Dissertations

Intelligent agents are becoming increasingly prevalent in a wide variety of domains including but not limited to transportation, safety and security. To better utilize the intelligence, there has been increasing focus on frameworks and methods for coordinating these intelligent agents. This thesis is specifically targeted at providing solution approaches for improving large scale multi-agent systems with selfish intelligent agents. In such systems, the performance of an agent depends on not just his/her own efforts, but also on other agent’s decisions. The complexity of interactions among multiple agents, coupled with the large scale nature of the problem domains and the uncertainties associated with the environment, make decision making very challenging. In this work, we specifically study the problem from the perspective of a centralized aggregator, that needs to maximize the revenue of the entire system.

To that end, we study this problem from strategic and operational point of view. With regards to strategic decision making, we propose planning and deep reinforcement learning based solution algorithms to improve the system performance by optimizing the adaptive operating hours of selfish agents and by providing flexible work schedules to them. From operational point of view, we propose novel mechanism to incentivise selfish agents, so that performance of all the agents and the overall system improve . Basically, through strategic and operational decision making, we assist selfish agents in making intelligent decisions that results in improved system performance.

In the first part of this thesis, we focus on making strategic decisions for the workers in the digital gig economy. To provide a concrete context, we focus on taxi drivers in the transport gig economy. Taxi fleets and car aggregation systems are an important component of the urban public transportation system. Taxis and cars in taxi fleets and car aggregation systems (e.g., Uber) are dependent on a large number of self-controlled and profitdriven taxi drivers, which introduces inefficiencies in the system. There are two ways in which taxi fleet performance can be optimized: (i) Operational decision making: improve assignment of taxis/cars to customers, while accounting for future demand; (ii) strategic decision making: optimize operating hours of (taxi and car) drivers. Existing research has primarily focused on the operational decisions in (i) and we focus on the strategic decisions in (ii).

We first model this complex real world decision making problem (with thousands of taxi drivers) as a multi-stage stochastic congestion game with a non dedicated set of agents (i.e., agents start operation at a random stage and exit the game after a fixed time), where there is a dynamic population of agents (constrained by the maximum number of drivers). We provide planning and learning methods for computing the ideal operating hours in such a game, so as to improve efficiency of the overall fleet. In our experimental results, we demonstrate that our planning based approach provides up to 16% improvement in revenue over existing method on a real world taxi dataset. The learning based approach further improves the performance and achieves up to 10% more revenue than the planning approach.

In second part of this thesis, We focus on: a) addressing the problem of handling schedule constraints of individual agents (e.g., breaks during work hours) to provide a flexible work schedule for them; and b) provide a scalable solution approach in such large scale problem settings. We introduced a simulation based (faster) equilibrium computation method that relies on policy imputation. We studied and analyzed different imputation methods and show that a good imputation method coupled with a well designed simulation based best response computation can help in achieving better symmetric equilibrium for large scale systems, in a time efficient manner. We demonstrate that our methods provide significantly better policies than the previous approach in terms of improving individual agent revenue and overall agent availability.

In the third/final part of the thesis, we focus of operational decision making, where we improve system performance by inducing cooperation among selfish agents. Here we focus on principal-agent problem setting. Principalagent relationships, where a principal employs several agents to accomplish tasks on its behalf, are prevalent in many domains (e.g., Manufacturer distributors for product distribution, Uber-taxi drivers for transportation, FoodPanda-delivery personnel for food delivery). Principal has a global observation on all the tasks, while agents only have local observations with regards to local tasks. This limited observability coupled with selfish interest of agents results in a misalignment between Principal and agents objectives. We provide Multi-Agent Reinforcement Learning (MARL) approaches for sequentially designing incentives that improves objectives for principal and agents. We demonstrate that our approaches are able to outperform the state of art approaches for sequential incentive design on Escape-Room and adapted StarCraft-2 environments.

Although studies have consistently indicated that heavier social networking sites (SNS) use perpetuates poorer self‑esteem outcomes, no study has examined potential intervention methods that can counteract the ill-effects of SNS use. We sought to examine whether SNS use in a self-affirmative manner could mitigate threats to self that are often experienced during its use. Specifically, we hypothesized that the viewing of one’s SNS profile (i.e., Instagram profile) would have self-affirmative effects on individuals and improve their self-perception, and these effects are mediated by self‑concept clarity. We tested these hypotheses through cross-sectional (Study 1) and intensive longitudinal (Study 2) studies. Across two studies, we found that participants who spent time on their own Instagram profile felt more positive about themselves. In Study 2, using multilevel latent variable path analyses, we found that SNS-influenced self‑concept clarity mediated the relations between self-affirmative SNS use and SNS-influenced self-esteem. Our findings provide preliminary evidence for our hypothesis that guided SNS use can have beneficial effects on one’s self-perception.

This dissertation studies different long memory models. The first chapter considers a time series regression model where both the regressors and error term are locally stationary long memory processes with time-varying memory parameters, and the regression coefficients are also allowed to be time-varying. We consider a frequency-domain least squares estimator with kernelized discrete Fourier transform and derive its pointwise asymptotic normality and uniform consistency. A specification test on the constancy of coefficients is provided. The second chapter studies a linear regression panel data model with interactive fixed effects where the regressors, factors and idiosyncratic error terms are all stationary but with potential long memory. The setup involves a new factor model formulation for which weakly dependent regressors, factors and innovations are embedded as a special case. Standard methods based on principal component decomposition and least squares estimation, as in Bai (2009), are found to suffer bias correction failure because the order of magnitude of the bias is determined in a complex manner by the memory parameters. To cope with this failure and to provide a simple implementable estimation procedure, frequency domain least squares estimation is proposed. The limit distribution of this frequency domain approach is established and a hybrid selection method is developed to determine the number of factors. The third chapter estimates the memory parameters and test them against spurious long memory of the latent factors in a linear regression model with interactive fixed effects, based on the estimated discrete Fourier transform of the factors. The same asymptotic properties hold as if we use the infeasible true factors for both the memory estimator and the test. This result illustrates how the frequency domain least squares estimator can be applied to further inference other than the regression coefficients.

Behavioral spillover occurs when performing an initial behavior increases the likelihood of performing a subsequent behavior (positive spillover) or decreases this likelihood (negative spillover). The current research focuses on negative spillovers of pro-environmental behaviors (PEB), which has the implication of limiting individuals’ environmental conservation efforts. To offer insights, three studies sought to explicate how and for whom negative spillovers would occur. I theorized that prior behaviors would negatively predict subsequent behaviors via greater perceived goal progress and that this negative association between perceived goal progress and subsequent engagement would be more pronounced for people with a strong (vs. weak) promotion focus. This is because promotion-focused individuals are more sensitive to gains (e.g., goal progress) and may discontinue their pursuits when they perceive a positive state has been attained (Zou et al., 2014). Across two studies, self-reported (Study 1, N = 161) and experimentally induced recall (Study 2, N = 481) of prior PEB led to greater perceived goal progress. However, its effect varied with a stronger promotion focus accentuating a negative spillover for PEB intentions in Study 1 but a positive spillover for environmental donation in Study 2. As Study 1 referenced a general collective goal of addressing climate change and Study 2 referenced a personal goal of addressing climate change, Study 3 (N = 501) sought to examine whether the observed differing spillover effects would be moderated by goal framing (i.e., collective vs. personal goal). Negative spillovers may be more pronounced for collective (vs. personal) goals as people feel that they can be relieved of the responsibility for expending further effort toward the collective goals if they have previously contributed. However, Study 3 could not reconcile the inconsistent spillover patterns found in Studies 1 and 2. The implications of these findings and future directions are discussed.

This thesis studies the estimation and inference problems for spatial panel data models when the panels are unbalanced, when the panels contain threshold effects, or when the panels contain time-varying network structures. These three scenarios divide the thesis naturally into three chapters.

The first chapter considers estimation and inferences for fixed effects spatial panel data models based on unbalanced panels that result from randomly missing spatial units. The unbalanced nature of the panel data renders the standard method of estimation inapplicable. In this chapter, we proposed an M-estimation method where the estimating functions are obtained by adjusting the concentrated quasi scores to account for the estimation of fixed effects and/or the presence of unknown spatiotemporal heteroscedasticity. The method allows for general time-varying spatial weight matrices without row-normalization, and is able to give full control of the individual and time specific effects for all the spatial units involved in the data. Consistency and asymptotic normality of the proposed estimators are established. Inference methods are introduced and their consistency is proved. Monte Carlo results show excellent finite sample performance of the proposed methods. An empirical application is presented on commodity tax competition among US states.

The second chapter introduces general estimation and inference methods for threshold spatial panel data models with two-way fixed effects (2FE) in a diminishing-threshold-effects framework. A valid objective function is first obtained by a simple adjustment on the concentrated quasi loglikelihood with 2FE being concentrated out, which leads to a consistent estimation of all common parameters including the threshold parameter. We then show that the estimation of threshold parameter has an asymptotically negligible effect on the asymptotic distribution of the other estimators, and thereby lead to valid inference methods for other common parameters after a bias correction. A likelihood ratio test is proposed for statistical inference on the threshold parameter. We also propose a sup-Wald test for the presence of threshold effects, based on an M-estimation method with the estimating functions being obtained by simply adjusting the concentrated quasi-score functions. Monte Carlo results show that the proposed methods perform well in finite samples. An empirical application is presented on age-of-leader effects on political competitions across Chinese cities.

The third chapter considers the specification and estimation of a three-dimensional (3-D) spatial panel data model with time-varying network structures. The model allows for endogenous and exogenous interaction effects, correlation of unobservables, and most importantly group-specific effects that are allowed to interact with the individual and time specific effects. The time-varying network structures provide information on the identification of various interaction effects but also yield time-varying sociomatrices whose row sums may not be constant, which renders the transformation-based quasi maximum likelihood inapplicable. In this chapter, we propose an adjusted quasi score method where the estimating functions are obtained by adjusting the concentrated quasi scores (with fixed effects being concentrated out) to account for the effects of concentration. The method is able to give full control of general specifications of three-way fixed effects. Consistency and asymptotic normality of the proposed estimators are established. Monte Carlo results show excellent finite sample performance of the proposed methods.

This dissertation investigates how the endorsement of certain social activities by CSR standards impacts stakeholders’ interpretation on firms’ motivation of doing CSR and how managers make decisions on which specific CSR activities they would like to participate in. The first essay examines how the standards release of CSR by Sustainability and Accounting Standards Board (SASB) affects the relationship between material CSR and firm performance outcomes in terms of stock returns (for investors) and sales growth (for customers), through shaping investor and customer perceptions on the motivation underlying a firm’s material CSR activities. I further argue that a sharp increase in material CSR after the SASB standards release, as a strong indicator of a firm’s opportunistic response to the endorsement, is more likely to be penalized by prosocial shareholders and customers. The second essay explores what drives a firm to select different CSR investment strategies, in terms of the financial materiality of CSR. I posit that firms with stronger financial orientation, which is reflected by more analyst coverage and higher institutional ownership, are more likely to engage in financial material CSR investment, but firms with stronger social orientation, which is reflected by higher female board proportion and more liberal CEOs, are more likely to engage in financial immaterial CSR investment. In addition, these effects are moderated by firm’s financial distress. The empirical results support most of arguments.

The dissertation consists of three essays on asset pricing by constructing new data set and developing new methodologies. In the first chapter, we conduct empirical studies on the volatility-managed portfolios in the Chinese stock market. Using data from the Chinese stock market, we have found that the main empirical findings in Moreira and Muir (2017) break down. Based on the empirical findings, we exploit a comprehensive set of $99$ equity strategies in the Chinese stock market to analyze the value of managed portfolios. Based on these $99$ equity trading strategies, we find that there exists no systematic gain from scaling the original portfolios using volatility. Our empirical results suggest that one should be careful to use volatility-managed portfolios in practice as the expected performance gains are rather limited.

In the second chapter, we review a Bayesian interpretable machine-learning method proposed by Kozak, Nagel, and Santosh (2020). We show how the method can link two strands of literature, namely the literature on empirical asset pricing and the literature on statistical learning. Based on a recently developed data-cleaning technique, we obtain 123 financial and accounting cross-sectional equity characteristics in the Chinese stock market. When applying the method of Kozak, Nagel, and Santosh (2020) to the Chinese stock market, we find that it is futile to summarize the stochastic discount factor (SDF) in the Chinese stock market as the exposure of several dominant cross-sectional equity characteristics in-sample. A cross-validated out-of-sample analysis further supports this finding.

In the third chapter, we propose several alternative parametric models for spot volatility in high frequency, depending on whether or not jumps, seasonality, and announcement effects are included. Together with these alternative parametric models, nonlinear non-Gaussian state-space models are introduced based on the fixed-k theory of Bollerslev, Li, and Liao (2021). According to Bollerslev, Li, and Liao (2021), the log fixed-k estimator of spot volatility equals the true log spot volatility plus a non-Gaussian random variable. Bayesian methods are introduced to estimate and compare these alternative models and to extract volatility from the estimated models. Simulation studies suggest that the Bayesian methods can in general work well. Empirical studies using high-frequency market indexes and individual stock prices reveal several important results. As an application of extracting volatility, we quantify the strategic value of information.

