Financial distress is a crucial issue when it comes to the financial health of the company. It shows the inability of the company to maintain the viability of its business. Those who survive from the problem are the toughest one. In Malaysia, the Asian financial crisis in 1997 had impacted the Malaysia's economy. Most of the businesses are affected with the slowdown of the economy, and they are not well prepared with the economy downturn. This had caused a large number of companies failed to continue its business and faced financial difficulties. The financial institutions and investors also affected with this crisis. They had suffered major losses on their investments.
The Edge Malaysia, 2002 (as cited in Wan Adibah, W.I., Raja Adzrin, R.A., Khairul Anuar, K, & Rusliza, Y.,2002) found that on 1 September 2002, 99 companies out of 861 listed in KLSE are categorized under PN4. These shows about 11.5% of the listed companies are having financial difficulties. PN4 refer to Practice Note 4/2001 which refers to those companies which cannot maintain the financial position and it is issued by Bursa Malaysia. It has taken into effect since 15 February 2001. To be listed in Bursa Malaysia, companies need to meet the listing requirement of Bursa Malaysia. Bursa Malaysia has implemented many regulations to maintain sound financial position of listed companies. All companies that are listed in Bursa need to have an adequate level of financial condition in order for them to trade in the Main market. As time changing, not all of these companies are in a healthy condition. The economic factor and business environment can directly change the company to better position or to the worse financial position.
In Malaysia, listed company do not file for bankruptcy, but for a moment they are suspended and will given a chance to restructure back their company. That is the reason why PN4 is introduced. The companies which have financial difficulties will be classified as PN4. Those companies which fall under PN4 are provided with restructuring plan to make them able to continue trading on the market. Some companies were able to continue its business and trading on the exchange after the restructuring plan, but after a moment these companies were facing the financial problem again (Mohd Norfian., Norhana S, Ismail A, 2011). PN4 was failed to achieve its objective. Therefore, Bursa Malaysia introduced PN17 to replace the PN4.
The PN17 was introduced to improve the qualities of the company traded in Main Market. PN17 is stands for Practice Note No.17/2005. PN17 has taken into effect since 3 January 2005. The amendments were made to lengthen the time taken by financial distress company to regularize back their financial condition and continue trading in Main Market. On 5 May 2006, Bursa Malaysia amended again the PN17. The aims of this amendments are to strengthen and increase the quality of the listed company, to make investor more confidence when invest with the listed companies and to provide more protections to the investors. Thus, criteria for a company to be classified as PN17 were become more rigorously.
When a company is announced as PN17, they have to follow the requirements of Bursa Malaysia. A PN17 company must regularize their condition within 12 months from the date they are list as PN17 Company. They also need to submit a plan which contains a report on how they want to restructure back their business. The restructuring plan needs to follow the requirements in Section 32 of the Securities Commission Act 1993. Thus, it requires Securities Commission of Malaysia to approve the restructuring plan before the company implements it.
Besides, they must implement the restructuring plan within the timeframe given by the authorities. If the company failing to comply with the requirements, they will be suspended from trading and de-listing procedures will be taken by the Bursa Malaysia Securities Berhad. The PN17 Companies List updated as at 25th January 2011 had list 37 companies in these categories.
Background of Study
A company with financial distress will face severe problems in continuing their business. They deal with distress by selling their major assets, reducing the capital spending and research and development, merge with other company or else they have to filing for bankruptcy. Thus, it is important to predict the potential of a company to become financially distress. A study carried by Zulkarnain, M.S., Mohamad Ali, A.H., Annuar, M.N. and Zainal Abidin, M., (2001), found that the findings of financial distress research from developed country cannot be applied to the developing economic country because of the differences in the market structure, economic situations and the law of the country. Most of the researches in Malaysia were use PN4 as financial distress company instead PN17 because most of the research are done before the PN17 were introduced.
In the late 1960's, the pioneer research, Beaver (1966) and Altman (1968) had study and predict the corporate failures. More research had been done by others after the pioneer research to enhance the ability to predict the corporate distress and to include other significant variables. One of the best ways to examine a company's financial health is by looking at its financial ratios. Financial ratios have been used widely in previous research to predict the corporate distress. This is due to financial ratio provide adequate information about the liquidity, activity or efficiency, leverage and profitability of the company. Thus, through the study of ratio, financial health of a company could be analyzed.
