
Agency Cost Models – continued.
Effort Level, Debt and bankruptcy (simple example).
Debtholders are hard- if not paid, firm becomes bankrupt, manager
loses job- manager does not like this.
Equity holders are soft.
Effort
Level
High
Low
Required
Funds
Income
500
100
200
What is Optimal Capital Structure (Value Maximising)?
53
Firm needs to raise 200, using debt and equity.
Manager only cares about keeping his job. He has a fixed
income, not affected by firm value.
a) If debt < 100, low effort. V = 100. Manager keeps job.
b) If debt > 100: low effort, V < D => bankruptcy.
Manager loses job.
So, high effort level => V = 500 > D. No bankruptcy =>
Manager keeps job.
High level of debt => high firm value.
However: trade-off: may be costs of having high debt
levels.
54
Free Cashflow Problem (Jensen 1986).
-Managers have (negative NPV) pet projects.
-Empire Building.
=> Firm Value reducing.
Free Cashflow- Cashflow in excess of that
required to fund all NPV projects.
Jensen- benefit of debt in reducing free cashflow.
55
Jensen’s evidence from the oil industry.
After 1973, oil industry generated large free cashflows.
Management wasted money on unnecessary R and D.
also started diversification programs outside the industry.
Evidence- McConnell and Muscerella (1986) – increases
in R and D caused decreases in stock price.
Retrenchment- cancellation or delay of ongoing projects.
Empire building Management resists retrenchment.
Takeovers or threat => increase in debt => reduction in
free cashflow => increased share price.
56
Jensen predicts:
young firms with lots of good (positive NPV) investment
opportunities should have low debt, high free cashflow.
Old stagnant firms with only negative NPV projects should
have high debt levels, low free cashflow.
Stultz (1990)- optimal level of debt => enough free
cashflow for good projects, but not too much free cashflow
for bad projects.
57
Income Rights and Control Rights.
Some researchers (Hart (1982) and (2001), Dewatripont and
Tirole (1985)) recognised that securities allocate income rights
and control rights.
Debtholders have a fixed first claim on the firm’s income, and
have liquidation rights.
Equityholders are residual claimants, and have voting rights.
Class discussion paper: Hart (2001)- What is the optimal
allocation of control and income rights between a single investor
and a manager?
How effective are control rights when there are different types of
investors?
Why do we observe different types of outside investors- what is
58
the optimal contract?
Conflict
Breaking MM
Benefits of Debt
Costs of Debt
Tax Relief
Fin’l Distress/
Debt Capacity
Agency Models
JM (1976)
Managerial
Perks
Increase Mgr’s
Ownership
Risk Shifting
Jensen (1986)
Empire Building
Reduce Freecash
Unspecified.
Stultz
Empire Building
Reduce Freecash
Underinvestment
.
Dewatripont and
Tirole, Hart.
Low Effort level
Bankruptcy threat
=>increased effort
DT- Inefficient
liquidations.
59
Signalling Models of Capital Structure
Assymetric info: Akerlof’s (1970) Lemons Market.
Akerlof showed that, under assymetric info, only bad things may be
traded.
His model- two car dealers: one good, one bad.
Market does not know which is which: 50/50 probability.
Good car (peach) is worth £2000. Bad car (lemon) is worth £1000.
Buyers only prepared to pay average price £1500.
But: Good seller not prepared to sell. Only bad car remains.
Price falls to £1000.
Myers-Majuf (1984) – “securities may be lemons too.”
60
Asymmetric information and Signalling Models.
- managers have inside info, capital structure has signalling
properties.
Ross (1977)
-manager’s compensation at the end of the period is
M  (1  r )  0 V 0   1V 1 if V 1  D
M  (1  r )  0 V 0   1V 1  C if V 1  D
D* = debt level where bad firm goes bankrupt.
Result: Good firm D > D*, Bad Firm D < D*.
Debt level D signals to investors whether the firm is good or bad.
61
Myers-Majluf (1984).
-managers know the true future cashflow.
They act in the interest of initial shareholders.
P = 0.5
Do
Nothing:
Issue
Equity
Good
Bad
Good
Assets
in Place
250
130
350
230
NPV of
new
project
Value of
Firm
0
0
20
10
250
130
370
240
Expected Value
190
305
New investors
0
100
Old Investors
190
205
Bad
62
Consider old shareholders wealth:
Good News + Do nothing = 250.
205
(370)  248.69.
Good News + Issue Equity =
305
Bad News and do nothing = 130.
Bad News and Issue equity =
205
(240)  161.31.
305
63
Old Shareholders’ payoffs
Good
News
Bad
News
Do
Issue
nothing and
invest
250 *
248.69
130
161.31*
Equilibrium
Good
News
Bad
News
Do
Issue
nothing and
invest
250 * 248.69
130
140 *
Issuing equity signals that the bad state will occur.
The market knows this - firm value falls.
Pecking Order Theory for Capital Structure => firms
prefer to raise funds in this order:
Retained Earnings/ Debt/ Equity.
64
Evidence on Capital structure and firm value.
Debt Issued - Value Increases.
Equity Issued- Value falls.
However, difficult to analyse, as these capital structure
changes may be accompanied by new investment.
More promising - Exchange offers or swaps.
Class discussion paper: Masulis (1980)- Highly
significant Announcement effects:
+7.6% for leverage increasing exchange offers.
-5.4% for leverage decreasing exchange offers.
65
Practical Methods employed by Companies (See
Damodaran; Campbell and Harvey).
-Trade off models: PV of debt and equity.
-Pecking order.
-Benchmarking.
-Life Cycle.
Increasing Debt?
time
66
Trade-off Versus Pecking Order.
• Empirical Tests.
• Multiple Regression analysis (firm size/growth
opportunities/tangibility of assets/profitability…..
• => Relationship between profitability and leverage
(debt): positive => trade-off.
• Or negative => Pecking order:
• Why?
• China: Reverse Pecking order
67
Capital Structure and Product
Market Competition.
• Research has recognised that firms’ financial
decisions and product market decisions not made
in isolation.
• How does competition in the product market affect
firms’ debt/equity decisions?
• Limited liability models: Debt softens
competition: higher comp => higher debt.
• Predation models: higher competition leads to
lower debt. (Why?)
68
Capital Structure and Takeovers
• Garvey and Hanka:
• Waves of takeovers in US in 1980’s/1990’s.
• Increase in hostile takeovers => increase in
debt as a defensive mechanism.
• Decrease in hostile takeovers => decrease in
debt as a defensive mechanism.
69
Garvey and Hanka (contiuned)
Trade-off: Tax shields/effort
levels/FCF/ efficiency/signalling
Vs financial distress
V
•
D/E
D/E*
70
Practical Capital Structure: case
study
•
71
Lecture 6: Dividend Policy
•
•
•
•
•
•
•
Miller-Modigliani Irrelevance.
Gordon Growth (trade-off).
Signalling Models.
Agency Models.
Gordon Growth (trade-off).
Lintner Smoothing.
Dividends versus share repurchases.
72
Early Approach.
•
•
•
•
Three Schools of ThoughtDividends are irrelevant.
Dividends => increase in stock prices.
Dividends => decrease in Stock Prices.
73
A. Dividend Irrelevance.
Assume All equity firm.
Value of Firm = Value of Equity = discounted value of
future cashflows available to equity holders = discounted
value of dividends (if all available cashflow is paid out).

