Smith School of Business Graduate Theses

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    Three Essays in Corporate Finance
    Drapeau, Line; Business; Gagnon, Louis
    Leniency laws constitute a powerful deterrent to the formation of new cartels and an effective tool facilitating the dismantling of existing cartels, thereby introducing a competition shock in the countries where they are enacted. While the connection between product market competition and firms’ financial decisions has been a subject of interest (e.g., Khanna and Tice (2000), Xu(2012), Jiang, Kim, Nofsinger, and Zhu (2015)), the lack of exogenous variation has hindered definitive inferences. This thesis explores the impact of the anticipated passage of leniency laws worldwide on firms’ valuations and financial decisions. In the first study (Chapter 2), I investigate the causal link between competition and firm value. My findings indicate that the investing and funding decisions made after the anticipated enactment of leniency laws translate into an increase in firm value for the average firm. However, this value-enhancing effect is concentrated among those firms that are domiciled in developed countries operating under common law, in countries where law enforcement is strong, and in countries where corporate governance standards are high. In the second study (Chapter 3), I investigate whether the impact of competition on firms' financial decisions, as documented by Dasgupta and Žaldokas (2019), is stronger for firms in tradable industries, where prices are set internationally, rather than for firms in nontradable industries, where prices are set domestically. I find that firms in tradable industries experience more asset growth, mainly explained by a rise in cash. Moreover, these firms undertake more funding by equity in response to a reshuffle of their asset base. In the third study (Chapter 4), I investigate the causal link between competition and tax avoidance. The results indicate that, on average, firms do not engage in more or less tax avoidance. However, firms operating in more concentrated industries reduce their tax avoidance. The latter is statistically significant only when U.S. firms are included in the sample. This finding suggests that the reduction in tax avoidance is primarily present among U.S. firms in more concentrated industries.
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    Affective Computing and Emotions: A Multimodal Analysis on Oscar-Nominated Films
    Lui, Jeffrey; Business; Dacin, Tina
    The field of Affective Computing (AC), or Emotion AI, has rapidly grown in popularity over the last three decades since its inception in 1995. Independent researchers, and academic and industrial organizations have all been discovering new ways to utilize automatic affect recognition to uncover new efficiencies, maximize net profits, and even design new media from a subject’s emotive state. To date, most AC research has primarily focused on the use of one single sensorial modality (unimodal) to extract emotions. However, as the field evolves, the use of multimodal sensory inputs (multimodality) is rising and provides researchers with more rich, accurate, and reliable insights from affect recognition and identification. In this thesis dissertation, a literature review of Affective Computing is included. It defines the main constructs of AC and the main modalities used to detect emotions. I draw from the most significant literature produced in the field, beginning from Rosalind Picard’s originating literature in 1995. I conduct a systematic review of the most notable modalities used in AC research and then how emotion appraisal is foundational to this field. Following this, I conducted an analytical study where affect data is extracted from all Oscar-nominated films for "Best Picture" since 2010 using three AC modalities: (1) Facial Features, (2) Language & Text, and (3) Prosody. Using a novel and proprietary extraction method, followed by descriptive statistical analyses, I answered the research question, "What is the volume and variety of emotions portrayed by actors in Best Picture Oscar-nominated films, and how might they impact the film’s potential for success?" It was discovered that there are moderately strong positive correlations between a film’s critic ratings and the volume of emotional language used. Similarly, results showed that there was a significant negative correlation between audience reviews and the volume of emotions detected from an actor’s face. And when comparing audience reviews to the co-efficient of variation from the variety of emotions detected, it was found that films with a more balanced and equal variety of emotions significantly produced higher audience film review scores.
