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Essays on the theory of corporate finance

Author: Klein, Manuel INSEAD Area: FinancePublisher: Fontainebleau : INSEAD, 2007.Language: EnglishDescription: 120 p. : Graphs ; 30 cm.Type of document: INSEAD ThesisThesis Note: For the degree of Ph.D. in management, INSEAD, June 2007Bibliography/Index: Includes bibliographical referencesAbstract: The first essay analyzes how two of the key tasks of (division) managers interact: the task to grow the business by creating new investment opportunities and the task to provide accurate information about these opportunities in the corporate budgeting process. We show how the interaction of these two tasks endogenously biases managers towards overinvesting in their own projects. The bias is further exacerbated if managers have to compete in an internal capital market. We show that incentive pay, which mitigates the bias, becomes steeper for managers of riskier and less profitable divisions. Finally our model lends itself to a new and parsimonious theory of the firm. The second essay introduces incomplete information and Bayesian learning into a structural model of default. Equity and debt are interpreted as contingent claims written on an earnings process whose growth rate id unknown. I characterize the value of equity by the means of a bivariate partial differential equiation and solve learning shareholders endogenous default problem, both analytically and numerically, as a free boundary problem. A numerical simuation of the model helps to reconcile the structural approach to credit risk with the empirical evidence by predicting (i) lower leverage, (ii) higher default probabilities, (iii) wider credit spreads. In particular, spreads at the upper ends of ratings spectrum rise relative to spreads at the lower end. The third essay introduces incomplete information and Bayesian learning into two standard models of irreversible investment. The payoff of the investment opportunity is perfectly observable but governed by a drift parameter that is unknown to the decision maker ex ante. I characterize the value of the option to invest by the means of a bivariate partial differential equation and solve the investment timing problem of a dearning decision maker, both analytically and numerically, as a free boundary problem. My results suggest that the impact of incomplete information on both the value of the option to invest and the optimal investment policy is ambiguous. In particular, I show that - counter to the classis real options effect - drift uncertainty does not necessarily increase the value of the option to invest. List(s) this item appears in: Ph.D. Thesis
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For the degree of Ph.D. in management, INSEAD, June 2007

Includes bibliographical references

The first essay analyzes how two of the key tasks of (division) managers interact: the task to grow the business by creating new investment opportunities and the task to provide accurate information about these opportunities in the corporate budgeting process. We show how the interaction of these two tasks endogenously biases managers towards overinvesting in their own projects. The bias is further exacerbated if managers have to compete in an internal capital market. We show that incentive pay, which mitigates the bias, becomes steeper for managers of riskier and less profitable divisions. Finally our model lends itself to a new and parsimonious theory of the firm.
The second essay introduces incomplete information and Bayesian learning into a structural model of default. Equity and debt are interpreted as contingent claims written on an earnings process whose growth rate id unknown. I characterize the value of equity by the means of a bivariate partial differential equiation and solve learning shareholders endogenous default problem, both analytically and numerically, as a free boundary problem. A numerical simuation of the model helps to reconcile the structural approach to credit risk with the empirical evidence by predicting (i) lower leverage, (ii) higher default probabilities, (iii) wider credit spreads. In particular, spreads at the upper ends of ratings spectrum rise relative to spreads at the lower end.
The third essay introduces incomplete information and Bayesian learning into two standard models of irreversible investment. The payoff of the investment opportunity is perfectly observable but governed by a drift parameter that is unknown to the decision maker ex ante. I characterize the value of the option to invest by the means of a bivariate partial differential equation and solve the investment timing problem of a dearning decision maker, both analytically and numerically, as a free boundary problem. My results suggest that the impact of incomplete information on both the value of the option to invest and the optimal investment policy is ambiguous. In particular, I show that - counter to the classis real options effect - drift uncertainty does not necessarily increase the value of the option to invest.

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