The liquidity ratios show the ability of a company to meet its short term obligations, whether the company has sufficient cash or not to meet its obligations. If the company has high level of liquidity ratios, it means that the company is having good financial health. While for the efficiency ratios, it indicate how well the company uses its resources to generate the sales. Leverage ratios show how much the firm using its shareholder equity or debt to finance its investments and the profitability ratios shows the ability of a firm to generate profit per each unit of their sales.
Therefore, this research will use financial ratios to predict financial distress of the company. This is due to most of the research in prediction of corporate failure are able to predict the financial failure before the failures (Altman, 1968; Beaver, 1966; Zulkarnain M.S, and Hasbullah A.J, 2009). In this study, 20 companies from financial distress and non financial distress that are listed at the Main Board of the Bursa Malaysia are selected. The company's financial information will be collected and the financial ratios of these companies are analyzed. This study attempts to examine the relationship of ratio with companies in financial distress and non distress in Malaysia. Through the research, the ratios that can predict the potential of the company's financial distress were determined.
Problem Statements
The research is intended to study on the financial ratios, and whether it can be as a predictor to financial distress companies which are listed in the Main Board of the Bursa Malaysia. The problem statements in this research are:
Do these 4 ratios (liquidity, efficiency, leverage and profitability ratios) have difference influences in predicting the company financial distress?
Whether these financial ratios can predict the financial distress a year before the failure?
Research Questions
The main research questions in this research are:
Which ratio is significantly important in determining the potential financial distress of the company?
How many years before the failure that the ratio can predict the potential financial distress?
Objectives of Study
The objectives of the study are:
To identify which independent variables gives significant in determining the company's performance. Through the research, the ratio that can predict the potential of the company's financial distress were determined. This is important since the ability to predict the financial distress can be used by the investor, the company and Bursa Malaysia to make decisions.
To determine how many years that the ratio can predict before the company became financial distressed. This is important since the management can take necessary action to avoid financial distress.
Significant of the Study
If compared to most of the previous research, especially research for financial distress in Malaysia, most of the data used are PN4 companies. While, the data used in this research are for PN17 companies. Thus, the data used in this research is more contemporary. It is very important to the company if they can predict their potential of financial distress in the future. Therefore, they can take an action to avoid any circumstances that will make them fall into financial distress. This is important since the ability to predict the financial distress can be used by the investor, the company and Bursa Malaysia to make decisions. This research would be valuable to the investor if they can predict the corporate failure earlier, thus they can liquidate their investment and minimize their losses.
Scope of Study
The scope of this study would cover the financial ratios and also the financial distress and healthy company listed in the Main Board of the Bursa Malaysia. The study is using yearly data of financial ratios of each company. The data consist of 20 financial distress and 20 healthy companies from 7 different industries for five years before financial distressed. The research will focus on the liquidity, activity, leverage and profitability ratios of each of the companies.
The time horizon used in this study is time series where the financial ratios are from 2001 to 2008. The period of study is taken from year 2001 to 2008 due to analyze whether it can predict the financial of the company for 5 years before it is classified as PN17. The sample consists of 15 companies which classified as PN17 on 2008 and 5 companies on 2006.
Limitations of Study
The limitations involved in this research are:
Number of sample and availability of data
This study is conducted based on the secondary data and the data is taken from Bursa Malaysia. Thus, the study is limited to Malaysian company. Some of the annual report was not available which make the sample for financial distressed were 20 out of 37 companies.
Period of study
The period of study limited to 5 years only. The study covers 5 years before financially distressed which from 2001 to 2008. The selected sample has different years of failure. The sample are consists of 15 companies which were financially distressed on 2009 and 5 companies were financially distressed on 2006.
Definitions of Terms
Financial distress
Financial distress is refers to a condition where a company cannot meets its obligations to the creditors. Financial distress companies show that the managements are unable to maintain the viability of its business. The possibilities of a company to be financial distress will be increase when a firm has high fixed costs, illiquid assets and their operation are sensitive with the economic conditions. Bursa Malaysia classified listed companies that have financial difficulties as PN17.
Liquidity ratios
Liquidity ratios represent the firm ability to meet its short term obligations. High liquidity ratio shows that the company is able to pay their short term debt.
