Thursday, April 20th, from 10:00 am to 11:30 am EST
in Room 314, Scheller College of Business.
Zoom: https://gatech.zoom.us/j/99796351547.
Area: Finance
Committee Members: Dr. Sudheer Chava (Chair), Dr. Manasa Gopal, Dr. Nikhil Paradkar (University of Georgia)
Title: Essays on FinTech, AI and Innovation in Finance
Dissertation Overview:
Essay 1: Buzzwords? Firms' Discussions of Emerging Technologies in Earnings Conference Calls
Emerging technologies 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. 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.
Essay 2: Do Managers Walk the Talk on Environmental and Social Issues?
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.
Essay 3: Measuring Firm-Level Inflation Exposure: A Deep Learning Approach
We develop a novel measure of firm-level inflation exposure by applying a deep learning model to firms' earnings conference call transcripts. Our methodology not only identifies sentences that discuss price changes but also differentiates price increases from price decreases and input prices from output prices. In the time series, our aggregate inflation exposure measure strongly correlates with official inflation measures. In the cross section, firms that have higher inflation exposure experience a strong negative stock price reaction to earnings calls. Firms' pricing power attenuates the negative market reaction. Consistent with the market reaction, firms with higher inflation exposure have higher future costs of goods sold and lower operating cash flows. Last, high inflation exposure firms perform worse on Consumer Price Index (CPI) release days, in particular when the CPI release is salient and the CPI is higher than the consensus forecast.
Essay 4: When FLUE Meets FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain
Pre-trained language models have shown impressive performance on a variety of tasks and domains. Previous research on financial language models usually employs a generic training scheme to train standard model architectures, without completely leveraging the richness of the financial data. We propose a novel domain specific Financial LANGuage model (FLANG) which uses financial keywords and phrases for better masking, together with span boundary objective and in-filing objective. 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. Our models, code and benchmark data are publicly available on Github and Huggingface.