This thesis presents a mathematical framework to model trading of financial assets on an exchange. The interaction between agents on the exchange is modeled as the Nash equilibrium of a demand schedule auction. The submission of demand schedules in the auction is meant to proxy for the submission of limit and market orders on an exchange. Chapter 1 considers this auction in a one-period setting, highlighting the importance of noisy flow for obtaining a unique Nash equilibrium.
\r\n\r\nChapter 2 is the core of the thesis and considers the auction in a continuous time setting. Here the agents trading on the exchange have quadratic-type preferences, and in equilibrium they must clear an exogenously specified stream of market orders. Chapter 3 considers alternative and more realistic dynamics for the exogenous market orders. Chapter 4 endogenizes the market orders by considering an agent executing orders on behalf of noisy clients.. Chapter 5 considers the same model as in Chapter 2, except with a consumption based utility function for each agent.
", "doi": "10.7907/9ks2-fa45", "publication_date": "2021", "thesis_type": "phd", "thesis_year": "2021" }, { "id": "thesis:13997", "collection": "thesis", "collection_id": "13997", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:11112020-185040796", "type": "thesis", "title": "Attention, Strategy, and the Human Mind", "author": [ { "family_name": "Li", "given_name": "Xiaomin", "orcid": "0000-0002-1286-4012", "clpid": "Li-Xiaomin" } ], "thesis_advisor": [ { "family_name": "Camerer", "given_name": "Colin F.", "orcid": "0000-0003-4049-1871", "clpid": "Camerer-C-F" } ], "thesis_committee": [ { "family_name": "Tamuz", "given_name": "Omer", "orcid": "0000-0002-0111-0418", "clpid": "Tamuz-O" }, { "family_name": "Adolphs", "given_name": "Ralph", "orcid": "0000-0002-8053-9692", "clpid": "Adolphs-R" }, { "family_name": "Jin", "given_name": "Lawrence J.", "clpid": "Jin-Lawrence-J" }, { "family_name": "Camerer", "given_name": "Colin F.", "orcid": "0000-0003-4049-1871", "clpid": "Camerer-C-F" } ], "local_group": [ { "literal": "div_hss" } ], "abstract": "The current dissertation chapters try to discover the role of visual attention in decision making from three different perspectives: 1) how attention bias affects strategic decision makings, 2) how to model eye movement data to better understand strategic decisions, 3) how to manipulate simple choices through visual saliency.
\r\n\r\nThe second chapter introduces a series of novel image games, where players need to match, hide, or seek against other players. We apply a pure computational way: the state-of-art visual saliency algorithm, Saliency Attentive Map (SAM) to measure visual saliency. We find that visual saliency can predict strategic behaviors well. The concentration of salience is correlated with the rate of matching when players are both trying to match location choices (r=.64). In hider-seeker games, all players choose salient locations more often than predicted in equilibrium, creating a ``seeker\u2019s advantage'' (seekers win 9\\% of games rather than the 7\\% predicted in equilibrium). The 9\\% win rate is robust for paying higher stakes and using a between-subjects design. Salience-choice relations are consistent with cognitive hierarchy and level-k models in which strategically naive level 0's are biased toward salience, and higher-level types are not directly biased toward salience, but choose salient locations because they believe lower-level types do. Other links between salience as understood in psychology and hypothesized in economics are suggested.
\r\n\r\nThe third chapter is a continuation of the second chapter, but with a different emphasis. The third chapter proposes a way to dynamically model gaze transitional data in games utilizing a class of machine learning model: hidden markov models(HMM). The HMM model reveals how the attentional bias affects strategies on different time point. Besides, this model well connects to the k level behavioral method and can make novel predictions on strategic levels. With further containing the fixation duration data, we developed a continuous-time hidden Markov model (cgtHMM), which can be used to predict how exactly time pressure changes choices and the seeker\u2019s advantage.
\r\n\r\nDistinct from the other two, chapter four aims at manipulating binary choice outcomes through the change of visual saliency distribution under SAM. We design a value-based choice paradigm where both the reward property and the attention property are well separated and controlled. The experimental results indicate that visual saliency can enhance the choice correction rates when the more rewarding outcome is also labeled salient. It can also shorten the decision time needed. Such a result can be explained by a saliency-enhanced rational inattention model by incorporating attention factors in the traditional RI model.
