From Words to Syntax: Identifying Context-specific Information in Textual Analysis

59 Pages Posted: 13 Apr 2020 Last revised: 20 Oct 2022

See all articles by Sean Cao

Sean Cao

University of Maryland - Robert H. Smith School of Business

Yongtae Kim

Santa Clara University - Leavey School of Business

Angie Wang

The Chinese University of Hong Kong (CUHK) - School of Accountancy

Houping Xiao

Georgia State University - J. Mack Robinson College of Business

Date Written: April 3, 2020

Abstract

When quantifying information from unstructured textual data, the traditional approach in the literature only captures semantic features of single words or phrases. The context, the sequence of words, and the relationship between words are ignored. This paper introduces a novel approach to incorporate complex syntactical features in textual analysis using two applications of machine learning (i.e., neural-network-based natural language parser and word embedding). We demonstrate the usefulness of this approach by analyzing the tone of financial narratives in earnings conference calls. We construct a new measure of sentiment that is specific to performance discussions and is adjusted for complex contextual negations. We find that this performance-specific sentiment explains cross-sectional returns and future operating performance better than the umbrella sentiment proxy and the simple rule-based measures used in the literature. An analysis of earnings-related forward-looking statements in conference calls confirms the value of this new approach in identifying context-specific information.

Keywords: textual analysis, machine learning, neural networks, artificial intelligence, natural language processing, sentiment analysis, conference calls

Suggested Citation

Cao, Sean S. and Kim, Yongtae and Wang, Angie and Xiao, Houping, From Words to Syntax: Identifying Context-specific Information in Textual Analysis (April 3, 2020). Available at SSRN: https://ssrn.com/abstract=3568504 or http://dx.doi.org/10.2139/ssrn.3568504

Sean S. Cao

University of Maryland - Robert H. Smith School of Business ( email )

College Park, MD 20742-1815
United States

Yongtae Kim (Contact Author)

Santa Clara University - Leavey School of Business ( email )

500 El Camino Real
Santa Clara, CA California 95053
United States
(408) 554-4667 (Phone)
(408) 554-2331 (Fax)

Angie Wang

The Chinese University of Hong Kong (CUHK) - School of Accountancy

Shatin, N.T.
Hong Kong

Houping Xiao

Georgia State University - J. Mack Robinson College of Business ( email )

P.O. Box 4050
Atlanta, GA 30303-3083
United States

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