This dissertation seeks to gain insight into the critical roles of consumers and marketers in a retail context using a variety of unique and rich data sources (e.g., tracking data, retail scanner data, ad intel data and publicly available data). The main aim of the two essays is to focus on unique aspects of retail analytics. The first essay examines how consumers conduct haptic search to make purchase decisions using a unique dataset collected by the state-of-the-art sensing technology. This research contributes to the literature by defining key attributes of the shoppers’ speed, consideration set, and shopping path at the shelf space and investigating the effects of consumer haptic search on price paid across food and non-food categories. This paper further provides managerial implications regarding in-store category management and shelf layout. The second essay investigates the spillover effects of recreational cannabis legalization (RCL) on related categories (i.e., alcohol, tobacco, candy, and salty snacks) using secondary data (Nielsen retailer scanner data and ad intel data). This study employs synthetic control method to show that RCL resulted in an increase in per capita dollar sales and per capita unit sales of alcohol, salty snacks, and candy, while this was not observed for tobacco sales. To rule out alternative explanations, this work identifies a "null category" (i.e., batteries) and demonstrates that RCL did not lead to changes in pricing or advertising. The findings are likely to help policymakers in understanding unintended consequences and potential problems associated with RCL such as excessive drinking and junk food consumption resulting in increasing health care expenses.

This thesis studies the externalities in the housing market and agglomeration economies. While knowledge-based externalities, or knowledge spillovers are one of the most important micro-foundations of agglomeration economies, the first chapter studies how knowledge spillovers from universities affect local innovation activities. In the second chapter, we propose a high-order spatiotemporal autoregression approach to study the externalities in the housing market. The third chapter studies another important but under explored aspect of the agglomeration economies – the role that marriage market plays in providing incentives to promote urbanization, along with the unique feminization phenomenon during this process.

The first chapter studies the impact of universities on local innovation activity by exploiting a unique university expansion policy in China as a quasi-experiment. In this chapter, we take a geographic approach, empowered by geocoded data on patents and new products at the address level, to identify knowledge spillovers as an important channel. We obtain three main findings. First, university expansion significantly increases universities’ own innovation capacity, which results in a dramatic boom of local industry patents. Second, the impact of university expansion on local innovation activities attenuates sharply within 2 kilometers of the universities. Third, university expansion boosts nearby firms’ new products and the number of nearby industrial patents that cite university patents but not industry patents that cite patents far away from universities.

In the second chapter, we propose a high-order spatiotemporal autoregression approach for analyzing large real estate prices data. Real estate prices arrive sequentially on different housing units over time in a large volume. In this paper, we propose a high-order spatiotemporal autoregressive model with unobserved cluster and time heterogeneity. When the numbers of clusters (C) and time segments (T) are finite and the errors are iid, quasi maximum likelihood method is used for model estimation and inference. In the presence of unknown heteroskedasticity, or C and/or T is large, an adjusted quasi score method is proposed for model estimation and inference. Methods for constructing the space-time connectivity matrices are proposed. Monte Carlo experiments are performed for assessing the finite sample properties of the proposed methods. An empirical application is presented using the housing transaction data in Beijing. We find that the estimation of the spatiotemporal interaction effects are largely affected after controlling for cluster heterogeneity at the community level.

The third chapter studies the relationship between urbanization and feminization, where the marriage market plays an important role in connecting the two. Previous literature studying urbanization and migration has mainly considered incentives arising from cross-city variation in productivity and the subsequent labour market outcomes. In this paper, we study an important but under explored migration incentives arising from the matching outcomes in the marriage market and the gender differences in responding to such incentives. To achieve identification, we exploit the setup of special economic zones (SEZs) as a pull force and China’s accession to the World Trade Organization (WTO) as a push force that exogenously trigger urbanization across locations, which leads to a unique feminization phenomenon during this process. The paper highlights important distributional implications on gender inequality and spatial disparity during the rapid urbanization process.

The dissertation consists of three chapters on information diffusion and stock market efficiency and analyst style. The first chapter examines the asset pricing implications of investors’ inattention to non-obvious firm relatedness hidden in earnings calls. This chapter documents that the overlap in attention allocation over various business aspects serves as a time-sensitive proxy for firm relatedness. By employing the unsupervised topic modelling methodology, I characterize the attention allocation of earnings conference call participants (executives, investors and analysts) over topics discussed. I construct a novel cross-firm topic similarity measure that captures difficultto-observe and time-varying firm relatedness compared with existing peer-firm classification systems. I verify that topic peers are fundamentally comoved. However, it is beyond human capacity to process information from a large number of earnings calls in a timely manner. A long-short strategy based on returns of topic peers yields a monthly alpha of approximately 69 basis points. The return predictability mainly stems from topic peers with similar business models, customer management and influential macroeconomic situations. The lead-lag return pattern is more pronounced among focal firms with less firm visibility, higher information complexity and less common information processors. The second chapter investigates whether investors incorporate the value-relevant information from peers with similar geographic locations. Using detailed information on establishments owned by U.S. public firms, we construct a novel measure of geographic linkage between firms. We show that the returns of geography-linked firms have strong predictive power for focal firm returns and fundamentals. A long-short strategy based on this effect yields monthly value-weighted alpha of approximately 60 basis points. This effect is distinct from other cross-firm return predictability and is not easily attributable to risk-based explanations. It is more pronounced for focal firms that receive lower investor attention, are more costly to arbitrage, and during high sentiment periods. Sell-side analysts similarly underreact, as their forecast revisions of geography-linked firms predict their future revisions of focal firms. Further tests suggest that the lead-lag relation we document results from innovation spillover among geographic peers in addition to their common exposure to the local economy. The third chapter examines whether abstract thinking facilitates generating investment insights. Exploiting the questions raised by analysts during earnings conference calls, we construct a (timevarying) Abstract Thinking Index (ATI) to quantify an individual analyst’s propensity to think in an abstract way. Analysts with a higher level of ATI are more likely to ask questions using abstract words and focus on logical reasoning, broader categories of topics and a firm’s future prospects. Abstract thinking analysts issue more accurate and informative earnings forecasts and recommendations. Such effects are stronger when analysts cover firms with more uncertain fundamentals and a poorer information environment. Abstract thinking analysts survive and improve the information environment of firms they cover, and oppositely concrete thinking analysts are less likely to be promoted. Overall, this chapter suggests that abstract thinking is valueenhancing for analysts and facilitates information discovery in financial markets.

Proliferation of Internet of Things (IoT) sensor systems, primarily driven by cheaper embedded hardware platforms and wide availability of light-weight software platforms, has opened up doors for large-scale data collection opportunities. The availability of massive amount of data has in-turn given way to rapidly growing machine learning models e.g. You Only Look Once (YOLO), Single-Shot-Detectors (SSD) and so on. There has been a growing trend of applying machine learning techniques, e.g., object detection, image classification, face detection etc., on data collected from camera sensors and therefore enabling plethora of vision-sensing applications namely self-driving cars, automatic crowd monitoring, traffic-flow analysis, occupancy detection and so on. While these vision-sensing applications are quite useful, their real-world deployments can be challenging for various reasons namely DNN performance drop on data collected in-the-wild, high energy consumption by vision sensors, privacy concerns raised by the captured audio/video data and so on. This dissertation explores how a combination of IoT sensors and machine-learning models can help resolve some of these challenges. It proposes novel vision-analytics techniques, aimed at improving the large-scale adoption of vision-sensing techniques, with their potential performance improvements demonstrated by using two different vision-sensing systems namely SmrtFridge and CollabCam.

First, this dissertation describes SmrtFridge system, which uses a combination of embedded RGB & Infrared (IR) camera sensors and a machine-learning model for automatic food item identification and residual quantity sensing. SmrtFridge adopts a user interaction-driven sensing approach which is triggered as and when a user is interacting (adding/removing items) with any food item. Using two different processing pipelines, i.e., motion-vector based and IR based, SmrtFridge isolates the food item from the other background objects that might be present in the captured images. The segmented items are then assigned a food label by an image classifier. SmrtFridge shows that using these segmentation techniques can help convert the item identification problem from a complex object-detection problem to a relatively simpler object-classification problem. Also, SmrtFridge proposes a novel IR based residual quantity estimation technique which can quantify the residual content inside food item containers (transparent/opaque) of various shapes, sizes and material types.

Secondly, this dissertation presents CollabCam, a novel and distinct multi-camera collaboration framework for energy efficient visual (RGB) sensing in a large-scale camera deployment. CollabCam exploits the partially overlapping FoVs of cameras to selectively reduce imaging resolution in their mutually common regions. This resolution reduction can enable overall energy savings of a camera sensor by reducing the energy consumption in image capture, optional storage and network transmission. CollabCam proposes novel techniques for (a) autonomous and accurate estimation of overlapping regions between a pair of cameras (b) mixed resolution sensing where selected regions of an image are captured at lower resolution, whereas the remaining regions are captured at default (higher) resolution and (c) collaborative object inference where a modified DNN model, called CollabDNN, utilizes the perspective of other collaborating cameras to enhance performance of object detection on low-resolution images. Application of CollabCam techniques on two publicly available datasets demonstrates the potential high energy savings for a multi-camera system and takes a step towards making energy efficient large-scale vision-sensing systems a reality

This dissertation consists of three studies in the areas of empirical asset pricing, market microstructure, and behavioural finance. I study the trading behavior and portfolio choices of institutions and retail investors in the equity and derivatives markets. Examining the ways in which different market participants make investment decisions allows us to understand their role in shaping financial market dynamics. This is important in order to know how to structure markets for enhanced market efficiency, and to protect less sophisticated investors through better policies and regulations. Although there is a considerable amount of literature disputing the ability of retail investors and different types of institutions to make informed investment decisions, their trading patterns and the various effects they have on the markets are not fully understood. My dissertation aims to explore this broad issue from several different angles.

Chapter I examines retail investor activity in the extreme portfolios of well-known cross-sectional anomalies. This study is co-authored with Prof. Ekkehart Boehmer. We show that retail investors tend to trade in the opposite direction of anomalies (buying stocks in the short portfolios and selling stocks in the long portfolios), both before and after the anomaly variables become public information. However, we do not find evidence that retail trading is the cause of mispricing and subsequent return predictability. Stocks with high retail participation do not appear to be more mispriced after controlling for confounding factors. Instead of pushing prices away from fundamentals, contrarian retail trades are likely to provide liquidity to arbitrageurs after firm announcements. In addition, we show that retail short sellers exploit anomaly information and help to correct mispricing of overvalued stocks in the short portfolios of value-versus-growth anomalies. Overall, the goal of this study is to show that retail participation in equity markets is not detrimental to market efficiency and in certain settings can even be helpful in correcting anomalies.

Chapter II investigates trading styles and profitability of institutional and retail investors in a leading derivatives market. This study is co-authored with Prof. Jianfeng Hu, Prof. Seongkyu Gilbert Park, and Prof. Doojin Ryu. Using comprehensive account-level transaction data, we provide a detailed description of the options market and the different types of investors. We find that retail investors tend to stick to one trading style. About 70% of retail investors predominantly hold simple positions such as long calls or long puts. Institutional investors are more likely to use multiple strategies with various levels of complexity. We use trading style complexity as an ex-ante measure of trading skills and show that it significantly affects investment performance. Specifically, retail investors using simple strategies lose to the rest of the market. For both retail and institutional investors, volatility trading is the most profitable strategy, although subject to large downside risk. After adjusting for risk, Greek neutral strategies outperform. These style effects are persistent and cannot be explained by systematic risk exposure or known behavioral biases.

Chapter III is about rational regulation and irrational investors. It is co-authored with Prof. Jianfeng Hu. We show that irrational response to regulatory reforms aimed at investor protection can lead to these reforms having the opposite effect and hurting investors. After the August 2011 crisis in the Korean equity market, regulators increase the contract size of equity index options fivefold, hoping to limit retail participation and excessive speculation in the market. Contradicting the purpose of the reform, we find that investors‚Äô propensity to exit the market decreases after the reform. The dollar risk exposure of remaining investors significantly increases after the reform, consistent with investor inattention to the reform. Our estimation shows that it takes six months for risk taking activity to return to the pre-reform level but there is no significant decrease afterward. Heightened risk taking also leads to worse performance in the post-reform period. Although these effects are always stronger on retail investors, institutional investors are not spared either. In addition, we find that investors who are adversely affected by the reform exhibit self-attribution bias which causes them to extrapolate their performance into the future. They tend to outperform their peers before the crisis and their trading activity becomes more responsive to past performance after the crisis. However, limited attention to the market reform exacerbates their losses when their performance reverts to the mean. These results highlight the importance of considering behavioural biases in policy research and setting to avoid unintended consequences.

