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Modigliani-Miller Theorem

Overview:

� The Modigliani-Miller Theorems.� MM Proposition I.

� MM Proposition II.

� MM Dividend Policy Irrelevance.

� Using MM sensibly.

D. Gromb Modigliani-Miller Theorem 1

MODIGLIANI-MILLER

Modigliani-Miller Theorem(s):Under some assumptions, corporate Þnancial policy is irrelevant.

Corollaries:

� Financing decisions are irrelevant.� Capital structure is irrelevant.� Dividend policy is irrelevant.� Cash management is irrelevant.� Risk management policy is irrelevant.� Cross shareholdings are irrelevant.� DiversiÞcation is irrelevant.� Etc.

MM is a paradigm shift, and the foundation of modern corporate Þnance.


MM-Proposition I (MM 1958):A Þrm�s total market value is independent of its capital structure.

MM-Proposition II (MM 1958):A Þrm�s cost of equity increases with its debt-equity ratio.

Dividend Irrelevance (MM 1961):A Þrm�s total market value is independent of its dividend policy.

Investor Indifference (Stiglitz 1969):Individual investors are indifferent to the Þrms� Þnancial policy.


Assumptions

� Frictionless markets: No transaction costs, etc.� Competitive markets: Individuals and Þrms are price-takers.� Individuals and Þrms can undertake the same Þnancial transactions at thesame prices (e.g., borrow at the same rate).

� All agents have the same information.� No taxes.� A Þrm�s cashßows do not depend on its Þnancial policy (e.g., no cost ofbankruptcy).

Remark:

� MM applies to all securities, not just debt and equity.

� It is simply a value additivity result.� If A and B are cashßow streams, absence of arbitrage opportunity implies:

V (A+B) = V (A) + V (B) .

� MM applies to all purely Þnancial transactions: Their NPV is zero.


ARBITRAGE APPROACH

Idea of the proof (�by contradiction�):

� Consider two Þrms differing only in their capital structures.� If they have different total market values, an arbitrage opportunity exists.� That is, one can make a risk free proÞt by trading securities.� Incompatible with equilibrium.

Remarks:

� MM�s proof requires two identical Þrms.� Single-Þrm proof (Miller 1988).� General Equilibrium approach (Stiglitz 1969).� MM did not have a theory of risk.

� MM introduced the idea of risk-classes. For our purpose, Þrms in the samerisk class will have identical cash ßows.

� Note: Firm-level irrelevance does not imply indeterminacy in aggregate, e.g.,Þrm-level irrelevance may hold only in equilibrium (as in Miller (1977)).


Model

Consider Þrm 1 and Þrm 2 in the same risk-class:

� At t = 1, 2, ..., both Þrms yield the same (random) return X.� At t = 0, they have different capital structures:

� Firm 1 has equity and a constant level of risk-free debt.

� Firm 2 has no debt.

Notation:

� At t = 0,� Risk-free rate: r.

� Market value of Þrm i�s debt: Di.

� Market value of Þrm i�s equity: Ei.

� Total market value of Þrm i: Vi = Di +Ei.

� Hence, at t:� Firm 1�s debtholders receive: rD1.

� Firm 1�s equityholders receive: X − rD1.� Firm 2�s equityholders receive: X.


Step 1: It cannot be that V2 > V1.

� Suppose V2 > V1.� Consider an investor holding a fraction α of Þrm 2�s shares.� At t, he would receive αX.� Instead, he could:

� Sell the shares for αV2.

� Buy a fractionαV2V1

of Þrm 1�s debt and equity as:

αV2 =

µαV2V1

¶·D1 +

µαV2V1

¶·E1.

� At t, the investor would receive:µαV2V1

¶rD1 +

µαV2V1

¶· (X − rD1) =

µαV2V1

¶X

> αX for all X.

� ⇒ An arbitrage opportunity exists.

� Intuition: Arbitrageurs can �undo Þrm 1�s leverage� by buying its debt andequity in proportions such that interest paid and received cancel out.


Step 2: It cannot be that V1 > V2.

� Suppose V1 > V2.� Consider an investor holding a fraction α of Þrm 1�s shares.� At t, he would receive α(X − rD1).� Instead, he could:

� Sell the shares for αE1.

� Borrow αD1.

� Invest the total in a fraction αV1V2of Þrm 2�s shares:

αE1 + αD1 =

µαV1V2

¶· V2.

� At t, the investor would get αV1V2X and pay interests rαD1:µ

αV1V2

¶X − rαD1 = α

µV1V2X − rD1

¶> α (X − rD1) for all X.

� ⇒ An arbitrage opportunity exists.

� Intuition: Arbitrageurs can �lever up� Þrm 2 by borrowing on individualaccounts (homemade leverage).


COST OF CAPITAL

� MM was controversial as debt seemed cheaper than equity:

� Interest rates on corporate debt ' 5%.� Equity earnings/price ratios (then the conventional measure of the costof equity capital) ' 20%.

� Under such conditions, how could Þnancing be irrelevant?� MM�s Proposition II shows that there is no contradiction.

Proposition II: A Þrm�s cost of equity increases with its debt-equityratio.

� Intuition: Raising debt makes existing equity more risky, hence more costly.


Proof:The Þrm�s Weighted Average Cost of Capital (WACC) is:

WACC =D

D +Er +

E

D +ErE =

Exp [X]

V,

where:

� r is the cost of risk-free debt capital, i.e., its return.� rE is the cost of equity capital, i.e., its expected return.

This can be rewritten as

rE = (WACC− r)DE+WACC.

By Proposition I, the WACC is independent of the D/E ratio.⇒ rE is linear in D/E.

Note:

� If WACC > r (i.e., rE > r) then rE increases with D/E.� In practice, rE > r essentially for all Þrms.� Therefore, the difference between the cost of debt and equity is compatiblewith the irrelevance proposition.

� MMdid not have a theory of risk but their results are consistent with CAPM.


MM vs. Clientele Theories

Clientele Theory:

� Investors with heterogenous preferences and needs value the same cash ßowstreams differently.

� ⇒ Financial policy choices affect the match between securities and het-erogenous preferences.

� ⇒ Financial policy can affect Þrm value (i.e., Þnancial marketing).

Example: An all-equity Þrm might fail to exploit the potential demands for riskyand safe securities. It may be worth more by separating riskier from safer cashßow streams (e.g., into debt and equity) so that investors can focus on theirpreferred security.

Intuition for MM:

� Modigliani and Miller (1958) show that this reasoning is ßawed.� Investors� preferences are over cashßows, not securities.� They are not limited to the securities issued by Þrms.� If investors can undertake the same transactions as Þrms, at the same prices,they will not pay a premium for the Þrms to undertake such transactions ontheir behalf.

� Put differently, if investors can freely reverse the Þrms� Þnancial decisions,these are immaterial.

Note: MM do not assume away heterogeneity. But, the match between pref-erences and cash ßow streams need not be organized by the Þrms. There is novalue to Þnancial marketing.


MM DIVIDEND POLICY IRRELEVANCE

� Pre-MM: �Bird-in-the-Hand� Theory.� Gordon model of stock valuation:

V0 =∞Xt=1

E0 [dt]

(1 + rt)t,

where

E0 [dt] is the expected dividend at date t,

rt increases with t due to greater risk.

Main idea:

� Dividends are safer than future payments.⇒ Dividends increase Þrm value.

� MM show that this theory is ßawed.

� The arbitrage proof relied on identical cashßows.� But shareholders receive dividends, not cashßows.� Do dividends have to be identical too?� If yes, MM would be much less interesting.


Proposition: A Þrm�s value is independent of its dividend policy.

� Each �period�, the Þrm:� Invests and retains cash (Investment Policy).

� Raises new capital (Financing Policy).

� Pays some dividends (Payout Policy).

� Accounting identity: Taking Investment Policy as given, a change in PayoutPolicy has to be met by a change in Financing Policy.

� For instance:� A dividend increase can be Þnanced with a new debt issue.

� A dividend decrease can be met by a retirement of debt.

� Existing shareholders and new investors form a �closed system�.⇒ The total value of their claims is unaffected (value conservation).

� New investors are competitive.⇒ The value of their claims is unchanged.

⇒ The value of the current shareholders� claims is unchanged.


USING MM SENSIBLY

� Financial decisions do matter at the Þrm level.� So what do we make of the irrelevance result?

� Avoid some fallacies: WACC fallacy, EPS fallacy,...

� Organize your thoughts.

Main message:

� Value is created only by operating assets (i.e., on LHS of BS).� If Financial Policy is unrelated to Investment Policy, then it is irrelevant: Itmerely divides a �pie� of Þxed size.

� Serves as a benchmark: If we know what does not matter, we may be ableto infer what really matters.

How can Þnancial decisions affect the size of the pie?

� Investors cannot undertake the same Þnancial transactions as Þrms due totaxes, transaction costs and short-sale restrictions, bankruptcy costs, infor-mation asymmetries, etc.

Yet, we are far from being done with MM.


Readings (starred articles are recommended):

(*) Grinblatt, Mark, and Sheridan Titman (1998), Financial Markets and Cor-porate Strategy, Irwin/McGraw-Hill, chapter 13.

Stiglitz, Joseph E. (1969), �A Re-Examination of the Modigliani-Miller Theo-rem,� American Economic Review, 59, 784-793.

Surveys, etc.

Miller, Merton (1988), �The Modigliani-Miller Propositions After Thirty Years,�Journal of Economic Perspective, 2, 99-120. (see the whole issue).

Related literature:

Miller, Merton (1977), �Debt and Taxes,� Journal of Finance, 32, 261-276.

Miller, Merton, and Franco Modigliani (1961), �Dividend Policy, Growth andthe Valuation of Shares,� Journal of Business, 34, 411-433.

Modigliani, Franco, andMertonMiller (1958), �The Cost of Capital, CorporationFinance, and the Theory of Investment,� American Economic Review, 48,261-297.

Stiglitz, Joseph E. (1974), �On the Irrelevance of Corporate Financial Policy,�American Economic Review, 64, 851-866.

Titman, Sheridan (2002), �The Modigliani and Miller Theorem and the Inte-gration of Financial Markets,� Financial Management, 31.


PROBLEMS

Problem 1 (MM, The Single-Firm Proof)

Consider a single Þrm at t = 0 that has (possibly risky) debt with face value Kmaturing at t = 1. At t = 1, the value of the Þrm�s assets takes a random valueX and the Þrm is liquidated.a) Write the value of the Þrm�s debt and equity as a function of those of arisk-free bond and of a call and a put on the Þrm�s asset.b) Derive MM Proposition I without resorting to a comparable Þrm.c) Compare this proof to the �risk-class� proof. What are, in your view, its mainmerits and weaknesses?

Problem 2 (MM, The General Equilibrium Approach)

The aim of this problem is to show that a version of MM is valid in a staticGeneral Equilibrium model, and that all agents are indifferent to the Þrms� capitalstructures (in a sense to be soon clariÞed).Consider an economy with a set I of Þrms and a set J of individual investors.At t = 0, Þrm i ∈ I has risk-free debt with value Di, equity with value Ei andtotal value Vi = Di + Ei. At t = 1, it generates a random gross return Xi. Att = 0, individual j ∈ J �s wealth wj is invested in Dj risk-free corporate debt anda fraction αji of Þrm i�s equity. The gross risk-free rate of return is denoted r.Show that for any given equilibrium, there exists another one with any Þrm havingany other debt-equity ratio but with the value of all Þrms and the yield on risk-free debt being unchanged. That is, for any equilibrium with Di, Vi and r and forany �Di, there exists an equilibrium with �Di, Vi and r. You may want to proceedas follows.a) Write individual j�s wealth at t = 1, yj, as a function of wj, αji , Vi and Xi.b) Consider an equilibrium with Di, Vi and r. Write the market clearing condi-tions for Þrm i�s equity and for risk-free debt.c) Consider a change from Di to �Di and assume that, indeed, Vi and r areunchanged. Show that the αji are unchanged.d) Show that the equity markets and the debt market clear.e) Conclude.f) Does this result imply the irrelevance of the capital structure at the aggregatelevel, i.e., of the economy-wide debt-equity ratio?


g) Compare this General Equilibrium version of the MM Theorem with the (stan-dard) arbitrage approach. What are the differences and similarities? What are,in your view, the relative strengths and weaknesses of the two approaches?h) Consider the same model as before but now suppose that, at t = 0, the Þrmscan also issue options to buy new equity at t = 1. Show that for any givenequilibrium, there exists another one with any Þrm issuing any debt/equity andoptions/equity ratios but with the value of all Þrms and the yield on risk-freedebt being unchanged.

