Topic Tones of Analyst Reports and Stock Returns: A Deep Learning Approach

41 Pages Posted: 20 Aug 2018 Last revised: 23 Dec 2020

See all articles by Hitoshi Iwasaki

Hitoshi Iwasaki

National University of Singapore (NUS) - Department of Statistics and Applied Probability

Ying Chen

National University of Singapore

Qianqian Du

Southwest University of Finance and Economics

Jun Tu

Singapore Management University - Lee Kong Chian School of Business

Date Written: August 8, 2018

Abstract

We disassemble the text of analyst reports into multiple pieces that represent three types of topics - opinion type (overall assessments of analysts), corporate fact type (helping investors to better digest corporate facts) and justification type (explaining quantitative numbers). We extract the tone for the text of each topic via a deep neural network supervised learning methodology. A baseline model without using text information has an adjusted R squared of 2:3% in predicting the cumulative twoday abnormal returns. When we include the topic tones, the adjusted R squared increases to 15:4%. This significant increase of R squared is not much driven by the justification type topics since it is sort of redundant given the quantitative numbers (e.g., earnings forecasts). In contrast, the opinion type and the corporate fact type topics provide substantial information beyond the quantitative numbers and are the main drivers of the significant increase of R squared.

Keywords: Textual Analysis; DNN Approach; Topic Tones; Information Content

JEL Classification: C89, G11, G12, G14

Suggested Citation

Iwasaki, Hitoshi and Chen, Ying and Du, Qianqian and Tu, Jun, Topic Tones of Analyst Reports and Stock Returns: A Deep Learning Approach (August 8, 2018). Available at SSRN: https://ssrn.com/abstract=3228485 or http://dx.doi.org/10.2139/ssrn.3228485

Hitoshi Iwasaki (Contact Author)

National University of Singapore (NUS) - Department of Statistics and Applied Probability ( email )

Block S16, Level 7
6 Science Drive 2
117546
Singapore

Ying Chen

National University of Singapore ( email )

Department of Mathematics, Faculty of Science
Block S17, Level 4, 10 Lower Kent Ridge Road
Singapore, Singapore 119076
Singapore

Qianqian Du

Southwest University of Finance and Economics ( email )

Riem of SWUFE
chengdu, Sichuan 611130
China

Jun Tu

Singapore Management University - Lee Kong Chian School of Business ( email )

50 Stamford Road
#04-01
Singapore, 178899
Singapore

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
604
Abstract Views
2,443
rank
53,505
PlumX Metrics