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    Behavioral Consumers and Behavioral AIs: Three Studies
    Chen, Yang; Business; Ovchinnikov, Anton
    In this thesis, we conduct three research projects in operations management with behavioral underpinnings. With most businesses, the behaviors of employees and customers require careful consideration since they are pivotal to business decision-making and firm profitability. Understanding consumer and AI behavior in business is the central topic throughout the thesis. In the first part of this thesis, we study the effect of tier rewards on customer loyalty. We report on the results of a large-scale field quasi-experiment in a loyalty program (LP) offering premium status. Our main goal is to understand strategic consumer behavior in such programs and quantify the tier reward's value to the firm. We follow the addition of a new reward tier to an existing LP and compare participant behavior before and after the program structure change. We find a ~3.3% lift in revenue among strategic participants, and document rich behavioral patterns. Our findings provide valuable information for the design of the LP tier structure. In the second part of the thesis, we study the effect of redeemable points on customer loyalty in an LP where consumers decide how much to redeem. We investigate the effect of reward redemption size and consumer "type" on long-term loyalty. We find that size does matter in reward redemptions, and so does the operationalization of size and prior reward redemption behaviors. Medium-sized redemptions are most consistently associated with increased loyalty. Small redemptions are sometimes associated with increased loyalty. With large redemptions, however, we sometimes observe a decrease in loyalty. Our findings suggest that the optimal redemption size for firms to encourage should be personalized. In the third part of this dissertation, we investigate whether ChatGPT exhibits behavioral biases commonly found in operations management tasks. We find that although ChatGPT can be much less biased and more accurate than humans in problems with explicit mathematical nature, it also exhibits many biases humans possess, especially when the problems are complicated, ambiguous, and implicit. This study characterizes ChatGPT's behaviors in decision-making and showcases the need for researchers and businesses to consider potential AI behavioral biases when developing and employing AI for business operations.
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    Essays on Secondary Financial Markets and Firm Strategic Decisions
    Goudarzi, Kamyar; Business; Chakrabarti, Abhirup
    This dissertation explores the relationship between stock valuations on secondary financial markets and firm strategic decisions. In separate empirical studies, I examine three aspects of this relationship. In the first essay, I explore the influence of corporate strategy and structure on how firms respond to stock valuations when determining their capital investments. The second essay examines how the extent of firm-specific information in stock valuations impacts firms' tendency to adopt unique, difficult-to-value corporate strategies. Finally, in the third essay, I study the influence of stock valuations on how firms invest during economic crises. The first essay shows that more unique firms are less sensitive to information and feedback from market valuations when making capital investment decisions, however, not when demand uncertainty is high or when their investors are more likely to trade frequently on private information. The second essay finds that firms adopt more unique corporate strategies in response to more informative valuations. In other words, firms appear to cater the uniqueness, and in turn valuation difficulty, of their corporate strategies to the level of their investors' firm-specific information inferred through market valuations. Finally, the third essay shows that valuations may (mis)lead firms in determining responses to economic crises. That is, valuation changes at the onset of a crisis may lead firms to discount internal information and undermine the risks of investing in downturns. These studies contribute to our understanding of the role of secondary financial markets in explaining firms' strategic decisions and highlight that trading in secondary financial markets can reveal signals to firms that can influence strategic decisions with significant performance implications.
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    Advanced Analytical Models for Analyzing Financial and Social Data
    Yang, Xingwei; Business; Li, Guang
    Depression is a common mental disorder affecting thoughts, feelings, behaviors, or moods. We develop a methodological language framework that incorporates psychological patterns and states, contextual information, and the influence of social interactions using NLP and ML techniques to detect user-level depression more effectively and accurately from social networks. Social support via social networks can affect mental health outcomes by promoting positive mental states, providing socialization opportunities, and supportive relationships. We define a new group search problem over social networks to find support groups for depressed users. We prove this problem is NP-hard and propose a 2-approximation algorithm. We also conduct experiments using real-world datasets to show that our greedy algorithm can efficiently find groups with shared interests and small proximity. We also use machine learning to enhance business decisions using financial data. We propose a new sampling method specifically suited to the bankruptcy prediction problem. We then calibrate a random forest model as an easy-to-interpret and easy-to-visualize analytic tool to support better investment decision-making by identifying companies at risk of default.