Corporate organizational structure, tax havens and analyst forecast properties

ABSTRACT

This study investigates how corporate organizational complexity affects analyst forecast behavior and the information environment of the firm. Using a unique sample of publicly traded Taiwan companies which are required to disclose information on all of their affiliates, we capture the feature of organizational complexity by measuring the number of investment layers connecting the parent firm and the lowest-tiered subsidiary. We argue that long chains of investment layers, which are associated with higher information asymmetry and agency costs, are likely to reduce the quality of financial reporting and complicate analysts’ forecast tasks. We find that, after controlling for other determinants of forecasting behavior, analysts experience greater difficulty in predicting the earnings of firms with more investment layers than in predicting the earnings of firms with fewer layers. In addition, we find that a long chain of investment layers influences analyst forecast accuracy and dispersion more if firms also have a larger number of investees in tax havens and if firms have a higher deviation between controlling rights and cash flow rights. Overall, we document that organizational complexity exacerbates the agency costs between investors and managers, which in turn complicates analyst forecast tasks.

KEYWORDS:

• Layers
• tax havens
• analyst forecast accuracy
• forecast dispersion

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. For example, by April, 2015, Google had acquired more than 180 subsidiaries; as of October 2015, Apple had acquired more than 70 subsidiaries, though the number of undisclosed actual acquisitions could be larger.

2. In the US, publicly traded firms are required to disclose their significant subsidiaries in Exhibit 21 of the 10K. However, the complete structure of pyramidal ownership is not available from Exhibit 21, as firms are not required to disclose all the subsidiaries and the number of the layer in which each subsidiary is located.

3. Using the mean EPS in year t instead of the stock price per share at the end of year t-1 as a scalar does not qualitatively alter the results.

4. For a robustness check, we also include WIDTH, the natural logarithm of the number of subsidiaries at the widest horizontal span in the firm’s pyramidal structure to control for another dimension of organizational complexity, and the coefficient on WIDTH is not significant.

5. We also use STD_ROA, the standard deviation of return on assets, over the five-year window to capture earnings performance volatility as a robustness check, and the results do not change.

6. We also identify tax havens based on the list in Dyreng and Lindsey (2009) as a robustness check and the results are qualitatively the same.

7. 4.21 investees based on Dyreng and Lindsey (2009) list of tax haven countries.

8. See Shipman, Swanquist, and Whited (2016) for a detailed discussion of the usefulness and limitations of propensity score matching relative to multiple regression analysis in accounting research.