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Title: Strategic information transmission in the stock market and the firm
Author: Blanes i Vidal, Jordi
Awarding Body: London School of Economics and Political Science
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 2004
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In this thesis I apply the tools of information economics to analyse the way that information is transmitted in the stock market and the firm. In particular, I use models of asymmetric information to explain a number of empirical regularities that affect the modem corporation and the modern capital market. In the first chapter I show that delegation of decision-making rights can stimulate the career concerns of subordinates in organisations. I show that, when an employer takes a decision following the proposal of her subordinate, a winner's curse reduces the subordinate's prospects in the labour market, muting incentives. This can be solved by delegating decision-making rights to the worker. The second chapter is a joint work with Marc Moller. We use tools of information economics to propose a model of leadership in order to understand why many leaders are unduly confident in their own judgment, a fact that frequently afflicts modern organisations. We show that overconfidence can improve a leader's use of private information although it harms the aggregation of external advice. Overconfidence can therefore improve overall efficiency if the cost of consulting externally is sufficiently high. In the last chapter I apply similar tools to study how investors in the stock market react to public messages that may be optimistically biased. I first construct a communication game between an investor and a (possibly) biased securities analyst. I find an equilibrium characterized by the following properties: first, the investor reacts more to bad news than to good news, and second, the difference in this reaction is higher when the investor has a greater prior suspicion that the analyst is a biased type. I then use parametric and nonparametric techniques and a large database of earnings and forecasts to test these predictions, and find that the evidence supports them.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available