It is desirable to combine machine learning and program analysis so that one can leverage the best of both to increase the performance of software analytics. On one side, machine learning can analyze the source code of thousands of well-written software projects that can uncover patterns that partially characterize software that is reliable, easy to read, and easy to maintain. On the other side, the program analysis can be used to define rigorous and unique rules that are only available in programming languages, which enrich the representation of source code and help the machine learning to capture the patterns better. In this dissertation, we aim to present novel code modeling approaches to learn the source code better and demonstrate the usefulness of such approaches in various software engineering tasks. The methods developed for the aims to utilise the advantages of both deep learning techniques and static code analysis techniques.

Interorganisational relations are critical resources that are enablers for organisations to achieve competitive advantage. Collaborative ties provide organisations access to new markets, distribution channels, information, and present opportunities to develop or enhance capabilities and competencies. However, interorganisational ties are dynamic and susceptible to relational tensions among collaborative, coordinative, and competitive elements. As such, primarily focusing on collaborative elements between organisations presents an incomplete representation. Social relations involve elements of collaboration and conflict that are not antithetical but dialectical determinants of one another. Despite these conjectures of dialectical tensions, the nature of interorganisational conflict remains elusive. Hence, this dissertation is devoted to: (i) explicating the conceptual underpinnings of interorganisational conflict, (ii) exploring conflict as an experience that develops organisations’ abilities to address interorganisational ties and brokerage and, (iii) examining the asymmetric role of conflict as opportunities for learning and strategic actions.

In chapter two, the dissertation discusses various conceptualisations of interorganisational conflict and highlights conflict as a distinct construct involving relational tensions between interacting organisations. I present an exploratory framework to capture prior perspectives of interorganisational conflict and claim that redefining conceptualisations of interorganisational conflict will present new opportunities for management research. The following chapters in the dissertation highlight that our theoretical understanding of interorganisational relations and beyond can be extended by inculcating the antecedents, processes, and outcomes of interorganisational conflict as part of future research considerations.

Chapter three focuses on firm-level triadic ego-network structures by examining firms’ ability to reside in brokerage positions based on their prior experiences with collaboration and conflict. The chapter develops on the basis that the dualistic experience of collaboration and conflict has implications for a firm’s ability to span structural holes. Empirical results indicate that ambidextrous experiences related to collaborative and conflictual experiences positively and significantly affect firms’ ability to reside in brokerage positions. However, such effects were found to be contingent on the levels of environmental volatility. It was found that environmental volatility reduced the learning effects of prior experiences on brokerage. This suggests that firm learning and the development of capabilities based on prior experiences are contingent on the magnitude of environmental shifts.

Chapter four focuses specifically on the role of conflictual ties on a firm’s ability and strategic positioning to bridge structural holes with the goal of explicating a firm’s role as an initiator or target of conflictual ties. The paper posits that the directionality of conflict impacts a firm’s strategic choices to reside in brokerage positions. The results highlight a significant increase in the likelihood that both targets and initiators of conflict span structural holes. However, when firm performance was considered as a trigger for firm motivation and risk predilection to broker, the effects of directed conflictual ties on brokerage formation were diminished. The results indicate that firm learning is contingent on event-specific determinants as well as firm-related aspirations of motivation and risk partiality.

Empirical chapters three and four are anchored by a 10-year longitudinal sample of contract litigation on breach of contracts supplemented by alliance and joint venture activities of publicly traded firms in the United States of America between 2009 to 2018.

In this dissertation, I have made several contributions to the literature on the multivariate stochastic volatility model. First, I have considered a new multivariate stochastic volatility (MSV) model based on a recently proposed novel parameterisation of the correlation matrix. This modeling design is a generalisation of Fisher's z-transformation to the high-dimensional case. It is fully flexible as the validity of the resulting correlation matrix is guaranteed automatically. It allows me to completely separate the driving factors of volatilities and correlations. To conduct an econometric analysis of the proposed model, I develop a new Bayesian method that relies on the Markov Chain Monte Carlo (MCMC) tool. For the latent variables, the traditional single-move or multi-move sampler is replaced by a novel technique called Particle Gibbs Ancestor Sampling (PGAS), which is built upon the Sequential Monte Carlo (SMC) method. Simulation results indicate that our algorithm performs well when a small number of particles are used. Empirical studies based on the exchange rate returns and equity returns are considered and reveal some interesting empirical results. Second, I further develop a multivariate stochastic volatility model with intra-day realised measures. A simple and consistent estimation technique is developed. The problem of under-identification is discussed. A two-stage approach is introduced to address the problem. A simulation study shows that the proposed method works well in finite samples. The new model is then implemented using two financial datasets. A comparison with some existing models is made. Third, I also incorporate the leverage effect and the heavy-tailed error distribution into the MSV model. A Particle Gibbs Sampling Algorithm is developed for the extended MSV model. Simulation results indicate that our algorithm performs well when a small number of particles are used. Empirical studies of the stock indices are considered. I have found strong evidence of the leverage effect and, more, importantly, heavy-tails in the errors.

Building on past studies that have found positive influence of minority member on team creativity, this research examined an underexplored yet crucial topic of a unique opinion holder‚Äôs happy and anger emotions on team creativity. Using a collective information processing perspective, this study examined whether the expression of anger and happiness would be beneficial for team creativity by spurring team members to respond qualitatively differently to each other‚Äôs ideas during the discussion. Additionally, this study examined whether the influence of a unique opinion holder‚Äôs emotions on team creativity through information-processing pathways would depend on individual members‚Äô working memory capacities. Three hundred and ninety-six undergraduate students (M = 22.07 years, SD= 1.84) were randomly assigned to work with three to five members, including a confederate who expressed anger, happy or neutral emotions. They were asked to brainstorm ideas that could improve online learning for future semesters in Singapore. As compared with teams with a neutral unique opinion holder, teams with a happy unique opinion holder showed an improvement in their creativity by expanding the active associations within the semantic network of ideas across members (i.e., generative pathway). On the other hand, teams with an angry unique opinion holder elicited improved team creativity by deliberating on expressed ideas (i.e., elaborative pathway). These mediational pathways, however, did not depend on teams‚Äô levels of working memory capacity. Future applications with technological tools and implications of this research for organisations would be discussed.

Creativity and innovation are vital for organizational growth and success, driving many organizations to increase pressure for employee creativity. Yet, researchers have neglected investigating how employees respond to creativity pressure at the workplace. This dissertation introduces and develops a new scale for the concept of organizational creativity pressure ‚Äì the pressure on employees to continually develop novel and useful ideas and solutions. The scale is further validated through extensive assessment of content and construct validity, empirically differentiating the construct from similar others such as performance pressure and support for creativity.

Drawing on the transactional theory of stress (Lazarus & Folkman, 1984) and the need-based theory of work motivation (Green, Finkel, Fitzsimons, & Gino, 2017), I theorize that organizational creativity pressure is appraised more strongly as a challenge stressor than a hindrance stressor, in turn promoting work engagement in employees. Building on the emerging research on gender and creativity, I further theorize that the positive effects of organizational creativity pressure on challenge appraisal and work engagement are stronger for men than for women. Four studies provide evidence consistent with the model. Interestingly, the pattern of interaction is such that men are significantly less motivated and engaged than women at low organizational creativity pressure. At high organizational creativity pressure, there is no significant gender difference in work engagement. Women are also not more likely to see organizational creativity pressure as a hindrance stressor compared to men. This essay has important theoretical contributions to research in creativity, gender, and workplace stress. In a separate chapter, I investigate whether organizational creativity pressure induces feeling of task uncertainty among employees, which in turn leads to negative perception of fairness in the workplace. In sum, this dissertation draws attention to the new construct and the related workplace phenomenon, develops a scale to provide a foundation for empirically rigorous research and investigates both positive and negative effects of organizational creativity pressure in the workplace.

This dissertation focuses on proposing statistical and deep learning models for software engineering corpora to detect bugs in software system. The dissertation aims to solve three main software engineering problems, i.e., bug localization (locating the potential buggy source files in a software project given a bug report or failing test cases), just-in-time defect prediction (identifying the potential defective commits as they are introduced into a version control system), and bug fixing patch identification (identifying commits repairing bugs for their propagation to parallelly maintained versions) to save developers’ time and effort in improving software system quality. Moreover, I also propose a neural network model learning a vector representation of code changes based on their commit messages. The vector representation of code changes contains its semantic intent and can be used to improve the performance of just-in-time defect prediction and bug fixing patch identification. This vector can also be applicable for potentially many other software engineering problems related to code changes, such as tangled change prediction, the recommendation of a code reviewer for a patch, etc.

My dissertation develops one statistical model and three deep learning models for various software engineering tasks. The first one introduces a statistical model which is a novel multi-modal approach for bug localization problem. The multi-modal approach is built by utilizing information from both bug reports and program spectra (or program elements) to effectively localize bugs in programs. Different from other multi-modal approaches for bug localization that treat bug reports (or program elements) as independent, my approach considers similarities between bug reports (or program elements). Hence, similar bugs should have model parameters that are close together. My novel multi-modal approach employs network Lasso regularization to incentivize the model parameters of similar bug reports (or program elements) to be close together.

The second one presents a novel deep learning framework to find likely defective code early; the problem is commonly referred to as Just-In-Time (JIT) defect prediction. While most existing JIT defect prediction approaches involve a manual feature engineering step, where researchers propose a number of features extracted from commits (e.g., the number of deleted and added lines, number of files, information of authors and code reviewers, etc.), I introduce an end-to-end deep learning framework, namely DeepJIT, which automatically extracts features from commit messages and code changes in the commits, and then uses them to identify defects.

The third one introduces a hierarchical deep learning-based approach, namely PatchNet, to find bug fixing patches in the Linux kernel. Bug fixing patch identification and JIT defect prediction are pretty similar as they take as input the same type of data (i.e., commits to version control systems). While DeepJIT simply merges the removed and added code in the code changes together, PatchNet separates the removed and added code and takes into account the hierarchical structure of the removed and added code.

Finally, the last one presents a neural network model, namely CC2Vec, that learns a representation of code changes based on the semantic information in commit messages. Unlike DeepJIT or PatchNet which only solve a specific software engineering task (i.e., just-in-time defect prediction or bug fixing patch identification), the vector representation represents the semantic meaning of the code changes and can be used to solve a number of software engineering problems related to commits (i.e., just-in-time defect prediction, identification of bug fixing patches, and tangled change prediction, etc.).

The dissertation includes three chapters on panel and factor models.

In the first chapter, we introduce a two-way linear random coefficient panel data models with fixed effects and the cross-sectional dependence. We follow the idea of the within-group fixed effects estimator to estimate parameters of interests. We establish the limiting distributions of the estimates and also propose the two-way heterogeneity bias test to check the desirability of the estimation strategy. The specification tests then are constructed to examine the existence of the slope heterogeneity and time-varyingness. We study the asymptotic properties of the specification tests and employ two bootstrap schemes to rectify the downward size distortion of the specification tests. We apply the specification tests to reveal the heterogenous relationship between the unemployment rate and youth labor rate in the working-age population.

In the second chapter, we devise a simple but effective procedure to test bubbles in the idiosyncratic components in the presence of nonstationary or mildly explosive factors in common components in panel factor models. We study the asymptotic properties of our test. We also propose a wild bootstrap procedure to improve the finite sample performance of our test. As an illustrative example, we consider testing the bubbles in the idiosyncratic components of cryptocurrency prices.

In the third chapter, we propose the tests constructed from estimated common factors for detecting bubbles in unobserved common factors when the idiosyncratic components follow a unit-root or local-to-unity process. We study the asymptotic properties of our proposed tests. We show that our proposed tests have non-trivial power to detect those bubbles in unobserved common factors under the alternative of local-to-unity. To implement our proposed tests, we propose to use the dependent wild bootstrap method to simulate the critical values in practice.

Even with comparable education and level of competence, workers with lower socioeconomic status (SES) origins are disadvantaged in terms of earnings and occupational attainment. This class gap, or the “class ceiling,” is as large as the gender gap, but poorly understood. In my dissertation, I designed a series of related projects to explain and potentially mitigate the class ceiling problem. Across three projects, I mainly focused on where the problem starts—labor market and newcomer adjustment in organizations. I find that, beyond discrimination and bias that has been the focus of past work, many challenges stem from workers’ own psychology and behaviors, which can be effectively addressed with a psychological intervention.

The Android platform is becoming increasingly popular and numerous applications (apps) have been developed by organisations to meet the ever increasing market demand over years. Naturally, security and privacy concerns on Android apps have grabbed considerable attention from both academic and industrial communities. Many approaches have been proposed to detect Android malware in different ways so far, and most of them produce satisfactory performance under the given Android environment settings and labelled samples. However, existing approaches suffer the following robustness problems:

In many Android malware detection approaches, specific API calls are used to build the feature sets, and their feature sets are fixed once the model has been trained. However, such feature sets lack of robustness against the change of available APIs. Since there are always new APIs released with old ones deprecated during the evolvement of Android specifications. If developers switch from old APIs to new ones in app development, older Android malware detection models which are trained before the release of new APIs may not be effective then, because these new APIs are not included in the previously fixed feature sets.