Div
t
V0  
t
t 0 (1   )
If everything not reinvested is paid out as dividends,
then
NCF t  I t
V0  
t
t 0 (1   )

74
Miller Modigliani’s Dividend Irrelevance.
MM used a source and application of funds argument to show that
Dividend Policy is irrelevant:
Source of Funds = Application of Funds
NCFt  NSt  I t  Div t
 NCFt  I t  Div t  NSt


Div
NS
NCF
t
t
t  It
 V 0  

t
t
(1   )
t 1
t 1 (1   )

75
NCF t  I t
V0  
t
t 1 (1   )

-Dividends do not appear in the equation.
-If the firm pays out too much dividend, it issues new
equity to be able to reinvest. If it pays out too little
dividend, it can use the balance to repurchase shares.
-Hence, dividend policy irrelevant.
-Key is the availability of finance in the capital
market.
76
Example of Dividend Irrelevance using Source and Application
of Funds.
Firm invests in project giving it NCF = 100 every year, and it needs
to re-invest, I =50 every year.
Cashflow available to shareholders = NCF – I = 50.
Now, NCF – I = Div – NS = 50.
If firm pays dividend of 50, NS = 0 (ie it pays out exactly the
cashflow available – no new shares bought or sold).
If firm pays dividend of 80, NS = -30 (ie it sells new shares of 30 to
cover dividend).
If firm pays dividend of 20, NS = 30 (ie it uses cashflow not paid out
as dividend to buy new shares).
In each case, Div – NS = 50.
77
Dividend irrelevance (from Lease et
al book chapter 2
• Appendix 2a.
78
B. Gordon Growth Model.
Where does growth come from?- retaining
cashflow to re-invest.
Constant fraction, K, of earnings retained for reinvestment.
Rest paid out as dividend.
Average rate of return on equity = r.
Growth rate in cashflows (and dividends) is g = Kr.
Div
NCF
1
1 (1  K )

.
V0 
g
  Kr
79
Example of Gordon Growth Model.
£K
19x5
Profits After Tax (NCF)
Retained Profit (NCF.K)
19x6
19x7
19x8
19x9
Average
2500
1550
2760
1775
2635
1600
2900
1800
3100
1900
950
985
1035
1100
1200
Share Capital + retentions
B/F
C/F (= BF + Retained Profit)
30000
31550
31550
33325
33325
34925
34925
36725
36725
38625
Retention Rate K
r on opening capital
0.62
0.083
0.64
0.087
0.61
0.079
0.62
0.083
0.61
0.084
Dividend (NCF(1-K))
0.62
0.083
g = Kr = 0.05.
How do we use this past data for valuation?
80
Gordon Growth Model (Infinite Constant
Growth Model).
Let
  12%
Div 0 (1  g ) Div1 1200(1.05)
1260
V0 



g
g
g
0.12  0.05
= 18000
81
Finite Supernormal Growth.
-Rate of return on Investment > market required return for T
years.
-After that, Rate of Return on Investment = Market required
return.
V0 
NCF1

