Finance - Theses
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ItemNew Approaches of Utilizing Firm Characteristics in Empirical Asset Pricing( 2022)Firm characteristics carry very important information about stock prices and can be widely used in various aspects of asset pricing. This thesis contains 3 new applications of firm characteristics combined with some new technologies, which brings new understandings to several important questions: In chapter 3, my co-authors and I apply the General Empirical Likelihood (GEL) estimator to ask and answer the question of, which characteristic-sorted portfolios provide valuable information about the stochastic discount factor (SDF). We estimate a non-parametric SDF from a set of portfolios, then test whether excluding a portfolio changes the implied SDF. Though related to traditional asset pricing tests, our approach has several advantages: we test all portfolios jointly and can incorporate trading costs easily. We show four portfolios provide independent information about the SDF after accounting for trading costs: the Market and Profitability factors, an Investment-based portfolio, and the Value-Momentum-Profitability anomaly portfolio. The remaining portolios are redundant. We show both the joint testing and transaction cost adjustments are important for inference, and provide a simple way to implement our tests. In chapter 4, I introduce a Machine Learning (ML) based approach to construct comparable groups that researchers often use to compute the abnormal part of stock returns. The characteristics shown in chapter 3 to be important to asset pricing are included in this study as candidate characteristics. In order for stock expected returns to be similar within groups and disperse across groups, I use the ML based approach to select characteristics that best distinguish expected returns, and cutoffs points where returns are most sensitive to the underlying characteristics. I show that: 1) the combination of chosen characteristics changes over time; 2) fewer fund managers are identified to be stock pickers once the time-variation in comparable groups is incorporated; 3) and the resulting portfolios exhibit desirable properties as basis assets. In chapter 5, I adopt the ML based comparable groups from chapter 4 to analyze the market timing and stock picking skills of actively managed mutual funds. Compared to traditional studies which are prone to model mis-specification due to the difficulties in handling large dimensions and non-linearity, my approach can extract information about returns from a large number of characteristics, and allows for complex and time-varying relations between stock returns and firm characteristics. To the contrary of the consensus in prior literature, I find strong evidence in support of fund timing skills. On average, funds exhibit significantly positive timing performance. Cross-sectionally, the best timers continue to outperform others for at least three years after the ranking period. There is some picking return for the average fund but out-performance in picking does not persist. These results have real-world implications. I further show that investors can use my timing measure to identify funds with high future risk-adjusted performance.
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ItemComputational complexity of decisions: Quantifying computational hardness and its effects on human computation( 2021)Humans are presented daily with decisions that require solving complex problems. In many cases, solving these problems is computationally hard. This raises a tension between the computational capacity of the agent and the computational requirements of a task. Whilst the underlying invariants of this mechanism remain unclear in cognition, they have been widely studied in computer science. I build on theoretical and empirical work in computational complexity, which characterizes the intrinsic computational hardness of problems. I first present an adaptation of this theoretical framework for the study of human cognition by introducing a set of metrics of hardness of instances of problems. I do this in a way that is independent of any algorithm or computational model and that can be generalized to other problems. Based on this, I explore empirically, in a set of lab experiments, how these task-independent metrics of hardness affect human problem-solving. I do this at two levels of analysis. Firstly, I study how these metrics affect human performance at the behavioral level in three canonical computational problems: the knapsack problem, the traveling salesperson problem and the Boolean satisfiability problem. Secondly, I examine the relation between computational hardness and the neural processes associated with problem-solving, employing ultra-high field functional MRI. I find that the metrics of intrinsic hardness put forward here predict human performance and time-on-task across the three computational problems in a similar way. Moreover, I identify the neural correlates of computational hardness in the knapsack task, a complex problem-solving task. I show that this framework can be used for the study of the neural underpinnings of problem-solving by providing a generic definition of cognitive demand. The results of these studies provide support for the conceptual premise that the quantification of intrinsic hardness is fundamental in the development of more refined theories of human decision-making and its neural underpinnings. Critically, they provide a framework to study how humans adapt to computational complexity and how intrinsic hardness of tasks affect the reliability of human decision-making. This could inform public policy by identifying which decisions over products involve solving problems that require computational resources beyond those available to an agent, and how this affects decisions.
