The Reliability of Bankruptcy Forecasting Based on Financial Ratio Analysis in The United Kingdom

Abstract

This thesis investigates the relationship between firms’ characteristics and financial distress among publicly listed companies on the London Stock Exchange, covering the period from 2008 to 2019. This is done by examining the extension of the Altman Z’-score (1983) model in assessing the financial health of UK firms through financial ratios and discriminant analysis. The motivation for this thesis stems from the limited number of studies applied in the UK and the need for more studies in predicting firms’ bankruptcy. Specifically, the purpose of this research is to assess the ability of the Altman Z’-model, which is addressed to private firms, to anticipate the financial distress of the public firms in the UK covering the period of the financial crisis and post-crisis, and to assess the power of prediction of this model. Another important objective of this thesis is to contribute to Altman's (1983) model based on the results of testing the significance of several financial ratios classified under four categories, which are, solvency, liquidity, activity, and profitability ratios. The empirical results revealed that the Altman Z’-score model (1983) was able to predict the public firm’s bankruptcy by 71.5% over the investigation period (2008-2019) and by 72 % over the financial crisis period (2008-2013) and 71% over the subsequent crisis period (2014-2019). In addition, the results of the analysis show that 4 of the solvency ratios have a significant
variance in means between bankrupt and non-bankrupt firms, when applying the t-test to test the study hypotheses, these ratios were; The Non-Current Liabilities to Total Liabilities, The Equity to Total Assets, The Fixed Assets to Total Equity for the crisis period, and The Current Liabilities to Total Liabilities for both periods. Also, the analysis results find three liquidity ratios, which are The Current Assets to Total Assets ratio during the crisis period, and The Current Assets to Current Liabilities, as well as The Cash to Total Assets for the post-crisis period. These ratios can enhance the predictive accuracy of the prediction model.

Date of Award	2024
Original language	English
Awarding Institution	Aberystwyth University
Supervisor	Sarah Lindop (Supervisor) & Chunmei Guo (Supervisor)