...Yasheng Chen; Yufei Liu; Xuehui Mao; Joseph H. Zhang SYNOPSIS We examine how well machine learning algorithms can leverage historical pricing data to reduce the initial bargaining gap between sellers and buyers, aiming to improve personalized price effectiveness for traditional offline firms...

.... In this study, we propose a framework that combines machine learning and natural language processing methods to develop a model capable of understanding the nature of a transaction and automating intelligent recognition. We demonstrate the feasibility of our framework by constructing preferred stock recognition...

...Joshua O. S. Hunt; James N. Myers; Linda A. Myers SYNOPSIS Using stepwise logit regression, Ou and Penman (1989) predicts the sign of future earnings changes and uses these predictions to form a profitable hedge portfolio. Increases in computing power and advances in machine learning allow us...

...Emily Hunt; Joshua Hunt; Vernon J. Richardson; David Rosser SYNOPSIS We investigate whether misstatement risk estimated using advanced machine learning techniques—hereafter, estimated misstatement risk (EMR)—approximates auditors' risk assessments in practice. We find that auditors price EMR...

...; G32; M21; M41. disclosure earnings announcement machine learning natural language processing neural network textual analysis transfer learning This study introduces and demonstrates the application of new machine transfer learning methods to classify the content of corporate...

... intelligence audit procedure machine learning data analytics This paper provides a conceptual explanation of how deep learning works and identifies the capabilities of deep learning when it comes to text understanding, speech recognition, visual recognition, and structured data analysis...