Besides, existing approaches are also lack of robustness towards the label noises. Recent research discovered that sample labels provided by malware detection websites may not be always reliable, and we also figure out that 10% of sample labels provided by VirusTotal change during a period of 2 years in our experiments. This indicated label noises cannot be ignored in the training of Android malware detection models, while existing approaches which directly use the provided labels will suffer from the label noise problem.

Furthermore, even if the sample labels are correct, there may still exist inconsistencies between the sample labels and the generated feature vectors in dynamic-based Android malware detection approaches. Since no triggering modules can perfectly trigger all potential malicious behaviors, and anti-analysis techniques are common in the apps. In this case, the triggered behaviour traces collected from samples labelled as “malware” may not contain “malicious” behaviors, thus feature vectors built from such traces may become noises in the model training.

Towards the above problems, three different works are presented in this dissertation to provide robustness to Android malware detection in different ways:

The first work in this dissertation proposes a slow-aging Android malware detection solution named SDAC. Towards solving the model aging problem, SDAC evolves its feature set effectively by evaluating new APIs’ contributions to malware detection using existing APIs’ contributions. In detail, SDAC evaluates the contributions of APIs using their contexts in the API call sequences. These sequences are extracted from Android apps demonstrating how the APIs are used in real world cases. Based on these sequences, an embedding algorithm named API2Vec is deployed to map APIs into a vector space in which the differences among API vectors are regarded as the semantic distances. Then SDAC clusters all these APIs based on the semantic distances among them to create a feature set in the training phase, and extends the feature set to include all new APIs in the detecting phase. By the feature extension, SDAC can adapt to the changes in Android specifications and thus produces a robust approach against changes in Android OS specifications.

The second work in this dissertation is named Differential Training, which is a general framework designed to reduce the noise level of training data for any machine learning-based Android malware detection approach. We discover that labels of samples provided by Anti-Virus organisations change over time. The changes imply certain labels are erroneous, and thus distort the performance when such labels are used in training Android malware detection models. Differential Training, which functions as a general framework, can detect label noises with different Android malware detection approaches. For the input sample apps, Differential Training firstly generates the noise detection feature vectors from all the intermediate states of two identical deep learning classification models. Then it applies outlier detection algorithms on these noise detection feature vectors, and the outliers detected are regarded as coming from noises. With the label noises being detected and reduced, Differential Training can thus help improve the detection accuracy of Android malware detection approaches.

The third work in the dissertation is a noise-tolerant dynamic-based Android malware detection approach named Dynamic Attention. In dynamic-based Android malware detection approaches, the triggered behaviour traces collected from samples with “malware” labels may not contain “malicious” behaviors due to the imperfect trigger procedure or anti-analysis methods, so they are in fact mislabelled when used in training Android malware detection models. Dynamic Attention is thus designed to solve this mislabelling problem: it identifies the label noises based on the variances of the attention weights associated within the behavior traces derived from malicious apps, and assigns correctly labelled behavior traces with high weights and wrongly-labelled ones with low weights during the model training. By doing so, Dynamic Attention makes the classification model learn less from wrongly-labelled feature vectors and gains resistances against the noises. This approach also enjoys high practicality, since it relies on neither domain knowledge nor manual inspection in the model training.

This dissertation contributes to the robustness of Android malware detection approaches in various ways. In particular, SDAC is robust towards changes in Android specifications, Differential Training provides robustness against label noises for Android malware detection in static analysis, and Dynamic Attention achieves the same goal for Android malware detection in dynamic analysis.

This dissertation consists of three chapters on Preferential Trade Agreements (PTAs) and trade policies. Increasing in numbers rapidly since 1990s, PTAs have extended their traditional focus on tariff reduction to deeper policy integration in areas such as competition policy, intellectual property rights, investment, and movement of capital. The first chapter of the dissertation uses a recently released dataset of PTA contents to quantify impacts of the horizontal depth of trade agreements on bilateral trade flows and national welfare for the period of 1980-2015. The results indicate that agreements that are deeper (covering a wider range of policy areas) contribute to larger trade growth and welfare gain. The second chapter of the dissertation expands the above analysis by using synthetic control matching (SCM) methods to obtain time-varying trade effects of PTAs, and isolates from the estimated total PTA effect the part contributed by different horizontal depths (coverages) of trade agreements. Built on the Anderson and van Wincoop (2003)’s set-up, we decompose and quantify the welfare effects of PTA deep integration for the different horizontal depths (coverages) of trade agreements for the period of 1988-2015, while controlling for the effect of tariff barriers. The third chapter of the dissertation analyses the short-run impact of 2018- 2019 U.S.-China trade war on the Chinese economy, following the micro-to-macro approach of Fajgelbaum et al. (2020) and analyze the impacts of the 2018–2019 U.S.-China trade war on the Chinese economy. We use highly disaggregated trade and tariff data with monthly frequency to identify the demand/supply elasticities of Chinese imports/exports, combined with a general equilibrium model for the Chinese economy (that takes into account input-output linkages, and regional heterogeneity in employment and sector specialization) to quantify the partial and general equilibrium effects of the tariff war. This complements the studies focused on the ex post response of the U.S. economy by Amiti et al. (2019), Flaaen et al. (2020), Fajgelbaum et al. (2020), and Cavallo et al. (2021).

Research on the interpersonal effect of anger expressions on others’ concessionary behaviour has found conflicting results about whether anger expressions increase or decrease concessionary behaviour. The Emotions as Social Information (EASI) model (Van Kleef, 2009, 2014) proposed that these conflicting findings can be resolved by looking at inferential and affective processes. Specifically, anger expressions increase concessionary behaviour via inferential processes but decrease concessionary behaviour via affective processes. However, previous research has mainly focused on dominance-related inferences and reciprocal anger reactions. I propose that the relationship between anger expressions and concessionary behaviour is determined by the type of inferential and affective processes, and not just whether inferential or affective processes are occurring. I explore other inferential processes, such as affiliation-related inferences, and other affective processes, such as complementary fear reactions, together with dominance-related inferences and reciprocal anger reactions, as possible mediators of the relationship between anger expressions and others’ concessionary behaviour. I also propose that the relative influence of these mediators depends on the perceived appropriateness of the anger expression and investigate the proposed model in a transgression setting. I found support for the mediating effect of dominance-related inferences and partial support for the mediating effect of reciprocal anger reactions, but not the other mediators. I also found partial support for the moderating effect of a counterpart’s transgression role on the relationship between anger expressions and perceived appropriateness. I also did not find any moderating effects of perceived appropriateness. Implications of these findings and future research plans for further testing of the EASI model are discussed.

Common across current research in healthcare operations is the conclusion that there exist many inefficiencies in today’s healthcare systems. Governments and healthcare organisations have sought to address these inefficiencies through the introduction of new policies and operational procedures or by relying on incentives to encourage specific behaviour. However, despite these attempts to reduce inefficiencies in the healthcare systems, the problem persists and is further exacerbated by growing medical complexities coupled with a rapidly ageing population. Against this backdrop, this dissertation investigates two issues within healthcare operations: (i) colorectal cancer (CRC) screening adherence, and (ii) blood donor management. A distinguishing feature of this dissertation looks at the incentivization of participants’ behaviours within the two main operations in healthcare operations management.

The first chapter of the dissertation empirically examines the determinants and barriers of CRC screening adherence. Using responses drawn from a nationwide survey, the data highlights that CRC screening adherence levels continue to remain low despite the government’s implementation of nationwide screening programs. To study the reasons behind the low adherence rate, I conduct a stepwise logistic regression model and identify several key predictors of the screening adherence. I found that age and individual perceived risk of developing CRC have significant quadratic trends towards screening participation. The results further show that participant's proficiency in probability literacy has an impact on perceiving an individual's risk of developing CRC towards screening adherence. Linear predictors consisting of CRC knowledge and factor of trust in government are also significant predictors towards screening participation. Motivated by the significant quadratic trend of age, I further investigate the nonmonotonic relationship between age and the adherence rate and provide policymakers with insights on possible interventions to CRC screening policies via a mediation model. I found that policy mediation factors in the form of financial means - CPF account balance and ownership of private insurance were statistically significant mediators that drives the nonmonotonic relationship.

The second chapter studies the strategic management of blood inventory through donor incentivization policies where under-incentivization may lead to shortage of critical blood supply while over-incentivization potentially causes excessive wastage. Incorporating key features of the blood donation such as perishable inventory, observation queue and stochastic demand and supply, I propose an optimization model to solve the donor incentivization decisions in the blood donor management problem by modelling both the blood inventory and donor flow process. Building on the techniques of the Pipeline Queues framework, the optimization model can be reformulated into a convex problem and be efficiently solved. Numerical experiments were further conducted to study how the structure of the optimal policies can change with respect to donors’ responsiveness, inventory levels, changes in demand for blood, new donor recruitment rate and distribution of donors in the observation window. Based on the results, the study also puts forward important practical implications relevant in supply chains with social impact.

Resource flexibility hedges against uncertainty in service and production systems. However, flexibility also brings complexity and difficulty in allocating resources. The thesis mainly studies managing flexible resources in two scenarios. The first scenario is a type of coordination of workers in a production or assembly line – bucket brigade. Specifically, the study shows how to manage a stochastic bucket brigade with discrete work stations. The second scenario is a service system with flexible service resources. The study proposes a distributive decision rule for the allocation of resources under both supply and demand uncertainty.

Chapter 2 studies a J-station, I-worker bucket brigade with preemptible work content. The time duration for each worker to serve a job at a station is exponentially distributed with a rate that depends on the station’s work content and the worker’s work speed. We analytically derive the throughput and the coefficient of variation (CV) of the inter-completion time. We study the system under three cases. (i) If the work speeds depend only on the workers, the throughput gap between the stochastic and the deterministic systems can be up to 47% when the number of stations is small. (ii) If the work speeds depend on the workers and the stations such that the workers may not dominate each other at every station, the asymptotic throughput can be expressed as a function of two factors. (iii) The work speeds depend on the workers, the stations, and the jobs. There is a trade-off between the intensification of the learning experience and the diversification of the skills.

Chapter 3 further studies a J-station, I-worker bucket brigade with non-preemptible work content. If the work content is non-preemptible, the work on each station can not be preempted and has to be processed by the same worker. We properly denote the waiting workers, re-analyze the state of the system, and the transition probability matrix of the reset vectors. Finally, we derive the average throughput. In the numerical experiments, we first verify the theoretical results by simulations. Then we compare the throughput difference between a non-preemptible line and a preemptible line. If the workers are sequenced slowest-to-fastest, the preemptible line dominates the non-preemptible line. However, if the workers are sequenced fastest-to-slowest, the non-preemptible line can possibly dominate the preemptible line. As such, the management needs to consider the actual setting to enhance the performance.

Chapter 4 studies a resource allocation problem, where the planner needs to decide simultaneously on both the supply and the allocation policy to fulfill the uncertain demand over a multi-period horizon. We introduce a distributive decision rule, which decides on the proportion of jobs awaiting dispatch to each of the possible resource supply pools. Our model has a convex reformulation that can be solved efficiently. Through simulations, we illustrate that the optimal solution evolves with changes in service distribution, initial conditions, temporal fluctuations in demand, and resource availability. At last, we benchmark our model against the static rule and a fluid model. In doing so, we justify the adaptivity of the proposed distributive decision rule and show the robustness of our model to different settings.

Comparison sites are widely used by consumers. Theory assumes that consumers visit these sites to discover new alternatives, raising questions about the role of the initial consideration set (alternatives considered at the start of search) when comparison sites are available. Will consumers ignore their initial consideration set and directly explore new alternatives? Will consumers with large initial consideration sets avoid comparison sites? Utilizing search and incomplete knowledge theories, the authors intuit that consumers first search their initial consideration set, and visit a comparison site to reduce the search costs of doing so. If a suitable alternative is absent, consumers subsequently visit a comparison site to discover new alternatives. The authors test their expectations on unique data capturing consumers’ initial consideration sets and online search and find strong support. Specifically, consumers search a greater proportion of their initial consideration set at the start of search and are more likely to visit a comparison site when their initial consideration set is large. Additionally, consumers are more likely to visit a comparison site when they expect to find a better deal, particularly at the end of search. Finally, only consumers expecting to find a better deal are more likely to explore alternatives not in their initial consideration set.

