Bankruptcy Risk and Income Smoothing Tendency of NBFIs in Bangladesh

Abstract

Purpose: The study mainly investigates bankruptcy risk and income smoothing tendency of Non-Banking Financial Institutions (NBFIs) in Bangladesh. External parties of NBFIs take investment decisions based on financial reports. Stable and predictable income is one of their preference. On the other hand, poor income is one of the signs of NBFIs having bankruptcy risk. Hence the study tries to find whether the NBFIs having bankruptcy are involved in income smoothing or not. Research design, data and methodology: Data were collected from the annual report of twenty-two listed NBFIs in Bangladesh. Data from 2013 to 2017 were used. Altman's Z score and Eckel's model are used to detecting bankruptcy risk and income smoothing respectively. Results: Result implies that most of the NBFIs which have bankruptcy risk are not involved in income smoothing. Therefore, NBFIs which has bankruptcy risk are involved less with income smoothing. Conclusions: The present study revealed that most of the listed NBFIs in Bangladesh are facing bankruptcy risk. They didn't use any fraudulent technique to show smooth income. The findings will help the investor to take an investment decision on NBFIs in Bangladesh. It will convey signals to the stock market in Bangladesh.

1. Introduction

Executives and employees use annual reports as their way of displaying the principal allocated funds and conducting their stewardship functions. The financial accounts are used to convey a company's financial condition, business performance and cash flows. One of the reasons why shareholders or principal rely heavily on the statement is that the company’s records are scarce or not available for all. Because of this, the managers and employees will report on the financial statements more favourable outcomes, since the shareholders are aware of these reports as a basis for decision or findings. Income smoothing is described as a special manipulation of fluctuations in the amount of income that the company considers natural. Income smoothing refers fundamentally to the fact that income adjustments are declining over time (Atik, 2009).

When a manager engages in income smoothing, he/ she does not document accurate economic earning for the reporting period and tries to make income less volatile among periods. As managers’ performances are evaluated at the end of the year, they are motivated to do income smoothing. If income is smooth, it will have a great effect on the manager's performance. Public companies are eager to sell their shares to satisfy their monetary needs. They can be engaged in income smoothing as investors are more interested in investing in organizations that have a secure income. Another is tax motivation. A company with stable income pays less tax. Other reasons for undertaking income smoothing is that it will facilitate the capability of predicting income and enhance the manager's welfare (Huang et al., 2018).

The non-banking financial institution is one of the promising sectors in Bangladesh. It provides diversified monetary services to its customers. It mainly deals with the customer's money. NBFIs need sufficient resources to operate their businesses properly. If it has a lack of resources, it will face bankruptcy risk. To conceal bankruptcy risk, Companies manage accounting profit by fraudulent accounting techniques to provide good news and information to the capital market (Hejazi et al., 2012).

Recent studies have found that both artificial and real income smoothing is positively correlated with stock price crash risk. Results are similar to the view that income smoothing fosters a manager's opportunistic behaviour and helps encumber the intrinsic performance of a firm (Chen et al., 2017). Therefore, to avoid spreading bad news of bankruptcy risk, there is a possibility that the manager will engage in income smoothing. The study investigates nonbanking financial institutions which have bankruptcy risk smooth their income or not.

Both Income smoothing and bankruptcy risk are crucial issues in today's world. Many types of research have been undertaken on the NBFI sector, income smoothing, and bankruptcy risk. But the income smoothing tendency of NBFIs having bankruptcy risk in Bangladesh has not been undertaken. Taking this as a research gap, an effort has been given to fill the research gap and enrich existing knowledge. It will help the investor to take an investment decision on NBFIs in Bangladesh. It will convey signals to the capital market in Bangladesh.

There are thirty-four NBFIs in Bangladesh. To attain the objectives, data of twenty-two NBFIs listed in DSE in Bangladesh are used as a sample. One listed company named Bangladesh Industrial Finance Company Limited isn't covered in this study because of the unavailability of data. Apart from listed companies, private firms are also operating their business in the economy. Therefore, the small sample size is the limitation of the study.

2. Literature Review

2.1. Income Smoothing

Income smoothing is labelled as "cookie jar accounting". It is an accounting technique used to reduce income fluctuation and to show more than the actual figure by reducing current expenses and record it in the future financial statement. Investors normally invest in companies that have stable and predictable returns. For this reason, many companies adopt accounting techniques that can show a steady growth of income. It can range from good business practices to fraudulent reporting as well as from the ethical accounting process to unethical indulgence (Moses, 1987).