", "doi": "10.7907/6zqy-pt73", "publication_date": "2021", "thesis_type": "phd", "thesis_year": "2021" }, { "id": "thesis:10913", "collection": "thesis", "collection_id": "10913", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:05172018-154911560", "primary_object_url": { "basename": "Mali Zhang_Dissertation_20180517_vf.pdf", "content": "final", "filesize": 725331, "license": "other", "mime_type": "application/pdf", "url": "/10913/1/Mali Zhang_Dissertation_20180517_vf.pdf", "version": "v4.0.0" }, "type": "thesis", "title": "Information and Strategic Decision-Making in the Oil and Gas Industry: An Empirical Assessment", "author": [ { "family_name": "Zhang", "given_name": "Mali", "orcid": "0000-0002-7762-5557", "clpid": "Zhang-Mali" } ], "thesis_advisor": [ { "family_name": "Shum", "given_name": "Matthew S.", "orcid": "0000-0002-6262-915X", "clpid": "Shum-M-S" } ], "thesis_committee": [ { "family_name": "Shum", "given_name": "Matthew S.", "orcid": "0000-0002-6262-915X", "clpid": "Shum-M-S" }, { "family_name": "Alvarez", "given_name": "R. Michael", "orcid": "0000-0002-8113-4451", "clpid": "Alvarez-R-M" }, { "family_name": "Katz", "given_name": "Jonathan N.", "orcid": "0000-0002-5287-3503", "clpid": "Katz-J-N" }, { "family_name": "Jin", "given_name": "Lawrence Jiaqi", "clpid": "Jin-Lawrence-J" } ], "local_group": [ { "literal": "div_hss" } ], "abstract": "This dissertation comprises three essays addressing questions from Industrial Organization Economics concerning the oil and gas industry. The essays offer substantive contributions to the study of joint decision-making (Chapter 2), extrapolative beliefs (Chapter 3), and auctions (Chapter 4).
\r\n\r\nChapter 2 investigates the quality of joint operations, where multiple oil and gas companies explore a piece of land together. By developing a discrete-choice model which can be matched to actual drilling data, I show that joint operators consisting of only large companies have the least accurate signals. Further counterfactual analyses show that the best policy governing joint operations depends on government priority: to maximize revenue or to avoid damage to the environment.
\r\n\r\nChapter 3, co-authored with Lawrence Jin and Matthew Shum, presents a model of dynamic investment and production in which producers over-extrapolate recent demand conditions into the future. We show theoretically and empirically that, in a volatile industry, these biased beliefs can be beneficial in the long-run by counteracting the general trend in the industry. Calibration of our model to Alaska oil exploration shows that the cushioning effect can be large in reducing price decline and accelerating price recovery.
\r\n\r\nChapter 4 examines whether common value or private value auction model best describes the bidding decisions made by oil and gas companies. The common value model suggests that more competition can lead to lower equilibrium bids from bidders and lower revenue. By analyzing tract auction data from Alaska, I find that common value components play a slightly larger role when observable heterogeneity is removed. However, expected revenue still increases with competition and plateaus when competition becomes sufficiently high.