Efficient management of scarce resources is critical to the improvement of economic, social and/or environmental performances. In this dissertation, I focus on the management of two scarce resources: i) healthcare resources and ii) water, and investigate two important problems: i) the estimation of patients’ health transition to support healthcare resources control under the context of sequential medical treatments, and ii) the urban water system control with a specific focus on the wastewater recycling capacity investment in the presence of climate change and urban water scarcity. The first chapter studies how to estimate patients’ health transition considering the effects of treatment-effect-based policies. Treatment-effect-based decision policies are increasingly used in healthcare problems, which leverage predictive information on patient health transitions and treatment outcomes for specific medical treatment decisions. However, treatment-effect-based policies will significantly censor patients’ observed health transitions and distort the estimation of transition probability matrices (TPMs). I propose a structural model to recover the underlying true TPMs from censored transition observations to address this issue. I show that the estimated TPMs from the structural model are consistent, asymptotically normally distributed and maximize the log-likelihood function on observed censored data. I compare the proposed model with other estimation methods through numerical experiments and demonstrate its advantages in various performance metrics, e.g., deviations from the ground truth TPMs. I also implement the proposed model to estimate patient health transitions using real censored data in ICUs extubation problems. Formulating the extubation problem as a classical optimal stopping Markov Decision Process model, I show that the proposed model, with more accurate estimated TPMs considering censored data, can reduce the length of stay of patients in ICU compared to other benchmark transition estimation methods. In the second chapter, considering multiple urban water resources (e.g., freshwater from reservoirs, recycled water, and desalinated water/imported freshwater) and multiple streams of urban water demand (e.g., household and non-household demands), I examine the economic and sustainable implications of wastewater recycling capacity investment under rainfall and recycling cost uncertainties. To this end, I formulate the problem as a two-stage stochastic minimization model and characterize the optimal wastewater recycling capacity. I find that the optimal recycling capacity first decreases and then increases in the freshwater capacity, suggesting that they are substitutes when the freshwater capacity is relatively small and complements otherwise. I also perform sensitivity analysis on how the uncertainties (rainfall and recycling cost variabilities and their correlation) affect the optimal recycling capacity and the optimal expected cost and find that the water utility always benefits from a higher correlation coefficient but a lower rainfall variability. In this chapter, I also discuss urban water sustainability using the measures such as urban water vulnerability and characterize the specific conditions under which urban water may become more vulnerable. The third chapter calibrates the economic model presented in the second chapter based on the publicly available data from the urban water supply practice in Adelaide, the capital city of South Australia. To complement the analytical results, I conduct comprehensive numerical analysis in this chapter to investigate the effects of uncertainties on the optimal expected cost and optimal recycling capacity. Moreover, I study the value of wastewater recycling and how rainfall and recycling cost variabilities, correlation and demand expansion affect it. For example, the results show that the value of wastewater recycling increases in the correlation coefficient and decreases in the rainfall variability. Based on the calibrated baseline scenario, I find that the expansion of both the household and non-household demands increase the value of recycling; moreover, the expansion of non-household demand tends to have a larger impact when the deviations from the baseline scenario become relatively large. I further study the leakage reduction, water vulnerability and overflow risk. The insights from the numerical analysis in this chapter complement the analytical results presented in Chapter 2. I put forward important practical implications relevant to both urban water utilities and water policymakers based on the findings.

This dissertation studies China’s economic growth from a perspective of industry dynamics. In chapter 1, I introduce the background and policies relating to China’s economic growth after 1978. In chapter 2, I find that the elasticity of the average R&D expenditure of firms on competition is -0.29 in weak-IPR (intellectual property right) provinces, and -0.06 in strict-IPR provinces. Next, I use the Schumpeterian growth model to explain this finding: When the market becomes more competitive, a firm prefers imitation to innovation to a larger extent, as a means of getting new technology. Due to enforcement of IPR laws, the imitation replaces innovation more slowly in strict-IPR provinces, compared to weakIPR provinces. In chapter 3, I estimate the TFPs of exporting and importing varieties for 6827 firms from 2002 to 2007 in the garment industry. I present three main channels of the growth of the aggregate TFPR(revenue) of continuous exporters: technology upgrade, reallocation of resources within continuous-exporting products, and switch of products. These three channels explain 27.2%,15.3%, and 9.46% of the aggregate TFPR growth, respectively. From the import side, the adjustment of import counts by firms explains 0.1% of the aggregate TFPR growth.

In the first chapter, we develop an estimation procedure to identify the partial (direct) effects of the GATT/WTO membership on variable and fixed trade costs, respectively. This procedure extends the techniques of Anderson and Van Wincoop (2003) on the structural relationship of multilateral resistance terms and of Helpman, Melitz and Rubinstein (2008) on the structural modeling of trade incidence. We then develop a general equilibrium framework (that allows for the presence of zero trade) to simulate the impact of variable, fixed, and total trade cost changes on the firm-level trade structure (including the bilateral export productivity cutoff, the weighted/unweighted extensive margin of export, the intensive margin, and the mass of active firms), the bilateral trade flow, and the aggregate welfare due to the GATT/WTO system (given the trade cost effects estimated in the first stage) for the period 1978–2015.

Information asymmetry can create substantial frictions when importing firms find it difficult to acquire information about foreign products. In the second chapter, I use detailed China Customs Data to show that firms tend to import from countries with which they already have an importing relationship. Motivated by this fact, I develop a dynamic model describing firms’ decisions on their choice of sourcing country. This model incorporates both communication cost and satisfaction uncertainty, which are lower with familiar countries than with unfamiliar ones. Using this model, I estimate the benefits of importing from familiar countries measured by the probability improvement of receiving satisfactory products. I find that this probability can be improved by a maximum of 89.0 percent when importing from familiar countries instead of from unfamiliar ones. These results also support the prediction that the effective unit cost of intermediates is lower when importing from familiar countries.

The third chapter presents a heterogeneous firm model à la Melitz (2003) in which firms suffer from both the agency problem internally and financial constraints externally. We show that conditional on the same raw productivity draw, managers of potential exporting firms around the export cutoff in financially underdeveloped
countries exert more effort than their counterparts in financially developed countries, as to induce their owners to export. This finding has very positive policy implications, as firms in financially underdeveloped countries can compete with their peers in financially developed countries by exerting more managerial effort. We find clear empirical evidence for this theoretical prediction using the World Management Survey data for more than 7,000 firms in 20 countries during 2002-2012.

The first chapter is a randomized controlled trial study that uses loss framing and information nudges to increase secondary school attendance in Bangladesh. Conditional cash transfers (CCTs) have become one of the most common policy interventions to increase school attendance, but the cost-effectiveness of such interventions has not attracted the attention it deserves. Hence, in addition to a standard CCT implementation, our rich unique dataset on daily attendance allows us to experimentally study two potential ways to improve the cost-effectiveness of school attendance interventions: (i) SMS information nudges and (ii) loss framing in CCTs. The former provides school attendance information to parents and the latter exploits the endowment effect. Consistent with the existing literature, CCT intervention significantly increases school attendance. Though the difference between gain and loss framing is not statistically significant, the point estimate of the Loss treatment is consistently higher than that of the Gain treatment. The SMS treatment has a modest impact on school attendance but the overall cost of treatment is low. We also find diminishing marginal impact of cash transfer amount on attendance, indicating that the intensive margin matters. Thus, both loss framing and SMS nudges can be considered as alternative cost-effective approaches to promote attendance in schools in developing and less developed economies. In the second chapter, I study the causal impact of alcohol consumption on incidence of intimate partner violence in the Indian context. A study by the World Health Organization shows that about 35% of women in the world have been victims of physical or sexual intimate partner violence in their lifetime. Using an overidentified model where I exploit the spatial variation in alcohol ban and minimum legal drinking age across states in India to instrument for the husband's alcohol consumption, I find that alcohol consumption by the husband increases incidence of less severe physical violence by 55 percentage points and severe physical violence for women by 23.6 percentage points, and also has negative consequences on women empowerment in general. I further show that the results are not driven by worse gender attitudes in states where alcohol is allowed. A heterogeneity analysis reveals that there is a vicious cycle of intimate partner violence whereby individuals who are the most vulnerable in terms of having previous exposure to domestic abuse or residing in poorly constructed houses are often the victims. The third chapter explores the causal impact of the mid day meal program on parental investment in education for primary school going children in India. Using the first round of the Indian Human Development Survey (IHDS) and exploiting the staggered implementation of the mid day meal program across different states in India, we find that the amount spent on school fees reduces significantly by 16 percent for children who are eligible to receive the mid day meal. The significant decrease in school fees can, in part, be attributed to transfer of children from private to government schools. We further find that such transfers do not lead to any improvement in learning or health outcomes. However, there is no evidence of gender discrimination in school expenditures that might adversely affect the girl child. The fourth chapter outlines Singapore’s major sustainability challenges and its policy response in the areas of land use, transportation, waste management, water, and energy. We review the current and past Concept Plans from the perspective of sustainable land use and provide an overview of transportation policy in Singapore. We also examine Singapore’s policies to manage increasing wastes and review the four tap water management plan. Finally, we look at various initiatives by the government for sustainable use of energy. We discuss the opportunities that new technologies will bring about and the role that Singapore can play in building a sustainable city.

Emotion regulation (ER) constitutes strategies that modulate the experience and expression of emotions. While past work has predominantly focused on each ER strategy independently, recent research has begun to examine individual-difference factors that are associated with the flexible implementation of ER strategies in line with environmental demands (i.e., ER flexibility). Considering that ER processes generally implicate executive function (EF)—a collection of adaptive, general-purpose control processes—it is plausible that EF could be involved in ER flexibility. Using a latent-variable approach based on a comprehensive battery of EF tasks, the present study investigated how the various aspects of EF (i.e., common EF, working-memory specific, and shifting-specific factors) are related to the flexible maintenance and switching of ER strategies in response to stimuli that elicit varying levels of emotional intensity. Results indicated that better working-memory-specific ability (i.e., the ability to manipulate and update information within a mental workspace) was associated with greater ER strategy variability and higher frequency of ER strategy switching in high-, relative to low-, intensity contexts. Further, more proficient common EF (i.e., the ability to sustain relevant goals in the face of competing goals and responses) corresponded to greater propensity to maintain ER strategy for contexts with low-, but not high-, negative intensity. The outcomes of this study offer a richer understanding of the cognitive mechanisms underlying ER flexibility.

The dissertation consists of three essays on empirical asset pricing. The first chapter proposes a novel inter-firm link - similar employee satisfaction. Based on the employee satisfaction data on Glassdoor, the returns of similar employee satisfaction (SES) firms are documented to predict focal firm stock returns. A long-short portfolio sorted on the lagged returns of SES firms yields the Fama-French six-factor alpha of 135 bps per month. The observed predictability cannot be explained by risk-based arguments or subsumed by other known inter-firm momentums. According to the international tests, we observe stronger return predictability in countries with more flexible labor markets. The return predictability across SES firms may reflect a new type of cross-firm link derived from the knowledge spillover about employee welfare policies via social transmissions.

The second chapter discovers a novel firm characteristic that contains information about firm stock performance. Inspired by the psychological findings that demographic similarity can promote trust and coordination within a team, we propose and find that firm performance is positively related to the facial resemblance between top management team (TMT) members due to the higher managerial efficiency. A long-short value-weighted portfolio sorted on the TMT facial similarity yields a significant Fama and French (2018) six-factor alpha of 40 bps per month. In addition, the firm TMT facial similarity is also documented to be informative in firm operating performance. In addition, our tests suggest that investors’ limited attention and limits of arbitrage are the potential mechanisms behind the documented return predictability.

The last chapter studies the effects of CEO tweeting on firm stock performance by creating a measure of CEO tweeting skill. Based on the U.S. public firms sample from 2012 to 2018, we discover that if CEOs are good at communicating on social media, firms can benefit from CEOs’ high exposure on Twitter. However, if CEOs cannot handle well on social media, tweeting frequently can be harmful to the firm stock performance. We find the results hold across different countries (such as France, Germany, and the United Kingdom). The possible mechanisms behind our documented findings are shown to be limited attention and limits to arbitrage. And our documented effects are more likely to be explained by the behavioral bias other than risk explanations.

This dissertation consists of three essays that contribute to the theory of nonstationary time-series analysis.

The first chapter explores the inference procedures for predictive regressions with time-varying characteristics. We extend the self-generated instrumentation, called IVX, to incorporate persistent regressors of functional local-to-unity, functional mildly explosive, and functional mildly stationary roots. The asymptotic distributions of IVX estimators under time-varying parameters are novel and nonpivotal but lead to pivotal distributions of the corresponding Wald statistics that are robust across various roots. The numerical experiments justify the robustness of IVX testing procedures in finite samples. We also verify the existence of time-varying coefficients and the predictability of fundamentals with such unstable parameters using the S&P 500 data.

The second chapter proposes a functional local-to-unity model with autoregressive coefficients that vary smoothly over time. Two sieve estimators, namely a time series and a panel autoregression estimators, are considered to estimate the local-to-unity function. The property of consistency is established. Besides, a consistent specification test to detect parameter instability is proposed. Numerical simulations demonstrate the finite sample performance of the specification test. Finally, we apply the panel estimator and specification test to the price index of China's real estate market and obtain significant empirical results in measuring time-varying growth rates in the data.

