An Empirical Study of the Altman’s Failure Prediction Model in Anticipating Corporate Financial Distress of Listed Firms at the Nairobi Securities Exchange.
Abstract
Anticipation of financial distress must be a technique that senior management should be
versed with since distress borders bankruptcy that later degenerates to failure. The impact
of bankruptcy is quite expensive to a firm financially and the extent of damage to
reputation of a firm to its lenders, suppliers among other stakeholders. Prediction of
financial distress is challenging and has attracted many scholars to undertake academic
studies.
This study sought to establish the effectiveness of Altman’s Z- model among all listed
firms in the republic of Kenya. All the listed firms at NSE between 2012 and 2016 were
used as the population of this study. The audited financial reports published on the NSE
investors’ handbook formed the source where secondary data was mined.
Results of the study indicated that the model predicted 6 out of 7 firms as financially
distressed. This represent 86 per cent correct prediction and 14 per cent incorrect
prediction of firms currently considered financially distressed. Further, results for 8 firms
currently considered to be financially healthy showed that the Z-model was able to
predict 7 firms correctly representing 87 percent success. The results obtained in this
study proves that Altman’s Z model is still reliable and effective and therefore all listed
firms in Kenya should use it for anticipating financial distress.
The study established that financial distress originates from internal operations of firms,
which is an obligation of CMA and NSE to keep watch of. The study recommends that
CMA and NSE should exercise its power and if possible, introduce financially stability
strategy as a requirement for listing at NSE. The study indicate gaps for areas that
require further studies among them prediction of financial distress for SMEs and effect of
the age of listing of a firm to financial health of a company.
Publisher
University of Nairobi
Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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