Essays In Corporate Finance

In the first chapter, Activist Settlements\u27\u27, I provide a theoretical framework to study the economics of settlements between activist investors and boards. The activist can demand that his proposal be implemented right away ( action settlement ) or demand a number of board seats ( board settlement ), which also gives the activist access to better information. I find that the incumbent\u27s rejection of board settlement reflects more of its private information than the rejection of action settlement does. Therefore, demanding board settlement increases the activist\u27s credibility to run a proxy fight upon rejection and leads to a higher likelihood of reaching a settlement in the first place. I draw several implications and empirical predictions of my model, e.g., related to shareholder value, costs of proxy fight, and activist expertise. The second chapter, Corporate Control Activism\u27\u27, co-authored with Doron Levit, studies the role of activist investors in the M&A market. Our theory proposes that activist investors have an inherent advantage relative to bidders in pressuring entrenched incumbents to sell. As counterparties to the acquisition, bidders have a fundamental conflict of interests with target shareholders from which activist investors are immune. Therefore, unlike activists, the ability of bidders to win proxy fights is very limited. This result is consistent with the large number of activist campaigns that have resulted with the target\u27s sale to a third party and the evidence that most proxy fights are launched by activists, not by bidders

Essays In Corporate Finance

In the first chapter, Activist Settlements\u27\u27, I provide a theoretical framework to study the economics of settlements between activist investors and boards. The activist can demand that his proposal be implemented right away ( action settlement ) or demand a number of board seats ( board settlement ), which also gives the activist access to better information. I find that the incumbent\u27s rejection of board settlement reflects more of its private information than the rejection of action settlement does. Therefore, demanding board settlement increases the activist\u27s credibility to run a proxy fight upon rejection and leads to a higher likelihood of reaching a settlement in the first place. I draw several implications and empirical predictions of my model, e.g., related to shareholder value, costs of proxy fight, and activist expertise. The second chapter, Corporate Control Activism\u27\u27, co-authored with Doron Levit, studies the role of activist investors in the M&A market. Our theory proposes that activist investors have an inherent advantage relative to bidders in pressuring entrenched incumbents to sell. As counterparties to the acquisition, bidders have a fundamental conflict of interests with target shareholders from which activist investors are immune. Therefore, unlike activists, the ability of bidders to win proxy fights is very limited. This result is consistent with the large number of activist campaigns that have resulted with the target\u27s sale to a third party and the evidence that most proxy fights are launched by activists, not by bidders

Corporate Control Activism

We identify a commitment problem that prevents bidders from unseating resisting and entrenched incumbent directors of target companies through proxy fights. We discuss potential remedies and argue that activist investors are more resilient to this commitment problem and can mitigate the resulting inefficiencies by putting such companies into play. This result holds even if bidders and activists have similar expertise and can use similar techniques to challenge the incumbents, and it is consistent with the evidence that most proxy fights are launched by activists, not by bidders. Building on this insight, we study the implications of activist interventions on the M&A market

Fast algorithms for smooth and monotone covariance matrix estimation

In this thesis the problem of interest is, within the setting of financial risk management, covariance matrix estimation from limited number of high dimensional independent identically distributed (i.i.d.) multivariate samples when the random variables of interest have a natural spatial indexing along a low-dimensional manifold, e.g., along a line. Sample covariance matrix estimate is fraught with peril in this context. A variety of approaches to improve the covariance estimates have been developed by exploiting knowledge of structure in the data, which, however, in general impose very strict structure. We instead exploit another formulation which assumes that the covariance matrix is smooth and monotone with respect to the spatial indexing. Originally the formulation is derived from the estimation problem within a convex-optimization framework, and the resulting semidefinite-programming problem (SDP) is solved by an interior-point method (IPM). However, solving SDP via an IPM can become unduly computationally expensive for large covariance matrices. Motivated by this observation, this thesis develops highly efficient first-order solvers for smooth and monotone covariance matrix estimation. We propose two types of solvers for covariance matrix estimation: first based on projected gradients, and then based on recently developed optimal first order methods. Given such numerical algorithms, we present a comprehensive experimental analysis. We first demonstrate the benefits of imposing smoothness and monotonicity constraints in covariance matrix estimation in a number of scenarios, involving limited, missing, and asynchronous data. We then demonstrate the potential computational benefits offered by first order methods through a detailed comparison to solution of the problem via IPMs