Problem 3 (MM Proposition II and CAPM)

Assume that the conditions for MM Proposition I are satisÞed and that CAPMholds. MM�s original Proposition II states that as a Þrm�s cost of equity capitalincreases linearly with its debt-equity ratio (with risk-free debt). What is theimplicit assumption about the Þrm for this to hold? Explain.


Models Based onIncentive Problems

Overview:

� Moral hazard.� Moral hazard and credit rationing.� Jensen and Meckling (1976).

� Effort problem.

� Risk-shifting problem.

D. Gromb Incentive Problems 1

INCENTIVE ISSUES

� One MM assumption: Operating and Þnancing decisions are independent.

� Relax that assumption, i.e., the pie�s size is affected by how it is split.� The incentives of the party taking operating decisions depend on her claims.� This party may not have incentives to maximize Þrm value.

Main idea:

(1) Conßicts of interests between the party making operating decisions(≡ insider) and outside investors.

(2) Outside Þnancing involves costs due to Moral Hazard:

� Deviations from value maximization.

� Credit rationing: Some valuable projects cannot be Þnanced.� Costs incurred to prevent the above such as:� Monitoring.� Bonding.

Note: No standard model in Corporate Finance. Here, a few simple models toconvey the main ideas. Hence, reading the papers is important.


Model

� An entrepreneur has a project.� At t = 0: Financing.

� Need I > 0.

� Entrepreneur�s resources available: W .

� At t = 1: Moral hazard.� The entrepreneur is key to the project.

� He can choose an �effort� level e ∈ {0, 1}.� Cost c (0) = 0 and c(1) = c.

� At t = 2: Cash ßow.� X ∈ ©XL, XH

ªwith ∆X ≡ XH −XL > 0,

� and Pr£X = XH

¤ ≡ θ + e ·∆θ.Assumption (*): Exerting the effort is efficient, i.e.,

∆θ∆X > c.

Assumption: The project�s value is positive if e = 1, i.e.,

V1 ≡ XL + (θ +∆θ)∆X − I − c > 0.

Remarks:

� Throughout the course, universal risk-neutrality, no discounting, and com-petitive capital markets are assumed unless otherwise speciÞed.

� We discuss entrepreneurial vs. managerial Þrms later.� �Effort� is a metaphor! We will be more speciÞc later.


Financial Contracts

� Financial claims are promises of payments at t = 2, contingent on X:RL if X = XL and RH ≡ RL +∆R if X = XH .

� Limited liability:RL ≤ XL and RH ≤ XH

Examples:

� Debt with face value K:RL = min{XL,K} and RH = min{XH ,K}.

� Fraction β of equity:RL = βXL and RH = βXH .

� Call option on the Þrm�s equity with strike K:RL = max{XL −K, 0} and RH = max{XH −K, 0}.

� Etc.

Remark:

� If XL = 0, all contracts are linear (in cashßows):

RL = 0 and RH ≥ 0.

� There is no difference between debt, equity, etc.� Useful modelling trick when one wants to concentrate on internal vs. exter-nal Þnance as opposed to the type of external Þnance.


First Best

If W ≥ I, the entrepreneur should:� Invest I and exert e = 1.

What if W < I?

� The entrepreneur needs to raise at least (I −W ).� He can sell a claim (RL, RH).� Competitive investors are willing to pay

RL + (θ +∆θ)∆R.

Assumption (for now): XL = 0.

� The entrepreneur raises at least (I −W ) with a claim such that:

RH ≥ RHmin ≡I −Wθ +∆θ

.

� Note: Possible since RHmin ≤ XH .

� For instance, he can sell a claim to the entire cash ßow, i.e., RH = XH .

� Irrespective of W , the entrepreneur can always Þnance the project.� MMapplies: Firm value is independent of whether and how much the projectif funded internally vs. externally.


Moral Hazard

� This result holds under the assumption that there is no incentive problem,i.e., effort is contractible or is not costly (i.e., c = 0).

Assumption: Effort is costly (i.e., c > 0) and non-contractible.

� This (conßict + non-contractibility) induces an incentive problem.� For instance:

� Suppose that the entrepreneur sells the entire cashßow at t = 0.

� He has no incentives to incur a cost c(e) > 0 at t = 1.

� Investors are willing to pay less for the Þrm�s claims.

� At t = 1, the entrepreneur chooses e = 1 iff:∆θ(X

H −RH) ≥ c or RH ≤ RHmax ≡ XH − c/∆θ.

� But Þnancing the project (given e = 1) requires:RH ≥ RHmin ≡ (I −W )/(θ +∆θ).

� Hence, the Þrst best is obtained iff:RHmin ≤ RHmax.

� Role of internal funds:� The condition is more likely to be satisÞed when W is large.

� Firms with more internal funds are less constrained in their investmentstrategy.

Note: If the entrepreneur were not key to the project, he could sell it to aninvestor who would run the project (i.e., choose e). More on this later.


What if RHmin > RHmax?

� The project�s value for e = 0 is:V0 ≡ XL + θ∆x − I.

� Credit rationing:� Suppose V0 < 0.

� The entrepreneur cannot raise (I −W ), irrespective of RH .� Deviation from value maximization:

� Suppose V0 > 0.

� The entrepreneur can raise (I−W ) but fails to use these funds optimally.

Commitment problem:

� The entrepreneur�s payoff isFirm value − Net payment to competitive investor(s)| {z }

= 0

.

⇒ He is best-off maximizing Þrm value, which requires e = 1.

� However, once some claims are sold to investors, his incentives are deter-mined only by the claims that he retains.

� Ultimately, the entrepreneur bears the costs of moral hazard.Costly commitment:

� To commit to e = 1, the entrepreneur is willing to pay up to:V1 −max{V0, 0}.

� Monitoring by a blockholder, a bank, an auditor, etc...� Bonding: Contractual commitment not to engage in certain actions (evenif potentially valuable): Loan ear-marking, etc.


CAPITAL STRUCTUREJensen and Meckling (1976)

Main idea:

(1) Conßicts of interests:

� Between inside and outside equityholders.� Between equityholders and debtholders.

(2) SpeciÞc costs:

� Outside equity ⇒ Low �effort.�

� Debt ⇒ Risk-shifting (a.k.a. asset substitution).

(3) Optimal capital structure minimizes these agency costs.


Model

Same as before except:

� XL > 0, to be able to discuss Þnancing choices.

� I > XL, for simplicity.

� W = 0, for simplicity.

First-Best: Modigliani-Miller

� Financing choices are irrelevant in the absence of Moral Hazard (i.e., if c = 0or e is contractible).

� Say the entrepreneur chooses to raise exactly:I = RL + (θ +∆θ)∆R.

� Debt with face value K:

RL = XL ∆R =I −XL

θ +∆θ.

K = RH = RL +∆R = XL +

I −XL

θ +∆θ.

� Equity: Sell a fraction β of existing shares, i.e.,RL = βXL RH = βXH ,

with β =I

XL + (θ +∆θ)∆X.

� Intuition: Competitive investors. ⇒ Irrespective of Þnancing, the entrepre-neur receives the investment�s entire value.


Optimality of Debt

� At t = 1, the entrepreneur chooses e = 1 iff:∆θ (∆X −∆R) ≥ c or ∆R ≤ ∆maxR ≡ ∆X − c/∆θ.

� Debt is the contract for which this constraint is least severe, i.e., bindingfor a smallest set of parameters. Indeed, it solves:

min(RL,RH)

∆R

RL ≤ XL RH ≤ XH

I ≤ RL + (θ +∆θ)∆R� Debt is an optimal response to the effort problem: Projects that can befunded (e.g., with equity) can also be debt Þnanced but the reverse is nottrue.

� Intuition: The optimal (debt) contract maximizes the fraction of the re-turn from effort that accrues to the entrepreneur. Hence, it maximizes hisincentive to exert effort.

Remarks:

� Jensen and Meckling (1976) show that debt dominates equity. We haveshown that it also dominates all other contracts.

� In a more general model where effort affects the distribution of cashßowsθ(X | e) over a continuum, Innes (1990) shows that debt is optimal undertwo conditions:

� Monotone Hazard Rate Property: ∂∂X

µ∂θ(X|e)∂e

θ(X|e)

¶> 0.

� Monotonic repayment schedule: R(X) non-decreasing.


Cost of Debt: �Risk-Shifting�

� Same model except for Moral hazard at t = 1.� Two mutually exclusive projects generating X ∈ {0, �X, 2 �X} at t = 2.

Pr[X = 2 �X] Pr [X = 0]

Project A: θ1 θ2Project B: θ1 +∆1 θ2 +∆2

with 0 < ∆1 < ∆2 and θ1 +∆1 + θ2 +∆2 < 1.

� Assume that Project A�s value is positive, i.e.,(1 + θ1 − θ2) �X − I > 0.

� Note: This problem does not satisfy the MHR property.

First Best

� Project B�s value is(1 + θ1 +∆1 − θ2 −∆2) �X − I.

� This is less than Value(Project A), the difference being:(∆2 −∆1) �X > 0.

� ⇒ I should be used for Project A.


Debt Finance?

� Suppose that I is raised in debt with face value K.� Assume (for simplicity) that K > �X, i.e.,

(1− θ2) �X < I.

� The entrepreneur gets a positive payoff only when X = 2 �X.

� With Project A, he gets:θ1(2 �X −K).

� With Project B, he gets:(θ1 +∆1)(2 �X −K).

⇒ Once I has been raised, the entrepreneur picks Project B.

Equity Finance?

� Suppose I has been raised in equity.� Once I raised and invested, the entrepreneur gets a Þxed share of cash ßows.� ⇒ He maximizes expected cash ßows.

� ⇒ He undertakes Project A.

� Equity is optimal since it induces no distortion in investment.


Intuition

� The difference between Project A and Project B has two parts.� An increase in θ1 and θ2 by ∆1 which preserves the mean:

∆12 �X − 2∆1 �X +∆1 · 0 = 0but increases the variance.

� An increase in θ2 by (∆2 −∆1) which decreases the mean:−(∆2 −∆1) �X + (∆2 −∆1) · 0 < 0.

Debt:

� Risky debt�s payoff is concave in cash ßows.⇒ Levered equity�s payoff is convex in cash ßows.

⇒ Equity holders have an incentive to take excessive risk.

� Value of call option increases with volatility. ⇒ Risk-Shifting problem.

Equity:

� The entrepreneur and the investors have the same claims. ⇒ No conßict.

� Linear claims. ⇒ No risk-shifting.

� Note: Equity dominates debt but also all other contracts. This holds inmore general models (see Green 1984).


Risk-Shifting Model�s Implications

� More debt when there is less risk-shifting potential: e.g.,� Regulated public utilities with less managerial discretion (Bradley, Jarrelland Kim 1984).

� Firms in mature industries with few growth opportunities (Barclay, Smithand Watts 1992).

� Risk shifting incentives are higher in Þnancial distress because limited liabilitykicks in (Gambling for Resurrection).

� For instance, managers may delay Þling for bankruptcy to keep equity�soption value alive.

� Or they may Þle for Chapter 11 rather than Chapter 7.Mitigating asset substitution:

� Covenants to debt contract, e.g., interest coverage requirements or prohibi-tion of investments into new, unrelated lines of business (Smith and Warner1979).

� Convertible Debt alleviates existing shareholders� risk-taking incentives byallowing debtholders to share in the upside, making shareholders� payoffpartly concave (Green 1984).



(*) Jensen, Michael C., and William Meckling (1976), �Theory of the Firm:Managerial Behavior, Agency Costs and Ownership Structure,� Journal ofFinancial Economics, 3, 305-360.

(*) Myers, Stewart C., (1977), �The Determinants of Corporate Borrowing,�Journal of Financial Economics, 5, 147-175.

Surveys, etc.

Allen, Franklin (1990), �The Changing Nature of Debt and Equity: A FinancialPerspective,� in Kopcke and Rosengren (eds.), Are the Distinctions betweenDebt and Equity Disappearing? (Federal Reserve Bank of Boston), Confer-ence Series, 33, 12-38.

Berle, Adolf A. Jr., and Gardiner C. Means (1932), The Modern Corporationand Private Property, New York: Commerce Clearing House, Inc.