The third chapter discusses about time-varying predictive regressions, which are useful in the applications of empirical finance. The relevant theory in this area is mainly restricted to the case in which the model contains the local-to-unity (LUR) or locally stationary regressors only. It is not universal as the prevalent evidence indicates the existence of both time-varying predictability and the mixed-root phenomenon. We investigate a nonparametric predictive regression model with mixed-root regressors and time-varying coefficients, evolving smoothly over time. Further, we present a new variant of the self-generated instrument, called Sieve-IVX, which attains robust inference irrespective of various degrees of persistence. We establish its consistency and provide a Wald test to detect the temporary predictability of economic fundamentals. Numerical simulations show satisfactory finite-sample performances, which support our results.

Due to increased aging populations and changes in lifestyles, we have witnessed an increased prevalence of various chronic and acute diseases and a drastic rise in healthcare expenditures in recent years. It is of paramount importance for public health to promote regular screening and close monitoring to detect the early onset of diseases. On the other hand, the increasing availability of healthcare data and advancement in data analytics offer a huge potential to facilitate this goal. We can analyze the vast amount of data and recommend more personalized diagnostic tests after receiving results and signals from screening tests and monitoring systems, which are critical decisions for the effective and efficient implementation of such screening programs and monitoring systems. Meanwhile, it is also necessary to consider human behavioral issues and their impact in making the recommendations. In particular, individual adherence to the recommended diagnostic tests can significantly affect the effectiveness and efficiency of the programs. This dissertation aims to integrate predictive analytics, optimization techniques, and behavioral models to improve risk monitoring and decision-making in patient monitoring systems and population screening programs. This dissertation first studies the real-time risk monitoring problem for patients in intensive care units (ICUs). We identify a critical lag in the provision of information due to the long lead time to measure some laboratory test variables (e.g., creatinine, platelets, and bilirubin) used in calculating the Sequential Organ Failure Assessment (SOFA) score, a well-established and important risk measure for patients in ICUs. We develop machine learning models to estimate such variables using easily measured bedside variables, the rate of changes in bedside variables, and time lag from the previous laboratory test, which mimics how physicians assess patient conditions in practice. Then the predicted laboratory test variables can be used to calculate an estimate of the real-time SOFA score. We further take advantage of the estimated standard deviations from these models to construct intervals of the real-time SOFA scores. We hypothesize that the estimated score intervals could capture the uncertainty in patient condition since the previous test and provide valuable information in a new dimension that complements the nominal SOFA scores. Using a dataset collected from an ICU in a tertiary hospital in Singapore, we calibrate our model and validate the hypothesis by comparing the prognostic accuracy of the proposed approach on patients’ 24-hour mortality and 30-day readmission with those from the SOFA score calculated using the conventional approaches. The proposed methodology could be applied to other risk measures to improve their prognostic accuracy and provide more reliable early warning for timely intervention. The methodologies developed in the previous chapter can help raise a warning of potential deterioration in a patient’s health condition, but the exact problem still has to be confirmed through follow-up diagnostic tests, which are typically more invasive and expensive. Medical resource overuse has become increasingly common in recent years and caused diverse problems, including unnecessary and risky diagnostic tests and overly intensive or expensive treatments. There is a growing call for more evidence-based decisions to reduce unnecessary diagnostic tests. The next part of the thesis dives into this problem to optimize the prescription of diagnostic tests during the health monitoring process, leveraging the improved risk monitoring tools developed in the previous chapter. In particular, we develop a finite-horizon, partially observable Markov decision process model to optimize the time to initiate a diagnostic test. Our model captures both measured and estimated clinical variables (including estimated intervals) in real-time to update the belief on a patient’s underlying health condition. We apply the model to monitor patients’ blood glucose levels to detect hyperglycemia, a common complication of critical illness. We calibrate the model using the same ICU dataset as in the previous chapter and demonstrate that the new approach can advance the detection time with fewer diagnostic tests. The methodology can also be applied to many other health monitoring systems, especially those powered by smart wearable health devices for chronic diseases. However, to optimally design the warning signals and recommend the diagnostic tests for such a monitoring system, one must consider the impact of human behavioral issues, especially individuals’ perception of the warning signals and adherence to the recommendations. We address this challenge in the next chapter in the optimal design of population screening programs for cancer surveillance and screening. Cancer remains one of the leading causes of human death, while early detection enables timely intervention and reduction in mortality rate. Two-stage screening programs are broadly implemented in practice among large average-risk populations to effectively and efficiently detect cancer in the early stages. Individuals receiving positive results in first-stage (initial) tests are recommended to undergo second-stage tests for further diagnosis. Notably, individuals’ adherence to the second-stage tests, which is closely associated with the initial test design (sensitivity and specificity) and personal characteristics, varies considerably across individuals and leads to different cancer detection rates and demands for second-stage tests. We adopt a Bayesian persuasion framework to model the optimal initial test design problem in the context of colorectal cancer screening. Our goal is to balance the trade-off between test effectiveness (i.e., detection rates of cancer incidences) and test efficiency (i.e., demands for second-stage tests), considering individuals’ adherence behavior. We conduct a nationwide survey in Singapore to calibrate the individual’s response to changes in the test design. With the embedded behavioral model, we next optimize the threshold selection in the initial test design (which decides the test sensitivity and specificity). We characterized the structural properties of an optimal initial test design. Using various data and information collected locally in Singapore and from the literature, we demonstrate that a well-designed initial test can detect more cancer incidences with fewer second-stage tests than the current practice. We further explore the benefits of using heterogeneous initial tests for different sub-populations and use the interpretable clustering technique to search for implementable rules to partition the population. We find that customized tests with simply an age-gender partition rule could bring significant extra benefits. To conclude, this thesis studies the optimal design of real-time patient monitoring systems and population screening programs, using a combination of techniques from machine learning, optimization, game theory and survey design. By analyzing the comprehensive datasets collected from various sources, we showcase that well-designed monitoring systems and screening programs can benefit individuals, healthcare service providers, and health systems through improved effectiveness and efficiency in healthcare service delivery.

Control-Flow Integrity (CFI) is an attractive security property with which most injected and code-reuse attacks can be defeated, including advanced attacking techniques like Return-Oriented Programming (ROP). CFI extracts a control-flow graph (CFG) for a given program and instruments the program to respect the CFG. Specifically, checks are inserted before indirect branch instructions. Before these instructions are executed during runtime, the checks consult the CFG to ensure that the indirect branch is allowed to reach the intended target. Hence, any sort of controlflow hijacking would be prevented. There are three fundamental components in CFI enforcement. The first component is accurately recovering the policy (CFG). Usually, the more precise the policy (CFG) is, the more security CFI improves, but precise CFG generation was considered hard without the support of source code. The second one is embedding the CFI policy securely. Current CFI enforcement usually inserts checks before indirect branches to consult a read-only table which stores the valid CFG information. However, this kind of read-only table can be overwritten by some kinds of attacks (e.g., Rowhammer attack and data-oriented programming). The third component is to efficiently enforce the CFI policy. In current approaches, no matter whether there are attacks, the CFI checks are always executed whenever there is an indirect control-flow transfer. Therefore, it is critical to minimize the performance impact of the CFI checks. In this dissertation, we propose novel solutions to handle these three fundamental components. We systematically study how compiler optimization would impact CFG recovery by investigating two methods that recover CFI policy based on function signature matching at the binary level and propose our novel improved mechanism to more accurately recover function signature. We also propose an enhanced deep learning approach to recover function signature by including domain-specific knowledge to the dataset. To embed CFI policy securely, we design a novel platform which encodes the policy into the machine instructions directly without relying on consulting any read-only data structure by making use of the idea of instruction-set randomisation. In it, each basic block is encrypted with a key derived from the CFG. To efficiently enforce CFI policy, we make use of a mature dynamic code optimization platform called DynamoRIO to enforce the policy so that it only requires to do the CFI check when needed.

Monetary incentives, such as matching subsidies, are widely used in traditional fundraising and crowdfunding platforms to boost funding activities and improve funding outcomes. However, its effectiveness on prosocial fundraising is still unclear from both theoretical (Bénabou and Tirole, 2006; Frey, 1997; Meier, 2007a) and empirical studies (Ariely et al., 2009; Karlan and List, 2007; Rondeau and List, 2008). This dissertation aims to examine the effectiveness of matching subsidies on prosocial fundraising in the crowdfunding context. Specifically, I study how the presence of matching subsidies affects overall funding outcomes and funding dynamics in the online prosocial crowdfunding environment.

The first essay utilises a quasi-experiment on a prosocial crowdfunding platform to examine the effectiveness of matching subsidies, in which third-party institutions provide a dollar-for-dollar match of private contributions on selected campaigns, on funding outcomes, and lender behavior. Although matching subsidies offer matched loans competitive advantages over unmatched loans, we find that the total private contributions to both matched and unmatched loans increase compared to their pre-matching counterparts, suggesting a positive spillover effect on unmatched loans. However, matching subsidies lead to decreased private contributions on the platform after the matching event, showing an intertemporal displacement effect on existing loans. Furthermore, we find matching subsidies effectively attract previously inactive lenders to contribute to matched loans, leading to a motivational crowding-out effect on active lenders to unmatched loans. These findings shed new light on the overall effectiveness of matching subsidies on the online crowdfunding platforms. These findings provide policy support to offer matching subsidies on prosocial crowdfunding websites to increase overall funding.

The second essay examines how matching subsidies affect the dynamics of prosocial crowdfunding, driven by herding behaviour and payoff externalities. First, in contrast to the previous literature documenting that prior contributions may crowd out subsequent contributions in prosocial crowdfunding, we find that both herding behaviour and positive payoff externalities exist, which suggests that higher cumulative contributions lead to an increase in the subsequent funding amount. Second, we identify the existence of the bystander effect, where the positive effect of prior contributions drops sharply when the campaign is close to success. Finally, we find a substitution effect between matching subsidies and prior cumulative contributions. Matching subsidies not only increase private contributions but also moderate the herding behaviour and payoff externalities. Our findings shed new light on the effective strategies to boost fundraising on prosocial crowdfunding platforms.

The dissertation consists of three chapters on empirical asset pricing. The first chapter examines whether the cross-sectional variation in private subsidiaries’ information disclosure predicts the cross-sectional dispersion in future equity returns of public parent firms. Information disclosure on private subsidiaries is not mandatory for public firms in the U.S., and thus these subsidiaries could be a good choice for public firms to hide bad news. We construct a private subsidiaries’ information disclosure (PSID) measure and find that a value-weighted portfolio that longs stocks in the highest PSID quintile and shorts stocks in the lowest PSID quintile yields a Fama and French (2015) five-factor alpha of 0.60% per month. This return predictability is robust controlling for various firm-specific characteristics and is stronger for stocks that receive less investor attention and stocks that are costlier to arbitrage, consistent with the hypothesis that PSID information is slowly incorporated into stock prices. The second chapter investigates whether locations of firms’ economically-important public subsidiaries contain valuable information about parent firms’ stocks returns. Stock returns of firms in the same headquarter state tend to move together (Pirinsky and Wang (2006)). Parsons, Sabbatucci, and Titman (2020) find that the return comovement of firms headquartered in the same state extends to a predictable lead-lag effect because investors are not able to fully process information arising from firms’ peers located in the same place. We reexamine whether returns of geographic peers based on the locations of both headquarters and economically relevant subsidiaries are useful for predicting the stock returns of focal firms. We find that focal firms whose geographic peers experience higher (lower) returns in the current month will earn higher (lower) returns in the next month. A strategy exploiting this pattern is distinct from other wellknown cross-firm momentum strategies, and it is more pronounced among firms that receive less investor attention and firms that are more costly to arbitrage, consistent with slow information diffusion in the geographic network into stock prices. The third chapter focuses on the well-known presidential puzzle, which refers to the striking empirical fact that stock market returns are much higher under Democratic presidencies than Republican ones. Since first noted by Huang (1985) and Hensel and Ziemba (1995) and carefully documented by Santa-Clara and Valkanov (2003), the pattern remains robust. It is only recently that Pastor and Veronesi (2020) provide an ingenious solution to this puzzle. In this paper, we document a different presidential puzzle in the cross-section of individual stocks. We construct a monthly Presidential Economic Approval Rating (PEAR) index from 1981 to 2019, by averaging ratings on president’s handling of the economy across various national polls. In the cross-section, stocks with high betas to changes in the PEAR index significantly under-perform those with low betas by 0.9% per month in the future, on a risk adjusted basis. The low-PEAR-beta premium persists up to one year, and is present in various sub-samples (based on industries, presidential cycles, transitions, and tenures) and even in other G7 countries. It is also robust to different risk adjustment models and controls for other related return predictors. Since the PEAR index is negatively correlated with measures of aggregate risk aversion, a simple risk model would predict the low PEAR-beta stocks to earn lower (not higher) expected returns. Contrary to the sentimentinduced overpricing, the premium does not come primarily from the short leg following high sentiment periods. Instead, the premium could be driven by a novel sentiment towards presidential alignment.

