Three Essays on Corporate Disclosure

Corporate disclosure is the most important source of information about the firm for the outside investors. While some disclosure of public firms is mandated by regulation, firm managers can provide extra information at their discretion by making voluntary disclosures. On the other hand, even the reports required by regulation can be disclosed untruthfully. This thesis is structured in three chapters, each addressing a specific issue in voluntary disclosure and misreporting. In the first chapter, titled Voluntary Disclosure and Informed Trading'', I study the impact of informed trading on voluntary corporate disclosure in the presence of two frictions: cost of disclosure and value of manager's information. In the absence of both frictions, informed trading has no impact on disclosure even when traders are not certain whether the manager has information. If disclosure is costly, then informed trading reduces disclosure. Since traders can discover favorable information about the firm, additional disclosure of the information is not necessary. If manager's information is valuable for the firm, then informed trading increases disclosure. Since traders can discover unfavorable information about the firm, the manager with such information has less incentives to pool with uninformed managers and discloses to show that he is informed. I also show that informed trading can have both a positive and a negative real effect on the firm value by crowding in or crowding out information production in the firm. These results hold for general information structures and are robust if traders can choose how much information to acquire. The second chapter, titled Misreporting and Feedback Effect'' and co-authored with Prof. Hui Chen of the University of Zurich, studies the incentives of firms to misreport information in the presence of feedback effect from financial markets. Stock price often provides firms with new information, which can be used in the firms' subsequent real decisions. We examine how this informational feedback from the financial market affects a myopic firm manager's incentive to misreport, and how the misreporting further affects the firm's price and value. We find that the manager overstates his report more in the presence of feedback, but this misreporting brings forth both positive price and real effects for the firm. Intuitively, overstating the report encourages information production in the market because (a) it renders accounting reports less reliable as a source of information, and (b) investors expect higher trading profits from larger capital investment. The new incremental information improves investment efficiency when it is revealed to the firm manager through trading and used in the firm's subsequent investment decisions. As a consequence, the capital investment is higher when there is feedback effect. In the third chapter, titled ``Voluntary Disclosure and Margin Constraints'', I develop a dynamic model of voluntary corporate disclosure that explains clustering of negative announcements observed in practice. A manager may receive a signal about the firm's asset value and can disclose it to traders with margin constraints. I show that the manager postpones delivery of a negative signal until the margin constraints tighten. In contrast to previous studies, the clustering of announcements happens even if there are no negative updates in traders' beliefs about the firm value.

Essays in Dynamic Corporate Finance

Francesca Maria Zucchi

This thesis examines the effects of financing frictions on corporate decisions using dynamic models. Accounting for financing frictions helps reconcile a number of regularities that are hard to explain within the Modigliani-Miller framework. For instance, financing frictions provide incentives for firms to keep liquidity on their balance sheets as a precautionary hedge---a pattern that has been heavily debated in light of the secular increase of cash-to-asset ratios of U.S. firms. The first chapter develops a model that investigates the relation between corporate policies and secondary stock market liquidity. I show that secondary market illiquidity limits a firm's ability to hold precautionary liquidity, exacerbates financial constraints, reduces investment, and decreases firm value. The model reproduces the positive relation between market liquidity and corporate cash holdings observed in the data. This relation might be surprising since firms with illiquid stocks, which have been documented to be more financially constrained than more liquid peers, should be willing to keep more precautionary cash. I also show that the illiquidity-driven drop in firm value can feed back into the secondary market by deterring the participation of liquidity providers, thereby making the market more illiquid. The self-reinforcing nature of this relation gives rise to an internal-external liquidity loop, which singles out a propagation mechanism between financial markets and the corporate sector. From a banking perspective, liquidity hoarding has been a hotly debated topic among academics and policy-makers in the aftermath of the 2007-2009 financial crisis. The second chapter, which is a joint work with Prof. Erwan Morellec and Marco Della Seta, develops a dynamic model of the effects of liability structure and liquid reserves on banks' insolvency risk. When a bank relies on short-term debt financing, negative operating shocks get amplified as the bank weaker fundamentals also translate into losses when rolling over short-term debt. This amplification mechanism leads to an increase in default risk that gets more pronounced as debt maturity decreases and rollover losses increase. Because of this amplification mechanism, banks with identical debt ratios and liquid reserves but different debt structures have different default risk. Heavy exposure to rollover risk can also lead banks to implement gambling strategies when close to default. The third chapter, which is a joint work with Prof. Semyon Malamud, analyzes firms' optimal policies in a general equilibrium setting. It characterizes optimal liquidity management, innovation, and production decisions for a continuum of firms facing financing frictions and the threat of creative destruction. We show that liquidity constraints lead firms to cut production and increase markups, which are then countercyclical with respect to firm-specific shocks. We also illustrate that liquidity constraints may spur firms' investment in innovation and give rise to a non-monotonic cash-investment relation. We embed our single-firm dynamics in a Schumpeterian model of endogenous growth and demonstrate that financing frictions have a non-monotonic effect on economic growth and may increase aggregate consumption. When the corporate sector is constrained, liquidity injections by the government have real effects and can stimulate growth.

