Measuring Corporate Culture Using Machine Learning

102 Pages Posted: 21 Oct 2018

See all articles by Kai Li

Kai Li

University of British Columbia (UBC) - Sauder School of Business; China Academy of Financial Research (CAFR)

Feng Mai

Stevens Institute of Technology

Rui Shen

The Chinese University of Hong Kong, Shenzhen - School of Management and Economics; Shenzhen Finance Institute

Xinyan Yan

University of Dayton

Date Written: May 26, 2020

Abstract

We create a culture dictionary using one of the latest machine learning techniques—the word embedding model (Mikolov et al. 2013)—and 209,480 earnings call transcripts. We obtain scores of the five top corporate cultural values proposed by Guiso, Sapienza, and Zingales (2015): innovation, integrity, quality, respect, and teamwork for 62,664 firm-year observations over the period 2001–2018. We show that corporate culture correlates with business outcomes, including operational efficiency, risk-taking, earnings management, executive compensation design, firm value, and deal making. We also present suggestive evidence that corporate culture is shaped by major corporate events such as mergers and acquisitions.

Keywords: machine learning; word embedding; semi-supervised learning; corporate culture; cultural fit; acculturation; mergers and acquisitions

JEL Classification: C45; G34

Suggested Citation

Li, Kai and Mai, Feng and Shen, Rui and Yan, Xinyan, Measuring Corporate Culture Using Machine Learning (May 26, 2020). Available at SSRN: https://ssrn.com/abstract=3256608 or http://dx.doi.org/10.2139/ssrn.3256608

Kai Li (Contact Author)

University of British Columbia (UBC) - Sauder School of Business ( email )

2053 Main Mall
Vancouver, BC V6T 1Z2
Canada
604-822-8353 (Phone)
604-822-4695 (Fax)

HOME PAGE: http://finance.sauder.ubc.ca/~kaili

China Academy of Financial Research (CAFR)

1954 Huashan Road
Shanghai P.R.China, 200030
China

Feng Mai

Stevens Institute of Technology ( email )

Hoboken, NJ 07030
United States

Rui Shen

The Chinese University of Hong Kong, Shenzhen - School of Management and Economics ( email )

2001 Longxiang Road, Longgang District
Shenzhen, 518172
China

Shenzhen Finance Institute ( email )

Xinyan Yan

University of Dayton ( email )

300 College Park
Dayton, OH 45469
United States

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