Text Sentiment’s Ability to Capture Information: Evidence from Earnings Calls

45 Pages Posted: 12 Nov 2013 Last revised: 21 Apr 2018

See all articles by Tatiana Chebonenko

Tatiana Chebonenko

University of Illinois at Urbana-Champaign

Lifeng Gu

The University of Hong Kong

Dmitriy Muravyev

Michigan State University - Department of Finance

Date Written: March 1, 2018

Abstract

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 intraday calls do convey value-relevant information to the market, but text sentiment cannot capture it. However, text sentiment explains overnight returns extremely well. Since overnight periods are dominated by fundamental news from earnings releases, this finding suggests that text sentiment more forcefully captures firms' fundamental information. In addition, we show that a Lasso-based sentiment measure explains returns significantly better than a dictionary-based sentiment, suggesting that Lasso approach has superior ability to capture information in textual analysis.

Keywords: text sentiment, earnings calls, supervised-learning methods, Lasso, fundamental information

JEL Classification: G12, G14, M41

Suggested Citation

Chebonenko, Tatiana and Gu, Lifeng and Muravyev, Dmitriy, Text Sentiment’s Ability to Capture Information: Evidence from Earnings Calls (March 1, 2018). Available at SSRN: https://ssrn.com/abstract=2352524 or http://dx.doi.org/10.2139/ssrn.2352524

Tatiana Chebonenko

University of Illinois at Urbana-Champaign ( email )

Urbana, IL 61820
United States

Lifeng Gu

The University of Hong Kong ( email )

Hong Kong
+852 3917-1033 (Phone)

HOME PAGE: http://https://sites.google.com/site/uiuclifenggu/

Dmitriy Muravyev (Contact Author)

Michigan State University - Department of Finance ( email )

315 Eppley Center
East Lansing, MI 48824-1122
United States

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