(r   )
 K . NCF1.T
 (1   )
If T = 0, V = Value of assets in place (re-investment at zero
NPV).
Same if r =  .
82
Examples of Finite Supernormal Growth.
NCF1  100.
  10%.
T = 10 years. K = 0.1.
A. Rate of return, r = 12% for 10 years,then 10% thereafter.
100
(0.12  0.1)
V0 
 0.1.(100).10
 1018
0.1
0.1(1  0.1)
B. Rate of return, r = 5% for 10 years,then 10% thereafter.
100
(0.05  0.1)
V0 
 0.1.(100).10
 955
0.1
0.1(1  0.1)
83
Are Dividends Irrelevant?
- Evidence: higher dividends => higher value.
- Dividend irrelevance : freely available capital for reinvestment. If too much dividend, firm issued new shares.
- If capital not freely available, dividend policy may matter.
C. Dividend Signalling - Miller and Rock (1985).
NCF + NS = I + DIV: Source = Uses.
DIV - NS = NCF - I.
Right hand side = retained earnings. Left hand side higher dividends can be covered by new shares.
84
Div - NS - E (Div - NS) = NCF - I - E (NCF - I)
= NCF - E ( NCF).
Unexpected dividend increase - favourable signal of NCF.
Prob
0.5
Firm A
0.5
Firm B
E(V)
NCF
400
1400
900
New Investment
600
600
600
Dividend
New shares
0
200
800
0
400
100
E(Div - NS) = E(NCF - I) = 300.
Date 1 Realisation: Firm B: Div - NS - E (Div - NS) = 500 = NCF - E
( NCF).
Firm A : Div - NS - E (Div - NS) = -500 = NCF - E ( NCF).
85
Dividend Signalling Models.
•
•
•
•
•
•
•
Bhattacharya (1979)
John and Williams (1985)
Miller and Rock (1985)
Ofer and Thakor (1987)
Fuller and Thakor (2002).
Fairchild (2009/10).
Divs credible costly signals: Taxes or borrowing
costs.
86
Dividends as signals of expected
cashflows: Bhattacharya 1979.
• Asymmetric Info about cashflows.
• Investors invest over short horizons.
• Dividends taxed at higher rate than capital
gains.
• => signalling equilibria.
• Shorter horizon => higher dividends.
87
Signalling, FCF, and Dividends.
Fuller and Thakor (2002)
• Empirical Contest between Signalling and
FCF hypotheses.
• Divs’ costly signals: signalling plus FCF.
• If dividend too low: FCF problem (cf
Jensen 1986).
• If dividend too high: costly borrowing.
88
Fuller and Thakor (continued).
• 2 types of firm: good and bad.
• Good firm’s future CF  {H , L}.
CF  {L,0}.
• Bad firm’s future
Pr( x  H / G )  Pr( x  L / B )  q
Pr( x  L / G )  Pr( x  0 / B )  1  q
89
Fuller and Thakor (continued)
•
•
•
•
At date 1, outsiders observe signal S  {0, L, H }
If firm G, S  {L, H }
S  {0, L}
If firm B,
Thus, if S  H or S  0, mkt knows firm
type. Divs used to eliminate FCF.
• If S  L, mkt cannot identify type. Thus,
divs used to signal type and eliminate FCF.
90
Fuller and Thakor (continued)
• Firms’ dividend announcement trades-off
costly borrowing versus FCF problem.
• Bayesian updating.
S  H  medium.div
S  0  low.div
S  L, goodfirm  High .div
S  L, badfirm  low.div
91
Dividend Signalling: Current Income/future
Investment:
Fairchild (2009/10).
• Conflicting signals:
• High/low dividends signal high/low income
• But high/low dividends affect ability to reinvest (cf Gordon Growth)
• If –ve NPV: FCF: High divs good.
• But if +ve NPV: high div bad =>
ambiguous.
92
Fairchild (2002): continued.
2 all-equity firms; manager i  {g , b}.
Date 0: Project investment.
Date 1: Net income, N , with N  N .
g
b
i
Revealed to the manager, but not to
investors.
• Mkt becomes aware of a new project P2,
with return on equity   0,   0.
• Manager commits to a dividend Di
•
•
•
•
93
Fairchild (2002) continued
•
•
•
•
Date 1.5: Mgr pays announced dividend
P2 requires investment I  ( N b , N g ].
Mgr b cannot take new project.
Date 2, If P2 taken, achieves net income.
Mgr has private benefits b  0.
94
Fairchild (2002) continued
Mgr maximises M  V1  B.
Bayesian Updating.
Adverse selection: N g  I  N b .
Mgr g can either signal current income
(but no re-investment),
• or re-invest (without signaling current
income).
•
•
•
•
95
Fairchild (2002) continued
• Signalling (of current income) Equilibria:
• A) Efficient re-investment: Pooling:
Dg  [0, N g  I ], Db  [0, N g  I ].
• B) Inefficient Non re-investment, or
• C) Efficient Non re-investment: separating:
Dg  [ N b , N g ], Db  [0, N b ].
96
Fairchild 2002 (continued)
•
•
•
•
•
•
•
Case 2: Moral Hazard:
Mgr can provide credible signal of type
Effective communication (Wooldridge and Ghosh)
Now, use divs only due to FCF.
Efficient re-investment.
Inefficient re-investment.
Efficient non re-investment.
97
Fairchild 2002: Summary
• Case 1: Adverse selection: inefficiency when mgr
refuses to cut dividend to take +ve NPV project.
• Case 2: Moral hazard: mgr reduces dividend to
take –ve NPV project.
• Integrated approach: Effective mgrl
communication/ increase mgr’s equity stake.
98
Agency Models.
•
•
•
•
Jensen’s Free Cash Flow (1986).
Stultz’s Free Cash Flow Model (1990).
Easterbrook.
Fairchild (2009/10): Signalling + moral
hazard.
99
D.
Lintner Model.
Managers do not like big changes in dividend (signalling).
They smooth them - slow adjustment towards target payout rate.
Div t  Div t 1  K .(T . epst  Div t 1)
K is the adjustment rate. T is the target payout rate.
FIRM
K
EPS
Dividend Policy -Lintner Model
YEAR
50.00
A
B
0.5
DIV
C
0
DIV
1
DIV
Values
40.00
30.00
20.00
10.00
0.00
1
2
3
4
5
Years
6
7
8
1
2
3
4
5
6
7
8
30.00
34.00
28.00
25.00
29.00
33.00
36.00
40.00
13.25
15.13
14.56
13.53
14.02
15.26
16.63
18.31
11.50
11.50
11.50
11.50
11.50
11.50
11.50
11.50
15.00
17.00
14.00
12.50
14.50
16.50
18.00
20.00
100
Using Dividend Data to analyse Lintner Model.
Div t  (1  K ) Div t 1  K .T . epst .
In Excel, run the following regression;
Div t  a  bDiv t 1  cEpst
The parameters give us the following information,
a = 0, K = 1 – b, T = c/ (1 – b).
101
Dividends and earnings.
• Relationship between dividends, past,
current and future earnings.
• Regression analysis/categorical analysis.
102
Dividend Smoothing V optimal
re-investment (Fairchild 2003)
• Method:• GG Model: derive optimal retention/payout
ratio
• => deterministic time path for dividends,
Net income, firm values.
• Compare with stochastic time path to
determine smoothing policy.
103
Deterministic Dividend Policy.
Div
N
1
0 (1  K )(1  Kr )

