Methods for linking individuals across historical data sets, typically in combination with AI based transcription models, are developing rapidly. Probably the single most important identifier for linking is personal names. However, personal names are prone to enumeration and transcription errors...

Using textual analysis of earnings conference calls, we quantify firm level risk arising from the reliability of the supply chain from 2002 to 2020. Our proxy for perceived supply chain risk exhibits cross-sectional and time-series variation that aligns with reasonable priors and is...

Disclosure of prudential information on environmental, social and governance (ESG) risks will become mandatory from June, 2022 for large institutions with securities traded on a regulated market of any Member State. This paper tries to assess how prepared European financial institutions are for...

Natural language is a key form of communication in the capital markets. Textual analysis is the application of Natural Language Processing (NLP) to textual data for automated information extraction or measurement. We survey publications in top accounting journals and describe the trend and...

This paper assess how priorities of the IMF’s membership have evolved over the past two decades, by using text mining techniques on a unique dataset combining IMFC communiqués and constituency statements. Our results reveal significant variation in priorities across time and constituencies....

The digital literacy that has developed in recent decades has resulted in internet playing an important role in the communication of wineries. Business websites, initially used as an exhibitor of products, quickly became one of the most important tools to implement communication strategies used...

This paper presents a novel technique to perform multilingual sentiment classification of financial documents drawing on methods from computational linguistics. The challenge is to efficiently analyze large volumes of text produced by corporations and companies in various languages to find...

Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that...

The qualitative information of companies’ financial statements provides useful information that can increase the accuracy of bankruptcy prediction models. In this research, a dataset of 924,903 financial statements from 355,704 German companies classified into solvent, financially distressed,...