", "doi": "10.7907/3GAD-4Z16", "publication_date": "2018", "thesis_type": "phd", "thesis_year": "2018" }, { "id": "thesis:10960", "collection": "thesis", "collection_id": "10960", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:05292018-140741507", "primary_object_url": { "basename": "Sui_Pengfei_2018.pdf", "content": "final", "filesize": 1434510, "license": "other", "mime_type": "application/pdf", "url": "/10960/1/Sui_Pengfei_2018.pdf", "version": "v5.0.0" }, "type": "thesis", "title": "Essays on Investor Beliefs and Asset Pricing", "author": [ { "family_name": "Sui", "given_name": "Pengfei", "orcid": "0000-0002-0364-4915", "clpid": "Sui-Pengfei" } ], "thesis_advisor": [ { "family_name": "Jin", "given_name": "Lawrence Jiaqi", "clpid": "Jin-Lawrence-J" }, { "family_name": "Cvitani\u0107", "given_name": "Jak\u0161a", "orcid": "0000-0001-6651-3552", "clpid": "Cvitani\u0107-J" } ], "thesis_committee": [ { "family_name": "Jin", "given_name": "Lawrence Jiaqi", "clpid": "Jin-Lawrence-J" }, { "family_name": "Cvitani\u0107", "given_name": "Jak\u0161a", "orcid": "0000-0001-6651-3552", "clpid": "Cvitani\u0107-J" }, { "family_name": "Shum", "given_name": "Matthew S.", "orcid": "0000-0002-6262-915X", "clpid": "Shum-M-S" }, { "family_name": "Camerer", "given_name": "Colin F.", "orcid": "0000-0003-4049-1871", "clpid": "Camerer-C-F" } ], "local_group": [ { "literal": "div_hss" } ], "abstract": "This dissertation is composed of three chapters addressing the connections between investor beliefs and asset pricing. Specifically, I focus on one prevailing pattern of investor beliefs in the finance literature, return extrapolation. The idea is that investor expectations about future market returns are a positive function of the recent past returns. In this dissertation, I use this concept to understand a number of facts in the asset pricing literature.
\r\n\r\nReturn extrapolation attracts growing attention in the literature, not only because it well explains real-world investors' expectations in the survey, but also because it significantly drives investor demand towards stocks. Therefore, we should anticipate a connection between return extrapolation measurement and the stock market dynamics. However, contrary to the intuition, previous empirical studies fail to document a significant connection. In Chapter 1, \"Time-varying Impact of Investor Sentiment\", I recover this connection. Specifically, I formally define investors who extrapolate past returns as extrapolators and incorporate their wealth level into analysis. My main finding is that return extrapolation interacts strongly with extrapolators' wealth level in predicting future market returns. Therefore, conditional on extrapolators' wealth level, return extrapolation significantly explains stock market returns.
\r\n\r\nThe return extrapolation concept also raises challenges to the asset pricing models under the rational expectation frameworks. Specifically, rational expectation theories lead to a positive correlation between expectations and future realized returns, whereas return extrapolation indicates a negative correlation. Given this discrepancy, there is a clear demand for a behavioral asset pricing model that can simultaneously explain survey evidence on investor expectations and the classical asset pricing puzzles. In Chapter 2, \"Asset Pricing with Return Extrapolation\", coauthored with Lawrence Jin, we present a new model of asset prices based on return extrapolation. The model is a Lucas-type general equilibrium framework, in which the agent has Epstein-Zin preferences and extrapolative beliefs. Unlike earlier return extrapolation models, our model allows for a quantitative comparison with the data on asset prices. When the agent's beliefs are calibrated to match survey expectations of investors, the model generates excess volatility and predictability of stock returns, a high equity premium, a low and stable risk-free rate, and a low correlation between stock returns and consumption growth.
\r\n\r\nIn Chapter 3, \"Dark Matter\" of Finance in the Survey, I investigate another attribute of investor beliefs\u2014tail risk perceptions. Although tail risks play significant roles in explaining asset pricing puzzles, researchers have very limited knowledge about them because tail events are difficult to observe. I use Shiller tail risk survey to empirically investigate tail risk perceptions. In this survey, investors are asked to report their estimated probability for a crash event in the U.S. stock market. However, when using survey data to understand investors\u2019 perception of tail risks, there are two fundamental challenges. First, is tail risks survey reliable? Second, to avoid cherry-picking, is there a unified framework to explain different attributes of investor beliefs? My analysis provides positive answers to both questions. First, I show that Shiller tail risk survey is reliable. More importantly, I show that return extrapolation can serve as a unified belief formation framework to explain not only variations in investor expectations but also in tail risk perceptions.