.
• Recall V 0 
g
  Kr
•
• Solving V0
 0,
K
• We obtain optimal retention ratio
•
K* 
  (   r )(   1)
r
.
104
Analysis of K *

• If r  [0, 1   ],
K*  0.


K
*
], K * [0,1], with
• If r  [0,
 0.
1 
r
• Constant r over time => Constant K* over
time.
105
Deterministic Case (Continued).
• Recursive solution:
Dt  N 0 (1  K *)(1  K * r )
N 0 (1  K *)(1  K * r )
Vt 
  K *r
t
t 1
.
When r is constant over time, K* is constant. Net
Income, Dividends, and firm value evolve
deterministically.
106
Stochastic dividend policy.
• Future returns on equity normally and
independently distributed, mean r.
• Each period, K* is as given previously.
• Dividends volatile.
• But signalling concerns: smooth dividends.
• => “buffer” from retained earnings.
107
Dividends V Share Repurchases.
• Both are payout methods.
• If both provide similar signals, mkt reaction
should be same.
• => mgrs should be indifferent between
dividends and repurchases.
108
Dividend/share repurchase
irrelevance
• Misconception (among practitioners) that
share repurchasing can ‘create’ value by
spreading earnings over fewer shares
(Kennon).
• Impossible in perfect world:
• Fairchild (JAF).
109
Dividend/share repurchase
irrelevance
• See Fairchild (JAF 2005)
• Kennon’s website
110
Evidence.
• Mgrs think divs reveal more info than
repurchases (see Graham and Harvey
“Payout policy”.
• Mgrs smooth dividends/repurchases are
volatile.
• Dividends paid out of permanent
cashflow/repurchases out of temporary
cashflow.
111
Motives for repurchases
(Wansley et al, FM: 1989).
•
•
•
•
•
•
•
Dividend substitution hypothesis.
Tax motives.
Capital structure motives.
Free cash flow hypothesis.
Signalling/price support.
Timing.
Catering.
112
Repurchase signalling.
• Price Support hypothesis: Repurchases
signal undervaluation (as in dividends).
• But do repurchases provide the same signals
as dividends?
113
Repurchase signalling:
(Chowdhury and Nanda Model: RFS 1994)
• Free-cash flow => distribution as
commitment.
• Dividends have tax disadvantage.
• Repurchases lead to large price increase.
• So, firms use repurchases only when
sufficient undervaluation.
114
Open market Stock Repurchase
Signalling:
McNally, 1999
• Signalling Model of OM repurchases.
• Effect on insiders’ utility.
• If do not repurchase, RA insiders exposed to
more risk.
• => Repurchase signals:
• a) Higher earnings and higher risk,
• b) Higher equity stake => higher earnings.
115
Repurchase Signalling :
Isagawa FR 2000
• Asymmetric information over mgr’s private
benefits.
• Repurchase announcement reveals this info
when project is –ve NPV.
• Repurchase announcement is a credible
signal, even though not a commitment.
116
Costless Versus Costly Signalling:
Bhattacharya and Dittmar 2003
• Repurchase announcement is not
commitment.
• Costly signal: Actual repurchase: separation
of good and bad firm.
• Costless (cheap-talk): Announcement
without repurchasing. Draws analysts’
attention.
• Only good firm will want this
117
Repurchase timing
• Evidence: repurchase timing (buying shares
cheaply.
• But market must be inefficient, or investors
irrational.
• Isagawa.
• Fairchild and Zhang.
118
Repurchases and irrational
investors.
Isagawa 2002
• Timing (wealth-transfer) model.
• Unable to time market in efficient market
with rational investors.
• Assumes irrational investors => market
does not fully react.
• Incentive to time market.
• Predicts long-run abnormal returns postannouncement.
119
Repurchase Catering.
• Baker and Wurgler: dividend catering
• Fairchild and Zhang: dividend/repurchase
catering, or re-investment in positive NPV
project.
120
Competing Frictions Model:
From Lease et al:
Taxes
•
Low
Payout
Low Payout
Agency
Costs
High
Payout
High Payout
Asymmetric
Information
High
Low Payout
Payout
121
Dividend Cuts bad news?
•
•
•
•
•
•
•
•
•
Fairchild’s 2009/10 article.
Wooldridge and Ghosh:=>
ITT/ Gould
Right way and wrong way to cut dividends.
Other cases from Fairchild’s article.
Signalling/FCF hypothesis.
FCF: agency cost: cutting div to take –ve NPV project.
New agency cost: Project foregone to pay high dividends.
Communication/reputation important!!
122
Lecture 9: Venture Capital/private
equity
• Venture capitalists typically supply start-up
finance for new entrepreneurs.
• VC’s objective; help to develop the venture over 5
– 7 years, take the firm to IPO, and make large
capital gains on their investment.
• In contrast, private equity firms invest in later
stage public companies to take them private….
123
Private Equity.
• PE firms generally buy poorly performing
publically listed firms.
• Take them private
• Improve them (turn them around).
• Hope to float them again for large gains
• Our main focus in this course is venture capital,
But will look briefly at PE later.
• “Theory of private equity turnarounds” plus PE
leverage article, plus economics of PE articles.
124
C. Venture Capital Financing
•
•
•
•
Active Value-adding Investors.
Double-sided Moral Hazard problem.
Asymmetric Information.
Negotiations over Cashflows and Control
Rights.
• Staged Financing
• Remarkable variation in contracts.
125
Features of VC financing.
• Bargain with mgrs over financial contract
(cash flow rights and control rights)
• VC’s active investors: provide value-added
services.
• Reputation (VCs are repeat players).
• Double-sided moral hazard.
• Double-sided adverse selection.
126
Kaplan and Stromberg
• Empirical analysis, related to financial
contract theories.
127
Financial Contracts.
•
•
•
•
Debt and equity.
Extensive use of Convertibles.
Staged Financing.
Control rights (eg board control/voting
rights).
• Exit strategies well-defined.
128
Fairchild (2004)
• Analyses effects of bargaining power,
reputation, exit strategies and value-adding
on financial contract and performance.
• 1 mgr and 2 types of VC.
• Success Probability depends on effort:
P  eM   i eVC
where
 i {0,1},
=> VC’s valueadding.
129
Fairchild’s (2004) Timeline
• Date 0: Bidding Game: VC’s bid to supply
finance.
• Date 1: Bargaining game: VC/E bargain
over financial contract (equity stakes).
• Date 2: Investment/effort level stage.
• Date 3: Renegotiation stage: hold-up
problems
• Date 4: Payoffs occur.
130
Bargaining stage
• Ex ante Project Value
V  PR  (1  P ) R.
• Payoffs:
2
em
S M  P( R  I )   ( R  I )
.
2
2
SVC
em
 (1   ) P( R  I )  I   ( R  I )
 Pr( R  I ).
2
131
Optimal effort levels for given
equity stake:
•

em *  ,

 (1    r )
eVC * 
.

