Non-Audit Services And Audit Quality: Blessing Or Curse?

This study investigates whether and how audit quality is associated with the provision of non-audit services by the statutory auditor. Using a sample of 1,008 firm observations of major German listed companies for the sample period 2004-2011, our study is one of the first to thoroughly analyze this issue empirically for the German audit market. Consistent with prior studies we choose discretionary working capital accruals as our proxy for audit quality. Our empirical results demonstrate that total non-audit fees in general and audit related fees in particular are negatively associated with audit quality, while provided tax and other advisory services have an insignificant impact on audit quality. Our results imply that non-audit fees are a significant factor with regard to auditor independence and economic auditor-client bonding while we are not able to detect compensating high knowledge spillover effects from these services. The empirical results are robust to alternative accrual measures and estimation model specifications, while our empirical evidence is not robust with regard to alternative fee measures

How deep is deep enough? -- Quantifying class separability in the hidden layers of deep neural networks

Deep neural networks typically outperform more traditional machine learning models in their ability to classify complex data, and yet is not clear how the individual hidden layers of a deep network contribute to the overall classification performance. We thus introduce a Generalized Discrimination Value (GDV) that measures, in a non-invasive manner, how well different data classes separate in each given network layer. The GDV can be used for the automatic tuning of hyper-parameters, such as the width profile and the total depth of a network. Moreover, the layer-dependent GDV(L) provides new insights into the data transformations that self-organize during training: In the case of multi-layer perceptrons trained with error backpropagation, we find that classification of highly complex data sets requires a temporal {\em reduction} of class separability, marked by a characteristic 'energy barrier' in the initial part of the GDV(L) curve. Even more surprisingly, for a given data set, the GDV(L) is running through a fixed 'master curve', independently from the total number of network layers. Furthermore, applying the GDV to Deep Belief Networks reveals that also unsupervised training with the Contrastive Divergence method can systematically increase class separability over tens of layers, even though the system does not 'know' the desired class labels. These results indicate that the GDV may become a useful tool to open the black box of deep learning