Activity ratios
Activity ratios measures on how efficient a company in managing its assets. It shows the ability of a company using its assets to generate the sales. The more effective a company using its assets, the more sales that the company can generated.
Leverage ratios
Leverage ratios measure the amount of debt being used to support its business. This ratio will indicate how much debt the company use compared with the stockholder's equity in finance their business. It also can measure the ability of the firm to service its debt. If the firm depends heavily on debt to support its operations, they have to generate more income in order for them to pay debt plus the interest charged.
Profitability ratios
Profitability ratios show the financial condition of a company. It measures the firms return by relating it with the assets, equity and sales of the company. High ratio of profitability indicates that a company earns high returns and profitability.
Summary
The company's financial health is always in investors' concern. Good financial health will bring profitable investments to the investors. Therefore, investors need to study the economic condition and the business of the company if they want to earn profitable investment. The financial health of a company can be seen through their financial statements and ratio analysis could be done. Ratio analysis is one of the most popular tools of financial analysis and it is widely used. The good interpretation of financial ratios is important in analyze the company. Hence, the usefulness of the ratio is depend on the how a person interpret the ratio, and these is the most challenging aspects in ratio analysis. Company that having financial difficulties would have severely problem when they want to continue its operations. They will face shortage of fund and it is hardly for them to continue their business and survive. Thus, it is important for a manager to predict corporate distress to avoid any consequences resulted from financial distress.
CHAPTER 2
LITERATURE REVIEW
Introduction
This chapter reviews the prior literature to identify the subject of matter. As the study was to identifying the financial ratios that have the ability to predict the potential of financial distress, the study begin with the prior study that has been conducted before. The prior studies had use different methodologies in identifying the relationship between financial ratios and financial distress. Most of the researchers were more critically in determining their methodologies rather on the selection of the ratios to predict the relations with the financial distress. By understanding this literature, hopefully this will give a clue why certain variables were chosen and the relationships between the variables and financial distress.
Previous Study
The earliest study in corporate bankruptcy was conducted by Beaver (1966). Beaver defined "failure" as the inability of a firm to meets its financial obligations. He used a sample of 79 failed companies in his research and he used a paired sample technique with 79 non-failed companies. By using univariate analysis, 30 financial ratios were selected to predict the failure for the period from 1954 to 1964. Beaver found that 6 financial ratios were significant in differentiated between failed firms and non-failed firms. The ratios were cash flow to total debt, net income to total assets, total debt to total assets, working capital to total assets, current ratio and total assets.
Following the Beaver research, Altman (1968) developed the Multiple Discriminant Analysis (MDA) in the prediction of corporate bankruptcy. He selected 66 companies which comprising of bankrupt companies and non-bankrupt companies. These companies were selected from manufacturing industries. 22 of financial ratios were chosen, based on the popularity and the ratios relevancy with the study conducted. The study found that 5 ratios were selected as the significant variable and 95% of the total sample was correctly accurate for one year before the bankruptcy. The percentage of accuracy was declined as the increase in the number of years before bankruptcy.
Most of the prior researches were based on the prediction of corporate bankruptcy. The term of corporate bankruptcy and corporate distress are different. Scoot, J. (1981) suggests that the failure of the firm does not always lead the company to the bankruptcy. Low, S.W., Fauziah M.N., and Yatim. P, (2001) stated that arguments by Scoot (1981) seems logical true as there are many other option for financial distress company than filing for bankruptcy. Thus, it is more appropriate to predict the financial distress using financial distress model.
In Malaysia, many of studies had been conducted to predict the financial ratios and the corporate distress. Most of the researches use corporate distress as their sample rather than corporate bankruptcy (Low, S.W., et.al, 2001; Wan Adibah, W.I. et.al, 2002; Zulkarnain, M.S et.al, 2001). Low, S.W., et.al, (2001), had suggested the model that they had been developed is more practical to predict the financial distress company compares to model that use to predict bankruptcy. This is due to most firm had likelihood to be financial distress before they being bankrupt. The sample of distresses companies are from companies that filed for court protection against the creditor under S176 in 1998.