132
Optimal equity proposals.
• Found by substituting optimal efforts into
payoffs and maximising.
• Depends on relative bargaining power, VC’s
value-adding ability, and reputation effect.
• Eg; E may take all of the equity.
• VC may take half of the equity.
133
Payoffs
E
VC
0.5
Equity Stake
134
E’s choice of VC or angel-financing
• Explain Angels.
• Complementary efforts
• Ex post hold-up/stealing threat
135
To come
•
•
•
•
Legal effects: (Fairchild and Yiyuan)
=> Allen and Song
=> Botazzi et al
Negative reciprocity/retaliation.
136
Ex post hold-up threat
•
•
•
•
•
VC power increases with time.
Exit threat (moral hazard).
Weakens entrepreneur incentives.
Contractual commitment not to exit early.
=> put options.
137
Other Papers
• Casamatta: Joint effort: VC supplies
investment and value-adding effort.
• Repullo and Suarez: Joint efforts: staged
financing.
• Bascha: Joint efforts: use of convertibles:
increased managerial incentives.
138
Complementary efforts (Repullo and
Suarez).
• Lecture slides to follow…
139
Control Rights.
• Gebhardt.
• Lecture slides to follow
140
Asymmetric Information
• Houben.
• PCP paper.
• Tykvova (lock-in at IPO to signal quality).
141
E’s choice of financier
• VC or bank finance (Ueda, Bettignies and
Brander).
• VC or Angel (Chemmanur and Chen,
Fairchild).
142
Fairness Norms and Self-interest in VC/E
Contracting: A Behavioral Game-theoretic
Approach
• Existing VC/E Financial Contracting Models
assume narrow self-interest.
• Double-sided Agency problems (both E and VC
exert Value-adding Effort) (Casamatta JF 2003,
Repullo and Suarez 2004, Fairchild JFR 2004).
• Procedural Justice Theory: Fairness and Trust
important.
• No existing behavioral Game theoretic models of
VC/E contracting.
143
My Model:
• VC/E Financial Contracting, combining
double-sided Moral Hazard (VC and E
shirking incentives) and fairness norms.
• 2 stages: VC and E negotiate financial
contract.
• Then both exert value-adding efforts.
144
How to model fairness?
Fairness Norms.
• r Fair VCs and Es in society.
• 1  r self-interested VCs and Es in society.
• Matching process: one E emerges with a
business plan. Approaches one VC at
random for finance.
• Players cannot observe each other’s type.
145
Timeline
• Date 0: VC makes ultimatum offer of equity
stake to E;   [0,1],1  
• Date 1: VC and E exert value-adding effort
in running the business
• Date 2 Success Probability P   E eE   E eVC
• => income R.
• Failure probability 1  P
• =>income zero
146
• Expected Value of Project
V  PR  ( E eE   E eVC ) R
  [0,1]
• Represents VCs relative ability (to E).
147
Fairness Norms
• Fair VC makes fair (payoff equalising)
equity offer  F
• Self-interested VC makes self-interested
ultimatum offer U   F
• E observes equity offer. Fair E compares
equity offer to social norm. Self-interested
E does not, then exerts effort.
148
Expected Payoffs
•
E  U PR  eE  r(F  U ) PR
2
VC  r[(1  U ) PS R]  (1  r)[(1  U ) PF R]  eVC
If VC is fair, by definition,
2
U   F
149
Solve by backward induction:
•
•
•
•
•
If VC is fair;
Since U   F
 E   F PR  eE
2
for both E types.
=> PS  PF
=>   (1   ) PR  e 2
VC
F
VC
150
VC is fair; continued.
• Given U   F
Optimal Effort Levels:
 F E R
(1   F ) E R
eE * 
, eVC * 
.
2
2
Fair VC’s equity proposal (equity norm):
1  2 2  1   4   2
F 
3(1   2 )
151
VC is self-interested:
U   F  PS  PF
• From Equation (1), fair E’s optimal effort;
•
[U  r ( F  U )] E R
eE * 
.
2
152
Self-interested VC’s optimal
Equity proposal
• Substitute players’ optimal efforts into V=
PR, and then into (1) and (2). Then, optimal
equity proposal maximises VC’s indirect
payoff =>
1    r (1   F )
U * 
.
2
2
2(1  r )  
2
2
153
Examples;
• VC has no value-adding ability (dumb
money) =>
2
•   0 =>  F 
3
•
1
• r =0 => U  .
2
2
• r => 1 , U   F  3 .
154
Example 2
• VC has equal ability to E;
1
=>
 1
F 
2
• r =0 => U  0.
1
• r => 1 , U   F  .
2
• We show that   [0,1],
