We investigate whether large language models (LLMs) can successfully perform financial
statement analysis in a way similar to a professional human analyst. We provide …

How can we use the novel capacities of large language models (LLMs) in empirical
research? And how can we do so while accounting for their limitations, which are …

Recent advances in large language models (LLMs) have unlocked novel opportunities for
machine learning applications in the financial domain. These models have demonstrated …

We examine how large language models (LLMs) interpret historical stock returns and
compare their forecasts with estimates from a crowd-sourced platform for ranking stocks …

We conduct the first analysis on the impact of democratized AI (ChatGPT) on the trading
activities of investors by leveraging a dataset of long textual information spanning 19 years …

This study introduces a novel suite of historical large language models (LLMs) pre-trained
specifically for accounting and finance, utilising a diverse set of major textual resources. The …

I examine the value of information from sell-side analysts by analyzing a large corpus of their
written reports. Using embeddings from state-of-the-art large language models, I show that …

Recent work within accounting and finance has highlighted that modern AI systems exhibit
superhuman performance on a variety of foundational activities within these fields. However …

We introduce a novel approach to learning the information that investors react to when
processing textual information. We use the attention mechanism that learns to identify …

Medical errors are consequential but difficult to study without laborious human review of past
cases. I apply algorithmic tools to measure the extent and nature of error in one of the most …