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This paper studies the impact of financial reporting scrutiny on (private) debt contracting in the presence of two capital market frictions: a cash-diversion problem and an asset-substitution problem. When cash flow realizations are not verifiable, firms have an incentive to divert cash by manipulating their accounting reports. When firms' project choices are not verifiable, post financing, they may have an incentive to choose riskier projects than desired by their financiers.
This dissertation focuses the corporate behaviors in a dynamic world with uncertainty. Especially, I am interested in how firms tradeoff their investment and cash savings when external financing is costly. The first two chapters fit into this theme. One considers optimal investment and financing policies when uncertainty itself is time-varying, the second investigates how firms prepare themselves against devaluation risks. Both chapters build dynamic corporate theories and test them empirically.
This study investigates a dynamic model of analyst forecasting where the ordering of forecasts and analysts' information endowments are endogenously determined. Analysts are probabilistically informed, potentially biased, and can increase their informedness through information acquisition. I characterize the unique equilibrium which holds for general distributions. The results show that analysts with less bias, greater precision, or a greater likelihood of being informed forecast earlier.
The rapid advance of information technologies largely facilitated firms' data-driven decision making. Particularly, in operations management practices, firms could continuously collect information to refine their demand knowledge, and integrate this process into their relevant operational decisions, e.g. pricing, inventory, and market entry, known as demand learning. Demand learning in complex business systems is often tangled with complex strategic interactions, thus requiring a deep understanding of how it affects the strategic relationship among players in various business setups.
A central topic in empirical asset pricing is how to explain anomalies in various trading horizons. This dissertation contains two essays that study several anomalies in medium-term/long-term investment in the equity market and in high-frequency trading in the foreign exchange market. In the first essay, I propose an investor underreaction model with heterogeneous truncations across time and stocks. In this setting, investors are more attracted to dramatic changes in stock prices than to gradual changes.
This thesis is concerned with addressing operational issues in two types of dynamic markets where queueing plays an important role: limit order books (financial industry), and dynamic matching markets (residential real estate). We first study the smart order routing decisions of investors in fragmented limit order book markets and the implications on the market dynamics. In modern equity markets, participants have a choice of many exchanges at which to trade.
In this thesis, we study economics and operations of cloud computing, and we propose new matching methods in observational studies that enable us to estimate the effect of green building practices on market rents. In the first part, we study a stylized revenue maximization problem for a provider of cloud computing services, where the service provider (SP) operates an infinite capacity system in a market with heterogeneous customers with respect to their valuation and congestion sensitivity.
Sequential decision making problems are ubiquitous in a number of research areas such as operations research, finance, engineering and computer science. The main challenge with these problems comes from the fact that, firstly, there is uncertainty about the future. And secondly, decisions have to be made over a period of time, sequentially. These problems, in many cases, are modeled as Markov Decision Process (MDP). Most real-life MDPs are ‘high dimensional’ in nature making them challenging from a numerical point of view. We consider a number of such high dimensional MDPs.
This thesis studies three game theoretic models of pricing, in which a seller is interested in optimally pricing and allocating her product or service to a market of agents, in order to maximize her revenue. These markets feature a large number of self-interested agents, who are generally heterogeneous with respect to some payoff relevant feature, e.g., willingness to pay when agents are consumers or private cost when agents are firms.
Conventional wisdom and a wealth of research suggest that effective networks are an important key to career success. Yet, why do so many people struggle to build and maintain professional relationships? In this dissertation I argue that, rather than not knowing how to network, most people feel conflicted about the idea of networking. The present research applies a motivational framework to networking. Building on the idea of lay theories in motivational psychology, this dissertation investigates how lay theories of social intelligence influence networking engagement.