This paper examines how accurately Swedish banks' internal probability of default (PD) models under IFRS 9 accounting rules predict changes in the borrowing firms' credit risk levels. Using a sample of matched bank lending and firm-level data, we find that PDs align well with aggregate...

We study the effectiveness of textual information in predicting the returns of crude oil futures and understanding the behavior of market participants. Using a machine learning method to extract oil market sentiment from news articles, we find that the computed sentiment is significantly...

We study the effectiveness of textual information in predicting the returns of crude oil futures and understanding the behavior of market participants. Using a machine learning method to extract oil market sentiment from news articles, we find that the computed sentiment is significantly...

In the contemporary financial landscape, predicting customer transactions plays a crucial role in enhancing customer service, personalizing marketing strategies, and improving operational efficiency. This research paper delves into the prediction of customer transactions using machine learning....