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ItemEssays on Fintech, AI, and Innovation in Finance(Georgia Institute of Technology, 2024-04-25) Du, WendiThe dissertation consists of five essays on FinTech, AI and innovation in finance. These essays center around how innovation and capital market influence each other, and how to use cutting-edge technologies like machine learning and AI to address the economic questions that would otherwise remain unanswered. In the first essay, I investigate the redeployability channel of trademarks' collateral value. Using a novel court decision that exogenously weakens trademark redeployability, I find a 3.4 percentage point reduction in affected firms’ book leverage, equivalent to a 16.9\% decrease in their average book leverage. By using firm-level trademark portfolio data and employing natural language processing (NLP) techniques, including ChatGPT, I show that firms with more licensed trademarks (i.e., those more exposed to the court ruling), experience a stronger negative impact. Additionally, affected firms are less likely to pledge their registered trademarks as collateral afterward. When they do pledge, they pledge a greater number of trademarks, as well as more valuable ones. Affected firms also register fewer new trademarks in the future. In sum, my results highlight the value of trademark collateral in enhancing firms' debt capacity through its redeployability channel. In the second essay, we develop a text-based measure of firm-level inflation exposure from earnings calls. Our deep learning model identifies sentences discussing price changes, while distinguishing price increases from decreases and inputs from outputs. Our aggregate inflation exposure measure strongly correlates with official inflation measures. Firms with higher inflation exposure experience negative stock price reactions to earnings calls. The price reaction is attenuated when a firm has pricing power. Further, firms with higher inflation exposure have higher future costs of goods sold and lower operating cash flows. They perform worse on Consumer Price Index (CPI) release days when CPI exceeds the consensus forecast. In the third essay, we explore the area of emerging technologies which can potentially transform business and society but are difficult to identify and prone to hype and uncertainty. We construct a dictionary of emerging technology phrases from earnings calls using deep learning techniques and document an immediate positive stock market reaction to firms’ discussions of emerging technologies. We find that the positive reaction is more pronounced when firms discuss emerging technologies early in their life cycle. Firms with lower ex-ante credibility, such as a prior history of earnings management, innovate less ex-post and experience poorer long-term returns. Overall, our results highlight when firms' discussions of emerging technologies convey credible information to investors. The fourth essay examines whether managers walk the talk on the environmental and social discussion. We train a deep-learning model on various corporate sustainability frameworks to construct a comprehensive Environmental and Social (E\&S) dictionary. Using this dictionary, we find that the discussion of environmental topics in the earnings conference calls of U.S. public firms is associated with higher pollution abatement and more future green patents. Similarly, the discussion of social topics is positively associated with improved employee ratings. The association with E\&S performance is weaker for firms that give more non-answers and when the topic is immaterial to the industry. Overall, our results provide some evidence that firms do walk their talk on E\&S issues. In the final essay, we address the limitations of generic training schemes in the realm of financial language models. We propose a novel domain specific Financial LANGuage model (FLANG) which uses financial keywords and phrases for better masking. We further extend it to include span boundary objective and in-filing objective, utilizing the fact that many financial terminologies are phrases. Additionally, the evaluation benchmarks in the field have been limited. To this end, we contribute the Financial Language Understanding Evaluation (FLUE), an open-source comprehensive suite of benchmarks for the financial domain. These include new benchmarks across 5 NLP tasks in financial domain as well as common benchmarks used in the previous research. Experiments on these benchmarks suggest that our model outperforms those in prior literature on a variety of NLP tasks.
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ItemAm I Paranoid or Did I Just Receive Advice?: The Impact of Disability Status on Recipient Behavior Following Unsolicited Advice(Georgia Institute of Technology, 2023-04-17) Speach, Mary Eve PatriceAlthough advice in the workplace is associated with positive outcomes, employees tend not to seek out advice from coworkers. As such, organizations may be tempted to encourage unsolicited advice between employees to maximize benefits. However, little is understood regarding its detrimental effects for particular groups of employees. Therefore, I leverage the literature on advice and model of stigma-induced identity threat to assert that those with disabilities, due to their unique backgrounds, are more likely to perceive unsolicited advice as an identity threat. This dissertation posits that this heightened perception, also known as an identity threat appraisal, will influence levels of paranoid cognition and decrease daily work engagement. Furthermore, given that not all advice is equal I propose that the nature of the unsolicited advice offered (message and context) may operate as features of advice, impacting paranoid cognition levels. Results of Study 1 indicate that recipients of unsolicited advice experience greater levels of paranoid cognition than those in the control condition. Study 2 explores the features of unsolicited advice through a vignette experimental design. Findings illustrate that paranoid cognition varies as a function of context but not message. Using an ESM approach, Study 3 replicates the main effect findings of the previous studies and links paranoid cognition to daily work engagement. Finally, for the moderating effects those with a disability who received public unsolicited advice reported higher levels of paranoid cognition than those without a disability in the third study. Theoretical and practical implications of this model are discussed.
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ItemInnovation and Supply Networks(Georgia Institute of Technology, 2023-01-13) Palit, ShubhobrataThe three essays in this dissertation aim to improve our understanding of the influence of a firm’s network of buyers and suppliers as a source of external technological knowledge and, subsequently on its innovation performance. Such understanding is important for effectively managing technological knowledge, which involves managing not only the firm’s internal but also external technological knowledge. In the first essay (Chapter 2), I focus on the available technological knowledge in a firm’s supplier network and examine factors that accrue innovation benefits from such knowledge for a buyer firm. Specifically, I study technological distance, technological breadth, and extent of global sourcing, and how these factors interrelate in influencing a firm’s innovation performance. In the second essay (Chapter 3), I focus on a buyer as a source of technological knowledge for a supplier, and examine factors that make a supplier accumulate technological knowledge from the buyer. Specifically, I study how buyer innovation, technological similarity between a supplier and a buyer, a supplier’s dependence on a buyer, buyer-supplier size asymmetry, and the interrelationships between them influence the extent of supplier’s knowledge accumulation from the buyer. In the third essay (Chapter 4), I study the extent of a supplier’s knowledge accumulation from its buyers as a mechanism through which buyer innovation positively influences supplier innovation performance. Additionally, I demonstrate the moderating role of the supplier’s position within its supply network on the indirect effect of buyer innovation on supplier innovation performance.