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ItemEssays on Political Economy of Finance( 2021)This thesis explores mechanisms that connect firms to politicians, as well as new channels through which firms benefit from being politically connected, under different ecopolitical environments. It contains three essays examining various aspects of finance at its intersection with political science. The first essay exploits Donald Trump’s nonpolitical background and surprise election victory to identify the value of sudden presidential ties among S&P 500 firms. In our setting firms did not choose to become politically connected, so we identify treatment effects comparatively free of selection bias prevalent in this literature. Firms with presidential ties enjoyed greater abnormal returns around the 2016 election. Since Trump’s inauguration, connected firms had better performance, received more government contracts, and were less subject to unfavorable regulatory actions. We rule out a number of confounding factors, including industry designation, sensitivity to Republican platforms, campaign finance, and lobbying expenditures. The second essay finds that borrowers from the same state as the Chairman of the US Senate Banking Committee, whom I term "connected", are able to borrow at spreads 14 basis points lower than other borrowers. Connected borrowers’ contributions toward the Chairman are influenced by their cost of loans, but the same is not true for nonconnected borrowers. Findings suggest the Chairman is incentivized by reelection to actively help connected borrowers obtain cheaper loans. Banks that offer a larger fraction of connected loans enjoy higher future excess stock returns. Results are consistent with the existence of a quid pro quo relationship triangle between firms, banks, and politicians. The third essay examines how changes in local political leadership affect firms’ gover- nance structures. Using a novel dataset, I document that following the appointment of a new city-level Chinese Communist Party (CCP) secretary, local firms increase (decrease) the fraction of directors who share a common birthplace with the incoming (departing) secretary. This appears to be a channel through which Chinese firms establish political connections. Firms with a higher percentage of birthplace-connected directors exhibit higher abnormal returns around secretary appointments. These firms enjoy superior accounting performances and attract institutional fund flows. I reject an alternative hypothesis that these directors are appointed to company boards on the "orders" of the politician, rather than actively recruited by firms.
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ItemThree Essays on CEO Compensation and the Agency Problem of Debt( 2020)Jensen and Meckling [1976] propose that compensating a manager with debt-like payment, a.k.a. “inside debt”, can be a mechanism to mitigate the agency problem of debt. Recent empirical papers document that higher inside debt held by a chief executive officer (“CEO”), which is measured by the present value of their supplemental retirement benefits, is associated with more conservative management and lower cost of debt. However, the grant of inside debt might originate from rent extraction purposes, and happens to encourage risk aversion among CEOs. Therefore, it remains unclear whether companies use CEO inside debt as a mechanism to mitigate the agency problem of debt. To address this question, my thesis examines how U.S. public companies adjust their use of CEO inside debt in three corporate events that involve changes to the agency problem of debt. In the first essay (Chapter 4), I show that new active blockholders adjust CEO inside debt-equity ratios to increase total firm value, not just equity value. These investors are more like to arise when CEO inside debt-equity ratios are not properly set up to maximize total firm value. The speed of adjustments towards the appropriate ratios triples in the presence of active blockholders, but returns to the normal level once these investors “exit”. Such compensation adjustments are associated with positive stock and bond abnormal returns over the active block holding period. I also find that new active blockholders arise and restructure compensation when the old structures over-align CEOs’ incentives to either shareholders’ or debtholders’ interests. I argue that superb stock-picking skill, a mean-reverting process of compensation changes, or co-founding firm characteristics cannot explain the large compensation adjustments during active blockholders’ presence. Instead, these blockholders actively affect CEO compensation structures by appointing their favored directors into the targeted firms’ boards, especially into the compensation or governance committees. In the second essay (Chapter 5), I show that companies raise their CEO inside debt to address the heightened agency problem of debt due to increased leverage, after they remove anti-takeover