A study carried by Sulaiman. M, Ang Jili and Ahmadu U.S. (2001) also used the similar sample with Low, S.W., et.al, (2001), which the sample were companies that sought for court protection. Meanwhile, Wan Adibah, W.I. et.al, (2002), had selected PN4 companies as their sample of financial distressed company. There is another research of the financial distress studies in Malaysia that were selected PN4 companies as their sample of the independent variable (Mohd Norfian et.al , 2011). However, there is no research that had done before to predicting the failure of PN17 companies.
Financial Ratios as Predictor of Corporate Distresses
According to the prior studies, there were different types of ratios that have been selected to be the independent variables to predict the financial distressed. Study conducted by Altman(1968) had selected only 5 ratios from the 22 ratios to predict the company bankruptcy. The ratios are Working Capital to Total Asset, Retained Earning/Total Asset, Earning before Interest and Taxes/Total Assets, Market Value of Equity/Book Value of Total Debt and lastly, Sales/Total Assets. Low, S.W., et.al, (2001), found that the cash flows ratios are more relevant in predict the financial difficulties of a company. Beaver (1966) argues that there are some ratios have predictive power than others because it can measures up to five years before distressed. His finding shows that cash flow to total debt is the best predictor in the financial difficulties.
The independent variable used in research done by Zulkarnain M.S, and Hasbullah A.J (2009) were based on the financial ratios that have been used commonly. The ratios used in his study are those that have been utilized by Altman (1968), Beaver (1966). The ratios that had been selected are consist of 64 ratios. Meanwhile, Wan Adibah.W.I et.al (2002) had selected the financial ratio that had been selected by Altman. E.I., Eom. Y.H., and Kim. D.M. (1995) to be independent variables in their studies. From prior studies, 20 ratios had been selected by Wan. While Sulaiman. M et. al (2001) had selected 11 financial ratios from 4 broad categories. The ratio are chosen based on their simplicity.
A variety of models have been developed to predict the financial distress such as multiple discriminant analysis (MDA), logit and hazard models. Altman (1968) had developed discriminant function which combines ratios in a multivariate analysis to analysis the selected sample. Altman found that his five ratios are significant to predict the corporate bankruptcy. Ohlson (1980) had raised questions about the MDA model, which is regarding the limitations of the model to predict the sample accurately. To overcome the limitations of the MDA, he used logistic regression to predict company failure. Ohlson (1980) used the logit model and US firms to develop probability of failure for each firm. He found that this method can overcomes some of the criticisms of MDA.
Palepu, 1986 (as cited in Zulkarnain, M. S., et al., 2001) suggests that the used of matched sample will lead to the potential firm failure bias. However, Zulkarnain, M. S., et al., (2001) stated that the bias is only important in certain situation depending on usage of the model. If the model is used to rank the firm to predict financial distress, then the bias is not important. The bias is significant when the model is used to identify investment portfolio. Platt & Platt, (1990,1991) (as cited in Zulkarnain, M. S., et al., 2001) suggest that the paired sample technique is still an acceptable method in prediction of corporate failure.
As mentioned earlier, there were many studies conducted in Malaysia. Zulkarnain et al.(2001) used twenty-four distressed and non-distressed companies from the period 1980-1996. By using the multivariate discriminant analysis to determine the significant variables, they found that total liabilities to total assets, sales to current assets, cash to current liabilities and market value to debts were important variables in determined the corporate failures in Malaysia. The original model was correctly classified 89.7% of the sample while the other model only correctly classified 87.9% of the sample.
Meanwhile, Low et al. (2001) analysed financial distress using the logit analysis. 6 distressed companies and 42 non-distressed companies in 1988 were chosen. In this study, they found that current assets to current liabilities, change in net income, sales to current assets, cash and marketable securities to total assets were important variables in determining the financial distress. The accuracy rate is 82.4% in the estimation sample and 90% in the hold-out sample.
Sulaiman. M et al. (2001) compared the MDA and the logit model in the analysis of bankruptcy. Their results showed that when using MDA, debt ratio and total assets turnover were found to be important variable but when logit analysis was used, an additional significant variable was found which is interest coverage. Thus, Sulaiman. M et al. (2001) study emphasized the importance of leverage ratio as a predictor of failure. The logit model predicted 80.7% of the companies in the estimation sample and 74.4% in the hold-out sample, while the MDA model predicted 81.1% of the companies in the estimation sample and 75.4% in the hold-out sample. The accuracy rate of Sulaiman. M et al. (2001) prediction model was lower than Low et al. (2001) and Zulkarnain et al. (2001).