Personalized recommendation, whose objective is to generate a limited list of items (e.g., products on Amazon, movies on Netflix, or pins on Pinterest, etc.) for each user, has gained extensive attention from both researchers and practitioners in the last decade. The necessity of personalized recommendation is driven by the explosion of available options online, which makes it difficult, if not downright impossible, for each user to investigate every option. Product and service providers rely on recommendation algorithms to identify manageable number of the most likely or preferred options to be presented to each user. Also, due to the limited screen estate of computing devices, this manageable number maybe relatively small, yet the selection of items to be recommended is personalized to each individual users.

The basic entities of a personalized recommendation system are items and users. Personalization can be achieved through custom alternatives for delivering the right experience to the right user at the right time on the right device. Therefore, personalized recommendation can appear in many forms, depending on the characteristics of the items and the desired experience that the system wants users to have. In this thesis, we encompass two perspectives on personalized recommendation: preference learning and similarity learning. The former refers to the personalization in which the recommendation is tailored towards users' preference. The latter, on the other hand, refers to personalization approach in which recommendation is generated based on the users' personal perceptions of similarity between the items.

In the preference learning perspective, we focus on the task of retrieving recommendations efficiently and propose two techniques for this objective. For the first technique, we rely on Euclidean embedding to learn user and item latent vectors from users' ordinal preferences. Since they operate in the Euclidean space, these latent vectors natively support efficient nearest neighbor search using geometric structures such as spatial trees. For the second technique, our key idea is to desensitize the effect of vector magnitudes when modelling users' preferences over items. That effectively reduces the recommendation retrieval problem to the nearest neighbor search problem with cosine similarity, which can be solved efficiently with various indexing methods such as locality sensitive hashing, spatial trees, or inverted index. Extensive experiments on publicly available datasets show significant improvement of proposed techniques over the baselines.

In the similarity learning perspective, we are interested in the setting where there are multiple similarity perceptions in the data. Towards modelling these perceptions effectively, we propose two approaches that are natively multiperspective. One is a graph-theoretic framework that yields a similarity measure for any pair of objects for a perspective. Another is a geometric framework that learns multiple low-dimensional representation of objects, each for one perspective. Experiments in both studies show that the adoption of multiperspective approach allows us to better model the similarity between objects, as compared to classical uniperspective methods, which ignore the multiperspectivity in the data.

This dissertation includes three essays on the quality of tradable products. The first chapter studies the supply-side determinants of quality specialization across Chinese cities. Specifically, we complement the quality specialization literature in international trade and study how larger cities within a country produce goods with higher quality. In our general equilibrium model, firms in larger cities specialize in higher-quality products because agglomeration benefits (arising from the treatment effect of agglomeration and firm sorting) accrue more to skilled workers, who are also more efficient in upgrading quality, although these effects are partially mitigated by higher skill premium in larger cities. Using firm-level data from China, we structurally estimate the model and find that agglomeration and firm sorting each accounts for about 50% of the spatial variation in the quality specialization. A counterfactual policy to relax land use regulation in housing production raises product quality in big cities by 5.5% and indirect welfare of residents by 6.2%. The second chapter examines how information frictions matter in the endogenous choice of product quality made by firms. We introduce quality choice into a trade-search model with information frictions Allen (2014). In our model, producers must search to learn about the quality-augmented price index elsewhere and decide whether to enter a specific destination market. Hence, a fall in information frictions such as the building of information and communications technologies infrastructure (i.e., faster mobile networks) will induce quality upgrading. We empirically test the predictions of our model using unit value data and variation in information and communications technologies infrastructure across Chinese cities. The third chapter provides empirical evidence on the effects of falling trade costs on product quality across cities within a country. We approach this question in the context of expanding the highway system in China in the past decades, which substantially reduces the trade costs across regions within the nation. Empirically, we combine two firm-level panels that provide unit-value information of products across Chinese cities with city-level data on transportation infrastructure for 2001-2007. We find that firms choose to upgrade product quality more in cities with a greater expansion of connecting highways. In addition, this effect is more pronounced in larger cities, which speaks to changes in the spatial concentration of higher-quality products. These results are also robust to the inclusion of an exhaustive battery of fixed effects and to changes in estimation specifications. Our findings shed important insights on the impact of falling intranational trade cost on quality specialization pattern across cities, which is difficult to model quantitatively due to the presence of agglomeration and sorting.

This dissertation investigates two important issues in agricultural commodity processing: (i) biomass commercialization; that is, converting organic waste into a saleable product, from economic and environmental perspectives, and (ii) optimal procurement portfolio design using multiple suppliers and spot market, and the impact of by-product introduction on this optimal portfolio.

The first chapter examines the economic implications of biomass commercialization from the perspective of an agri-processor that uses a commodity input to produce both a commodity output and biomass. We characterize the value of biomass commercialization and perform sensitivity analysis to investigate how spot price uncertainty (input and output spot price variabilities and the correlation between the two spot prices) affects this value. We find that commercializing biomass makes the profits less sensitive to changes in spot price uncertainty. Using a model calibration in the context of palm industry, we show that the value of biomass (palm kernel shell) commercialization can be as high as 26.54% of the processor (palm oil mill)’s profits.

The second chapter examines the environmental implications of biomass commercialization. To this end, we characterize the expected carbon emissions considering the profit-maximizing operational decisions using the economic model of the first chapter. In comparison with the common perception in practice, which fails to consider the changes in operational decisions after commercialization, we identify two types of misconceptions (and characterize conditions under which they appear). In particular, the processor would mistakenly think that commercializing its biomass is environmentally beneficial when it is not, and vice versa. Using a model calibration, we show that the former misconception is likely to be observed in the palm industry. we perform sensitivity analyses to investigate how a higher biomass price or demand (which is always economically superior) affects the environmental assessment and characterize conditions under which these changes are environmentally superior or inferior. Based on our results, we put forward important practical implications that are of relevance to both agri-processors and policy makers.

The third chapter studies the procurement portfolio design of an agri-processor that sources a commodity input from two suppliers that use quantity flexibility contracts---characterized by reservation cost and exercise cost---to produce and sell a commodity output under input and output spot price uncertainties. We characterize the optimal procurement portfolio that is composed of three strategies---single sourcing from the supplier with lower reservation price, and single sourcing from the supplier with lower exercise price, and dual sourcing. We investigate how the spot price correlation shapes the optimal procurement strategy and the value of using suppliers. We then study the impact of introducing a non-commodity by-product on the optimal procurement portfolio. Based on our results, we put forward important managerial implications about the procurement strategy and by-product management in agricultural processing industries.

This dissertation studies the capacity investment decision of a manufacturing firm facing demand uncertainty in the presence of shortage possibility in production resources, as often ignored in the literature. These production resources can be physical resources (component / raw material) or financial resources (working capital / budget). The shortage in these resources can be caused by a variety of supply chain disruptions; examples include global disruptions like COVID-19 and financial crisis in 2008 and local disruptions like shortage of components/workforce. The dissertation analyses two important issues related to capacity management: (i) the effect of production resource disruption on the capacity investment strategy and the profitability of the firm (including the significance of profitability loss incurred when the resource shortage possibility is ignored, and (ii) the role of production resource disruption management strategies, i.e. using pre-shipment financing to mitigate the effect of financial resource disruption and using hedging to mitigate physical resource disruption.

The first part examines a two-stage capacity-production framework that capacity investment decision is in anticipation of demand and production resource uncertainties and production quantity is decided after the revelation of uncertainties. I characterize the optimal decisions and investigate how the uncertainties (demand and production resource variability and the correlation between the two) affect the optimal capacity investment level and profitability.

My results provide a rule of thumb for the managers in capacity management. I also study the significance of profitability loss incurred when the resource uncertainty is ignored in choosing a capacity level. Through both analytical and extensive numerical analysis, I show that the profitability loss is high when 1) correlation is high; 2) either production resource variability is sufficiently high or sufficiently low; and 3) either demand variability is sufficiently high or sufficiently low.

The second part examines the role of pre-shipment finance in managing financial production resource (working capital/budget) disruption. Pre-shipment finance allows the firm to transfer the purchase orders (which will be paid after production) to an external party that provides immediate cash flow (at a cost) that can be used for financing the production process. To this end, I characterize the optimal pre-shipment finance level (proportion of sales revenues transferred) and the production volume in the production stage and the optimal capacity investment level in the capacity stage. I make comparisons with the results in the first chapter to understand how pre-shipment financing alters the effects of demand and production resource uncertainties on the optimal capacity investment level, expected profit and profitability loss due to ignoring resource uncertainty. I identify that applying pre-shipment finance makes the capacity investment and profits more resilient to changes in spot price uncertainty.

The third part studies the role of procurement hedging contract in managing physical production resource (e.g., component/raw material) disruption. With the hedging contract, the firm can engineer the production resource uncertainty at the capacity investment stage—for example, with full hedging this uncertainty can be completely removed. I provide the joint characterization of the optimal hedging level and capacity investment decisions. I find that these decisions critically depend on the covariance between demand and production resource uncertainties and the unit capacity investment cost. For example, I find that fully hedging is always optimal when the correlation is non-positive. I highlight conditions under which the firm optimally does not hedge at all or use partial hedging strategy. I then investigate the significance of the profitability loss due to i) misspecification of capacity level by ignoring production resource uncertainty and ii) misspecification of hedging strategy (using full hedging which is easy to implement), and provide conditions under which these profitability losses are significant.

The dissertation consists of three chapters which studies a mechanism design approach to the problem of bilateral trade and public good provision.

Chapter 1 characterizes mechanisms satisfying Bayesian incentive compatibility (BIC) and interim individual rationality (IIR) in the classical public good provision problem. We propose a stress test for the results in the standard continuum type space by subject- ing them to a finite type space. The main contribution of this paper is to propose a set of techniques that allow us to characterize the efficient and optimal mechanisms in a discrete setup. Using these techniques, we conclude that many of the known results gained within the standard continuum type space also hold when it is replaced by a discrete type space.

Chapter 2 seeks for more positive results by employing two-stage mechanisms (Mezzetti (2004)), as efficient, voluntary bilateral trade is generally not incentive compatible in an interdependent-value environment (Fieseler, Kittsteiner, Moldovanu (2003) and Gresik (1991)). First, we show by means of a stylized example that the generalized two-stage Groves mechanism never guarantees voluntary trade, while it satisfies efficiency and in- centive compatibility. In a general environment, we next propose Condition α under which there exists a two-stage incentive compatible mechanism implementing an effi- cient, voluntary trade. Third, within the same example, we confirm that our Condition α is very weak because it holds as long as the buyer’s degree of interdependence of preferences is not too high relative to the seller’s counterpart. Finally, we show by the same example that if Condition α is violated, our proposed two-stage mechanism fails to achieve voluntary trade.

Chapter 3 clarifies how the interdependence in valuations and correlation of types across agents affect the possibility of efficient, voluntary bilateral trade in a model with discrete types, as efficient, voluntary bilateral trades are generally not incentive compat- ible in a private-value model with independently distributed continuous types (Myerson and Satterthwaite (1983)). First, we identify a necessary condition for the existence of in- centive compatible mechanisms inducing an efficient and voluntary trade in a finite type model. Second, we show that the identified necessary condition becomes sufficient for a two-type model. Using this characterization in a model with linear valuations and two types, we next conduct the comparative statics for how possibility results rely on the inter- dependence and correlation. Third, using the linear programming approach, we establish the general existence of an efficient, incentive compatible trade in a model with two types. This suggests that voluntary trade becomes a stringent requirement in an interdependent values model with correlated signals.

The rapid advancement in technology and internet penetration have substantially increased the number of economic transactions conducted online. Platforms that connect economic agents play an important role in this digital economy. The unbridled proliferation of digital platforms calls for a closer examination of the factors that could affect the welfare of the increasing number of economic agents who participate in them.

This dissertation examines the factors that could affect the welfare of agents using the setting of a crowdfunding platform where fundraisers develop campaigns to solicit funding from potential donors. These factors can be broadly categorized into three distinct groups: (1) campaign and its corresponding fundraiser characteristics, (2) other factors within the platform, and (3) other factors outside the platform. The first group of factors has been examined in a large number of studies. The second and third groups, which encompass factors external to the campaigns and fundraisers remain under-explored and therefore are the focus of this dissertation.

The first essay in this dissertation explores a factor within the platform; how displaying certain campaigns more prominently on the platform affects the performance of other campaigns. Such selective prominent practice is often viewed negatively because it is perceived to place less prominent sellers at a disadvantage (Kramer & Schnurr, 2018). The findings from the first essay provide a counterpoint to this popular view by documenting a positive spill-over effect from an increase in the performance of the prominent campaigns. In particular, when the prominent campaigns perform well, market expansion occurs with more donors entering the platform, benefiting the less prominent campaigns. These findings mitigate the concern that non-neutral practices on digital platforms naturally lead to the rich getting richer and the poor getting poorer.

The second essay explores a factor external to the platform; how public statements from a government official affect private donations to charitable crowdfunding campaigns. A clear pattern of ethnic homophily among fundraisers and donors, where Hispanic fundraisers receive disproportionately more donations from Hispanic donors, is observed in this setting. This pattern of homophily becomes stronger following statements from President Donald Trump. This essay documents how social media usage, particularly by a government official, can influence the dynamic within and across ethnic groups. In sum, the findings from the two essays help inform platform designers, policymakers, and government officials of the potential effects of their actions on the digital economy.

