In this paper, we introduce a framework that simplifies the process of finding information about a city. This is helpful when people are looking for information on various cities before planning a trip. As visiting various online portals and collecting information is a time consuming and tedious...

We construct a new numerical measure of earnings announcement surprises, standardized unexpected earnings call text (SUE.txt), that does not explicitly incorporate the reported earnings value. SUE.txt generates a text-based post-earnings-announcement drift (PEAD.txt) larger than the classic PEAD...

Investors read news from social and news media to make financial decisions. I used a mixture of distributions model on Thomson Reuters textual indices for these sources to build a monthly ambiguity proxy. This ambiguity proxy reflects disagreement and the monthly information uncertainty of the...

Authored by Katharine M. Donato, Lisa Singh, Ali Arab, Elizabeth Jacobs and Douglas Post, this Migration Research Series paper builds on prior studies of migration misinformation by analysing Twitter conversations in Spanish, the fourth most widespread language in the world. The paper assesses...

Using the dictionary-based approach to measure the sentiment of finance-related texts is primarily focused on English-speaking content. This is due to the need for domain-specific dictionaries and the primary availability of those in English. Through the contribution of Bannier et al. (2019b),...

In spite of the relatively scarce use of textual analysis and sentiment analysis techniques in finance and accounting, they have great potential in accounting, both because of the volume of documents used for the communication of information and due to the growth in the use of digital tools and...