Theoretical Framework
Figure 1: Schematic Diagram (Relationship Diagram)
INDEPENDENT VARIABLES DEPENDENT VARIABLE
4 independent variables were selected to predict the probability of a company to be financial distress. The selections of independent variables were based on their simplicity and relevancy to the environment and it is based on the research done by Sulaiman. M, et.al, (2001) and Low, S.W., et. al (2001). The independent variables are consists of:
Liquidity
Liquidity is concerned with the ability of the company to meet its operating expenses and short terms obligations. The indicator to this variable is liquidity ratios. The calculations involves in liquidity ratios are:
Current Ratio = Current Assets / Current Liabilities. (1)
Quick Ratio = (Current Assets - Inventories) / Current Liabilities. (2)
Efficiency
Efficiency is in terms of how well a company utilizing its assets in their business. The indicator to this variable is efficiency or activity ratios. The calculations in efficiency ratios consist of:
Fixed Asset Turnover = Sales / Net Fixed Assets. (3)
Total Asset Turnover = Sales / Total Assets. (4)
Leverage
Leverage indicates the amount of debt being used by the company to finance its business. High depend on the debt will make the company have high leverage. The indicator to this variable is leverage ratios. The calculations in leverage ratios consist of:
Debt Ratio = Total Liabilities / Total Assets. (5)
Debt to Equity Ratios = Long Term Debt / Stockholder Equity. (6)
Times Interest Earned = Earnings before Interest and Taxes /
Interest Expenses. (7)
Profitability
Profitability measures the returns of the company. It also measures the success of the company. The calculations involved in profitability ratios are:
Gross Profit Margin = Gross Profit / Sales. (8)
Net Profit Margin = Net Profit / Sales. (9)
Return On Equity = Net Profit / Shareholder Equity. (10)
Return On Assets = Net Profit / Total Asset. (11)
Summary
Through the review of previous research, it helps to find the literature gap for this study. Most of the previous research used the MDA approach and the researches were used to predict the corporate bankruptcy rather than corporate distress. The methodology that will explain in next chapter are based on previous research. This study examines the PN17 companies listed in Bursa Malaysia and the relations with the financial ratios. The study would like to investigate whether there are any of the financial ratios that can predict the financial distress before the event. The finding from this study would increase the understanding of the financial distress companies in Malaysia.
CHAPTER 3
METHODOLOGY AND DATA
Introduction
The objective of this paper is to explore the possibilities to predict probability of financial distress and to identify which financial ratio that distinguishing the financial distress companies from healthy company by using logistic regression. To achieve the objective, the study will be conducted by following methodology.
Data Collection
This population in this research are consists of companies that were listed as PN17 by Bursa Malaysia. The Bursa Malaysia website (www.bursamalaysia.com) stated that the PN17 Companies List updated as at 25th January 2011 had list 37 companies. There are 3 different years which the companies take an effect to be list as PN17. There is only 1 company listed in PN17 since 2005, 11 companies since 2006 and 25 companies were list as PN17 on 2009.
The 20 financial distressed companies were selected from 7 different industries. The sampling procedure is based on convenience sampling. The samples were selected and time series data were collected between 2001 until 2008, 5 years prior to being listed under PN17. Another 20 companies of non-distressed company will be selected and will be match with the financial distressed company according to their industries.
The paired sample technique used in this study is consistent with previous studies by Beaver (1966), Altman (1968), Zulkarnain, M. S., et. al, (2001) and Zulkarnain M.S, and Hasbullah A.J. (2009). The five years relative to the financial distress data are defined as year t-1, t-2, t-3, t-4, t-5 that are consistent with the previous studies. The t-1 is defined as the first year before financial distress. The t-2 is defined as the second years before financial distress. While t-3,t-4 and t-5 are similarly defined.
Sampling Frame
The population of distress companies is about 37 companies. This study decided to use 20 financial distressed out of 37 PN17 companies. About 20 of non-financial distressed were selected as a sample to compare with the financial distress.
