Assessing the Effects of Disruptive Events on Technology Firms Using Non-Negative Matrix Factorization

Abstract

[Abstract] This paper presents sector-level analysis of voting decisions in corporate meetings. Non-negative matrix factorization (NNMF) algorithms are applied to detect disruptive events in the technology sector. The sample consists of 255 NASDAQ technology firms. The annual data used in this study capture votes on managerial proposals for the election and compensation of directors over the period 2003 to 2017. Voting decisions reflect two major disruptive events during that period: the financial crisis and the Facebook initial public offering. These two disruptive events are observed to substantially change the underlying trends behind voting decisions in the technology sector. Implications for researchers and practitioners are discussed.