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Essays in Corporate Finance

In the first chapter, Activist Settlements\u27\u27, I provide a theoretical framework to study the economics of settlements between activist investors and boards. The activist can demand that his proposal be implemented right away ( action settlement ) or demand a number of board seats ( board settlement ), which also gives the activist access to better information. I find that the incumbent\u27s rejection of board settlement reflects more of its private information than the rejection of action settlement does. Therefore, demanding board settlement increases the activist\u27s credibility to run a proxy fight upon rejection and leads to a higher likelihood of reaching a settlement in the first place. I draw several implications and empirical predictions of my model, e.g., related to shareholder value, costs of proxy fight, and activist expertise. The second chapter, Corporate Control Activism\u27\u27, co-authored with Doron Levit, studies the role of activist investors in the M&A market. Our theory proposes that activist investors have an inherent advantage relative to bidders in pressuring entrenched incumbents to sell. As counterparties to the acquisition, bidders have a fundamental conflict of interests with target shareholders from which activist investors are immune. Therefore, unlike activists, the ability of bidders to win proxy fights is very limited. This result is consistent with the large number of activist campaigns that have resulted with the target\u27s sale to a third party and the evidence that most proxy fights are launched by activists, not by bidders

Essays in Corporate Finance

In the first chapter, Activist Settlements\u27\u27, I provide a theoretical framework to study the economics of settlements between activist investors and boards. The activist can demand that his proposal be implemented right away ( action settlement ) or demand a number of board seats ( board settlement ), which also gives the activist access to better information. I find that the incumbent\u27s rejection of board settlement reflects more of its private information than the rejection of action settlement does. Therefore, demanding board settlement increases the activist\u27s credibility to run a proxy fight upon rejection and leads to a higher likelihood of reaching a settlement in the first place. I draw several implications and empirical predictions of my model, e.g., related to shareholder value, costs of proxy fight, and activist expertise. The second chapter, Corporate Control Activism\u27\u27, co-authored with Doron Levit, studies the role of activist investors in the M&A market. Our theory proposes that activist investors have an inherent advantage relative to bidders in pressuring entrenched incumbents to sell. As counterparties to the acquisition, bidders have a fundamental conflict of interests with target shareholders from which activist investors are immune. Therefore, unlike activists, the ability of bidders to win proxy fights is very limited. This result is consistent with the large number of activist campaigns that have resulted with the target\u27s sale to a third party and the evidence that most proxy fights are launched by activists, not by bidders

Corporate Governance in the Presence of Active and Passive Delegated Investment

We examine the governance role of delegated portfolio managers. In our model, investors decide how to allocate their wealth between passive funds, active funds, and private savings, and fund fees are endogenously determined. Funds' ownership stakes and fees determine their incentives to engage in governance. Whether passive fund growth improves governance depends on whether it crowds out private savings or active funds. In the former case, it improves governance even though it is accompanied by lower fund fees, whereas in the latter case it can be detrimental to governance. Overall, passive fund growth improves governance only if it does not increase fund investors' returns too much. Regulations that decrease funds' costs of engagement can be opposed by both fund investors and fund managers even though they are value-increasing

Covariance matrix estimation for interest-rate risk modeling via smooth and monotone regularization

Estimating covariance matrices in high-dimensional settings is a challenging problem central to modern finance. The sample covariance matrix is well-known to give poor estimates in high dimensions with insufficient samples, and may cause severe risk underestimates of optimized portfolios in the Markowitz framework. In order to provide useful estimates in this regime, a variety of improved covariance matrix estimates have been developed that exploit additional structure in the data. Popular approaches include low-rank (principal component and factor analysis) models, banded structure, sparse inverse covariances, and parametric models. We investigate a novel nonparametric prior for random vectors which have a spatial ordering: we assume that the covariance is monotone and smooth with respect to this ordering. This applies naturally to problems such as interest-rate risk modeling, where correlations decay for contracts that are further apart in terms of expiration dates. We propose a convex optimization (semi-definite programming) formulation for this estimation problem, and develop efficient algorithms. We apply our framework for risk measurement and forecasting with Eurodollar futures, investigate limited, missing and asynchronous data, and show that it provides valid (positive-definite) covariance estimates more accurate than existing methods

Covariance matrix estimation for interest-rate risk modeling via smooth and monotone regularization

Estimating covariance matrices in high-dimensional settings is a challenging problem central to modern finance. The sample covariance matrix is well-known to give poor estimates in high dimensions with insufficient samples, and may cause severe risk underestimates of optimized portfolios in the Markowitz framework. In order to provide useful estimates in this regime, a variety of improved covariance matrix estimates have been developed that exploit additional structure in the data. Popular approaches include low-rank (principal component and factor analysis) models, banded structure, sparse inverse covariances, and parametric models. We investigate a novel nonparametric prior for random vectors which have a spatial ordering: we assume that the covariance is monotone and smooth with respect to this ordering. This applies naturally to problems such as interest-rate risk modeling, where correlations decay for contracts that are further apart in terms of expiration dates. We propose a convex optimization (semi-definite programming) formulation for this estimation problem, and develop efficient algorithms. We apply our framework for risk measurement and forecasting with Eurodollar futures, investigate limited, missing and asynchronous data, and show that it provides valid (positive-definite) covariance estimates more accurate than existing methods