Sources of Data
This sample was taken from the companies listed on Bursa Malaysia. The distress companies are classified under PN17. The data were collected from Bursa Malaysia Company Annual Report (www.bursamalaysia.com). The data used in this study are consists of secondary data instead of primary data. The secondary data are more suitable in conducting this research. The company income statements and balance sheet data were used in this study. These samples were come from 7 different industries: 6 companies from the industrial product sector and trading and services sector, 3 companies from consumer product, 2 companies from construction, and 1 each from hotels, properties and technologies sector. Due to the restrained sample volume, the samples were focuses on blended industry sector.
Variables and Measurement
The variables used in this study are consists of independent variables and dependent variables. Thus, this study attempts to predict whether the independent variables have significant effect to the dependent variable.
Dependent variable
The dependent variables in this study are the financial condition of listed companies in Bursa Malaysia. The companies including financial distress company which classified as PN17 and non-financial distress company. The financial distressed company, the dependent variable is coded as 1. Otherwise, the dependent variable will be coded as 0, if the company is non-financial distressed.
Independent variables
Based on the prior research, there is lack of theoretical guide to selection of variables. Thus, the selection of the indicator to the independent variables is based on the popularity from the past research. Besides, there is other research that select the independent variables based on the environment of the study and the simplicity (Sulaiman. M., et. al, 2001). The independent variables is consists of 2 liquidity ratios, 2 efficiency ratios, 3 leverage ratios and 4 profitability ratios.
Research Design
Purpose of study
The purpose of this study is to analyze which financial ratios have significant effect in forecasting the probability of the company before the company is classified as financial distress.
Types of investigation
There are 3 types of investigation that is exploratory studies, descriptive research and causal research. This study is focus on causal research because the causal research is conducted to identify cause and effects relationship among variables when the research problem has already been narrowly defined. This study are conducted to know the relationship between liquidity, activity, leverage and profitability ratios, whether these ratios have significant relationships towards the performance of the company, either the company will financially distressed or vice versa.
Unit of analysis
For this research, the unit of analysis is the companies who had financial distress and non-financial distress.
Time Horizon
Under this study, it used longitudinal studies because the data gathered is from 2001 -2008.
Theoretical Framework
Figure 1: Schematic Diagram (Relationship Diagram)
INDEPENDENT VARIABLES DEPENDENT VARIABLE
The theoretical framework elaborates the relationship between the independent variables and dependent variable.
Liquidity
Liquidity ratios represent the ability of a firm to meet its short term obligations. Thus, it is expects to have negative relationship between liquidity ratios (current asset and quick asset) and financial distress. The lower the ratio, the more the company will be in financial distress.
Efficiency
High efficiency ratios show that the companies are able to generate high sales per unit of assets. This ratio is expects to have negative relationship between efficiency ratios (fixed asset turnover and total asset turnover) and financial distress.
Leverage
The relationship is expects to be positive relationship between the leverage ratios and financial distress. This is because the greater the value, the more the company will be financial distress. High leverage ratios show the company is heavily depends on debt to continue its business.
Profitability
A negative relationship is expects between the profitability ratios (gross profit margin, net profit margin, return on assets, return on equity) and financial distress. The lower the return on a company's investments, the more the company will be in financial distress.
Data Analysis and Treatment
The data in this study will be analyzed by using the Microsoft Excel, and Statistical Package for Social Science (SPSS) Version 13 for Windows. The Microsoft Excel will be used to calculate the ratios used as independent variables in this study. Meanwhile, the SPSS will be used to measure the relationship between the independent variables and dependent variables.
The statistical tool used in this study is Logistic Regression Model. This model is suitable with this study which the independent variable in this study is dichotomous. Dichotomous variable refer to the variable that only have two characteristics. Which in this study, the data for independent variable are coded as 0 and 1. The logistic regression is different with linear regression, which in linear regression, dependent variable, Y, will increase as the value of independent variable, X is increase. While in this study have to predicting the probabilities of the company to be financial distress or non-distress companies.
The logistic equation is;
or equivalently,
Where Z is the linear combination
The probability of the event not occurring is:
Where prob(event) is the probability of financial distress for the company. X is the set of n independent variables, which is a financial ratio. β0 and β1 refer the intercept and coefficients of the independent variables. The model uses a maximum likelihood method, which it will maximizes the probability of getting the result.
Hypothesis Statement
Based on the theoretical framework, the hypotheses were developed to examine the relationship between independent variables and dependent variable. The following are the set of hypothesis that were developed in this study.