Chapter 1: Analyst report content and stock market anomalies A series of recent papers document that security analyst recommendations tend to contradict stock-mispricing signals. This seems at odds with the large prior literature on the investment value of analyst recommendations. What justifications do analysts make when they write reports on mispriced stocks? I use the latest techniques in machine learning and textual analysis to categorize the qualitative information in a large sample of analyst reports. I find that report content can be intuitively classified into five categories or topics: 1) Growth, 2) Earnings, 3) New developments, 4) Management transactions, and 5) Conviction. I then relate the frequency of each topic and the tone surrounding the topic to stock-anomaly mispricing signals. I find that although analysts are incorrectly optimistic about overvalued stocks in general, reports on new developments and management transactions have investment value after controlling for the predictive power of the mispricing signals. For undervalued stocks, while analysts are on average incorrectly pessimistic, reports on growth, new developments, and management transactions have investment value. Overall, this paper helps to understand how analysts provide value in their reports even when the report ratings appear to contradict well-known signals of mispricing.

Chapter 2: The information cycle and return seasonality (with Roger Loh) Heston and Sadka (2008) find that the monthly cross-sectional returns of stocks depend on their historical same calendar-month returns. We propose an information-cycle explanation for this seasonality anomaly—that firms’ seasonal release of information coincide with higher returns during months with such dissolution of information uncertainty, and lower returns during months with no information releases. Using earnings announcements and changes in implied volatility as proxies for scheduled information releases, we find that seasonal winners in information-release months and seasonal losers in non-information release months indeed drive the seasonality anomaly. Our evidence shows that scheduled firm-level information releases can give rise to the appearance of an anomalous seasonal pattern when stock returns are in fact responding to information uncertainty.

Chapter 3: Managerial and analyst horizons during conference calls It is alleged that public-firm managers face short-term pressures from investors. In this paper, I examine managers’ tendency to talk about the short versus the long term by analyzing the language in quarterly analyst conference calls. Using the word embedding model, I determine whether conference calls focus on the short or long term. I find that when firms fail to meet analyst expectations, both managers and analysts focus on the short term rather than the long term. However, in macro bad times, analysts question managers about the short term rather than the long term, while managers maintain the same long term-short term balance whether in good or bad macro conditions. Finally, I show that firms whose conference call participants focus more on the long term have negative initial market reactions, but stock prices recover in the subsequent months. subsequent months. The results are consistent with Wall Street exerting excessive short-term pressures on public firm managers.

Wearable devices are gaining in popularity, but are presently used primarily for productivity-related functions (such as calling people or discreetly receiving notifications) or for physiological sensing. However, wearable devices are still not widely used for a wider set of sensing-based applications, even though their potential is enormous. Wearable devices can enable a variety of novel applications. For example, wrist-worn and/or finger-worn devices could be viable controllers for real-time AR/VR games and applications, and can be used for real-time gestural tracking to support rehabilitative patient therapy or training of sports personnel. There are, however, a key set of impediments towards realizing this vision. State-of-the-art gesture recognition algorithms typically recognize gestures, using an explicit initial segmentation step, only after the completion of the gesture, thereby being less appropriate for interactive applications requiring real-time tracking. Moreover, such gesture recognition & hand tracking is relatively energy-hungry and requires wearable devices with sufficient battery capacity. Such battery-driven operation further restricts widespread adoption, as (a) the device must be periodically recharged, thereby requiring human intervention, and (b) the battery also adds to the wearable device’s weight, which potentially affects the wearer’s motion dynamics.

In this thesis, I explore the development of new capabilities in wearable sensing along two different dimensions which we believe can help increase the diversity and sophistication of applications and use cases supported by wearable- based systems: (i) Low-latency, low-complexity gesture tracking, and (ii) Ultra-low-power or Battery-less operation. The thesis first proposes the development of a battery-less wearable device that permits tracking of gestural actions by harvesting power from appropriately beamformed WiFi signals. This work requires innovations in both wearable and WiFi AP operations, which work together to support adequate energy harvesting over distances of several meters. Through a combination of simulations and real-world studies, I show that (a) smart WiFi beamforming techniques can help support sufficient energy harvesting by up to 3-4 battery-less devices in a small room, and (b) the prototype battery-less wearable device can support uninterrupted tracking of significant gestural activities by an individual. The thesis then explores the ability of smartwatch to recognize hand gestures early and to track the hand trajectory with low latency, so that it can be used in realizing interactive applications. In particular, I show that our techniques allow a wrist-worn device to be used as a real-time hand tracker and gesture recognizer for an interactive application, such as Table Tennis. The dissertation also demonstrates that my proposed method provides a superior energy-vs-accuracy trade-off compared to more complex gesture tracking algorithms, thereby making it more conducive to operation on battery-less wearable devices. Finally, I evaluate whether my proposed techniques for low- latency gesture recognition can be supported by WiWear-based wearable devices, and establish the set of operating conditions under which such operation is feasible. Collectively, my work advances the state-of-the-art in low-energy wearable-based low-latency gesture recognition, thereby opening up the possible use of battery-less, WiFi-harvesting based devices for gesture-driven applications, especially for sports & rehabilitative training.

Disagreement measures are known to predict cross-sectional stock returns but fail to predict market returns. This paper proposes a partial least squares disagreement index by aggregating information across individual disagreement measures and shows that this index significantly predicts market returns both in- and out-ofsample. Consistent with the theory in Atmaz and Basak (2018), the disagreement index asymmetrically predicts market returns with greater power in high sentiment periods, is positively associated with investor expectations of market returns, predicts market returns through a cash flow channel, and can explain the positive volume-volatility relationship.

Dynamic malware analysis schemes either run the target program as is in an isolated environment assisted by additional hardware facilities or modify it with instrumentation code statically or dynamically. The hardware-assisted schemes usually trap the target during its execution to a more privileged environment based on the available hardware events. The more privileged environment is not accessible by the untrusted kernel, thus this approach is often applied for transparent and secure kernel analysis. Nevertheless, the isolated environment induces a virtual address gap between the analyzer and the target, which hinders effective and efficient memory introspection and undermines the correctness of semantics extraction. Code instrumentation mixes the analyzer code together with the target, thus they share the same execution flow as well as the virtual address space at runtime. The instrumentation code has native access capabilities to the target’s virtual memory, which seamlessly introspects and controls the target. However, code instrumentation based schemes are inadequate to tackle malicious execution since the analysis can be detected, evaded, or even tampered with as noted in many recent works.

We securely bridge the virtual address gap by designing a system called the On-site Analysis Infrastructure(OASIS) based on hardware virtualization technology. OASIS features a one-way address space sharing: on the one hand, the analyzer, as an independent full-fledged application, runs in a fused virtual address space comprising both its own space and the target’s; on the other hand, the analyzer’s space is separated and isolated from the target which still runs within its unmodified address space. We also extend OASIS with a significant instrumentation technique which allows secure transitions between the analyzer and the target without precipitating any CPU mode/privilege switch. In total, OASIS offers three capabilities to the analyzer: to reference the target virtual memory in a native way with mapping consistency; to dynamically control and instrument the target execution; and to transparently receive unmodified host OS services. With these capabilities, the analyzer performs onsite analysis on a malicious user/kernel thread running in the guest VM.

We propose two new dynamic analysis models based on OASIS: Onsite Memory Analysis (OMA) and Execution Flow Instrumentation (EFI). In OMA, the analyzer examines the user/kernel thread’s live virtual memory without interposing on its execution. We developed four tools to demonstrate its capability. The first one is a virtual machine introspection tool which is up to 87 times faster than the state of the art. The second one delineates the target’s virtual memory layout without trusting any kernel objects. The third one captures the target’s system call events along with their parameters without intercepting its execution. The last one generates the control flow graph for Just-In-Time emitted code. In EFI, the analyzer is provisioned with two options to directly intercept the user/kernel thread execution and dynamically instrument it. Despite being securely and transparently isolated from the target, the analyzer introspects and controls it in the same native way as the instrumentation code. We have also conducted three case studies. The first one is a cross-space control flow tracer which shows OASIS based EFI has better performance than existing hardware trapping based schemes. The second one works in tandem with Google Syzkaller which demonstrates EFI’s agility in controlling and introspecting the target thread. The last one examines how a user-space program exploits the vulnerability in dynamically loaded kernel modules. EFI tools are well-suited for targeted and fine-grained analysis.

We have implemented a prototype of OASIS on an x86-64 platform and have rigorously evaluated it with various experiments including performance and security tests. OASIS and its tools remain transparent and effective against targets armed with anti-analysis techniques including packing.

This dissertation consists of three chapters that evaluate the impacts of public policies on labour and health.

The first chapter studies a wage supplement scheme in Singapore, called the Workfare Income Supplement, which targets older low-income workers. I exploit differences in maximum benefits across age and over time to find that increasing benefits generosity encourages labour market participation and selfemployment. I also find improved life satisfaction and happiness among those with low education, who are likely to be eligible for the scheme. These results suggest that wage supplements can ease some burdens of an ageing population.

The second chapter investigates the effects of raising a non-pension retirement age on labour market outcomes and subjective well-being in Singapore. Adopting a difference-in-differences identification strategy, I find an increase in employment and a decrease in retirement of older workers. Additional analyses suggest that mental anchors may be an important mechanism. I also find improved satisfaction with life as whole and with health, especially among those who are less educated, less prepared for retirement or dissatisfied with household income.

The third chapter examines heterogeneous health effects of medical marijuana legalization on young adults in the United States. Using a difference- in-differences approach accounting for spatial spill-over, I find that states with stricter regulations generate health gains, but not states with lax access to marijuana. Subsample analysis reveal that subgroups such as Blacks, individuals from lower-income households and the uninsured experience larger gains under strict regulations. However, the low-educated, individuals from lower-income households and the uninsured are more likely to suffer worse health under lax regulations.

The first essay is about how high and moderate aspiration levels compare in terms of affecting the decision making and reinforcement learning in an uncertain environment. After developing a thought experiment and a computational model, I used lab experiments to test the model’s predictions: a high (moderate) aspiration level reduces (increases) feedback ambiguity about the relative attractiveness of different options, thus increases the exploitation (exploration) tendency of the decision maker. The behavioural difference suggests that high aspirations lead to better performance in stable environments, but worse performance after disruptive shocks. The second essay investigates whether organizations should commit more (or less) to exploration in response to an increased environmental dynamism. Using a computational model, I address the literature contradictions by disentangle exploration intensity and width. I demonstrate that the phenomenon of “chasing a moving target” (Posen & Levinthal, 2012) – the decreasing optimal exploration level under increased environmental dynamism – is caused by the entanglement of exploration intensity and width. The third essay addresses the question about how ambiguous performance feedback across organizational levels affects resource allocation. Attribution theory suggests organizations and organizational members will attribute success internally while attributing failures externally, resulting different learning and response patterns following organizational success and failure. Using professional basketball data, I demonstrate the resources (minutes) allocated to players are subject to the players prior performance. Team performance (game win) positively moderates the relationship between allocated resource and a player’s performance. The moderating effect is the weakest when the team experience a loss with large point-deficit.

This dissertation two issues related to business ethics: how corporate social responsibility (CSR) affects the value creation in an acquisition and how corporate decoupling behaviors are driven by the CEO narcissism, consisting of two essays. The first essay examines how target corporate social responsibility affects the economic gains for acquirers, as reflected in market reaction to acquisition announcement, from two distinct perspectives: stakeholder preservation versus stakeholder appropriation. The stakeholder preservation perspective suggests that positive market reaction to an acquisition stems from potential new value creation by honoring implicit contracts and maintaining good relationships with target stakeholders. By contrast, the stakeholder appropriation perspective posits that positive market reaction is primarily derived through wealth transfer to acquirers by defaulting on implicit contracts with target stakeholders. Findings from this essay indicate that target CSR is positively associated with acquirer abnormal returns upon acquisition announcement. Moreover, stakeholder value congruence between the merging firms strengthens this positive relationship, whereas business similarity between them weakens it. These findings align with the stakeholder preservation perspective and challenge the stakeholder appropriation perspective. The second essay investigates antecedents of corporate decoupling behaviors from the perspective of CEO attributes. This essay is conducted in the context of corporate buyback program. Corporate decoupling happens when a firm announces a buyback policy but does not implement the buyback program. Findings from this essay suggest that there is a positive relationship between CEO narcissism and buyback policy adoption whereas, following a buyback policy adoption, there is a negative relationship between CEO narcissism and buyback program implementation. Also, this essay examines the peer influence on a focal firm’s buyback practice and finds that peer buyback policy adoption will weaken the relationship between CEO narcissism and firm buyback policy adoption. In addition, the buyback policy adoption initiated by more narcissistic CEOs receives less favorable stock market reactions.