\r\n\r\n", "doi": "10.7907/F2BV-8Y73", "publication_date": "2018", "thesis_type": "phd", "thesis_year": "2018" }, { "id": "thesis:10264", "collection": "thesis", "collection_id": "10264", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:06042017-015517588", "primary_object_url": { "basename": "Song_Myungkoo_2017.pdf", "content": "final", "filesize": 1089002, "license": "other", "mime_type": "application/pdf", "url": "/10264/1/Song_Myungkoo_2017.pdf", "version": "v4.0.0" }, "type": "thesis", "title": "Essays on the Impact of Information Asymmetry", "author": [ { "family_name": "Song", "given_name": "Myungkoo", "orcid": "0000-0001-8602-3825", "clpid": "Song-Myungkoo" } ], "thesis_advisor": [ { "family_name": "Cvitani\u0107", "given_name": "Jak\u0161a", "orcid": "0000-0001-6651-3552", "clpid": "Cvitani\u0107-J" } ], "thesis_committee": [ { "family_name": "Cvitani\u0107", "given_name": "Jak\u0161a", "orcid": "0000-0001-6651-3552", "clpid": "Cvitani\u0107-J" }, { "family_name": "Ewens", "given_name": "Michael J.", "orcid": "0000-0002-6968-8451", "clpid": "Ewens-M-J" }, { "family_name": "Jin", "given_name": "Lawrence Jiaqi", "clpid": "Jin-Lawrence-J" }, { "family_name": "Tamuz", "given_name": "Omer", "orcid": "0000-0002-0111-0418", "clpid": "Tamuz-O" } ], "local_group": [ { "literal": "div_hss" } ], "abstract": "This dissertation consists of three essays focusing on how information asymmetry affects agents\u2019 behavior across different environments. The first essay characterizes the optimal contract when a firm can employ two incentive schemes, promotion and pay for performance, simultaneously (Chapter 2). In the second essay, I study how information asymmetry can lead a firm to choose a less profitable short-term over a more profitable long-term project (Chapter 3). The other essay analyzes a career choice problem when agents have private information about their ability (Chapter 4).
\r\n\r\nChapter 2 presents the effect of information asymmetry on executive pay structure to examine the cause of the rise in CEO compensation and wage inequality between CEO and other executives. To analyze the effect of the interaction of two incentive schemes, promotion and pay for performance, on CEO compensation and within-firm wage inequality, I embed a pay for performance framework into a tournament structure. The model shows that when CEO and managers contribute to a firm\u2019s output independently, it is optimal for the firm to provide the CEO a compensation far beyond her reservation value in order to provide promotion incentives for managers. However, I find that the promotion incentive motive can disappear if there is interdependency between the CEO\u2019s and managers\u2019 outputs. In this case, the main purpose of a high CEO compensation is to induce the CEO to exert effort. The tension between incentives for CEO and managers makes it difficult to interpret the meaning of within-firm wage gap. As a possible solution, this paper suggests the use of CEO\u2019s base salary to identify which incentive factor is driving the pay gap.
\r\n\r\nIn Chapter 3, I study the optimal contract problem when a firm faces a long-term project. I consider a long-term project as one that requires an indefinite amount of time to complete its objective. I assume that the long-term project generates profits once it is accomplished. Using a continuous-time moral hazard model, I characterize the incentive compatibility condition in a relatively general contracting space. Moreover, I find a unique optimal contract under a restricted contracting space which consists of the two components: the termination level and the completion payment. The firm might invest in a short-term project: one that generates an instantaneous profit to the firm without any effect on the future, as analyzed by DeMarzo and Sannikov (2006). Comparison of optimal contracts for long and short-term projects provides an interesting insight to managerial short-termism: the firm, not the agent, could prefer a short-term project to a long-term project if there is a moral hazard problem.
\r\n\r\nChapter 4 analyzes the role of asymmetry information on one\u2019s career choice. I examine how people choose their career when they do not know ability of the rest of the applicant pool. The goal is to understand labor supply in the markets where ability is widely distributed. In particular, I consider a situation where there are two exclusive labor markets and the upper and lower bounds of one market\u2019s payoffs are both higher than those of the other market. Under the market setting, agents decide which market to participate in. I find that the symmetric Bayesian Nash equilibrium of this problem is unique. In the equilibrium, agents are divided into two groups according to their ability. Members of the high ability group use a pure strategy and only apply to the more desirable market. Members of the low ability group apply to both markets with positive probability.
\r\n\r\n", "doi": "10.7907/Z9571925", "publication_date": "2017", "thesis_type": "phd", "thesis_year": "2017" } ]