SummaryCredit scoring has been regarded as a core appraisal tool of different institutions during the last few decades, and has been widely investigated in different areas, such as finance and accounting. Different scoring techniques are being used in areas of classification and prediction, where statistical techniques have conventionally been used. Both sophisticated and traditional techniques, as well as performance evaluation criteria are investigated in the literature. The principal aim of this paper is to carry out a comprehensive review of 214 articles/books/theses that involve credit scoring applications in various areas, in general, but primarily in finance and banking, in particular. This paper also aims to investigate how credit scoring has developed in importance, and to identify the key determinants in the construction of a scoring model, by means of a widespread review of different statistical techniques and performance evaluation criteria. Our review of literature revealed that there is no overall best statistical technique used in building scoring models and the best technique for all circumstances does not yet exist. Also, the applications of the scoring methodologies have been widely extended to include different areas, and this subsequently can help decision makers, particularly in banking, to predict their clients" behaviour. Finally, this paper also suggests a number of directions for future research.

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Neural nets have become one of the most important tools using in credit scoring. Credit scoring is regarded as a core appraised tool of commercial banks during the last few decades. The purpose of this paper is to investigate the ability of neural nets, such as probabilistic neural nets and multi-layer feed-forward nets, and conventional techniques such as, discriminant analysis, probit analysis and logistic regression, in evaluating credit risk in Egyptian banks applying credit scoring models. The credit scoring task is performed on one bank's personal loans' data-set. The results so far revealed that the neural nets-models gave a better average correct classification rate than the other techniques. A one-way analysis of variance and other tests have been applied, demonstrating that there are some significant differences amongst the means of the correct classification rates, pertaining to different techniques.

For a sample of 94 firms, using data up to 1999, we find that retentions are more significant than dividends in determining prices of shares that are actively traded on the Egyptian stock market. However, for non-actively traded shares, the accounting book value is the most important determinant. Reductions in dividends are associated with a lack of liquidity and profitability. Dividend increases are linked to higher pre-tax operating profit effects, which outweighed post-tax effects. As to aspects that influence dividend payout ratios of actively traded firms, important factors are gearing and the market to book value, the latter a surrogate for investment opportunities. For non-actively traded firms, a more complex pattern emerges.

PurposeThe aim of this paper is to investigate differences in capital structures across industries in Egypt paying particular attention to: corporate characteristics, such as liquidity, asset structure, growth, and size; fiscal characteristics, namely, the application of differential corporate tax rates; and stock market activity.Design/methodology/approachComparisons are made between the four main industrial sectors: food, heavy industries, contracting and services. For each industry four aspects of capital structure are assessed. Firms are also classified according to whether their shares are actively traded on the Egyptian stock market. Multiple regressions are run to test a range of hypotheses. ANOVA and multiple comparison procedures are also employed.FindingsAcross Egyptian firms, higher business risks do not generally result in lower levels of long‐term capital structure. The contracting sector is significantly different from food, heavy industries and services in its determinants of its short‐term financing and interest ratios. The sector also has a higher level of debt, and so a hypothesised tax‐induced higher debt level for the services sector, which has the highest corporate tax rate, is rejected. Asset‐backing is particularly important in heavy industries, and in non‐actively traded firms. Size and growth are positively related to short‐term financing in heavy industries and services.Originality/valueThe value lies in the comprehensiveness of the study, covering both short‐ and long‐term capital structures across industries, both income measures and capital indebtedness, and distinctions according to whether the shares are actively traded or not.