Most of peer-to-peer (P2P) online borrowers are small business managers. The learning behavior of borrowers in the P2P market is rarely studied. The aim of this paper is to identify the existence of borrowers' learning behavior in the P2P market using a large sample from renrendai.com, which is...

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...

We introduce a general framework for analyzing large-scale text-based data, combining the strengths of neural-network language processing and generative statistical modeling. Our methodology generates textual factors by (i) representing texts using vector word embedding, (ii) clustering words...

We analyze whether newspaper content can predict aggregate future stock returns. Our study is based on articles published in the Handelsblatt, a leading German financial newspaper, from July 1989 to March 2011. We summarize newspaper content in a systematic way by constructing word-count indices...

We survey the textual sentiment literature, comparing and contrasting the various information sources, content analysis methods, and empirical models that have been used to date. We summarize the important and influential findings about how textual sentiment impacts on individual, firm-level and...

Fiscal policy is made in parliament. We go to the roots of changes of fiscal policy in Germany and use a novel data set on all parliamentary speeches in the Bundestag from 1960 to 2021. We propose an embedding-based approach, which allows the representation of words and documents in a shared...