A Machine Learning Approach to Credit Allocation

This dissertation seeks to understand the shortcomings of contemporaneous credit allocation, with a specific focus on exploring how an improved statistical technology impacts the credit access of societally important groups. First, this dissertation investigates a variety of limitations of conventional credit scoring models, specifically their tendency to misclassify borrowers by default risk, especially for relatively risky, young, and low income borrowers. Second, this dissertation shows that an improved statistical technology need not to lead to worse outcomes for disadvantaged groups. In fact, the credit access for borrowers belonging to such groups can be improved, while providing more accurate credit risk assessment. Last, this dissertation documents modern-day disparities in debt collection judgments across white and black neighborhoods. Taken together, this dissertation provides valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders and across societally important groups, as well as macroprudential regulation

Social Media Emotions and IPO Returns

I examine potential mechanisms behind two stylized facts of initial public offerings (IPOs) returns. By analyzing investor sentiment expressed on StockTwits and Twitter, I find that emotions conveyed through these social media platforms can help explain the mispricing of IPO stocks. The abundance of information and opinions shared on social media can generate hype around certain stocks, leading to investors' irrational buying and selling decisions. This can result in an overvaluation of the stock in the short term but often leads to a correction in the long term as the stock's performance fails to meet the inflated expectations. In particular, I find that IPOs with high levels of pre-IPO investor enthusiasm tend to have a significantly higher first-day return of 29.54%, compared to IPOs with lower levels of pre-IPO investor enthusiasm, which have an average first-day return of 16.91%. However, this initial enthusiasm may be misplaced, as IPOs with high pre-IPO investor enthusiasm demonstrate a much lower average long-run industry-adjusted return of -8.53%, compared to IPOs with lower pre-IPO investor enthusiasm, which have an average long-run industry-adjusted return of -1.1%

EmTract: Investor Emotions and Market Behavior

We develop a tool that extracts emotions from social media text data. Our methodology has three main advantages. First, it is tailored for financial context; second, it incorporates key aspects of social media data, such as non-standard phrases, emojis and emoticons; and third, it operates by sequentially learning a latent representation that includes features such as word order, word usage, and local context. This tool, along with a user guide is available at: https://github.com/dvamossy/EmTract. Using EmTract, we explore the relationship between investor emotions expressed on social media and asset prices. We document a number of interesting insights. First, we confirm some of the findings of controlled laboratory experiments relating investor emotions to asset price movements. Second, we show that investor emotions are predictive of daily price movements. These impacts are larger when volatility or short interest are higher, and when institutional ownership or liquidity are lower. Third, increased investor enthusiasm prior to the IPO contributes to the large first-day return and long-run underperformance of IPO stocks. To corroborate our results, we provide a number of robustness checks, including using an alternative emotion model. Our findings reinforce the intuition that emotions and market dynamics are closely related, and highlight the importance of considering investor emotions when assessing a stock's short-term value.Comment: Substantial changes to the projec

Racial Disparities in Debt Collection

A distinct set of disadvantages experienced by black Americans increases their likelihood of experiencing negative financial shocks, decreases their ability to mitigate the impact of such shocks, and ultimately results in debt collection cases being far more common in black neighborhoods than in non-black neighborhoods. In this paper, we create a novel dataset that links debt collection court cases with information from credit reports to document the disparity in debt collection judgments across black and non-black neighborhoods and to explore potential mechanisms that could be driving this judgment gap. We find that majority black neighborhoods experience approximately 40% more judgments than non-black neighborhoods, even after controlling for differences in median incomes, median credit scores, and default rates. The racial disparity in judgments cannot be explained by differences in debt characteristics across black and non-black neighborhoods, nor can it be explained by differences in attorney representation, the share of contested judgments, or differences in neighborhood lending institutions