Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Vamossy, Domonkos F."'
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 mis
Externí odkaz:
http://arxiv.org/abs/2409.00296
Autor:
Vamossy, Domonkos F.
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 r
Externí odkaz:
http://arxiv.org/abs/2404.03792
Autor:
Delhommer, Scott, Vamossy, Domonkos F.
This paper presents the first 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 Amer
Externí odkaz:
http://arxiv.org/abs/2404.03794
Autor:
Vamossy, Domonkos F.
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 expla
Externí odkaz:
http://arxiv.org/abs/2306.12602
Autor:
Vamossy, Domonkos F., Skog, Rolf P.
Publikováno v:
In International Review of Financial Analysis January 2025 97
Autor:
Vamossy, Domonkos F., Skog, Rolf
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
Externí odkaz:
http://arxiv.org/abs/2112.03868
Autor:
LaVoice, Jessica, Vamossy, Domonkos F.
Publikováno v:
In Journal of Banking and Finance July 2024 164
Autor:
Vamossy, Domonkos F.
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 announ
Externí odkaz:
http://arxiv.org/abs/2006.13934
Autor:
LaVoice, Jessica, Vamossy, Domonkos F.
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 p
Externí odkaz:
http://arxiv.org/abs/1910.02570
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 l
Externí odkaz:
http://arxiv.org/abs/1908.11498