U   F
as r => 1
155
Table 1.
156
Graph
157
Table of venture performance
158
Graph of Venture Performance.
159
Future Research.
• Dynamic Fairness Game:ex post
opportunism (Utset 2002).
• Complementary Efforts.
• Trust Games.
• Experiments.
• Control Rights.
160
Private Equity
• JCF paper: slides to follow…
• PE and leverage: slides to follow….
161
Lecture 10: Introduction to Behavioural
Corporate Finance.
•Standard Finance - agents are rational and selfinterested.
•Behavioural finance: agents irrational
(Psychological Biases).
•Irrational Investors – Overvaluing assetsinternet bubble? Market Sentiment?
•Irrational Managers- effects on investment
appraisal?
•Effects on capital structure?
•Herding.
162
Development of Behavioral Finance I.
• Standard Research in Finance: Assumption:
Agents are rational self-interested utility
maximisers.
• 1955: Herbert Simon: Bounded Rationality:
Humans are not computer-like infinite
information processors. Heuristics.
• Economics experiments: Humans are not
totally self-interested.
163
Development of Behavioral Finance II.
•
•
•
•
•
Anomalies: Efficient Capital Markets.
Excessive volatility.
Excessive trading.
Over and under-reaction to news.
1980’s: Werner DeBondt: coined the term
Behavioral Finance.
• Prospect Theory: Kahnemann and Tversky
1980s.
164
Development III
• BF takes findings from psychology.
• Incorporates human biases into finance.
• Which psychological biases? Potentially
infinite.
• Bounded rationality/bounded
selfishness/bounded willpower.
• Bounded rationality/emotions/social factors.
165
Potential biases.
•
•
•
•
•
•
•
•
Overconfidence/optimism
Regret.
Prospect Theory/loss aversion.
Representativeness.
Anchoring.
Gambler’s fallacy.
Availability bias.
Salience….. Etc, etc.
166
Focus in Literature
• Overconfidence/optimism
• Prospect Theory/loss aversion.
• Regret.
167
Prospect Theory.
U
Risk-averse in
gains
W
Eg: Disposition Effect:
Risk-seeking in losses
Sell winners too quickly.
Hold losers too long.
168
Overconfidence.
• Too much trading in capital markets.
• OC leads to losses?
• But : Kyle => OC traders out survive and
outperform well-calibrated traders.
169
Behavioral Corporate Finance.
• Much behavioral research in Financial
Markets.
• Not so much in Behavioral CF.
• Relatively new: Behavioral CF and
Investment Appraisal/Capital
Budgeting/Dividend decisions.
170
Forms of Irrationality.
a) Bounded Rationality (eg Mattson and Weibull 2002, Stein
1996).
- Limited information: Information processing has a cost of
effort.
- Investors => internet bubble.
b) Behavioural effects of emotions:
-Prospect Theory (Kahneman and Tversky 1997).
-
Regret Theory.
-
Irrational Commitment to Bad Projects.
-
Overconfidence.
C) Catering – investors like types of firms (eg high dividend). 171
Bounded rationality (Mattson and Weibull 2002).
-Manager cannot guarantee good outcome with probability of 1.
-Fully rational => can solve a maximisation problem.
-Bounded rationality => implementation mistakes.
-Cost of reducing mistakes.
-Optimal for manager to make some mistakes!
-CEO, does not carefully prepare meetings, motivate and monitor
staff => sub-optimal actions by firm.
172
Regret theory and prospect theory (Harbaugh 2002).
-Risky decision involving skill and chance.
-manager’s reputation.
Prospect theory: People tend to favour low success probability
projects than high success probability projects.
-Low chance of success: failure is common but little reputational
damage.
-High chance of success: failure is rare, but more embarrassing.
Regret theory: Failure to take as gamble that wins is as
embarrassing as taking a gamble that fails.
=> Prospect + regret theory => attraction for low probability
gambles.
173
Irrational Commitment to bad project.
-Standard economic theory – sunk costs should be ignored.
-Therefore- failing project – abandon.
-But: mgrs tend to keep project going- in hope that it will improve.