Kustono et al. (2021) found that the quality of business profits is affected by income smoothing. The income smoothing management aims to express the prospects of the business for gains instead of opportunistic reasons.

Income smoothing has a desirable effect on the market and share price by ensuring non-changing returns. Generally, two types of income smoothing can be found which are natural and intentional smoothing. Natural smoothing means that the income process inherently generates a smooth income (ECKEL, 1981). An example of this type of income smoothing is public utilities. On the other hand, intentional smoothing is to manipulate earnings growth artificially by the management of an entity. It can identify the other two subtypes of Intentional Smoothing. One is real smoothing where management manipulates income by reducing research and development cost and changing selling and administrative expenses. Another is artificial smoothing which is a non-natural process to reduce the variability of income (Atik, 2009). Real Smoothing has an impact on the cash flows of an entity. Without directly affecting cash flow, changes in income can also result from accounting dimensions, allocation methods, and classification of expenses (Huang et al., 2009). For example: if revenue is good in one year and it is predicted that the next year's revenue will not be good, then the company will defer its revenue to next year. Several types of research are based on the objective of income smoothing.

DANG et al. (2020) found that the quality of earnings is positively linked with a statistically important firm value. On the other hand, such determinants affect firm valuation adversely, such as financial leverage, business value-to-book ratio and sales growth. Company size determinants, fixed asset spending cost, the dividend paid rate impact company valuation positively. In comparison, financial leverage determinants, income growth rate and book value are inversely associated with the firm value by economic value, Tobin's Q or price.

More recent research has found that operating income after depreciation, pre-tax income, income before extraordinary items, and net income are the aim of income smoothing (Atik, 2009). Fully diluted Earnings Per Share (EPS), net income, net income before extraordinary items, operating income and gross margin are the possible measures of income smoothing Several studies have discussed explanatory variables of income smoothing. Firm size is one of the explanatory variables, which is measured by total sales. Larger firms are engaged in more income smoothing than smaller firms. The reasons behind it are that larger firms are more visible and have greater public scrutiny. To hold down their good reputation in the community, they try to show smooth income by using income smoothing techniques (Moses, 1987).

Ashari et al. (1994) discovered that larger firms are less engaged in income smoothing because of more available information and more examinations by analysts. As such smoothing does not contribute much to firm value. Another explanatory variable is the industry. Political, social, economic circumstances are different from industry to industry. Different industries smooth their income in different degrees. A high level of smoothing has been found in the oil and gas and sedate businesses. The service industry has less likely income smoothing behaviour than financial institutions. Peripheral industrial sectors tend to smooth income rather than core industrial sectors. In contrast, less profitable companies are involved in income smoothing rather than profitable companies (Ashari et al., 1994). Other explanatory variables are divergence of actual earnings from the expectations of the industry, the nationality of the companies, the presence of bonus compensation plans (Moses 1987; Ashari et al., 1994).

Several types of research have been done to find a link between income smoothing and other variables. Gassen and Fülbier (2015) have examined the relationship between creditor financing and income smoothing. They have found that creditors preferred firms who have smooth income. Chen et al. (2017) have investigated the relationship between income smoothing and stock price crash risk and found that a higher level of income smoothing leads to stock price crash risk especially for the firms with smaller institutional holdings, fewer analysts following the company, and positive aggregate discretionary accruals.

Habib (2005) has investigated the presence of income smoothing and firm-specific determinants in Bangladesh and found that a fair number of corporations are engaged in income smoothing. Firms whose sponsors having the largest ownership stake and higher leverage are engaged in income smoothing.

2.2. Bankruptcy Risk

Corporate failure is one of the crucial issues in today's world. It has a great effect not only on business but also on its surroundings. Failure is a process that varies in length. It depends on various factors, which are industry, size, age, and location, among others. Bankruptcy is the final stage of this process. It is a situation when liabilities exceed assets, and the firm is unable to repay its obligation. The firm is the inability to survive market competition causes bankruptcy (Aleksanyan & Huiban, 2016).

Bankruptcy risk is the possibility of anticipated corporate failure. Firms face bankruptcy risk at 6 stages of the life cycle. Risk is greater when the firm faces great liquidity problem and have insufficient resources to operate a business. It is also called insolvency risk (Sun et al., 2014).

