A large body of research studies shows that the quantitative outputs of analyst reports (e.g., earnings forecasts and recommendations) have informational value. In this study, by analyzing the textual tone of headlines in analyst reports, I reveal that textual opinion in the reports – a...

This study uses text and data mining to investigate the relationship between the text patterns of annual reports published by US listed companies and sales performance. Taking previous research a step further, although annual reports show only past and present financial information, analyzing...

In this research, two estimation algorithms for extracting cross-lingual news pairs based on machine learning from financial news articles have been proposed. Every second, innumerable text data, including all kinds news, reports, messages, reviews, comments, and tweets are generated on the...

We investigate the type of information text sentiment uncovers using earnings conference call transcripts and find that text sentiment fails to explain returns during intraday calls, while average trading volume and return volatility are higher during the call. This finding indicates that...

Although there is wide recognition of the negative consequences of policy volatility for countries' long-term economic growth, there is limited empirical work on this subject. One of the reasons is the difficulty of measuring policy volatility over long periods of time, especially in developing...

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...