Leicester Castle Business School

Permanent URI for this community

https://hdl.handle.net/2086/1428

Browse

Browsing Leicester Castle Business School by Author "Abdou, H. A."

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Financial development and economic growth in China
    (LLC Consulting Publishing Company Business Perspectives, 2015-08-12) Wang, Yan; Li, X.; Abdou, H. A.; Ntim, Collins G.
    The purpose of this paper is to examine the relationship between financial development and economic growth. In particular, the authors examine the impact of financial development on the growth of primary, secondary, and tertiary industries in China. Ordinary Least Square (OLS) multiple regressions are applied on a set of data from China for the period 1978 to 2013 to determine the effects of financial development on economic growth, while controlling for other macroeconomic variables, namely labor force, capital growth, inflation rate and export growth. The empirical results show that financial development has a negative effect on economic growth in general, but on the growth of the tertiary industry in particular. By contrast, it is found that financial development has no significant effect on the primary and secondary industries. The findings offer policymakers some useful insights that more attention may need to be paid on developing capital markets and providing more investment choices/opportunities for Chinese households. This paper is different from most of the previous studies as it uses up-to-date data (1978-2013) from China capturing the effects of financial development on economic growth in addition to other macroeconomic factors.
  • Thumbnail Image
    ItemOpen Access
    Prediction of financial strength ratings using machine learning and conventional techniques
    (LLC Consulting Publishing Company Business Perspectives, 2017-12-26) Collins Nitm; Abdou, H. A.; Abdalla, W. M.; Mulkeen, J.; Collins, N.; Wang, Yan
    Financial strength ratings (FSRs) have become more significant particularly since the recent financial crisis of 2007-09 where rating agencies failed to forecast defaults and the downgrade of some banks. The aim of this paper is to predict Capital Intelligence banks’ financial strength ratings (FSRs) group membership using machine learning and conventional techniques. Here we use five different statistical techniques, namely CHAID, CART, multilayer-perceptron neural networks, discriminant analysis and logistic regression. We also use three different evaluation criteria namely average correct classification rate, misclassification cost and gains charts. Our data is collected from Bankscope database for the Middle Eastern commercial banks by reference to the first decade in the 21st Century. Our findings show that when predicting bank FSRs during the period 2007-2009, discriminant analysis is surprisingly superior to all other techniques used in this paper. When only machine learning techniques are used, CHAID outperform other techniques. In addition, our findings highlight that when a random sample is used to predict bank FSRs, CART outperform all other techniques. Our evaluation criteria have confirmed our findings and both CART and discriminant analysis are superior to other techniques in predicting bank FSRs. This has implications for Middle Eastern banks as we would suggest that improving their bank FSR can improve their presence in the market.