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(Dis)aggregated earnings forecasts and acquisition financing

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  • Paul Ordyna

Abstract

Purpose - The purpose of this paper is to examine how a firm’s mergers and acquisitions (M&A) goals influence its voluntary disclosure policy. Specifically, this paper examines how a firm’s M&A financing intentions influence the degree of aggregation in management guidance prior to and after the M&A transaction. Design/methodology/approach - Using a logistic model, this study tests the relation between M&A financing and the decision to issue disaggregate earnings guidance for 3,929 acquiring firms from 2007 to 2011. Findings - The logistic regression results show that firms are more likely to provide disaggregate earnings guidance when using mostly stock to finance M&A and that the incentives to disaggregate guidance vary throughout the M&A transactional window. Alternatively, because the value of cash is independent of the true value of the acquirer, the results show that firms offering mostly cash to finance M&A are less likely to issue disaggregate earnings forecasts. Additional analysis reveals that the decision to issue disaggregate earnings guidance also influences post-merger outcomes such as CEO turnover. Research limitations/implications - The choice to disaggregate earnings guidance and the choice to use stock as a means to finance an acquisition is made by management, thus are endogenous which could introduce bias. Originality/value - This study provides insights into management’s incentives and attitudes toward the use of management forecasts to effect a potential merger and acquisition. Given the flexibility management has in issuing voluntary forecasts, management can tailor a financial message toward investors and potential targets in attempt to facilitate a merger and acquisition and to further the firm’s goals.

Suggested Citation

  • Paul Ordyna, 2019. "(Dis)aggregated earnings forecasts and acquisition financing," Asian Review of Accounting, Emerald Group Publishing Limited, vol. 28(2), pages 255-271, December.
  • Handle: RePEc:eme:arapps:ara-03-2019-0077
    DOI: 10.1108/ARA-03-2019-0077
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