Tax Specific Versus Generic Accounting-Based Textual Analysis and the Relationship with Effective Tax Rates: Building Context

Journal of Information Systems, Forthcoming

58 Pages Posted: 27 Oct 2020

See all articles by Eric J. Allen

Eric J. Allen

University of California, Riverside (UCR) - School of Business Administration

Daniel E. O'Leary

University of Southern California - Marshall School of Business; University of Southern California - Leventhal School of Accounting

Hao Qu

University of Rochester - Simon Business School

Charles W. Swenson

University of Southern California - Leventhal School of Accounting

Date Written: September 1, 2020

Abstract

A growing literature, typically using “bags of words” dictionaries, examines the information content of text in financial accounting disclosures. We generate context for our text analysis to help predict effective tax rates using two approaches. First, we create tax-specific, expert-derived, dictionaries and second, we generate the counts for those bags of words using text taken from tax-related discussions of the Form 10K, as opposed to its entirety. We find that using expertise provides more information than simply using general accounting and finance dictionaries. In addition, we find that generating general accounting text variable values from tax-related content in the Form 10K provides statistically significant improvement in model fit. Contrary to more generic accounting and finance word-based text analysis, we find that the signs on our positive and negative tax event dictionaries are different and are consistent with theoretical expectations through each of our modeled time periods.

Keywords: Text Analysis, Tax Specific Dictionary, Effective Tax Rates, Tax Haven Dictionary

JEL Classification: M41,

Suggested Citation

Allen, Eric J. and O'Leary, Daniel E. and Qu, Hao and Swenson, Charles W., Tax Specific Versus Generic Accounting-Based Textual Analysis and the Relationship with Effective Tax Rates: Building Context (September 1, 2020). Journal of Information Systems, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3684838 or http://dx.doi.org/10.2139/ssrn.3684838

Eric J. Allen

University of California, Riverside (UCR) - School of Business Administration ( email )

United States

Daniel E. O'Leary (Contact Author)

University of Southern California - Marshall School of Business ( email )

701 Exposition Blvd
Los Angeles, CA California 90089
United States

University of Southern California - Leventhal School of Accounting ( email )

Los Angeles, CA 90089-0441
United States

Hao Qu

University of Rochester - Simon Business School ( email )

Rochester, NY 14627
United States
2133049521 (Phone)
14620 (Fax)

Charles W. Swenson

University of Southern California - Leventhal School of Accounting ( email )

Los Angeles, CA 90089-0441
United States

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