Hypothesis 1:
H0 : The independent variable (liquidity, efficiency, leverage and profitability) cannot predict the potential financial distress 1 year before the distressed.
H1 : The independent variable (liquidity, efficiency, leverage and profitability) can predict the potential financial distress 1 year before the distressed.
Hypothesis 2:
H0 : The independent variable (liquidity, efficiency, leverage and profitability) cannot predict the potential financial distress 2 year before the distressed.
H1 : The independent variable (liquidity, efficiency, leverage and profitability) can predict the potential financial distress 2 year before the distressed.
Hypothesis 3:
H0 : The independent variable (liquidity, efficiency, leverage and profitability) cannot predict the potential financial distress 3 year before the distressed.
H1 : The independent variable (liquidity, efficiency, leverage and profitability) can predict the potential financial distress 3 year before the distressed.
Hypothesis 4:
H0 : The independent variable (liquidity, efficiency, leverage and profitability) cannot predict the potential financial distress 4 year before the distressed.
H1 : The independent variable (liquidity, efficiency, leverage and profitability) can predict the potential financial distress 4 year before the distressed.
Hypothesis 5:
H0 : The independent variable (liquidity, efficiency, leverage and profitability) cannot predict the potential financial distress 5 year before the distressed.
H1 : The independent variable (liquidity, efficiency, leverage and profitability) can predict the potential financial distress 5 year before the distressed.
Summary
In conclusion, this chapter provides the research design that was used to conduct this study. The study aims to determine the significant ratios that can predict the corporate financial distress. The selections of the ratios are based on the popularity. Thus, this research was done according to the objective of the study. Since the study focuses on data from PN17, if would give a better information that can be used to make investment decisions.
CHAPTER 4
FINDINGS AND ANALYSIS
Introduction
This chapter represents the data findings and analysis which is conducted using SPSS. It was already mentioned in the previous chapter that Logistic Regression is used to predict the probability of financial distress. It includes which variable is the most significant to the regression equation. Under Logistic Regression, forward stepwise method is used to determine the most significant variable. This method will enter all the variables at one time by using likelihood ratio and estimates which variables will be in the equation.
Findings and Analysis
First Year before Financial Distress
Model Summary
Step
-2 Log likelihood
Cox &Snell R Square
Nagelkerke R Square
1
36.177(a)
.382
.510
2
16.424(b)
.623
.831
This model summary measure is used to indicate how well the model fits the data. The -2 Log likelihood value is 16.424 which mean that the model fits the data better. Smaller -2 log likelihood values mean that the model fits with the data by which a perfect model has value of zero. Meanwhile, the Cox & Snell R Square and the Nagelkerke R Square are use to estimates the value, which indicate what percentage of the dependent variable may be accounted for by all included independent variables. The Cox & Snell R Square and the Nagelkerke R Square values for this model are 0.623 and 0.831. It means that 62.3% and 83.1% the variable is explained by the set of variables. This is consistent with the previous research done by M.Norfian et.al , 2011 which the value are 53.5% and 71.3% respectively.
Table 1 shows the percentage of prediction financial distress first year before financial distress. The classification table below compares the actual values with the predicted values based on the regression method. It shows the correct percentage of 95% that represent 19 companies which is not in financial distress. While, the correct percentage of 85% describes about 17 companies is in financial distress. The overall percentage is 90% correct in predicting the corporate failure.
Table : Classification Table for first year before financial distress
As shown in table 2, only one independent variable made statistically significant contribution to the model. The independent variable is total asset turnover. Pallant, 2007 (as cited in M.Norfian et.al , 2011) stated that variable that contributes significantly to the model should have less than 0.05. Based on table 10, total asset turnover has negative B coefficient value. It means that a low total asset turnover are more likely to be financial distress. This is consistent with the hypothesis of this study which stated there is negative relationship between efficiency ratio and financial distress. The constant is not significant, so β0 is set to 0.
Table 2: Variable in the equation for first year before financial distress
Therefore, based on Table 2, the equation to determine whether the company is financially distressed or not for a first year before financal distressed is:
Where,
X1 = Total Asset Turnover
Second Year before Financial Distress
Based on Table 3, the result provide the classification table for the second year before the financial distressed. The overall percentage is about 85%. For the non-distressed company, the correct percentage is 80% that represent 16 companies. While the correct percentage for financial distressed is 90% which represent by 18 companies.