-Especially if manager controlled initial investment decision.
-More likely to abandon if someone else took initial decision.
174
Real Options and behavioral aspects of ability to revise (Joyce
2002).
-Real Options: Flexible project more valuable than an inflexible
one.
-However, managers with an opportunity to revise were less
satisfied than those with standard fixed NPV.
175
Overconfidence and the Capital Structure (Heaton 2002).
-Optimistic manager overestimates good state probability.
-Combines Jensen’s free cashflow with Myers-Majluf Assymetric
information.
-Jensen- free cashflow costly – mgrs take –ve NPV projects.
-Myers-Majluf- Free cashflow good – enables mgs to take +ve
NPV projects.
-Heaton- Underinvestment-overinvestment trade-off without
agency costs or asymmetric info.
176
Heaton (continued).
-Mgr optimism – believes that market undervalues equity =
Myers-Majluf problem of not taking +ve NPV projects => free
cash flow good.
-But : mgr optimism => mgr overvalues the firms investment
opportunities => mistakenly taking –ve NPV project => free cash
flow bad.
-Prediction: shareholders prefer:
-Cashflow retention when firm has both high optimism and good
investments.
- cash flow payouts when firm has high optimism and bad
investments.
177
Rational capital budgeting in an irrational world. (Stein 1996).
-Manager rational, investors over-optimistic.
- share price solely determined by investors.
-How to set hurdle rates for capital budgeting decisions?
- adaptation of CAPM, depending on managerial aims.
- manager may want to maximise time 0 stock price (short-term).
-May want to maximise PV of firm’s future cash flows (long term
rational view).
178
Effect of Managerial overconfidence, asymmetric Info, and
moral hazard on Capital Structure Decisions.
Rational Corporate Finance.
-Capital Structure: moral hazard + asymmetric info.
-Debt reduces Moral Hazard Problems
-Debt signals quality.
Behavioral Corporate Finance.
-managerial biases: effects on investment and financing decisions
-Framing, regret theory, loss aversion, bounded rationality.
-OVERCONFIDENCE/OPTIMISM.
179
Overconfidence/optimism
• Optimism: upward bias in probability of
good state.
• Overconfidence: underestimation of asset
risk.
• My model =>
• Overconfidence: overestimation of ability.
180
Overconfidence: good or bad?
• Hackbarth (2002): debt decision: OC good.
• Goel and Thakor (2000): OC good: offsets
mgr risk aversion.
• Gervais et al (2002), Heaton: investment
appraisal, OC bad => negative NPV
projects.
• Zacharakis: VC OC bad: wrong firms.
181
Overconfidence and Debt
• My model: OC => higher mgr’s effort
(good).
• But OC bad, leads to excessive debt (see
Shefrin), higher financial distress.
• Trade-off.
182
Behavioral model of overconfidence.
pˆ  p, qˆ  q.
Both Managers issue debt:
2 pˆ I
M g  pˆ R 
 (1  pˆ )b.
pq
2qˆI
M b  qˆR 
 (1  qˆ )b.
pq
183
Good mgr issues Debt, bad mgr issues equity.
pˆ
M g  pˆ R  I  (1  pˆ )b.
p
qˆ
M b  qˆR  I .
q
Both mgrs issue equity.
2 pˆ
M g  pˆ R 
I,
pq
2qˆ
M b  qˆR 
I.
pq
184
Proposition 1.
a) If
qˆ ( p  q)
I  (1  qˆ )b  (1  pˆ )b,
q( p  q )
{S g  Sb  D}.
b)
qˆ ( p  q)
(1  qˆ )b 
I  (1  pˆ )b,
q( p  q )
{S g  D, Sb  E}.
c)
qˆ ( p  q)
(1  qˆ )b  (1  pˆ )b 
I,
q( p  q )
{S g  Sb  E}.
Overconfidence leads to more debt issuance.
185
Overconfidence and Moral
Hazard
•
•
•
•
•
•
Firm’s project: 2 possible outcomes.
Good: income R. Bad: Income 0.
Good state Prob: P  (    )e  (0,1].
True:
  0.
Overconfidence:
  0.
True success prob: P  e.
186
Manager’s Perceived Payoffs
2
ˆ
ˆ
ˆ
M D  P( R  D )  (1  P )b  e  PD  I .
2
ˆ
ˆ
M E  PR  e  (1   ) PR  I .
187
Optimal effort levels
(   )( R  D  b)
eD * 
2
(   )( R  D )
eE * 
2
188
Effect of Overconfidence and
security on mgr’s effort
• Mgr’s effort is increasing in OC.
• Debt forces higher effort due to FD.
189
Manager’s perceived Indirect
Payoffs
2
2
(