Several factors can increase bankruptcy risk. Poor decision making is one of them. If the manager takes an inappropriate decision on important issues, the company may suffer from it. Another factor is financing. Many firms take a loan to give financial support to the business. When a firm faces financial problems, the lender may not be willing to give loans. It also increases bankruptcy risk. Other factors are the loss of key employees, market conditions, and unfavourable business location, etc. (Korol, 2017).

The pioneer of predicting bankruptcy was William H. Beaver. Beaver (1966) introduced the univariate bankruptcy prediction model where financial distress will be predicted through a single variable. He selected 79 failed and another 79 non-failed firms and used 30 financial ratios on those firms. He analysed those ratios to identify ratios with the best discriminating power between bankruptcy and nonbankruptcy firms. There were some limitations to this model. Edward Altman added four more variables into the model to give a more accurate prediction of manufacturing bankruptcy risk. (Altman, 1968).

Financial leverage is a determinant of bankruptcy risk. Rahman et al. (2020) determined the association between financial leverage and profitability. They found that profitability is lower when there is higher leverage.

Ullah et al. (2021) determined the link between financial distress and bankruptcy. The Z-score study shows that all banking institutions are not bankrupt and they have sufficient power to control bankruptcy. Around the same time, international banks struggled financially and will not be able to be maintained in the future because they do not have the money to cover the short-term and long-term debt.

2.3. Income Smoothing and Bankruptcy Risk

Income smoothing can be used to maximize the financial institution's value. According to asymmetric information theory, the estimation of investors is not biased and best based on the information they have. As such it is sensible to assert income which is congruent with their expectation (Acharya & Lambrecht, 2012).

It is presumed by a shareholder that the quality of reported income will be high, and the firm's value will be high if income surprise is smaller (i.e. reported income is closer to expected earnings). Therefore, the manager tries to report income according to the shareholder's expectations rather than true income (Kirschenheiter & Melumad, 2002).

Managers smooth income to maximize the value of the business. When income is higher, managers manage to show it downward to conceal bad news in the future. Managers withhold the bad financial condition of the company. If things may turn around in the future, they will be able to hide the bad news. Managers primarily disclose bad news to a certain level and then try to disclose good news as soon as possible. Their intention may be good, but sometimes it can increase bankruptcy risk when the news is particularly bad and they can't keep it secret any longer. Income smoothing can cause a sudden decline in share price if the manager discloses all the bad news instantaneously (Chen et al., 2017).

A study examined how income smoothing has an impact on the likelihood of bankruptcy risk in liquidating private firms in the UK. Firms that would be liquidated from 2012 to 2017 are taken as a sample. Approximately Seventy-six percent of firms indicated bankruptcy risk. Maximum firms have negative income, negative Earnings before income and tax (EBIT), negative operating cash flow because most of the firms were under liquidation. Low liquidity, low insolvency, high leverage are the functions of those firms. It has also found that 69.9% of the sampled firm's income is less volatile than their cash flows, which means no income smoothing. The findings imply that firms having bankruptcy risk are less engaged in income smoothing. Another finding is that firms do not become involved in income smoothing if they are getting more distressed. Creditors monitoring activities have an impact on this relationship. Firms were getting more credit when they have bankruptcy risk and they hold cash to hedge against distressed times. Private firms do not have the chance to be involved in income smoothing anymore (Shabani & Sofian, 2018).

3. Overview of Non-Banking Financial Institutions in Bangladesh

NBFIs have gained popularity in recent times. It provides diversified products and services to meet consumer demands. Mainly it provides leasing services. Other products and services are loans, project financing, equity financing, short term financing, real-estate financing, venture capital financing, term loan, merchant banking and working capital financing, fixed-term deposit scheme, a monthly savings scheme, corporate bond, etc. Both asset side and liability side products provided by NBFIs of Bangladesh are categorized in broader categories. Categories of asset side products are Corporate Finance, Consumer Finance, Agriculture Finance, Small and Medium Enterprise (SME) Finance, Housing/real estate Finance, Equity Finance, Syndication Finance, Capital Market Investment, Bonds, and Others. Asset side services are also categorized in broader categories which are Advisory Services, Arranger, Trustee, and Guarantor. On the other hand, categories of liability side products are Savings Scheme, Term Deposit Scheme, Wealth-Builder Scheme, and Issuance of Bonds. (Bangladesh, 2013)

