MCOM Theses and Dissertations (2017)

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Browsing MCOM Theses and Dissertations (2017) by Author "Muya, Linus Chiira"

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    A Study comparing financial distress prediction models in Kenya listed non-financial sector
    (Strathmore University, 2017) Muya, Linus Chiira
    Business financial distress for most companies is an absolute affirmation of their inability to endure current operations given their current debt obligations. If the financial distress was expected ahead of time, investors and other stakeholders of the companies would have the ability to take action to reduce risk or avoid loss of business. This research aimed to compare financial distress prediction models and their applicability to predict financial distress of Kenya non-financial sector for period of 2005-2014, the number of Kenyan companies faced with financial distress, be it high debts, declined business operations, lack of cash flow to run its operations or payment of its creditors have increased over time and in some cases have resulted to company’s suspension from NSE trading. Data was collected from the company’s financial report plus a questionnaire administered to the company’s risk officers. Financial profile of thirty-one companies is examined and a model is built using the inferential statistic technique, this is then compared with results of other models used to predict financial distress. The research found that Altman’s emerging market model is applicable in Kenya, with adjusted R2 of 71.20%, this however when compared with other models like the O-Score model showed the lowest prediction score, with a combination of four models showing a 86.34% prediction power, this was consistent with findings from the questionnaires where the respondent agreed that their current company models is as a result of different model combined together. The study had some limitations some of which were lack of qualitative aspects such as the company’s strategy, age of the firm and quality of management, Altman’s model is an accounting based model with historical accounting data that are subject to management manipulations. The model can be used to assist regulators, investors, creditors and scholars to predict financial distress. The incremental information content of different ratios as per different model is examined and a financial distress model is developed.