Table 3: Classification Table for second year before financial distress
As shown in Table 4, only one independent variable made a significant contribution to the model. The independent variable that is significant for second year before financial distressed is return on assets. The variable has significance value of .007 which is less than 0.05. Based on Table 4, ROA has a negative B coefficient value which explain that 1 unit decrease in the return on asset, a probability of getting financial distressed roughly by 62% (0.622).
Table 4: Variable in the equation for second year before financial distress
Third Year before Financial Distress
For the third year before financial distressed, Table 5 show the correct percentage for non distress company is 80%, which is represents by 16 companies. While the correct percentage of 85% is represent by 17 financial distress companies. The model can predict 82.5% correct for prediction third year before the financial distress.
Table 5: Classification Table for third year before financial distress
As shown in Table 6 below, there is two variable in the equation, but only one made a statistically significant contribution to the model. The variable is same like first year before financial distress, Total Asset Turnover. The Total Asset Turover has negative B coeeficient value means that a decrease in total asset turnover will increase the probability of a company to be financially distress.
Table 6: Variable in the equation for third year before financial distress
Fourth Year before Financial Distress
Table 7 show the classification table for fourth years before the failure. The prediction for non-distress company is 70% correct which predict 14 companies non distress. Meanwhile for distress company, the percentage correct is 85% which about 17 companies. The overal percentage is about 77.5%. This show that the prediction accuracy is less than prediction for first year before financial distress.
Table 7: Classification Table for fourth year before financial distress
Based on the variable that is in the equation for fourth year before financial distress shown in Table 8, only total asset turnover is significant difference at 5% level, while gross profit margin is not significant because .095 is more than 0.05. Thus, only one independent variable was significant contribution to the model for fourth year before financial distress.
Table 8: Variable in the equation for fourth year before financial distress
Fifth Year before Financial Distress
Table 9 show the percentage of prediction financial distress fifth year before financial distress. The classification table below compares the actual values with the predicted values based on the regression method. It shows the correct percentage of 85% that represent 17 companies which is not in financial distress. While, the correct percentage of 95% describes about 19 companies is in financial distress. The overall percentage is 90% correct in predicting the corporate failure. It shows that the model can predict the financial distress 90% correct for 5 years before the failure.
Table 9: Classification Table for fifth year before financial distress
As shown in table 10, only one independent variable made statistically significant contribution to the model. The independent variable is total asset turnover. Based on table 10, total asset turnover has negative B coefficient value. It means that a low total asset turnover are more likely to be financial distress. While the other variable which is times interest earned, gross profit margin and net profit marginis less significant because the significance level is more than 0.05.
Table 10: Variable in the equation for fifth year before financial distress
Summary
From the findings, it shows that TATO is the most significant variable in predicting the financial distress in the company. It shows that the financial distress of the company is depends on their efficiency or their activity. A lower result of efficiency ratio will increase the probability of a company to be financial distress. The efficiency ratio is related on how well a company manages their assets in generating its sales. Thus, if the company fails to manage their assets efficiently, the company may faces problem in generating sales and it will make the company unable to generate the income. The company also will face a problem in order to pay the company's obligations. The finding also supported the hypothesis which the model can predict the financial distress 1 year before the failure which is 90% correct.
CHAPTER 5
CONCLUSIONS AND RECOMMENDATIONS
Conclusions
Logistic Regression analysis were used to predict the probability of these companies and from the results, it shows that the model can predict 5 year before the failure. Thus, a company should take an action if they found that their company will face financial distress in the future. This can help them to save the company from bankruptcy. It is important to know how a company can predict the financial distress before it is happen. Thus, this study attempts to determine which financial ratio is significant in predicting the financial distress. From the findings, TATO was the significant variable in determining the corporate distress and the model can predict 90% accuracy for the first year before financial distress.
Recommendations
There are few recommendations that should be taken into considerations. The recommendations are follows:
In the future research, it is better for researcher to take economic variables as one of the predictor in financial distress. This can help to give early warning to the company before financial distress.
The researcher should find exactly the number of the financial distress because this study has limited number of sample. The number of sample should large because with the larger sample it can give more significant result.
Instead of using logistic regression, there is another method that can be use to predict financial distress such as MDA and Z scores.
