Do Director Networks Help Managers Plan and Forecast Better?

Schabus, Mario

doi:10.2139/ssrn.2824070

Cited by 1 publication

(1 citation statement)

References 69 publications

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“…Horton et al (2012);Larcker et al (2013); and Omer et al (2014) take a closer look at firm performance and how directors' connectedness impacts this. Schabus (2016) concludes that the management forecast of earnings from firms with better connected directors are much more accurate. In earnings management, social networks may also have an effect.…”

Section: Related Researchmentioning

confidence: 97%

Predicting tax avoidance by means of social network analytics

Lismont

Cardinaels

Bruynseels

et al. 2018

Decision Support Systems

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This study predicts tax avoidance by means of social network analytics. We complement previous literature by being the first to build a predictive model including a larger variation of network features. We construct a network of firms which are connected through shared board membership. Three analytical techniques are applied creating five models using either firm characteristics or network characteristics or different types of combinations of both. A random forest which includes firm characteristics, network characteristics of firms and network characteristics of board members provides the best performance with an increase of 7% in AUC. Hence, including network effects significantly improves the predictive ability of tax avoidance models, implying that board members exhibit specific knowledge which can carry over across firms. We find that having board members with no connections to low-tax companies lowers the likelihood of being a low-tax firm.

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