arXiv subjects

Domonkos F. Vamossy

Publications and source records attributed to Domonkos F. Vamossy.

9 recordsLinked to original sources

arXiv2025

Retail Investor Horizon and Earnings Announcements

This paper moves beyond aggregate measures of retail intensity to explore investment horizon as a distinguishing feature of earnings-related return patterns. Using self-reported holding periods from StockTwits (2010-2021), we observe that separating retail activity into "long-horizon" and "short-horizon" cohorts reveals divergent price anomalies. Long-horizon composition is associated with underreaction, characterized by larger initial reactions and pronounced Post-Earnings Announcement Drift (PEAD), suggesting a slow but persistent convergence toward fundamental value. In contrast, short-horizon activity parallels sentiment-driven overreaction, where elevated pre-event sentiment precedes weaker subsequent performance and price reversals. A zero-cost strategy exploiting this heterogeneity, going long on long-horizon stocks and short on short-horizon stocks, yields risk-adjusted alphas of 0.43% per month. These findings suggest that accounting for investment horizon helps disentangles the fundamental signal in retail flow from speculative noise.

q-fin.PR↗

arXiv2025

Effect of State and Local Sexual Orientation Anti-Discrimination Laws on Labor Market Differentials

This paper presents quasi-experimental research examining the effect of both local and state anti-discrimination laws on sexual orientation on the labor supply and wages of lesbian, gay, and bisexual (LGB) workers. To do so, we use the American Community Survey data on household composition to infer sexual orientation and combine this with a unique panel dataset on state and local anti-discrimination laws. Leveraging variation in law implementation across localities over time and between same-sex and different-sex couples, we find that anti-discrimination laws significantly narrow gaps in labor force participation and employment for men in same-sex couples relative to men in different-sex couples, and also increase their percentile rank in the wage distribution. Our analysis reveals mostly null effects for female same-sex couples; however, in metropolitan areas these laws significantly reduce their employment compared to women in different-sex couples. One explanation for the reduced labor supply is that female same-sex couples begin to have more children in response to the laws. Finally, we present evidence that state anti-discrimination laws significantly and persistently increased support for same-sex marriage. This research shows that anti-discrimination laws can be an effective policy tool for reducing labor market inequalities across sexual orientation and improving sentiment toward LGB Americans.

econ.GN↗

arXiv2024

Credit Scores: Performance and Equity

Credit scores are critical for allocating consumer debt in the United States, yet little evidence is available on their performance. We benchmark a widely used credit score against a machine learning model of consumer default and find significant misclassification of borrowers, especially those with low scores. Our model improves predictive accuracy for young, low-income, and minority groups due to its superior performance with low quality data, resulting in a gain in standing for these populations. Our findings suggest that improving credit scoring performance could lead to more equitable access to credit.

q-fin.RM↗

arXiv2024

Social Media Emotions and IPO Returns

I examine potential mechanisms behind two stylized facts of initial public offerings (IPOs) returns. By analyzing investor emotions 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 enthusiasm tend to have a significantly higher first-day return of 29.73%, compared to IPOs with lower levels of pre-IPO investor enthusiasm, which have an average first-day return of 17.59%. 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 -8.22%, compared to IPOs with lower pre-IPO investor enthusiasm, which have an average long-run industry-adjusted return of -0.14%. Diving deeper into the qualitative aspects of investor discourse, I find that messages rich in financial language or that bolster prevailing information drive my results. Additionally, a trend towards caution emerges among users who frequently engage with IPOs, perhaps a byproduct of lessons from past endeavors. Intriguingly, firms that enjoy high levels of pre-IPO optimism consistently garner post-launch enthusiasm, a trend at odds with their long-term under-performance.

q-fin.PR↗

arXiv2024

Social Media Emotions and Market Behavior

I explore the relationship between investor emotions expressed on social media and asset prices. The field has seen a proliferation of models aimed at extracting firm-level sentiment from social media data, though the behavior of these models often remains uncertain. Against this backdrop, my study employs EmTract, an open-source emotion model, to test whether the emotional responses identified on social media platforms align with expectations derived from controlled laboratory settings. This step is crucial in validating the reliability of digital platforms in reflecting genuine investor sentiment. My findings reveal that firm-specific investor emotions behave similarly to lab experiments and can forecast daily asset price movements. These impacts are larger when liquidity is lower or short interest is higher. My findings on the persistent influence of sadness on subsequent returns, along with the insignificance of the one-dimensional valence metric, underscores the importance of dissecting emotional states. This approach allows for a deeper and more accurate understanding of the intricate ways in which investor sentiments drive market movements.

q-fin.PR↗

arXiv2023

Racial Disparities in Debt Collection

This paper shows that black and Hispanic borrowers are 39% more likely to experience a debt collection judgment than white borrowers, even after controlling for credit scores and other relevant credit attributes. The racial gap in judgments is more pronounced in areas with a high density of payday lenders, a high share of income-less households, and low levels of tertiary education. State-level measures of racial discrimination cannot explain the judgment gap, nor can neighborhood-level differences in the previous share of contested judgments or cases with attorney representation. A back-of-the-envelope calculation suggests that closing the racial wealth gap could significantly reduce the racial disparity in debt collection judgments.

econ.GN↗

arXiv2023

EmTract: Extracting Emotions from Social Media

We develop an open-source tool (EmTract) that extracts emotions from social media text tailed for financial context. To do so, we annotate ten thousand short messages from a financial social media platform (StockTwits) and combine it with open-source emotion data. We then use a pre-tuned NLP model, DistilBERT, augment its embedding space by including 4,861 tokens (emojis and emoticons), and then fit it first on the open-source emotion data, then transfer it to our annotated financial social media data. Our model outperforms competing open-source state-of-the-art emotion classifiers, such as Emotion English DistilRoBERTa-base on both human and chatGPT annotated data. Compared to dictionary based methods, our methodology has three main advantages for research in finance. First, our model is tailored to financial social media text; 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. Using EmTract, we explore the relationship between investor emotions expressed on social media and asset prices. We show that firm-specific investor emotions are predictive of daily price movements. Our findings show that emotions and market dynamics are closely related, and we provide a tool to help study the role emotions play in financial markets.

q-fin.PR↗

arXiv2020

Investor Emotions and Earnings Announcements

Armed with a decade of social media data, I explore the impact of investor emotions on earnings announcements. In particular, I test whether the emotional content of firm-specific messages posted on social media just prior to a firm's earnings announcement predicts its earnings and announcement returns. I find that investors are typically excited about firms that end up exceeding expectations, yet their enthusiasm results in lower announcement returns. Specifically, a standard deviation increase in excitement is associated with an 7.8 basis points lower announcement return, which translates into an approximately -5.8% annualized loss. My findings confirm that emotions and market dynamics are closely related and highlight the importance of considering investor emotions when assessing a firm's short-term value.

q-fin.PM↗

arXiv2019

Predicting Consumer Default: A Deep Learning Approach

We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard credit scoring models while accurately tracking variations in systemic risk. We argue that these properties can provide valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders, as well as macroprudential regulation.

econ.GN↗