Analyzing the temporal sequence of texts from sources such as social media, news, and parliamentary debates is a challenging problem as it exhibits time-varying scale-free properties and fine-grained timing irregularities.
In this paper, we investigate the influence of claims in analyst reports and earnings calls on financial market returns, considering them as significant quarterly events for publicly traded companies.
We use novel hourly-resolution data and Kendall's Tau correlation to explore the interconnectedness of the cryptocurrency market.
Recently large language models (LLMs) like ChatGPT have shown impressive performance on many natural language processing tasks with zero-shot.
To set the benchmark for the dataset, we develop and test a weak-supervision-based framework for the NER task.
Using a comprehensive sample of 2, 585 bankruptcies from 1990 to 2019, we benchmark the performance of various machine learning models in predicting financial distress of publicly traded U. S. firms.
To this end, we contribute the Financial Language Understanding Evaluation (FLUE), an open-source comprehensive suite of benchmarks for the financial domain.
Further, we develop a set of sequence-to-sequence hyperbolic models suited to this multi-span identification task based on the power-law dynamics of cryptocurrencies and user behavior on social media.