Bester, Helmut, and Martin Hellwig (1987), �Moral Hazard and Credit Ra-tioning: An Overview of the Issues,� in Bamberg and Spremann (eds.),Agency Theory, Information and Incentives, Heidelberg: Springer Verlag.

Bradley, Michael, Gregg A. Jarrell and E. Han Kim (1984), �On the Existence ofan Optimal Capital Structure: Theory and Evidence,� Journal of Finance,39, 857-878.

Harris, Milton, and Artur Raviv (1991), �The Theory of Capital Structure,�Journal of Finance, 46, 297-355.

Related Literature:

Admati, Anat, Paul Pßeiderer, and Josef Zechner (1995), �Large Sharehold-ers Activism, Risk Sharing, and Financial Market Equilibrium,� Journal ofPolitical Economy, 102, 1097-1130.

Barclay, Michael J., Clifford W. Smith, and Ross L. Watts (1992), �The Invest-ment Opportunity Set and Corporate Financing, Dividend and CompensationPolicy,� Journal of Financial Economics, 32, 263-292.

Burkart, Mike, Denis Gromb, and Fausto Panunzi (1997), �Large Shareholders,Monitoring and the Value of the Firm,� Quarterly Journal of Economics, 33,693-728.


Burrough, Bryan, and John Helyar (1990), Barbarians At The Gate, Harper-Collins.

Demsetz, Harold, and Kenneth Lehn (1985), �The Structure of Corporate Own-ership: Causes and Consequences,� Journal of Political Economy, 43, 1155-1177.

Dewatripont, Mathias, and Jean Tirole (1994), �A Theory of Debt and Eq-uity: Diversity of Securities and Manager-Shareholder Congruence,� Quar-terly Journal of Economics, 109, 1027-1054.

Dybvig, Philip H., and Jaime F. Zender (1991), �Capital Structure and DividendIrrelevance with Asymmetric Information,� Review of Financial Studies, 4,201-219.

Green, Richard C. (1984), �Investment Incentives, Debt and Warrants,� Journalof Financial Economics, 13, 115-136.

Hellwig, Martin (1994), �A Reconsideration of the Jensen Meckling Model ofOutside Finance,� working paper Basel University.

Innes, Robert D. (1990),�Limited Liability and Incentive Contracting with Ex-Ante Action Choices,� Journal of Economic Theory, 52, 45-67.

Morck, Randall, Andrei Shleifer, and Robert W. Vishny (1988), �ManagementOwnership and Market Valuation: An Empirical Analysis,� Journal of Finan-cial Economics, 20, 293-315.

Shleifer, Andrei, and Robert W. Vishny (1989), �Management Entrenchment:The Case of Manager-SpeciÞc Projects,� Journal of Financial Economics,25, 123-139.

Persons, John (l994), �Renegotiation and the Impossibility of Optimal Invest-ment,� Review of Financial Studies, 7, 419-449.

Ritter, Jay R. (1984),�Signalling and the Valuation of Unseasoned New Issues:A Comment,� Journal of Finance, 4, 1231-1237.

Smith, Clifford W., and Jerold Warner (1979), �On Financial Contracting,�Journal of Financial Economics, 7, 117-161.

Titman, Sheridan, and Roberto Wessels (1984), �The Determinants of CapitalStructure Choice,� Journal of Finance, 43, 1-19.


PROBLEMS

Problem 1 (Monitored Finance)

An entrepreneur with wealth W has a project requiring an investment I > W att = 0 and generating a random cashßow X ∈ {0, XH} at t = 1. To undertakethe project, he has to raise funds from competitive investors. The entrepreneurcan exert an effort e ∈ {0, 1} such that Pr[X = XH ] = θ + e∆θ and theentrepreneur derives private beneÞts (1− e)B. In particular, B cannot be sharedwith investors. Moreover, e = 1 is efficient and the project�s value is positive ife = 1 but negative if e = 0.a) Show that when effort is not contractible and with limited liability for theentrepreneur, external Þnancing is feasible only if W exceeds some threshold.How does the threshold vary with the different parameters? Explain.b) Suppose now that at a (non-contractible) cost c, a Þnancial intermediarycan monitor the entrepreneur, thereby reducing potential private beneÞts fromB to b. Assume that b + c ≤ ∆θX

H . Show that some Þrms that cannot beÞnanced directly can get an intermediated loan if b + c < B. That is, investorsare willing to fund the intermediary which in turn enters a Þnancial contract withthe entrepreneur. (Note: Monitoring being assumed to be non-contractible, theintermediary�s incentive to monitor need to be considered). Explain. Which Þrmswill use intermediated Þnance?

Problem 2 (Effort Model)

An entrepreneur has a project requiring an investment I = 50 at t = 0 andgenerating a cash ßowX ∈ {30, 120} at t = 1. If and after the project is funded,the entrepreneur chooses to incur a cost c(e) = 45e2 to exert a level e ∈ [0, 1]such that Pr[X = 120] = e. The Þrm has no assets in place. Everybody is riskneutral and there is no discounting.a) If the entrepreneur can self-Þnance, what level of effort will he choose? Is itworthwhile investing in the project?b) Assume now that the entrepreneur is cash constrained and that the project isÞnanced with debt. Is it possible to Þnance the project with risk-free debt? Fora given face value K, what is the equilibrium level of effort? What level of debtshould the entrepreneur choose? How does the Þrm�s value compare with theone in a)? Interpret your answer.


c) If the entrepreneur Þnances the project by selling a fraction α of equity, whatis the effort level? Show that the project cannot be equity-Þnanced. Interpretyour answer.d) Is it possible to Þnd a contract {RL, RH} for which the Þrm�s value is higherthan the one obtained in b)? Interpret your answer.


Corporate Finance and Investment:Empirics

Overview:

� Fazzari, Hubbard and Petersen (1988).� Hoshi, Kashyap and Scharfstein (1991).� Lamont (1997).� Kaplan and Zingales (1997).

Note:

� Here, we focus on allocation of Þnance to Þrms, i.e., functioning of externalcapital markets.

� As important for corporate investment if not more, is the capital allocationwithin Þrms, i.e., functioning of internal capital markets.

D. Gromb Corporate Finance and Investment: Empirics 1

Fazzari, Hubbard and Petersen (1988)

Main idea:

� Use dividend policy as a proxy for Þnancial constraint.� Investment-cashßow sensitivity is greater for low- than for high-dividend paying Þrms (controlling for investment opportunities).

� 422 large manufacturing publicly traded Þrms over 1970-84.Note: Less likely to be Þnancially constrained. So if Þnd something here, likelyto be present with other Þrms.

� Firms classiÞed according to their earnings retention practices.� If the cost disadvantage of external Þnance is small, the retention practiceshould reveal little about investment.

� Firms that retain and invest most of their income may have no low costsources of external Þnance. Their investment should be driven by ßuctua-tions in cash ßows.

� Exclude Þrms that did not grow (real sales) over the period.� Divide Þrms into three classes:

� Class 1: Payout ratio below 10% for 10 years or more.

� Class 2: Payout ratio in 10− 20% range for 10 years or more.

� Class 3: All other Þrms.


Class 1 vs. Class 3 Þrms (from summary statistics):

� Smaller (although still large Þrms).� Grow faster (sales and capital stock).� Invest more.� Invest a larger fraction of cash ßows (almost all).� Have more volatile investment and cash ßows.� Have greater aggregate investment-cashßow correlation.


Class 1 vs. Class 3 Þrms (from summary statistics):

� Use equity issues more frequently and for a larger fraction of total Þnancing(although still small relative to cash ßow).

� Have higher debt ratios and lower interest coverage.Consistent with pecking order story where Class 1 vs. Class 3 Þrms:

� Can rely less on internal funding of investment.� Raise more external Þnance ⇒ Lower leverage ratios.

� Cannot afford high dividends,Note: Cashßow and ∆Debt are positively correlated (even if use Tobin�s tocontrol for investment opportunities). So no offsetting.


In each time period, for each class run:

INV=α+βCF+γQ+Firm and year Þxed effects

Coefficient on Cash ßow:

� Large for all classes (Not surprising, e.g., mismeasured Q).� For Class 1 Þrms, greater for shorter/earlier period.� Larger for Class 1 Þrms + Substantially higher R̄2.

� Difference greater for shorter period: Eventually, Class 1 Þrms pay dividends.Possibly: Decreasing asymmetric information.

Note: Similar conclusions for alternative estimation methods and speciÞcationsand models of investment other than Q-theory.


Comments

Dividend policy is the classiÞcation criterion:

� But why do Þrms pay dividends?� How discretionary are they?� Moreover, one would prefer a less endogenous variable.� Subsequent studies use other criteria.

How do we control for investment opportunities?

� No obvious precise measure.� Average Q= Marketvalue of Þrm/Replacement cost of assets.

� Correct for taxes, etc.?

� Simpler methods estimate investment better.

� Tobin�s Q may not be right:� Average vs. marginal.

� SigniÞcant measurement errors of asset value (computed from account-ing numbers).

� Do we trust stock prices� informational content?

� Theory relies on strong assumptions.


Do mismeasured investment opportunities bias β?

� Cashßow is highly correlated with investment opportunities.� More generally, liquidity is likely correlated with the proÞtability of invest-ment: Firms that did well in the (recent) past are more likely to do well inthe (near) future.

� Mismeasurement of investment opportunities biases β.� Not too surprising that β > 0 even with best effort to control for investmentopportunities.

What about differences in β across classes?

� This more sophisticated approach is now widely used.� The hope is that differences in β are not as biased.� The difference is unbiased estimate of the true difference if there is nosystematic difference in measurement error across classes.

� But always subject to that question.

� Here, measurement error for investment opportunities is likely more severefor Class 1 Þrms which tend to be smaller.

� Moreover, endogeneity of dividends does not help: Class 1 Þrms may beretaining earnings because they have better investment opportunities thatare not observable by the market.


Does the theory really predict that sensitivity decreases with wealth?

� See Kaplan and Zingales (1997).� Begin with model:

maxF (I)− C(E, k)− IwhereI =W +E.

� First Order Condition:dI/dW = C11/(C11 − F11).

� For sensitivity to be decreasing in wealth:

d2I/dW 2 =F111C

211 − C111F 211

(C11 − F111)3 .

� Not obvious that this expression should be negative.


Hoshi, Kashyap, and Scharfstein (1991)

Main idea:

� Compare Japanese Þrms within and outside industrial groups.� Group membership as proxy for Þnancial constraints.� Investment-liquidity sensitivity is greater for group Þrms.� Suggestive of the role of banks.

� Sample: 337 Japanese manufacturing Þrms listed over 1965-86.� Group membership has been very stable over several decades.

Firm classiÞcation:

� Members of six largest industrial groups (121).� Independent Þrms (24).� Other Þrms (192):

� Group connected but no close Þnancial ties (25).

� Quasi-independent Þrms (152).

� Subsidiaries of group Þrms (15).


Group Members are Likely Less Financially Constrained

� Main Þnancial link is between Þrms and the large banks at the center of thegroup.

� Concentration of claims. ⇒ Less free riding among creditors.

� Non-affiliated Þrms tend to spread their borrowing.

� Group members borrow mostly from group banks.

� Greater incentives to monitor and/or to reÞnance.

� Less debt-equity conßicts (especially near distress) ⇒ More debt capacity.

� Group banks hold up to 10% equity (less now).

� Life insurance companies also own large stakes.

� Direct monitoring:� Former bank employees hold key managerial positions in group Þrms.


Compared to group Þrms, independent Þrms:

� Investment similar level and more volatile.� Liquidity and production are higher and more volatile.� Slightly higher Tobin Q.� Lower debt ratios.


For each class, run regression of investment on:

� Cash ßow.� Liquid assets:

� No precise data on cash balances.

� Rely on Þrms� holding of short-term securities.

� Lagged production = Sales + ∆Inventories.� Empirical �accelerator effect�.

� Not well understood.

� Included because production is correlated with liquidity.

� Firm and year Þxed effects.


Main result:

� Coefficients on Cash ßow and Liquid assets are larger for indepen-dent Þrms.

� In fact, cashßow coefficient is small and insigniÞcant for group Þrms.Note: Since don�t understand the role of production, run the same regressionwithout lagged production. Find similar result.


� Although imprecise, the results go in the same direction.� Quasi-independent Þrms could belong to minor groups.


Competing Explanations

� Why might the bias not be the same across classes?

