The purpose of this study was to predict financial distress in Kenyan listed firms. The specific objectives were to determine the contribution of financial ratios towards the prediction of corporate financial distress, to evaluate the predictive ability of the logistic regression model in making accurate forecasts of financial distress, to determine the predictive accuracy of the model in predicting financial distress before and during financial crisis, and also to compare the predictive accuracy of the model in predicting financial distress over the two periods, that is, before financial crisis (2004- 2006) and during financial crisis (2007- 2009). The study adopted a correlational research design. The target population included all the firms listed at the Nairobi Securities Exchange as at 2008 which were 66 firms. Secondary data were used in this study and were obtained from the Capital Markets Authority. Purposeful sampling was employed. Both descriptive statistics such mean, mode, median and standard deviation. Also inferential statistics such as correlation to determine the association between financial ratios and financial distress. Regression analysis were performed to test the hypotheses. The results indicated that an increase in the ratio of working capital to total assets, EBIT to total assets, current liabilities to total assets, and retained earnings to total assets of the surveyed firms was likely to increase the financial distress of the surveyed firms. However, the ratio of debt to total assets was found to be marginally related to financial distress. It was concluded that the earnings before interest and tax to total assets ratio significantly affected financial distress; having large amounts of retained earnings could possibly increase financial distress; and that financial ratios played a crucial role in determination of financial distress among listed firms. The study recommended that the listed firms should maintain high liquidity, be appropriately leveraged and have a positive trajectory of profitability in order to effectively mitigate financial distress.

Background of the study 
The National Bureau of Economic Research (NBER, 2010) defines recession as a significant decline in the economic activity across the country lasting more than a few months and normally visible in real GDP growth, real personal income, employment, and industrial production and wholesale- retail sales. These factors are very important in determining whether there is a recession or not (Rachlin, 2009). The study by Koksal and Ozgul (2007), found that managers are usually asked to either delay or abandon investment projects altogether during an economic downturn and get back to normal operations after that period is over in order to avoid risks. It is worth therefore noting that companies that are affected more severely during economic crisis may be forced to either liquidate and cease business or curtail their operations, retrench some of their workers, ask employees to accept a smaller compensation package and find ways and means to cut costs so as to remain competitive. 

The year 2008 saw economies in the world dip into a recession. This economic downturn began with industrialized countries and then moved to developing economies creating big cracks in the global world economy. Shortly after that credit flows froze and lender confidence dropped, investors stopped investing in countries and ultimately the value of stocks and domestic currencies plunged (IMF, 2009). A study by Cirmizi, Klapper and Uttamchandani (2012), found that this financial crisis had a big effect on companies around the world resulting in reduced demand for goods and services, reduction in availability of business financing and a declining flow of inter-border investment funds. There was also a rise in the level of insolvency among business entities due to declining demand for goods and services and decreasing availability of external finance. 

Another study by Erkens, Hung and Matos (2012) stated that since there was a large number of collapsing financial institutions around the world, there was a freeze of global credit markets that required widespread government interventions. Developing economies may not have played a big part in the recession but due to the fact that their economies are not resilient enough to counter the actions of the global markets, the sudden turn of events affected them. For many countries that do not hold United States securities, the impact of the crisis was initially transmitted through the exchange rate and the financial markets. With rapidly changing asset prices, the deteriorating financial conditions became a very important source of macroeconomic vulnerability. In their January 2009 update on World Economic Outlook, IMF points out that global growth was projected to slow from less than 3½percent to about ½ percent in 2009 before recovering in 2010.This means that as a result of the recession, the world economy faced a deep downturn. 

