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How well does management deliver? Creation of shareholder wealth by large public and private Brazilian firms in 2018

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  • Sanvicente, Antonio Zoratto

Abstract

This paper examines the performance of 157 of the 400 largest nonfinancial firms in Brazil in 2018. The metric used is the creation of value for their shareholders, represented by the difference between return on assets (operating income/assets) (ROA) and the weighted average of the opportunity cost of debt and equity (WACC) used to finance those assets. Among the 157 firms, one finds 66 privatelyowned and 91 publicly-owned companies. Of those, the management of 18 privately-owned (27,3%) and 13 publicly-owned firms (14,3%) were able to produce value for shareholders, because ROA > WACC. Therefore, this positive outcome occurred in less than half of the companies surveyed, and it is apparent that the proportion of such an outcome in private firms was higher than that for public firms.

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  • Sanvicente, Antonio Zoratto, 2019. "How well does management deliver? Creation of shareholder wealth by large public and private Brazilian firms in 2018," Textos para discussão 519, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    • Handle: RePEc:fgv:eesptd:519
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    References listed on IDEAS

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    1. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    2. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
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