Industry Effects?

� Possible explanation: Compared to group Þrms, independent Þrms are inhigh(er) growth industries where liquidity is a better proxy for investmentopportunities.

� Group and independent Þrms do not really differ along industries. Regres-sions conÞrm the results.

Measurement Error?

� Possible explanation: Cash ßow is less precisely measured for group Þrmthan for of independent Þrms.

� Transactions with other group Þrms not at market prices?

� Reallocate funds within the group?

� Group Þrms are publicly traded.� Lower level of coordination than required for the argument.� Group Þrm cash ßows are not much less volatile.

Endogeneity of Group Membership?

� Possible explanation: Maybe Þrms with better investment opportunities donot join groups.

� However, group affiliations are very stable.� Moreover, no evidence that independent Þrms have performed better overthe sample period.


Over- or Under-investment?

� Free cash ßow theory (Jensen 1986): Assume that managers� will invest internal funds even in negative NPV projects,

� but cannot get external funds for negative NPV projects.

� Firms with bad investment opportunities will exhibit investment cashßowsensitivity.

� If banks mitigate this problem, the sensitivity will be less for group Þrmswith bad investment opportunities.

� Implication: Other things equal, ∆β should be larger for Þrms with worseinvestment prospects.

� Difference in investment is most severe for high not low Q Þrms.


Role of banks for corporate investment:

� Indirect evidence from greater stock price reaction to announcement of bankloan vs. bond issue (James 1987).

� Questions the wisdom of previous US banking laws, i.e., Glass-Steagall act.


Lamont (1997)

Main idea:

� To separate cashßow and investment proÞtability:� Multi-segment Þrms: oil + non-oil;

� Exogenous shock to cashßow to oil segments;

� Examine effect on non-oil segments� investment.

� Non-oil segment of multi-segment Þrms cut investment.

� Use 1986 oil shock as exogenous instrument for cash:� Exogenous, large, unanticipated variation: Firms revised their invest-ment plans.

� 50% decline in oil prices: $26.6 in 12/85 to $12.67 in 04/86.

� Uncorrelated (at least not positively) with investment proÞtability.

� Two biases against Þnding a relationship:� Large Þrms (recall FHP).

� Survivorship bias: Constrained Þrms may have sold non-oil segments.


� Consider non-oil segments of Þrms with oil-segments:� Look at time-series correlation of proÞt and investment with real oilprices.

� Use own judgement.

� Investment:� Raw.

� Normalized by median same industry stand-alones.


Comments

� Also examine reasons for the result:� Evidence of overinvesting before the shock.

� Little evidence of underinvesting after the shock.

� Raises interesting questions:� How does internal capital allocation works?

� See lecture on conglomerates and capital allocation.

� Trade-off:� Sample size.

� Well identiÞed natural experiment.

� Should replicate this type of study for other events and industries.

� ClassiÞcation based in part on author�s judgement. Would prefer a lesssubjective method (e.g., input/output matrix?).

� Ozbas (1999): Decline in investments was part of a downward trend thatbegan in the early 1980s. Questions Lamonts� conclusions.


Kaplan and Zingales (1997)

� Some low dividend paying Þrms in FHP actually had Þnancial slack.� Examine each Þrm�s annual report and 10-K.

� FHP�s basic results are reversed for this small sample.

� Claim ICF sensitivities do not have to be monotone in Þnancial slack.

� Surprising - contradicts a large body of evidence that supports FHP�s results.� Objections:

� Small sample size.

� Subjective judgement used to classify Þrms.

� Most importantly, serious selection bias: Firms which KZ classify asNot Financially Constrainted are most likely to have very conservativemanagement that closely match investment and cash ßow.


Kaplan and Zingales Groups

� NFC - Not Financially Constrained if:� Cash dividend initiated or increased.

� Repurchased stock.

� CEOs� report claims the company has ample liquidity.

� LNFC - Likely Not Financially Constrained if::� Unused lines of credit.

� Cash reserves.

� PFC - Probably Financially Constrained if:� Unsure.

� Contradictory evidence.

� LFC - Likely Financially Constrained if:� Postpone equity or convertible offering.

� Claim in need of equity.

� Cut dividend.

� FC - Financially Constrained� Violation of debt covenant.

� Cutout of credit source.

� Renegotiating debt.



(*) Fazzari, Steven M., R. Glenn Hubbard, and Bruce C. Petersen (1988), �Fi-nancing Constraints and Corporate Investment,� Brookings Papers on Eco-nomic Activity, 141-195.

Fazzani, StevenM., R. Glenn Hubbard, and Bruce C. Petersen (2000), �Investment-Cash Flow Sensitivities are Useful: A Comment on Kaplan and Zingales,�Quarterly Journal of Economics, 115, 695-705.

Gilchrist, Simon, and Charles Himmelberg (1996), �Evidence on the Role ofCash Flow for Investment,� Journal of Monetary Economics, 36, 541-572.

(*) Hoshi, Takeo, Anil Kashyap, and David Scharfstein (1991), �CorporateStructure, Liquidity, and Investment: Evidence from Japanese IndustrialGroups,� Quarterly Journal of Economics 56, 33-60.

Hubbard, R. Glenn (1998), �Capital-Market Imperfections and Investment,�Journal of Economic Literature, 36, 193-225.

Kaplan, Steven N., and Luigi Zingales (1997), �Do Financing Constraints Ex-plain Why Investment is Correlated with Cash Flow?,� Quarterly Journal ofEconomics, 112, 169-215.

Kaplan, Steven N., and Luigi Zingales (2000), �Investment-Cash Flow Sensitiv-ities are not Valid Measures of Financing Constraints,� Quarterly Journal ofEconomics, 115, 707-712.

Kashyap, Anil, Owen Lamont, and Jeremy Stein (1994), �Credit Conditions andthe Cyclical Behavior of Inventories,� Quarterly Journal of Economics 109,565-592.

James (1987)

Jensen (1986)

(*) Lamont, Owen (1997), �Cash Flow and Investment: Evidence from InternalCapital Markets,� Journal of Finance, 52, 83-109.

Poterba, James M. (1988), �Comment on Financing Constraints and CorporateInvestment,� Brookings Papers on Economics Activity, 196-206.

Stein, Jeremy C. (2002), �Agency, Information and Corporate Investment,�forthcoming in Handbook of the Economics of Finance, Constantinides, Har-ris and Stulz (eds.).

Whited, Toni (1992), �Debt, Liquidity Constraints, and Corporate Investment:Evidence from Panel Data,� Journal of Finance, 47, 1425-1470.

PROBLEMS


Problem 1 (from MIT Final 1999)Fazzari, Hubbard, and Petersen (1988) and others document that year-to-yearchanges in Þrms� capital expenditures are highly correlated with changes in theircash ßows (earnings plus depreciation). Give a possible interpretation of this Þnd-ing (taking it at face value). What are possible objections to this interpretation?What other interpretation can you think of? What type of empirical strategycould provide evidence making one interpretation more convincing? Give two orthree examples of such experiments (assuming your are not constrained by data,time, etc.). If you know of empirical studies that have followed such a route,summarize their general idea and main Þndings.

Problem 2 (Cash ßow constraint) (from MIT Finance Generals 2000)Think about whether Þrms behave as if they are Þnancially constrained, so thattheir investment is constrained by the extent of cash available.a) Give some suggestive summary statistics that cash at hand constrains invest-ment. No need for regressions, only some simple means.b) What is the �standard� regression that argues for cash ßow constraininginvestment (e.g., Fazzari-Hubbard-Petersen)? What are some studies using thisgeneral methodology?i) What are the fundamental limitations in demonstrating a cash ßow constrainton investment? And to what extent does this methodology overcome theselimitations?ii) What other criticisms have been made of this methodology?iii) Describe any �cleaner� evidence we have and the problems with this evidence.c) Suppose you had all the data and any sort of variation in the world that youwanted. Describe a way to test for cash ßow constraints on investment.d) Consider the following argument: �Many large Þrms in the US have superbdebt ratings and easy access to credit. So these Þrms should not show anyconstraint of cash ßow on investment.� Do you Þnd this argument compelling?


Theories of Interactions betweenProduct Markets and Capital Markets

Overview:

� Direct effects.� Strategic effects.

� Debt makes you tough: e.g., Limited Liability Effect.

� Debt makes you weak: e.g., Predation.

D. Gromb Product/Capital Markets Interaction: Theories 1

Introduction

�Standard� Corporate Finance:

� Considers a Þrm�s cash ßows as affected by stakeholders in the Þrm (insiders,outside investors, managers...).

� Ignores interaction between different Þrms.� Yet, it is likely to impact on cash ßows.

�Standard� Industrial Organization:

� Focuses on interactions between Þrms.� Takes each Þrm as a black box.� Ignores aspects �internal� to Þrms, in particular the relation between theÞrm and (potential) Þnanciers and its inßuence on strategies.


DIRECT EFFECTSOF CAPITAL STRUCTURE

Main idea:

� Capital structure can affect Þrms� investment.� ⇒ Affects product market competition.

� Debt makes you �tough� (à la Jensen 1986).� Debt has a disciplinary effect.⇒ The Þrm keeps costs down.⇒ More aggressive in the product market (i.e., debt makes you �leanand mean�).

� Debt makes you �weak�(à la Myers 1977).� Debt overhang⇒ Distorted investment policy.⇒ e.g., lower investment in cost reduction.⇒ Less aggressive on the product market.


STRATEGIC EFFECTSOF CAPITAL STRUCTURE

Main idea:Capital Structure affects Þrms� strategic interactions in the product mar-ket.Debt makes you tough:

� Debt induces over-investment (à la Jensen and Meckling 1976).� Debt serves as a commitment to over-invest.

Debt makes you weak:

� Leveraged Þrms Þnd it harder to raise external funds.� Induces under-investment (à la Myers 1977).� This can be exploited by competitors.


LIMITED LIABILITY EFFECT

Brander and Lewis (1986).

Main idea:

(1) Debt induces risk-shifting by shareholders (limited liability effect).

(2) In Cournot competition under uncertainty, risk increases with thequantity produced.

(3) Debt is a commitment to be aggressive in Cournot.

Model

� Firms 1 and 2 compete à la Cournot: q1 and q2.� Zero marginal cost of production.� Price = a− bQ where a ∈ {aL, aH} with θ ≡ Pr £a = aH¤ .


All-Equity Firms(standard Cournot model)

� Denote:�a ≡ E [a] = θ · aH + (1− θ) · aL.

� Firm i�s shareholders/management maximize:E [Xi] = qi · [�a− bqi − bqj] .

� Reaction functions:q∗i =

�a− bqj2b

.

� Equilibrium quantities:

q∗1 = q∗2 =�a

3b,

Q∗ =2�a

3b.

� Expected proÞts:Π∗1 = Π

∗2 =

�a2

9b.

Remark:

� The quantity q∗i is ex-ante optimal.� Ex-post over-production if a = aL.� Ex-post under-production if a = aH .


Effect of Leverage

Assume now:

� Firm 1 has debt of K high enough so that K > XL1 .

� Firm 2 is all equity Þnanced.

� Under Limited Liability, Þrm 1�s shareholders maximize:E£(X1 −K)+

¤= θ · £q1 · ¡aH − bq1 − bq2¢−K¤+ (1− θ) · 0.

� That is, Þrm 1�s shareholders ignore the bad state and maximize:q1 ·

¡aH − bq1 − bq2

¢.

� Hence, Þrm 1�s reaction function is:

q1 =aH − bq22b

.

� Firm 2�s reaction function remains:

q2 =�a− bq12b

.

� Equilibrium quantities:

q1 =�a

3b+ 2

(1− θ)∆a3b

,

q2 =�a

3b− (1− θ)∆a

3b,

Q = q1 + q2 =2�a

3b+(1− θ)∆a

3b.


Compared to the standard Cournot outcome:

� Levered Þrm produces more,� Unlevered Þrm produces less,� Total industry output is greater,� Price is lower.

Intuition:

� An increase by Firm 1 from Cournot quantity leads to:� A higher XH

1 : less under-production,

� A lower XL1 : more over-production,

� A higher variance and lower mean.

� Limited liability⇒ Risk-shifting by shareholders at the expense of debthold-ers (as in Jensen-Meckling 1976).

� Firm 2 accommodates Firm 1�s more aggressive strategy.


Will Firm 1 Issue Debt?