The Kenya National Bureau of Standards (KNBS, 2009) highlights key economic indicators of financial downturn for the main Organization for Economic Co-operation and Development (OECD) countries. In the United States for example, the economy experienced a slackened real GDP growth estimated at 1.4 per cent in 2008 compared to 2.0 per cent in 2007.In Japan, the expansion of the economy experienced a slowdown following the recession in the global economy. The country‟s real GDP grew at an estimated 0.5 per cent compared to 2.1 per cent in 2007. This was occasioned by external shocks leading to contraction in the country‟s export markets, reduction in domestic demand, and an appreciation of the Japanese Yen against other major currencies. The unemployment rate in the country increased from 3.9 per cent in 2007 to 4.1 per cent in 2008. In the United Kingdom Real GDP was estimated to have grown at 0.8 per cent in 2008 with the economy facing adjustments in the construction sector, falling house prices and decelerated domestic demand. 

Unemployment is estimated to have increased by 0.1 percentage points to 5.5 per cent in 2008. For Germany the real GDP growth is estimated to have reduced from 2.6 per cent recorded in 2007 to 1.4 per cent in 2008. In China real GDP growth is estimated to have contracted in 2008 to 9.5 per cent compared to 11.9 per cent in 2007 and in the emerging Asian economies, countries in experienced slowed real GDP growth in 2008 at a rate of 7.7 per cent compared to 9.3 per cent in 2007. The Global recession was also felt in African countries and the ways in which it affected it include a significant contraction in their trade globally and a related collapse in exports of primary commodities, which many countries are dependent. Foreign investment and remittances to migrant workers decreased significantly. Some analysts also predicted that the recession would lead to cuts in foreign aid in the medium term with persistence of the crisis. Economies considered being the most powerful in Africa proved to be the most vulnerable to the downturn: South Africa experienced a recession for the first time in nearly two decades, and Nigeria and Angola reported revenue shortfalls due to the fall in global oil prices. Several countries seen as having solid macroeconomic governance, notably Botswana, sought international financial assistance to cope with the impact of the crisis. (Alexis, Martin & Vivian, 2010). 

Many analysts argue that even with the recent reforms in the economy, growth and development in many African countries is hampered by policies that restrict competition. According to World Bank (2008), Africa is the world‟s second most trade-restrictive region after South East Asia. The region, also displays the worst attributes in business environment, governance, logistics, and other trade facilitation indicators (World Bank, 2008) 

The Kenyan economy did not also escape the recession period and according to the Kenya National Bureau of Standards (KNBS, 2009) Economic Survey report, the economic growth in Kenya was restrained by the 2008 post-election violence, the global financial crisis and high fuel and food prices and though the post-election violence was experienced only in the first quarter of 2008, its spill-over effects were manifest throughout 2008 resulting in substantial declines in growths of most of the sectors of the economy leading to a slump in economic growth from 7.1% in 2007 to 1.7% in 2008. Similarly, employment creation was adversely affected by the slow economic growth, in the same period, the annual average inflation rate almost tripled from 9.8 per cent in 2007 to 26.2 per cent in 2008, a record high since that of 28.8 per cent in 1994. This led to a reduction in real average earnings by 16.2 per cent, the stock market also saw a downturn with the 20 share index shedding 1924 points at the end of 2008, while market capitalization remained at 854 billion Kenya shillings in 2008. 

The effects of the global financial turmoil explains this stagnation that resulted in reduced investor confidence who offloaded their investments in the Nairobi Securities Exchange due to the anticipation of a global credit crunch and falling stock prices. The Kenya Shilling also weakened against the US dollar to record an average exchange rate of 69.18 Kenya shillings in 2008 compared to 67.32 Kenya shillings per US dollar in 2007. In addition, the earnings from tourism sector which deteriorated in 2008, impacted negatively on the foreign exchange rate. The Central Bank of Kenya (CBK, 2009) on the other hand reported the 91 day Treasury bill rates for January 2008 to be at 6.950 while that of 2009 was at 8.464.The composite consumer index for lower income groups with October 2005 as the base year was 124.88 in 2008 and 142.08 in 2009.The treasury bill rate for Jan 2009 was 8.464 up from 6.950 in Jan 2008. 