Presently there are 34 NBFIs from which 3 are government-owned, 12 are joint venture and the rest are privately owned. The Minimum paid-up capital is BDT 1 billion according to the Financial Institution act, 1994. At the end of 30th June 2017, the asset has been increased by 5.81% from 713.87 billion in 2016 to 755.33 billion in 2017. NBFIs mostly invest in the industry sector. They too contribute to other divisions such as real estate, margin loan, trade and commerce, merchant banking, and agriculture. They can invest in the capital market up to 25% of their paid-up capital according to section 16 of the Financial Institution act, 1993. At the end of June 30, 2017, the total investment in the capital market becomes BDT 19.9 billion. As of June 2017, Total deposit, liability and equity have become 418.85 billion, 777.91 billion, and 109.63 billion respectively. The percentage of return on asset and return on equity is 5.9 and 0.8% respectively (Annual Report 2016-2017, 2020).

As a regulator, Bangladesh Bank has set responsibility and function to the board of directors, executive committees, management, chief executive officer, and audit committees of NBFIs. Basel-II has already executed and Capital Adequacy and Market Discipline (CAMD) has been issued for capital adequacy. Five core risk areas of NBFIs are credit risk management, asset-liability management, ICT security, prevention of money laundering and terrorist financing, and internal control and compliance. Guidelines have issued on risk management, cost of funds, products, and services of NBFIs (Annual Report 2016-2017, 2020).

4. Methods

4.1. Sampling

Twenty-two out of twenty-three companies listed in the Dhaka Stock Exchange has been used to evaluate the pattern in NBFIs in Z scores and income smoothing over five years starting between 2013 and 2017 and to develop an early warning system. However, data of one nonbanking financial institution named Bangladesh Industrial Finance Company Limited was not available. Hence, data of twenty-two companies have been used.

Table 1: Sampling Details

4.2. Hypothesis

The following null hypothesis has been framed for the present study:

H1: The NBFIs having bankruptcy risk are not involved in income smoothing.

4.3. Variables

Dependent variable: Bankruptcy risk Independent variables: Main variable: Income smoothing Other variables: leverage, liquidity, solvency, profitability.

4.4. Measures

Data will be analysed using both qualitative and quantitative techniques. To test the hypothesis, Bankruptcy risk has been measured first. Bankruptcy risk has been predicted by using ALTMAN (1983) z-score model.

Z score = 6.56 × X1 + 3.26 × X2 + 6.72 × X3 + 1.05 × X4

Where: X1=working capital /total assets; X2= retained profit / total assets; X3 =EBIT / total assets; X4 = book value of shareholders' equity / total liabilities. If z is below 1.81, it is in the red zone which means firm have bankruptcy risk. If it is greater than 1.81 and less than 2.99, it is in the grey zone which means moderate bankruptcy risk. If it is greater than 2.99, it is in the green zone which means the firm is safe from bankruptcy risk.

The reason behind using this model is that the Z score gives a quantitative measurement of the financial soundness of firms. It features factors adding to a firm's financial health and helps to indicate progress or declination of financial condition. It helps to raise capital and secure credit. It is an essential vehicle to survey financial health. It ensures transparency of financial condition to creditors and stakeholders. It exhibits creditworthiness to bankers and the soundness of business model to investors. Operating performance can be understood through the Z score. It is the conclusive assessment and avoids biasedness. It plays an imperative part to understand financial health.

Vaziri et al. (2012) predicted financial distress of financial institutions by taking several models including Z score. They found that the z-score model could predict it more appropriately than the other models. Hamid et al. (2016) used it for predicting financial distress of NBFIs in Bangladesh and got relevant results

After detecting NBFIs having bankruptcy risk, income smoothing tendency has been measured of those companies having bankruptcy risk. There are seven methods to detect income smoothing. A popular method to measure income smoothing is given below:

CV(∆l) < CV(∆S)

∆I=One period change in income;

∆S=One period change in sales;

CV=Coefficient of variation

If the value of the coefficient of variation of the annual change in income to the coefficient of variation of the annual change in sales is less than 1, it will indicate income smoothing behaviour. Several researchers have utilized this model, such as ECKEL (1981), Albrecht & Richardson (1990).

The method measures income smoothing by using smoothing variables which have a potential effect over time. Generally, the accounting technique is selected based on the expected effect on income. The method can detect income smoothing more appropriately than other methods. It is a simple and useful method.

Some other variables have also been measured for research purposes.