� Equilibrium expected proÞt (remember that debtholders break even)E[X1] = q1 · (�a− bQ)

=

µ�a

3b+ 2

(1− θ)∆a3b

¶×µ�a− b ·

µ2�a

3b+(1− θ)∆a

3b

¶¶=

1

9b· (�a+ 2(1− θ)∆a)× (�a− (1− θ)∆a) .

� To be compared with�a2

9b

� Firm 1 is better off with debt if and only if�a− 2(1− θ)∆a > 0

3(1− θ)aL > (2− 3θ)aH

� SatisÞed for� θ large enough.

� aL large enough.

� aH small enough.

� Indeed, this is when the expected loss w.r.t. standard Cournot (low state)is lowest.


Industry Equilibrium

Remark:

� Debt can constitute an entry barrier.� Indeed, Firm 2�s proÞt are lower than in standard Cournot.

Assume now both Þrms in the market:

� Firm 1 and 2 choose their capital structure.� Both may want to lever up.

⇒ Both over-produce w.r.t. Cournot.⇒ Both are worse off w.r.t. Cournot.⇒ They would be better-off committing not to lever up.


Comments

More general:

� Commitment to over-invest could be used against other parties (e.g., em-ployees, government, etc.)

� Sometimes might prefer committing to under-invest (e.g., Perotti and Spier1993).

Robustness?

� Does debt really make you tough?� Robustness to other competition games? Not much.� Debt might commit you to be soft, i.e., help collusion (Glazer 1994).

Commitment?

� Role of debt: Commit shareholders (ex-ante) to an ex-post inefficient reac-tion function.

� Renegotiation? �Secret recontracting�?What kind of Þrm?

� Same problems as usual. Why is management aligned with shareholders?� Management may try to avoid default ⇒ Excessively conservative.

General approach?

� Nice idea but: Do we really believe that Þrms choose their capital structurestrategically as a commitment to inßuence their competitors?


PREDATION

�Old theories�

� Cash rich Þrms drive cash poor Þrms out of business by competing aggres-sively in the short run.

� Costly predatory strategies (e.g., predatory pricing) are compensated by in-creased proÞts following the prey�s exit.

Critique

� Why would investors terminate valuable Þrms? (renegotiation).⇒ Why do rivals follow costly predatory strategies?

More recent theories

� Based on information asymmetry.� Predator tries to convince rivals (or their investors) that the continuationvalue is negative.

� Examples:� Predator convinces investors that he (his rival) has low (high) costs.

� Predator builds a reputation for being aggressive.


Agency-Based Theory

Bolton and Scharfstein (1990).

Resurrect the old idea of Þnancially driven predation.

Main idea

(1) Outside Þnance involves agency problems ⇒ A Þrm�s investmentcapacity increases with cashßow.

(3) Predation reduces short-run cashßows to reduce their rival�s invest-ment capacity.

Model

Main idea:

� The entrepreneur makes the repayments because of the threat of termina-tion, i.e., exclusion from further credit.

Assumption (key): Non-veriÞable cash ßows.

� This generates investment-cashßow correlation in a rational model becauseinvestors will not want to lend in the last period.

Assumption (simplifying): The investor has full bargaining power initially.


Optimal Contract Absent Predation

Contract:

� Repay RL = 0 and be reÞnanced with probability βL.� Repay RH and be reÞnanced with probability βH ≡ βL +∆β.� The entrepreneur gets θXH in continuation and is willing to pay

RH = ∆β · θXH .

� The investor�s proÞt is:Πnp = −F − (1− θ) · βL · F + θ · £∆β · θXH − βH · F ¤

= −F − βL · F + θ ·∆β ·£θXH − F ¤ .

Optimal Contract:

� RH = θXH .

� ∆β = 1, i.e., βH = 1 and βL = 0.

� The investor�s payoff is:Πnp = θ

2XH − (1− θ)F.


Predation

� Firm 1 is Þnanced internally.� Firm 2 needs outside Þnance.

Predation:

� At cost c, Þrm 1 can decrease Þrm 2�s θ by ∆θ� If Þrm 2 exits, Þrm 1�s proÞt in second period increases by ∆π

Assumption: Predation may be valuable, i.e.,

Φ ≡ c

∆θ ·∆π < 1.

� Predation increases Þrm 2�s exit probability by ∆θ ·∆β.� Hence, Þrm 1 preys if and only if

(∆β ·∆θ) ·∆π > c or Φ < ∆β.

Remarks:

� The contract minimizing agency problems within the Þrm (βH = 1 andβL = 0) maximizes the incentives to prey.

� Predation occurs even if observed (or anticipated) by all parties.


Accommodating Predation

If Þrm 2 anticipates predation:

� The optimal Þnancial contract is the same as before.� The investor�s proÞt is only

Πp = −F + (θ −∆θ) · (θXH − F )= Πnp −∆θ · (θXH − F ).

Predation-Proof Contract

� Assume for now that Þrm 2�s Þnancial contract is observed by Þrm 1.� Firm 2 can deter predation by reducing∆β, i.e., making its Þnancial contractless sensitive to performance.

Deep-Pocket Strategy:

� Keep βH = 1.� Increase βL until ∆β = Φ.� Investors are softer on the Þrm.

Shallow-Pocket Strategy

� Keep βL = 0.� Decrease βH until ∆β = Φ.� Investors are tougher on the Þrm.


Optimal Predation-Proof Contract

� The entrepreneur is willing to payRH = ∆β · θXH = Φ · θXH .

� Investor�s proÞtΠpp = −F − βL · F + θ ·∆β ·

£θXH − F ¤

= −F − βL · F + θ · Φ · £θXH − F ¤ .� Hence, Shallow Pocket is optimal:

βL = 0 and βH = Φ.

� Investor�s proÞtΠpp = −F + θ · Φ · (θXH − F ).

Deter vs. Accommodate

� Deterrence is optimal if and only if Πpp > Πp, i.e.,−F + θ · Φ · (θXH − F ) > −F + (θ −∆θ) · (θXH − F )

� This can be written as:θ · Φ > θ −∆θ.


Comments

Remark 1: The predation threat can constitute an entry barrier.

� Cash poor Þrms will enter the market if and only if−F +max{θΦ; θ −∆θ}(θXH − F ) > 0.

� More restrictive than absent a predation threat:−F + θ(θXH − F ) > 0.

Remark 2: If contracts are not observable, they cannot deter predation.

� Given Þrm 1�s strategy (i.e., given θ or θ−∆θ), setting βH = 1 and βL = 0is optimal.

� ⇒ Firm 1 will always prey.

Implications:

� Competition depends on a Þrm�s access to internal funds.� May depend on cross-sectional variation in availability of internal funds.� Competition can affect internal incentive problems.� Competitive threats limit the extent to which access to capital depends onperformance.

� Important point in optimal capital structure checklist: �Do competitors havedeep pockets?�


Signal Jamming Theory

Fudenberg and Tirole (1986).

Main idea:

(1) Investors observe signals about the Þrm�s proÞtability.

⇒ Incentives for rivals to distort the signals.

(2) Maybe true even if investors are not fooled in equilibrium.

Model

� At t = 1, 2, a Þrm generates i.i.d. cashßows Xt ∈ {−∞; +∞}.� Distribution h ∈ {hB, hG} with ν ≡ Pr [h = hG] .� Good Þrm�s NPV>0 while Bad Þrm�s NPV<0:Z +∞

−∞X · hG(X) · dX > 0 >

Z +∞

−∞X · hB(X) · dX.

Assumption: Monotone Likelihood Ratio Property (MLRP):

∂

∂X

µhG(X)

hB(X)

¶> 0.


No Predation

Optimal policy: Terminate the Þrm after t = 1 if and only if

X1 < �X.

Predation

� A rival can secretly incurs a (tiny) cost so that:X1 → X1 −∆X .

� If the Þrm exits, the rival gains ∆π.

Equilibrium?

� If the investors anticipate no predation:� They mistakenly liquidate if �X −∆X < X1 −∆X < �X.

⇒ If predation is not anticipated, there will be predation.⇒ Not an equilibrium.

� If the investors anticipate predation:� Absent predation, investors will mistakenly continue if

�X −∆X < X1 < �X.

⇒ If predation is anticipated, there will be predation.⇒ This is an equilibrium.

Note:

� Predation does not fool the investors (i.e., they take the same decisions aswithout predation).

� Not really general: Here predation does not affect the information contentof X1.


Comments

� BS and FT models are based on investment-cashßow correlation.� In FT, the correlation is between cashßows and investment opportunities.� In BS, the correlation is between cashßows and investment capacity.� In FT, rivals would jam any observable variable a priori correlated withinvestment opportunities.

� In BS, rivals need to distort cash-ßows.� In BS, predation affects (only) cash-ßow Þrms.� In FT, it affects cash-poor Þrms only in so far as they need to raise fundsfrom investors who are less informed than they are.



(*) Bolton, Patrick, and David S. Scharfstein (1990), �A Theory of PredationBased on Agency Problems in Financial Contracting,� American EconomicReview, 80, 93-106.

(*) Brander, James A., and Tracy R. Lewis (1986), �Oligopoly and FinancialStructure: The Limited Liability Effect,� American Economic Review, 76,956-970.

Chevalier, Judith A. (1995a), �Capital Structure and Product Market Compe-tition: Empirical Evidence from the Supermarket Industry,� American Eco-nomic Review, 85, 206-256.

Fudenberg, Drew, and Jean Tirole (1986), �A �Signal-Jamming� Theory of Pre-dation,� Rand Journal of Economics, 17, 366-376.

Surveys, etc.

Harris, Milton A., and Artur Raviv (1991), �The Theory of Capital Structure,�Journal of Finance, 46, 297-355.

Maksimovic, Vojislav (1995), �Financial Structure and Product Market Compe-tition,� in Jarrow, Maksimovic and Ziemba (eds.), Handbook of OperationsResearch and Management Science, Vol 9, North-Holland.

Ravid, S. Abraham (1988) �On the Interactions of Production and FinancialDecisions,� Financial Management, Tampa, 17, 87-99.

Tirole, Jean (1988), The Theory of Industrial Organization, MIT Press, Cam-bridge, MA.

Related Literature:

Chemla, Gilles, and Antoine Faure-Grimaud, (2001), �Dynamic Adverse Selec-tion and Debt,� European Economic Review, 45, 1773-1792.

Chevalier, Judith A. (1995b), �Do LBO Supermarkets Charge More? An Em-pirical Analysis of the Effect of LBOs on Supermarket Pricing,� Journal ofFinance, 50, 1095-1112.

Chevalier, Judith A., and David S. Scharfstein (1996), �Capital-Market Imper-fections and Countercyclical Markups: Theory and Evidence,� AmericanEconomic Review, 86, 703-725.

Dotan, Amihud, and S. Abraham Ravid (1985), �On the Interaction of Real andFinancial Decisions of the Firm Under Uncertainty,� Journal of Finance, 40,501-517.


Gertner, Robert, Robert Gibbons, and David S. Scharfstein (1988), �Simul-taneous Signaling to the Capital and Product Markets,� Rand Journal ofEconomics, 19, 173-190.

Glazer, Jacob (1994), �The Strategic Effects of Long-Term Debt in ImperfectCompetition,� Journal of Economic Theory, 62, 428-443.

Guedes, José C., and Tim C. Opler (1992), �The Strategic Value of Leverage:An Exploratory Study,� mimeo, Cox School of Business, Southern MethodistUniversity.

Kovenock Daniel, and Gordon Phillips (1995a), �Capital Structure and ProductMarket Rivalry: How Do We Reconcile Theory and Evidence?� AmericanEconomic Review, 85, 403-408.

Maksimovic, Vojislav (1988), �Optimal Capital Structure in Repeated Oligopolies,�Rand Journal of Economics, 19, 389-407.

Maksimovic, Vojislav (1990), �Product Market Imperfections and Loan Com-mitments,� Journal of Finance, 45, 1641-1653.

Maksimovic, Vojislav, and Sheridan Titman (1991), �Financial Reputation andReputation for Product Quality,� Review of Financial Studies, 2, 175-200.

Poitevin, Michel (1989), �Financial Signalling and the �Deep Pocket� Argu-ment,� Rand Journal of Economics, 20, 26-40.

Phillips, Gordon (1995) �Increased Debt and Product Markets: An EmpiricalAnalysis,� Journal of Financial Economics, 37, 189-238.