Recession can therefore be seen as a threat to the world economy at large since it has far reaching effects on both individual and corporate businesses. According to Wanjohi, (2011) the global economic slowdown and the accompanying cash crunch have hit businesses across all sectors forcing them to take drastic measures like reduce expenses in order to keep afloat. Distress is certainly not desirable and so the ability to adequately predict failure is crucial for both investors and creditors. This is because they have an incentive in early detection given that they would never like to make a decision that is disadvantageous to them. According to (Altman, 1993) the prediction of company distress and failure has gained much attention to financial economists and accountants alike and although values like corporate governance and ethics have been used to prevent financial distress, early detection of distress is still essential for protection of investments. Early failure prediction enables firms to take action to reduce bankruptcy costs, avoid failure to all stakeholders and contribute towards stability of the financial environment (Gharaibeh et al., 2013). 

According to Vuran (2009), the development and use of models can be very important in two different ways. First, as early warning systems, such models are very useful to managers and other authorities. Second, they can be useful in aiding decision making of financial institutions in firms‟ evaluation and selection. There are many research projects that have been conducted in order to find the early warning signs of distress. Finding a method to identify corporate financial distress as early as possible is clearly a matter of considerable interest to investors, creditors, auditors and other stakeholders. The significance of this issue has stimulated a lot of research concerning the prediction of corporate bankruptcy or financial distress. These studies often used the statistical approach or iterative learning approach to develop prediction models. Researchers used statistical models in the 60s to identify ratios that could help classify companies into failed and non-failed .This statistical approach includes univariate and multivariate models. In his work, Beaver (1966) used a dichotomous classification test to identify financial ratios for corporate failure prediction. He used 30 financial ratios and 79 pairs of companies (failure/non-failure). The best discriminant factor was the working capital/debt ratio, which correctly identified 90 percent of the firms one year prior to failure. The second best discriminant factor was the net income/total assets ratio, which had 88 percent accuracy. Altman (1968) was the first researcher to develop a multivariate statistical model to discriminate failure from non-failure firms. He used multivariate discriminant analysis (MDA), Martin (1977) used the logit model for bank failure prediction. 

This study will evaluate the impact of accounting information on the prediction of financial distress for Kenyan Listed firms using the logistic regression approach. Ohlson (1980) pioneered the application of Logistic Regression Analysis in prediction of bankruptcy and described the Logit model as a non-linear transformation of the linear regression and a technique that weights independent variables and assigns a score. The logit approach incorporates non-linear effects and uses the logistic cumulative distribution function to maximize the joint probability of default for the distressed firms and the probability of non- failure for the healthy companies in a sample. Much of the early research in the area of financial distress focused on MDA and then in later years on logit analysis. 

Logistic Regression Analysis (logit analysis) involves the determination of conditional probabilities of variables in a sample using the logistic regression model (logit model). Logistic regression allows one to predict a discrete outcome, such as group membership, from a set of variables that may be continuous, discrete, dichotomous, or a mix of any of these. Generally, the dependent or response variable is dichotomous, such as presence/absence or success/failure. Logistic regression combines independent variables to estimate the probability that a particular event will occur, i.e. a subject will be a member of one of the groups defined by the dichotomous dependent variable. If the probability for group membership in the modeled category is above some cutoff point, the subject is predicted to be a member of the modeled group. If the probability is below the cutoff point, the subject is predicted to be a member of the other group. For any given case, logistic regression computes the probability that a case with a particular set of values for the independent variable is a member of the modeled category. The logit model however suffers a shortcoming with respect to data collection of bankrupt firms. According to Ohlson (1980), realistic evaluation of a model‟s predictive relationships requires that the predictors are (would have been) available for use prior to the event of failure. The shortcoming arises because annual audited reports would not be publicly available at the end of the financial year; since audit takes place the following year. The timing issue can be expected to be serious for firms which have a large probability of failure in the first place. Another researcher who used conditional probability models, and more specifically Logit Analysis, to predict financial distress was Zavgren (1985). She argued that models which generated a probability of failure were more useful than those that produced a dichotomous classification as with the MDA.

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Item Type: Kenyan Topic  |  Size: 66 pages  |  Chapters: 1-5
Format: MS Word  |  Delivery: Within 30Mins.


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