EPFL2015

Structure-preserving approaches and data-driven closure modeling for model order reduction

Nicolò Ripamonti

In this thesis, we propose model order reduction techniques for high-dimensional PDEs that preserve structures of the original problems and develop a closure modeling framework leveraging the Mori-Zwanzig formalism and recurrent neural networks. Since high-fidelity approximations of PDEs often result in a large number of degrees of freedom, the need for iterative evaluations for numerical optimizations and rapid feedback is computationally challenging.The first part of this thesis is devoted to conserving the high-dimensional equation's invariants, symmetries, and structures during the reduction process. Traditional reduction techniques are not guaranteed to yield stable reduced systems, even if the target problem is stable. In the context of fluid flows, the skew-symmetric structure of the problem entails the preservation of the kinetic energy of the system. By preserving the same structure at the level of the reduced model, we obtain enhanced stability, and accuracy and the reduced model acquires physical significance by preserving a surrogate of the energy of the original problem. Next, we focus on Hamiltonian systems, which, being driven by symmetry, are a source of great interest in the reduction community. It is well known that the breaking of these symmetries in the reduced model is accompanied by a blowup of the system energy and flow volume. In this thesis, geometric reduced models for Hamiltonian systems are further developed and combined with the dynamically orthogonal methods, addressing the poor reducibility in time of advection-dominated problems. The reduced solution is expressed as a linear combination of a finite number of modes and coincides with the symplectic projection of the high-fidelity Hamiltonian problem onto the tangent space of the approximating manifold. An error surrogate is used to monitor the approximation ability of the reduced model and make a change in the rank of the approximating system if necessary. The method is further developed through a combination of DEIM and DMD to reduce non-polynomial nonlinearities while preserving the symplectic structure of the problem and applied to the Vlasov-Poisson system.In the second part of the thesis, we consider several data-driven methods to address the poor accuracy in the under-resolved regime for Galerkin reduced models via a closure term. The closure term is developed systematically from the Mori-Zwanzig formalism by introducing projection operators on the spaces of resolved and unresolved scales, thus resulting in an additional memory integral term. The interaction between different scales turns out to be nonlocal in time and dominated by a high-dimensional orthogonal dynamics equation, which cannot be solved precisely and efficiently. Several classical methods in the field of statistical mechanics are used to approximate the memory term, exploiting the finiteness of the memory kernel support. We conclude this thesis by showing through numerical experiments how long short-term memory networks, i.e., machine learning structures characterized by feedback connections, represent a valid tool for approximating the additional memory term.

EPFL2022

Structure-preserving approaches and data-driven closure modeling for model order reduction

Nicolò Ripamonti

EPFL2022

Essays in Dynamic Corporate Finance

Francesca Maria Zucchi

EPFL2015