Rotemberg, Julio, and David S. Scharfstein (1990), �Shareholder Value Max-imization and Product Market Competition,� Review of Financial Studies,3, 367-393.

Spence, A. Michael (1985). �Capital Structure and the Corporation�s ProductMarket Environment,� in: B. Friedman, Ed. Corporate Capital Structuresin the United States, University of Chicago Press, Chicago, II, 353-382.


PROBLEMS

Problem 1 (Markups and the Business Cycle) (from MIT Finance Generals2000)

Consider a model with two periods (t = 1, 2), universal risk-neutrality and nodiscounting. Two all-equity Þrms, A and B, compete in prices with differentiatedgoods (goods A and B) and have identical linear technologies, with constantmarginal cost c. They face consumers indexed by their �transportation cost� forgood A, y, uniformly distributed over [0, 1].

At t = 1, the Þrms set prices pA1 and pB1 . Consumer y�s valuation for q units of

good A (resp. B) is q · (U1 − y) (resp. q · (U1 − (1− y))) for q ≤ D and ßat forq ≥ D. Assume that after pA1 and pB1 have been set, D takes one of two values,DL = 0 and DH > 0 with Pr

£D = DH

¤ ≡ θ. Denote �D = θDH . To simplify,assume that U1 is large enough so that each consumer chooses q = D.

At t = 2, the Þrms set prices pA2 and pB2 . Switching costs are large enough so

that each consumer is locked with the Þrm she purchased from at t = 1. Eachconsumer�s valuation for the good is q · U2 for q ≤ 1 and ßat for q ≥ 1 (i.e., notransportation costs).

a) Solve for the equilibrium mark-ups at t = 1, i.e., for mi1 = pi1 − c with

i = A,B.b) Assume that a boom (resp. recession) at t = 1 corresponds to the demand θbeing large (resp. small). In this model, are mark-ups pro- or counter-cyclical?Explain brießy.Now, Þrm A has debt with face value K maturing at the end of period t = 1,wile Þrm B remains all-equity. Assume non-veriÞable proÞts in both periods (àla Bolton-Scharfstein (1990)). If Þrm A defaults, creditors seize its assets andoperate them at t = 2. However, their linear technology at t = 2 has marginalcost c+∆c with ∆c > 0.c) Under what condition will Þrm A not default when DH > 0? From now on,assume this condition to be satisÞed.d) Solve for the equilibrium mark-ups at t = 1, assuming that Þrms maximizethe value of debt and equity.e) How do both Þrms� markups compare to those in a), and to each other?Explain brießy.f) Show that mark-ups can be pro- or counter cyclical. (Give conditions). Explainbrießy.g) When both Þrms� mark-ups are pro-cyclical (resp. counter-cyclical), how dothe sensitivities of both Þrms� markups to the business cycle compare to those ina), and to each other? Explain brießy.

h) What are, in your view, the main strengths and weaknesses of this model?


Product Market/Capital MarketInteractions: Empirics

Overview:

� Introduction.� Phillips (1995).� Chevalier (1995).� Chevalier and Scharfstein (1994).

D. Gromb Product/Capital Markets Interactions: Empirics 1

Methodological Difficulties

� Causality.� Do Þnancial choices drive product market decisions? Or is it the other wayaround?

� What if both are driven by an unobserved third factor?

Inter-Industry Studies

Spence (1985)

� Fact: Firms in more concentrated industries have less leverage.Interpretations?

� Does low leverage lead to high concentration, say because incumbents withdeep pockets deter entry?

� Does high concentration lead to low leverage, say because Þrms in concen-trated markets/industries are more proÞtable and need less debt to Þnanceinvestment?

Guedes and Opler (1992)

� Try to explain inter-industry differences in median leverage based on mea-sures of product market competition.

� Also try to explain intra-industry differences in leverage.� Result: None.

Intra-Industry Studies

Strategy: Within a particular industry, examine the product market effects ofsigniÞcant capital structure changes, e.g., LBOs, recapitalizations, etc.


Phillips (1995)

� Industry selection criteria:� Increase of at least 25% in ratio of debt-to-market value of leading Þrm.

� C(4) ratio of at least 50%.

� Product homogeneity.

� Industry leader with at least 50% of its sales in industry.

� Four industries qualify:� Fiberglass RooÞng and Insulation.

� Tractor Trailers.

� Polyethylene Chemicals.

� Gypsum.

� Hope: LBO exogenous to changes in product market competition.� Questions:

� What happens to LBO Þrms?

� What happens to industry?

Basic Finding:

� In Þrst 3 industries:� LBO Þrms either lost market share or did not increase market sharewhen other Þrms exited the industry.

� LBO Þrms experienced decrease in sales and increase in operating mar-gins.

� Supply curves shift up after the leverage increase, suggesting a decreasein competition.

� For gypsum: just the opposite.


Fiberglass:

� 4 major Þrms accounting for 75% of sales: Owens-Corning Fiberglass (OCF)(the largest), Manville Corp., Certainteed Corp., PPG Industries.

� After its recap, OCF�s market share dropped from 30.5% to 26.5%.

� Manville was also Þnancially constrained:� Filed for Chapter 11 bankruptcy in 1983.

� Lost 5% of total market share.

� Plant closings:� Only one before OCF�s recapitalization, six after.

� Annual report: Closings are designed to generate funds to meet sched-uled interest and principal payments.

� Accounting measures indicate that OCF became more conservative after therecap:

� Higher margins.

� Lower sales and capital expenditures.


Tractors Trailers:

� Basically commodity pricing.� Dealer and service networks pose only barriers to entry.

� 5 major Þrms: Fruehauf (leader), Dorsey, Monon, Trailmobile, Great Dane.� All undertook recapitalization or LBO.� After Fruehauf recapitalization:

� Prices increase.

� Quantity falls.

� Not driven by input prices.

� Plant closings:� 10 plant closings during the period.

� Fruehauf closed 1 the year of the recap., and 4 more after.

� Trailmobile and Dorsey each close a plant.

� Trailmobile CEO: �In the past, capacity may have changed hands, butrarely removed.�

� Accounting measures indicate that OCF became more conservative after therecap:

� Higher margins.

� Lower sales and capital expenditures.


Polyethylene:

� Produced in large plants that cannot be converted to other use.� 3 largest Þrms: Quantum Chemical (largest), Union Carbide, Cain Inc.(smaller).

� All are highly levered:� Quantum Chemical and Union Carbide recapitalized.

� Cain formed by LBO of a DuPont division.

� Plant closings:� No plant closings before recaps.

� After recap, Quantum closes 1 plant and mothballed 2.

� Reduction in capacity might have been greater without demand increase(capacity utilization rose from 82% to 93%).

� Quantum increased margins, but expanded sales by buying a small competi-tor.


Gypsum:

� Used in construction and home-building (and in Har-Tru - a synthetic clayused in tennis courts).

� 2 Þrms account for50% of sales: U.S. Gypsum (USG), and National Gypsum.� Both underwent recaps drastically increasing leverage.

� USG increased debt-to-market by 50 percentage points.

� National Gypsum use a LBO.

� Market shares:� Two smaller, lowly levered Þrms gained some market share.

� USG�s market share also increased.

� Price and quantity:� Total industry quantity remain stable (opposite of expected).

� Product price dropped (opposite of expected).

� Input prices rose (⇒ expect prices to increase even more).

� Plant closings:� 4 of 6 plant closings by the two leaders came after USG�s recap.

� But small fraction of industry capacity.

� USG operating margins decreased, competitors� margins rose.� Maybe industry is different:

� Minimum efficient scale is small relative to market size.

� Few entry barriers.


What does this behavior mean for the industries?

� Increases in price and decrease in quantity.� But again, Gypsum is different.

Is this good?

� Are Þrms more prudent? Are they too prudent?� Look at management compensation on shareholder wealth and sales.

� Aligned incentives are an indirect check that Þrms do the right thing.

� Before recaps, management compensation increases with sales, and isnot sensitive to shareholder wealth.

� After recaps, returns to shareholders is the dominant predictor of man-agement compensation.

Identifying industry supply and demand:

� IdentiÞcation is problematic.� IO literature usually relies on truly exogenous shocks, e.g., Great Lakesfreezing in Porter (1983).

� Otherwise, coefficients will be very sensitive to speciÞcation (functionalform, explanatory variables, etc.)

� Unclear why debt enters into supply but not demand.


Questions:

� Cannot rule out alternative interpretations.� Do LBOs occur in the 3 industries because Þrms forecast less competition,higher proÞtability, and thus greater debt capacity?

� Is the increase in competition in Gypsum the result of distress in the industrybrought on by plummeting demand from construction industry (as a resultof recession)?

� What explains differences across industries? (Can�t really answer becausethere are only 4 observations.)

� An alternative research strategy may be to narrow the scope.


Chevalier (1995a)

Supermarket Industry

� LBOs:� LBOs were common during the 1980s: (19/50 largest supermarketchains ' 1/4 sales).

� One leveraged recap (i.e., Þrm borrows to pay a large dividend to share-holders, > 50% of equity value).

� Sudden and large changes in leverage: Good for event study.

� Most LBOs were in response to hostile takeover attempts.

� One industry with many local markets:� Industry is not concentrated at the national level but local markets canbe highly concentrated.

� LBOs concern many markets.

� Avoids pitfalls of inter-industry comparisons.

� But allows for cross-sectional effects of leverage within industry.

Questions:

� Are LBOs good or bad news for competitors?� How do competitors react?


Event Study

� Consider largest leveraged transactions over 1985-88:� 3 LBOs.

� 1 leveraged recapitalization.

� Examine the stock price reaction of 13 supermarket chains over 30-daywindow (to also include takeover announcement).

� Distinguish between:� Direct rivals of the LBO Þrm, i.e., with some stores in same city,

� Other supermarkets.

Idea:

� If debt makes you soft (tough), positive (negative) stock price reaction isexpected for rivals only.

� Control using non-rivals to account for possibility that LBO announcementincreases the probability of LBOs in other markets (⇒ premium).


SUR setup to estimate δij in:

Rit = αi + βiRmt +Xj

δijDjt + εij

where

� Rit: Þrm i�s return at date t,� Rmt: market return (V.W. NYSE/AMEX) at date t,� Djt : dummy for window of event j ∈ {1, ..., 4} .

Note: Small positive effect on rival chains. Underestimate: Direct rivals overlapwith LBO Þrm only over a subset of their own markets.


Entry, Exit and Expansion after LBOs

� Entry, exit and expansion of rivals of LBO chains.Idea:

� If debt makes you weak, one would expect:� Expansion by unlevered rival incumbents.

� Entry into local markets if incumbents have undertaken LBOs.

� Opposite results if debt makes you tough.

Note: Distinction is also helpful because events were recent. One expects in-cumbents to expand sooner.Data:

� 50 largest chains in 1985.� Entry, exit, and expansion decisions in 85 Metropolitan Statistical Areas(MSAs). Account for 6,068 of the 13,512 supermarkets in these MSAs.

� Classify all Þrms according to whether they did LBO in the 1980s.Tricky Issue:

� Asset sales and spin-offs are common after LBOs.� Should not be counted as entry or expansion by rivals.� Conservative approach: Assign the assets to the purchaser as of 1985 andlook at changes from this level.

� Similar treatment for mergers among non-LBO Þrms.


Do more/less stores Þt in a city? Regress the % change in # of stores ina MSA between 1985-91 on fraction of stores held by a chain that eventuallyundertook a LBO (+ controls).

� Total # store growth between 1985 and 1991 is higher in markets wherethe share of LBO Þrms is higher.

� This effect is more pronounced if the LBOs mainly occurred pre-1988: Re-action takes some time.


� Non-LBO incumbents are more likely to expand in markets where the shareof LBO Þrms is higher


� Firms are more likely to enter markets where the share of LBO Þrms is higher(Table 7).


An Alternative Hypothesis

� Could it be that LBO Þrms were always poor performers so that entry andexpansion would have occurred even if LBO did not take place?

� Responses:(a) Asset Sales.

(b) Accounting Performance.

(c) Event study evidence.

(d) Early vs. late LBO�s.


Chevalier and Scharfstein (1994)

� �Fact�: Markups seem to be countercyclical, i.e., during recessions, outputprices rise relative to real factor prices.

Theories:

� Increasing returns to scale. ⇒ Costs are procyclical. (But, in fact, realfactor prices decrease).