provisions (“ATPs”). By using a difference-in-difference-in-difference analysis, I document significant increases in CEO inside debt-equity ratios after companies remove ATPs. Inside debt also rises significantly after ATP removals, which accounts for 70% of increases in the overall ratios. In contrast, inside equity significantly decreases after companies remove ATPs, as these companies reduce the stocks and options awarded to their CEOs. These findings are robust to different explanatory variables, matching samples, and removals of certain ATPs. Further analysis displays that inside debt-equity ratios increase continually for the first three years after ATP removals. This trend coincides with the after-ATP-removal spike in leverage, which is caused by increased debt issuance. I also show that increasing inside debt-equity ratios, especially increasing inside debt, helps companies reduce the cost of borrowing after ATP removals. However, increasing inside debt can only address part of the heightened agency problem of debt, as companies still face more costly debt financing after ATP removals. Despite the increase in inside debt and decrease in inside equity, CEOs take more risks after their companies remove ATPs. This result suggests that removing ATPs can substitute for the role of inside equity in providing CEOs with more risk-taking incentives. In the third essay (Chapter 6), I explore the timing strategies for bond issuances based on disclosures of CEO inside debt. During the months before proxy statement releases, new changes in CEO inside debt are private information to companies’ insiders. Companies can exploit this information asymmetry to issue bonds when the market, based on publicly available information of inside debt, perceives these companies’ debt agency problems as relatively insignificant. I find that companies cluster their bond offerings in the immediate quarters after (before) disclosures of positive (negative) inside debt changes. The tendencies to time bond issuances based on inside debt disclosures also increase with the magnitudes of the disclosed changes. In addition, the adoption of these timing strategies is more observable when the issuing firms are regular issuers or when the new issues do not include covenants, especially the debt-restriction covenants. Finally, I verify that these timing strategies help reduce the cost of borrowing. The bonds issued at favorable times, i.e. right after positive change disclosures or before negative change disclosures, have lower offering yield spreads than those issued at non-favorable times.
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ItemEssays on Perfect Foresight in Asset Pricing( 2020)Through experimental and theoretical analysis, this thesis addresses the question of how the context of information in capital markets can affect the occurrence of perfect foresight equilibria. It contains three essays that build on theoretical and experimental applications of the perfect foresight assumption. The first essay contrasts theoretical and experimental strains of information aggregation – the ability of prices to aggregate disparate pieces. It contains a methodological guide for designing robust information aggregation experiments with a detailed description of the pilot studies that were used to develop the experimental study in the second essay. The second essay introduces two new theoretical concepts to the analysis of rational expectations equilibrium models. These concepts stem from “stability” characteristics inherent to the perfect foresight state-price mapping. Differences in stability characteristics are shown to arise from differences in the initial information structure underlying the aggregation problems. In the experiment, we test information aggregation with two fundamental information structures in continuous time double auction asset markets: The first information structure is motivated by the canonical information aggregation model in theoretical asset pricing. In this setting, the asset traded pays according to the average privately held information signal in the market. This setting has a stable state-price mapping and is shown to aggregate information well. The second information structure is motivated by prediction markets and studies in experimental finance. Both feature winner-take-all contracts where binary pay depends on the signal type held by the majority of agents. This setting is unstable under the theoretical stability concepts and is shown to aggregate information less efficiently. The third essay examines the use of perfect foresight when modelling disagreement in financial markets. In particular, we examine the conditions under which the perfect foresight approach can be used in a rational expectations equilibrium model. We show that an agent’s perfect foresight may be inconsistent with their own beliefs (based on subjective probabilities) unless their higher-order beliefs (about other participants’ beliefs) are correct.