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This paper aims to empirically examine the determinants affecting audit fees in the Egyptian context concerning different organizational forms and governance mechanisms.

This study adopts financial and non-financial data from 62 Egyptian firms listed on the Egyptian Stock Exchange from 2015 to 2020. The proposed audit fees model is developed by adopting panel data analysis to examine the effect of auditee, auditor and engagement attributes on audit fees. The validity of the proposed equation for determining audit fees on an annual basis was established by applying the fixed effect model results for the year 2020.

The results revealed that the most significant determinants that affect audit fees are liquidity, audit committee independence, audit report lag and the status of the audit firm. Audit fees of 95.7% are determined by these factors. The validation test proved that the proposed model was more accurate and closer to the estimated data at nearly 90.2%.

The results of this paper would send early signals to audit firms, stakeholders and regulators regarding the determinants of audit fees, and provide an objective standard for fee-setting to be used by stock market regulators and professional bodies, in determining a minimum amount of audit fees that ensure a reasonable level of audit quality.

To the best of the authors’ knowledge, for the first time, this paper empirically examines the determinants of audit fees in an emerging market like Egypt and presents evidence for a period of six years.

This study examines the effect of non-financial variables related to governance on the accuracy of financial distress prediction among Egyptian listed small and medium-sized enterprises (SMEs), by using the logistic regression technique.

This study used a sample of 24 Egyptian-listed SMEs in each year, totaling 120 firm observations, of which 25 were classified distressed and 95 of them non-distressed between 2014 and 2018. The variables for the study included five financial variables and thirteen non-financial variables related to governance. The models were developed using financial variables alone as well as combining financial and non-financial variables related to governance.

The results showed that the model with financial variables had a prediction accuracy of 91.7% , whereas models with a combination of financial and non-financial variables related to governance predict with comparatively better accuracy of 92.7 and 93.6% .

Although the results seem to be conclusive, it could be noted that the non-distressed sample was not paired with the distressed sample. Other studies showed that paired samples increase the financial distress prediction rate. Furthermore, due to the small sample size, this study was unable to create a hold-out sub-sample for the accuracy test.

The proposed distress prediction model for SMEs is effective for stakeholders, including banks and other financial institutions, in the assessment of the credit risk of SMEs. Using such a model, they could better identify SMEs with a higher risk of failure in their lending decisions. Moreover, SME managers' could be interested in using such models as a tool for planning corrective action, in addition to planning and controlling current operations to avoid financial failure in the future.

This study contributes to financial distress prediction literature in different ways. First, few studies were conducted in the area of financial distress among SMEs. Second, neither of these studies was conducted within the Egyptian context, nor any of them had used non-financial variables related to governance in the prediction of financial distress among SMEs.