� Less elastic demand in recessions (+ imperfect competition).� Collusion is more difficult in booms (high stakes in deviations).� Greater Þnancial constraints in recessions:

� Pricing for market share due to switching costs.

� Low prices are an investment: Lower short-run proÞt, higher long-runmarket share and proÞt.

� Financially constrained Þrms cannot price for market share.

Predictions:

� Hypothesis 1: A Þrm�s markups should be more countercyclical if it is moreÞnancially constrained.

� Hypothesis 2: Markups should be more countercyclical if rivals are moreÞnancially constrained because prices are strategic complements.

� Hypothesis 3: Average industry-wide markup should be more countercyclicalif Þrms are more Þnancially constrained.


Empirical Approach

� Examines supermarkets at the local market (i.e., city) level.� Consider exogenous events in which:

� the liquidity of supermarkets in a city is reduced;

� within the same city, some supermarkets (Group 1) are more affectedthan others (Group 2).

� Hypothesis: In response to a shock affecting a city,∆m1 > ∆m2 or ∆(∆m) > 0

� However, we observe prices, but not marginal costs.

Idea 1: Look at changes in prices

∆(∆p) = ∆(∆m) +∆(∆c)

If we observe (as we will) that ∆(∆p) > 0 we can conclude:

� either ∆(∆m) > 0,� or ∆(∆c) > 0, i.e., the marginal costs of more Þnancially constrained Þrmsincreased relative to less constrained Þrms.

Idea 2: Use ∆(∆p) in cities not (or less) affected by the shock:

� Hard to see why ∆(∆c) would differ across local markets.� If ∆(∆p) = ∆(∆c) > 0, we should expect ∆(∆(∆p)) = 0.� If ∆(∆(∆p)) > 0 then part of the action is likely due to ∆(∆m).


Experiment 1: 1986 Oil Shock

� Exogenous shock:� Oil prices drop 50% ⇒ Severe downturn in �oil states�.

� Clean example of local shock.

� Distinguish between supermarkets owned:� by local and regional chains (Group 1),

� or by national chains (Group 2).

� Assumption: In oil states, the oil shock affected local chains more thannational chains.

Data:

� Period:� from 1985Q4: prices begin falling in 1986Q1;

� to 1987Q1: Texas employment at trough + oil prices level off.

� 100 MSAs, including 22 cities in oil states.� Price index for each MSA:

� Basket of 27 grocery products, i = 1,...

� City average price for grocery product i.

� City price index

pj =Xi

wi ·µ

pijPk pik

¶.

� PRICE: Average over all (if several) cities in MSA.


� ∆PRICE: % �increase� in MSA�s price index over the period.

� Measures of a city�s exposure to the oil shock:� OILDUM: �oil state dummy�: Alaska, Colorado, Louisiana, Montana,Oklahoma, Texas, and Wyoming.

� OILIMP: share of oil and gas extraction in 1985 earnings.

� ∆EMP: % change in employment in city�s state over period as mea-sure of direct effect of shock on local economy. (Note: Change inunemployment would ignore migration).

� ∆WAGE: % change in wages of �sales and occupation� workers at MSAlevel over period.


Hypothesis 3: Average industry-wide markup should be more countercyclical ifÞrms are more Þnancially constrained.

(1) Regress ∆PRICE on NATSHARE × OILDUM controlling for

� NATSHARE,� OILDUM, e.g., the fall in demand in the oil states could reduce prices relativeto other states,

� ∆WAGE, e.g., labor costs could rise in oil state cities dominated by nationalchains.

(2) and (3): Repeat with alternative measures of city�s exposure to the oil shockOILIMP and ∆EMP.


� NATSHARE: Indistinguishable from zero, i.e., National chains� importancehas no impact on prices in non-oil state cities.

� OILDUM: Statistically insigniÞcant.� ∆WAGE: Positive as expected; Statistically insigniÞcant.� NATSHARE × OILDUM: Negative and signiÞcant.� Similar results with alternative measures of exposure to shock.

Interpretation: In states more severely hit by the oil shock, prices fall more incities with a larger share of national chains.


Experiment 2:LBOs and the 1990-91 Recession

� Exogenous shock:� Recession.

� Clean example of local shock.

� Distinguish between supermarkets owned:� by LBO chains (Group 1),

� or non-LBO chains (Group 2).

� Assumption: Recession put more pressure on LBO Þrms to boost short-runcash ßows to meet higher principal and interest payments.

Data:

� Period:� from quarter previous to peak: 1990Q2,

� to quarter of trough: 1991Q1.

� 59 MSAs.


� LBOSHARE: Share of all stores in a city owned by LBO chains.

Hypothesis 3: Average industry-wide markup should be more countercyclical ifÞrms are more Þnancially constrained.

Regress ∆PRICE on LBOSHARE×∆EMP controlling for� ∆EMP: city�s exposure to recession,� LBOSHARE,� ∆WAGE.

Results:

� LBOSHARE not statistically signiÞcantly different from zero.� LBOSHARE×∆EMP is negative and signiÞcant.� ∆EMP positive but statistically insigniÞcant.� ∆WAGE positive but statistically insigniÞcant.


Experiment 3:LBOs after 1990-91: Firm-SpeciÞc Data

� Implications for Þrm-level prices.� Hypotheses: A Þrm�s markups should be more countercyclical:(1) if it is more Þnancially constrained;

(2) if its rivals are more Þnancially constrained.

� Again, separate LBO vs. non-LBO Þrms.� Use geographic heterogeneity in the recovery from recession:

� Trough in 1991Q1.

� But negative growth in California and northeast until 1992Q4.

� Period: from 1991Q1 to 1991Q4.Data:

� 110 Þrms in local market.� Price:

� Average over a basket of products.

� Per chain in an area.

� Note: nominal price.


� For each Þrm:� LBO: dummy for LBO chains between 1981-1990Q1.

� OLBOSHARE: share of stores in local market owned by LBO chainother than the Þrm itself.

� ∆EMP.

� ∆WAG.E

� ∆EMP: average 2% (recovery) but negative in some states (California).


Results:Hypothesis 1:

� LBO: positive and statistically signiÞcant:� LBO Þrms tend to raise prices more than non-LBO Þrms;

� Two possible interpretations:

∗ LBOs increase markups more;∗ marginal costs increase more for LBO chains.

� LBO×∆EMP: negative and statistically signiÞcant:� LBO Þrms tend to raise price more relative to non-LBO Þrms in citiesin which economic recovery is slower;

� Hard to justify this by difference in difference in marginal costs betweenLBOs and non-LBOs across different states.

Hypothesis 2:

� OLBOSHARE: positive and statistically signiÞcant:� Firms tend to raise prices more in market where LBO rivals are impor-tant;

� OLBOSHARE×∆EMP: negative and statistically signiÞcant:� Effect is more pronounced in local markets in which economic recoveryis slower;



(*) Chevalier, Judith A. (1995a), �Capital Structure and Product Market Com-petition: Empirical Evidence from the Supermarket Industry,� AmericanEconomic Review, 85, 415-435.

(*) Chevalier, Judith A., and David S. Scharfstein (1996), �Capital Market Im-perfections and Countercyclical Markups: Theory and Evidence,� AmericanEconomic Review, 86, 703-725.

Phillips, Gordon (1995), �Increased Debt and Product Markets: An EmpiricalAnalysis,� Journal of Financial Economics, 37, 189-238.

Surveys, etc.

Maksimovic, Vojislav (1995), �Financial Structure and Product Market Compe-tition,� in Jarrow, Maksimovic and Ziemba (eds.), Handbook of OperationsResearch and Management Science, Vol 9, North-Holland.

Ravid, S. (1988) �On the Interactions of Production and Financial Decisions,�Financial Management, 17, 87-89.

Tirole, Jean (1988), �The Theory of Industrial Organization,� MIT Press, Cam-bridge, MA.

Related Literature:

Chevalier, Judith A. (1995b), �Do LBO Supermarkets Charge More: An Em-pirical Analysis of the Effects of LBOs on Supermarket Pricing,� Journal ofFinance, 50, 1095-1112.

Dotan Amihud, and S. Abraham Ravid (1985), �On the Interaction of Real andFinancial Decisions of the Firm Under Uncertainty,� Journal of Finance, 40,501-517.

Guedes, José C., and Tim C. Opler (1992), �The Strategic Value of Leverage:An Exploratory Study,� mimeo, Cox School of Business, Southern MethodistUniversity.

Kovenock Dan, and Gordon Phillips (1995a), �Capital Structure and ProductMarket Rivalry: How Do We Reconcile Theory and Evidence?� AmericanEconomic Review, 85, 403-408.

Maksimovic, Vojislav (1990). �Product Market Imperfections and Loan Com-mitments,� Journal of Finance, 45, 1641-1653.

Spence, A. Michael (1985). �Capital Structure and the Corporation�s ProductMarket Environment,� in: B. Friedman, Ed. Corporate Capital Structuresin the United States, University of Chicago Press, Chicago, II, 353-382.


This version: April 19, 2004

CORPORATE FINANCE(Ph.D. Seminar in Financial Economics 3)

Denis [email protected]

http://www.london.edu/faculty/dgromb/

� What is the course about? A thorough, doctoral level survey of both theory and empir-ical work in Corporate Finance. We will cover a wide range of topics (see the tentativeschedule below).

� Who can attend? The course�s content and style are targeted at doctoral students inAccounting, Economics and Finance. All other London Business School students arewelcome to attend. All doctoral students from other universities are also welcome.

� Where/When? Wednesdays, 2:00pm-5:00pm at London Business School, room E202.

� Course format: There are 9 meetings. I will distribute lecture notes and papers inclass. There is no textbook.

� Course spirit: Good.� How to get credit for the course? Weekly assignments (problem sets, referee reportson a paper, etc.). To be discussed in class.

Very Tentative Schedule

� This schedule is tentative. It will be updated. This schedule is also excessively full.We will not actually cover all these topics in class. I will distribute lecture notes andreadings for topics we cover but also for some of those we do not. Which topics weactually cover will depend on students (and faculty)� interests and mood.

Week 1: Wednesday 30th April

The Modigliani Miller TheoremTaxes and Bankruptcy Costs

� Grinblatt, Mark, and Sheridan Titman (1998), Financial Markets and Cor-porate Strategy, Irwin / McGraw-Hill, chapter 13.

Incentive Problems� Jensen, Michael C., andWilliamMeckling (1976), �Theory of the Firm: Man-agerial Behavior, Agency Costs and Ownership Structure,� Journal of Finan-cial Economics, 3, 305-360.

Information Asymmetry Problems

� Ross, Steven (1977), �The Determination of Financial Structure: The Incen-tive Signalling Approach,� Bell Journal of Economics, 8, 23-40.

Week 2: Wednesday 7th May

Capital Structure: Theories

� Harris, Milton, and Artur Raviv (1991), �The Theory of Capital Structure,�Journal of Finance, 46, 297-355.

� Myers, Stewart C. (1977), �The Determinants of Corporate Borrowing,� Jour-nal of Financial Economics, 5, 147-175.

� Myers, Stewart C. (1984), �The Capital Structure Puzzle,� Journal of Fi-nance, 39, 575-592.

Capital Structure: Empirics� Loughran, Tim, and Jay R. Ritter (1995), �The New Issues Puzzle�, Journalof Finance, 50, 23-51.

� MacKie-Mason, Jeffrey (1990), �Do Taxes Affect Financing Decisions?�, Jour-nal of Finance, 45, 1417-1493.

� Rajan, Raghuram, and Luigi Zingales (1994), �What Do We Know Aboutthe Capital Structure? Some Evidence from International Data,� Journal ofFinance, 50, 1421-1460.

� Shyam-Sunder, Lakshmi, and Stewart C. Myers (1999), �Testing Static Trade-Off Against Pecking Order Models of Capital Structure,� Journal of FinancialEconomics, 51, 219-244.

Corporate Investment� Fazzari, Steven, R. Glenn Hubbard, and Bruce Petersen (1988), �Financ-ing Constraints and Corporate Investment,� Brookings Papers on EconomicActivity, 141-195.

� Hoshi, Takeo, Anil Kashyap, and David S. Scharfstein (1991), �CorporateStructure, Liquidity, and Investment: Evidence from Japanese IndustrialGroups,� Quarterly Journal of Economics 56, 33-60.

� (s) Hubbard, R. Glenn (1998), �Capital-Market Imperfections and Invest-ment,� Journal of Economic Literature, 36, 193-225.