)
(
R

D

b
)
 (   )( R  D  b) D
ˆ
MD 

 I b
4
2
2
2
(



)
(
R

D
)
 (   )( R  D) D
ˆ
ME 

I
4
2
2
2
(



)
(
2
b
(
R

D
)

b
)  (   )bD
ˆ
M D 

 b.
4
2
190
True Firm Value
 (   )( R  D  b)( R  b)
VD  PD ( R  b)  b 
 b.
2
 (   )( R  D ) R
VE  PE R 
.
2
191
Effect of OC on Security Choice
2
2
2

(
2
b
(
R

I
)

b
)

bD
ˆ
M D (  0) 

b 0
4
2
Mˆ D
0


 Mˆ D (   C )  0.
  [0,  C ],
  C,
Manager issues Equity.
Manager issues Debt.
192
Effect of OC on firm Values
2 ( R  D) R
VE (  0) 
.
2
 (   )( R  D  b)( R  b)
VD (   C ) 
 b.
2
(2   )( 2bR  Db  b2 )  R( R  D)
VD 
b
2
193
Results
•
•
•
•
•
•
•
For given security: firm value increasing in OC.
If VD (   C )  0,
Firm value increasing for all OC: OC good.
Optimal OC:  *   max .
If VD (   C )  0,
Medium OC is bad. High OC is good.
Or low good, high bad.
194
Results (continued).
• If
VD (   C )  0,
• 2 cases: Optimal OC:  *   max .
•
• Or Optimal OC:  *   C   .
195
Effect of Overconfidence on Firm Value
1200
1000
800
Value
600
400
200
0
0
-200
0.1
0.2
0.3
0.4
Effect of Overconfidence on Firm Value
0.5
-400
2000
-600
1500
Overconfidence
1000
Value
500
Effect of Overconfidence on Firm Value
-500
2500
Value
0
2000
-1000
1500
-1500
1000
-2000
500
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Overconfidence
0
-500
1
2
3
4
5
6
7
8
9
10
-1000
-1500
-2000
Overconfidence
196
0.9
Conclusion.
•
•
•
•
Overconfidence leads to higher effort level.
Critical OC leads to debt: FD costs.
Debt leads to higher effort level.
Optimal OC depends on trade-off between
higher effort and expected FD costs.
197
Future Research
•
•
•
•
•
•
•
Optimal level of OC.
Include Investment appraisal decision
Other biases: eg Refusal to abandon.
Regret.
Emotions
Hyperbolic discounting
Is OC exogenous? Learning.
198
Herding
199
Hyperbolic Discounting
200
Emotional Finance
• Fairchild’s Concorde case study.
201