Leverage is measured by dividing current liabilities with the book value of shareholder’s equity. It determines the ability of firms to meet the financial obligation. Generally, a financial institution is the most leveraged institutions. Higher leverage ratio is a threat for any firm.

Profitability is measured by dividing Net income with total assets. It determines how effectively a firm has utilized assets to generate income. Lower profitability represents the bad condition of a firm.

Liquidity is measured by dividing current assets with current liabilities. It shows the company’s ability to pay current liabilities. Normally the ratio should be 2:1. The lower liquidity position is a bad signal for any firm.

Solvency is measured by dividing loans and advances (investment) with deposits. It determines the ability of a firm to meet its debt obligation. If loans and advances are higher than the deposit, it is not good for any financial institution.

Statistical measurements have been used to test the hypothesis.

5. Results

5.1. Detection of Bankruptcy Risk and Income Smoothing

After organizing collected data into different variables, NBFIs which have bankruptcy risk, have been detected. Among the twenty-two companies, seventeen companies have bankruptcy risk. Details are shown in table 2. Z scores are shown in the appendix. After that income smoothing behaviour of those companies which have a bankruptcy, risk has been detected. Results show that most of the sampled NBFIs' value of income smoothing is not less than 1. Therefore, eleven NBFIs are not engaged in income smoothing. Details are shown in Table 3.

Table 2: NBFIs Which Have Bankruptcy Risk

Table 3: Income Smoothing Behaviour of NBFIs Which Have Bankruptcy Risk

5.2. Descriptive Statistics

Table 4 shows distributional properties and correlation between independent and dependent variables. On average, the Z score is 0.5365656 and the median is 0.581068 which is less than 1.81. Most of the NBFIs have bankruptcy risk. On average, the value of income smoothing is 1.53872, and the median is 1.486949. This indicates that most of the NBFIs coefficient of variation of the annual change in income is smaller than the coefficient of variation of the annual change in sales. Most of the NBFIs are not engaged in income smoothing

Table 4: Descriptive Statistics

The ideal liquidity ratio is 2:1. On average, the liquidity ratio of NBFIs is 1.06876:1. There is a warning sign for sampled NBFIs to face liquidity problems (current asset to the current liability of 106.8%). Profitability is also low (ROA of 0.90805%). On average, most of the sampled NBFIs are highly insolvent. They could face problems of repaying their loans and advances (loans and advances to the deposit of 128.6459%). Leverage is very high (current liabilities to book value of equity of 368.5258%). There is a possibility of facing difficulty in repaying the debt. Therefore, distributional properties of control variables exhibit that the financial condition of NBFIs is not so good. The size of the firm is 23000 million (total asset). Many of the sampled NBFIs have low EBIT and low net income.

6. Discussion

The main purpose of this research was to determine whether the NBFIs which have bankruptcy risk are involved in income smoothing or not. For this research purpose, Data from 2013 to 2017 have been used. Results imply that the NBFIs having bankruptcy risk are not involved in income smoothing. When NBFI has bankruptcy risk, it has less tendency to smooth income. According to distributional properties, it shows that the majority of NBFIs that is facing bankruptcy risk doesn’t smooth income.

The present study revealed that income smoothing affects the value of public NBFIs. Their behaviour of reporting income in the financial statement depends on their creditors. If NBFI has higher leverage, they are less engaged in income smoothing. Finally, NBFIs which have bankruptcy risk do not use fraudulent techniques to show smooth income because accruals are no longer be used for income smoothing.

7. Conclusion

The study investigates the impact of income smoothing on the Bankruptcy risk of NBFIs in Bangladesh. Data for 2013-2017 have been used. It is found that NBFIs are engaged less with income smoothing when it faces bankruptcy risk. If they are getting more distressed, they will be involved less in income smoothing. Generally, there is a smooth and stable income when the firm is in a good financial position. It is an indication for NBFI that it does not use any fraudulent technique to show smooth income when it has bankruptcy risk. Therefore, it can be concluded that NBFI does not manage their earning when it is going through financial difficulties.

The study opens the door for future application to other sectors in Bangladesh. It may foster a better understanding of income smoothing behaviour and bankruptcy risk. That matter should be considered in different areas. More research is needed in this particular matter. Further research may consider income smoothing and bankruptcy risk in private firms of Bangladesh. Similar research can also be done in other listed sectors in Bangladesh. Real income smoothing is another emerging issue. This income behaviour is dominant in the accounting practice of the corporate world.