� Lamont, Owen (1997), �Cash Flow and Investment: Evidence from InternalCapital Markets,� Journal of Finance, 52, 83-109.

� Stein, Jeremy C. (2002), �Agency, Information and Corporate Investment,�forthcoming in Handbook of the Economics of Finance, George Constanti-nides, Milton Harris and Rene Stulz (eds.), Amsterdam: North Holland.


Financial Distress and Bankruptcy: Theories� Gertner, Robert, and David S. Scharfstein (1991), �A Theory of Workoutsand the Effect of Reorganization Law,� Journal of Finance, 48, 1189-1221.

� Shleifer, Andrei, and Robert W. Vishny (1992), �Liquidation Values andDebt Capacity: A Market Equilibrium Approach,� Journal of Finance, 47,1367-1400.

How Firms Fair in Financial Distress� Asquith, Paul, Robert Gertner, and David S. Scharfstein (1994), �The Anatomyof Financial Distress: An Examination of Junk-Bond Issuers,� QuarterlyJournal of Economics, 109, 625-658.

� Pulvino, Todd (1998), �Do Asset Fire-Sales Exist?: An Empirical Investiga-tion of Commercial Aircraft Transactions,� Journal of Finance, 53, 939-978.

Theories of Optimal Financial Contracting� Aghion, Philippe, and Patrick Bolton (1992), �An Incomplete Contracts Ap-proach to Financial Contracting,� Review of Economic Studies, 77, 388-401.

� Bolton, Patrick, and David S. Scharfstein (1990), �A Theory of PredationBased on Agency Problems in Financial Contracting,� American EconomicReview, 80, 93-106.

� Gale, Douglas, and Martin Hellwig (1985), �Incentive Compatibility DebtContracts: The One Period Problem�, Review of Economic Studies, 52, 647-663.

Week 4: Wednesday 21st May

Corporate Control Theories

� Aghion, Philippe, and Patrick Bolton (1992), �An Incomplete Contracts Ap-proach to Financial Contracting,� Review of Economic Studies, 77, 388-401.

� Burkart, Mike, Denis Gromb, and Fausto Panunzi (1997), �Large Sharehold-ers, Monitoring and the Value of the Firm,� Quarterly Journal of Economics,113, 693-728.

� Hart, Oliver D. (1995), Firms, Contracts and Financial Structure, OxfordUniversity Press. Chapters 1, 2, 3 and 5.

Financial Intermediaries: Theory

� Diamond, Douglas W. (1984), �Financial Intermediation and Delegated Mon-itoring,� Review of Economic Studies, 51, 393-414.

� Rajan, Raghuram (1992), �Insiders and Outsiders: The Choice between In-formed and Arm�s-Length Debt,� Journal of Finance, 47, 1367-1400.

� Diamond, Douglas W., and Philip Dybvig (1983), �Bank Runs, Deposit In-surance and Liquidity,� Journal of Political Economy, 91, 401-419.

� Gorton, Gary, and George Pennacchi (1990), �Financial Intermediaries andLiquidity Creation,� Journal of Finance, 45, 49-71.

� Holmström, Bengt, and Jean Tirole (1997), �Financial Intermediation, Loan-able Funds and the Real Sector,� Quarterly Journal of Economics, 112, 663-691.


Relationship Banking: Empirics

� Hoshi, Takeo, Anil Kashyap, and David S. Scharfstein (1990), �The Roleof Banks in Reducing the Costs of Financial Distress in Japan,� Journal ofFinancial Economics, 27, 67-88.

� Petersen, Mitchell, and Raghuram Rajan (1994), �The BeneÞts of LendingRelationships: Evidence from Small Business Data,� Journal of Finance, 49,3-37.

� Petersen and Rajan (1995), �The Effect of Credit Market Competition onLending Relationships,� Quarterly Journal of Economics, 110, 407-443.

Banks as Liquidity Providers: Empirics

� Kashyap, Anil K., Raghuram Rajan, and Jeremy C. Stein (2000), �Banks asLiquidity Providers: An Explanation for the Co-Existence of Lending andDeposit-Taking,� Journal of Finance.

Debt Structure: Theory

� Diamond (1991), �Monitoring and Reputation: The Choice Between BankLoans and Directly Placed Debt,� Journal of Political Economy, 99, 689-721.

� Diamond (1993), �Seniority andMaturity Structure of Debt Contracts,� Jour-nal of Financial Economics, 33, 341-368.

Trade CreditBanking: Empirics, by Prof. Viral Acharya.

Corporate Governance, by Prof. Paolo Volpin

� (s) Shleifer, Andrei, and Robert W. Vishny (1997), �A Survey of CorporateGovernance,� Journal of Finance, 52, 737-783.

Week 6: Wednesday 4th June

Theories of Takeovers and Control Transfers

� Bebchuk, Lucian A. (1994), �Efficient and Inefficient Sales of Corporate Con-trol,� 957-993.

� Grossman, Sanford J., and Oliver D. Hart (1980), �Takeover Bids, the FreeRider Problem and the Theory of the Corporation,� Bell Journal of Eco-nomics, 11, 42-64.

� Grossman, Sanford J., and Oliver D. Hart (1988), �One Share One Voteand the Market for Corporate Control,� Journal of Financial Economics, 20,175-202.

Takeovers: Empirics

� Grinblatt, Mark, and Sheridan Titman (1998), Financial Markets and Cor-porate Strategy, Chapter 19, Irwin/McGraw-Hill.

Dividend Policy

� Allen, Franklin and Roni Michaely (1995), �Dividend Policy,� in Jarrow,Maksimovic and Ziemba (eds.), Handbook of Operations Research and Man-agement Science, Vol 9, North-Holland.

� Bernheim and Wantz (1995), �A Tax-Based Test of the Dividend SignalingHypothesis,� American Economic Review, 85, 532-551.

� Benartzi, Michaely and Thaler (1997), �Do Changes in Dividends Signal theFuture or the Past?,� Journal of Finance, 52, 1007-1034.

Initial Public Offerings: Theories

� Benveniste, Lawrence P., and Paul A. Spindt (1989), �How Investment BankersDetermine the Offer Price and Allocation of New Issues,� Journal of FinancialEconomics, 24, 343-361.

� Rock, Kevin (1986), �Why New Issues are Underpriced,� Journal of FinancialEconomics, 15, 187-212.

� Welch, Ivo (1992), �Sequential Sales, Learning and Cascades,� Journal ofFinance, 47, 695-732.

� (s) Ibbotson, Roger G., and Jay R. Ritter (1995) �Initial Public Offerings,�in Jarrow, Maksimovic and Ziemba (eds.), Handbook of Operations Researchand Management Science, Vol 9, North-Holland.

Initial Public Offerings: Empirics, by Prof. David Goldreich.


Managerial Behavior (and How to Curb It): Theories

� Bikhchandani, Hirshleifer and Ivo Welch (1992), �A Theory of Fads, Fash-ion, Custom, and Cultural Change as Informational Cascades�, Journal ofPolitical Economy, 100, 992-1026.

� Hermalin, Benjamin, and Michael Weisbach (1998), �Endogenously ChosenBoards of Directors and their Monitoring of the CEO,� American EconomicReview, 88, 96-118.

� Holmström and Tirole (1993), �Market Liquidity and Performance Monitor-ing�, Journal of Political Economy, 01, 678-709.

� Jensen, Michael C. (1986), �Agency Costs of Free Cash Flow, CorporateFinance and Takeovers�, American Economic Review, 76, 323-329.

� Scharfstein, David S., and Jeremy Stein (1990), �Herd Behavior and Invest-ment�, American Economic Review.

� Schmidt, Klaus (1997), �Managerial Incentives and Product Market Compe-tition,� Review of Economic Studies, 64, 191-214.

� Stein, Jeremy (1989), �Efficient capital Markets, Inefficient Firms: A Modelof Myopic Behavior�, Quarterly Journal of Economics, 104, 655-669.

� Zwiebel, Jeffrey (1996), �Dynamic Capital Structure Under Managerial En-trenchment,� American Economic Review, 86, 1197-1215.

Corporate Risk Management� Froot, Kenneth, David S. Scharfstein, and Jeremy C. Stein (1993), �RiskManagement: Coordinating Corporate Investment and Financing Policies,�Journal of Finance, 48, 1629-1658.

� Graham, John R., and Clifford W. Smith, Jr. (1999), �Tax Incentives toHedge,� Journal of Finance.

� Geczy, Christopher, Bernadette A. Minton, and Catherine Schrand (1996),�Why Firms Use Currency Derivatives,� Journal of Finance. 52, 1323-1354.

� Kim, S. and Sheridan Titman (1998), �Managerial Incentives and Risk Man-agement Policy,� Working Paper.

� Smith, Clifford W., and René Stulz (1985), �The Determinants of Firm�sHedging Policies,� Journal of Financial and Quantitative Analysis, 20, 391-405.

� Tufano, Peter (1996), �Who Manages Risk? An Empirical Examination ofRiskManagement Practices in the GoldMining Industry,� Journal of Finance,51, 1097-1137.

� Stulz, René M., Derivatives, Risk Management, and Financial Engineering,Southwestern College Publishing, forthcoming.

Corporate DiversiÞcation and Internal Capital Markets: Theories� Stein, Jeremy C. (2002), �Agency, Information and Corporate Investment,�forthcoming in Handbook of the Economics of Finance, edited by GeorgeConstantinides, Milt Harris and Rene Stulz, Amsterdam: North Holland.

� Stein, Jeremy C., and David S, Scharfstein (2000), �The Dark Side of In-ternal Capital Markets: Divisional Rent-Seeking and Inefficient Investment,�Journal of Finance 55, 2537-2564.

Corporate DiversiÞcation: Empirics, by Prof. Henri Servaes


Corporate Finance and Product Markets� Bolton, Patrick, and David S. Scharfstein (1990), �A Theory of PredationBased on Agency Problems in Financial Contracting,� American EconomicReview, 80, 93-106.

� Brander, James A., and Tracy R. Lewis (1986), �Oligopoly and FinancialStructure: The Limited Liability Effect,� American Economic Review, 76,956-970.

� Chevalier, Judith A. (1995), �Capital Structure and Product Market Compe-tition: Empirical Evidence from the Supermarket Industry,� American Eco-nomic Review, 85, 206-256.

� (s) Maksimovic, Vojislav (1995), �Financial Structure and Product MarketCompetition,� in Jarrow, Maksimovic and Ziemba (eds.), Handbook of Op-erations Research and Management Science, Vol 9, North-Holland.

Corporate Finance, Financial Development and Growth


Entrepreneurial Finance

� Gompers, Paul, and Josh Lerner (1999), The Venture Capital Cycle. Cam-bridge, Mass.: MIT Press.

� Gromb, Denis, and David S. Scharfstein (2002), �Entrepreneurship in Equi-librium,� mimeo LBS and MIT.

� Kaplan, Steven N., and Per Strömberg (2000), �Financial Contracting Theorymeets the Real World: An Empirical Analysis of Venture Capital Contracts,�Mimeo Chicago.

� Landier, Augustin (2002), �Entrepreneurship and the Stigma of Failure,�mimeo MIT.

� Lerner, Josh (1995) �Venture Capitalists and the Oversight of Private Firms,�Journal of Finance 50, 301-317.

� Sahlman, W. A., 1990. The Structure and Governance of Venture-CapitalOrganizations, Journal of Financial Economics, 27, 473-521.

Corporate Finance Implications for Financial Markets

� Gromb, Denis, and Dimitri Vayanos (2002), �Equilibrium and Welfare inMarkets with Financially Constrained Arbitrageurs,� Journal of FinancialEconomics, forthcoming.

� Holmström, Bengt, and Jean Tirole, 2001, LAPM: A liquidity-based assetpricing model, Journal of Finance 56, 1837-1867.

� Kiyotaki, Nobuhiro, and JohnMoore (1998) Credit cycles, Journal of PoliticalEconomy 105, 211-248.

� Krishnamurthy, Arvind, 2000, Collateral constraints and the ampliÞcationmechanism, Working paper, Northwestern University.

� Shleifer, Andrei, and Robert W. Vishny (1997), �The Limits of Arbitrage,�Journal of Finance, 52, 35-55.

Other Topics We Might Cover: Managerial Behavior: Empirics; Monetary Policyand Corporate Finance; Law and Finance; Corporate Finance and Behavioral Finance;Behavioral Corporate Finance; Historical Perspective on Corporate Finance.

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