Full paper corporate rating

The Determinants of Corporate Credit Rating in Mongolian Stock Exchange Munkhtsetseg Sandagsuren* * - the Faculty of Economics of Mandakh Burtgel Institute Email: S_munkhtsetseg@mandakh.mn Abstract This paper aims to find out determinants of corporate credit rating and rating changes.

Principally, we consider factors that determining credit ratings of listed firms in Mongolian Stock Exchange (MSE). As a frontier market, the MSE has encountered a number of challenges such as unstable business courses of listed firms and lagged information flow. Results from this paper are important and relevant for designing and constructing credit rating models and the credit rating scale which is critically essential to understand a capital market development.

Our methodology introduced in this study is significantly related to a literature review of Alexander B. Matthies (2013). We focus mostly on the background papers specialized in determinants of corporate credit ratings. The determinants of credit ratings fall into three main categories. The first are financial ratios and financial data.

These variables proxy firm specific factors such as leverage, liquidity, and firm size (e.g. Ederington (1985), Kamstra et al. (2001), and Blume et al. (1998)). The second category are corporate governance mechanisms. Here, factors such as ownership structure and board independence are measured (Bhojraj & Sengupta (2003) and Ashbaugh-Skaife et al. (2006)). The third group comprises macroeconomic factors that could influence credit ratings like GDP growth measures (e.g. Amato & Furfine (2004)).

As the main result of the paper, we are expecting to gain basic empirical findings for modeling corporate credit rating in MSE. In this case, the model can produce a useful information about listed firms’ financial capability and their financial vulnerability for investors and lenders.

Keywords: Corporate credit rating, Determinants, Initial-stage modelling for rating I. Introduction Mongolian Stock Exchange (MSE) provides its listed firms’ financial statements publicly. Corporate government indicator are recently released on market information flow. The Central bank and other agencies report publicly available macroeconomic data. These information influence significantly investors and lenders’ decision making process on investment. The MSE also release a large scale rating of its listed firms’ market efficiency that distinguish only three classes among listed firms. If one could consider that the market behavior is truly rational, the information that i firm obtains at time t would be identical to that of j firm at time t. However, it is challenging to obtain the same information on the same time for any agency who participates in the market.

Our prospective object is to provide reliable and efficient information for all of agencies in the economy. To do that, we researchers seeks to rate different listed firms in their financial capability and vulnerability. Corporate credit rating can be useful to predict default or bankruptcy and to assess firms’ financial capability for investors 1

and lenders. If we, nevertheless, allure to this measuring problem, finding determinants of corporate credit rating shall be the very first step. In the paper, we will examine 30 selected listed firms’ financial data (which we consider an external information) and external environment data for finding determinants of corporate credit rating.

This paper falls into 5 sections: section 2 introduces previous and current background studies on this issue, section 3 will describe the methodology applied in this paper, section 4 is partly related to the prior section and shows specification of our developing model (which is indeed an uncompleted form, full model work in progress), then section 5 describe the model result, eventually section 6 sums up coherently major findings of the paper.

II. Literature review Corporate credit rating literatures are often dates back to Altman Z index (1968). This work was a pioneering one to predict bankruptcy, moreover corporate credit rating.

The ratios of Altman was revolutionary effect on the overall corporate financial studies.

Kaplan & Urwitz (1979) use interest coverage, the long term debt to total assets ratio, the long term debt to net worth ratio, the net income to total assets ratio, the coefficient of variation of total assets, the coefficient of variation of net income, and total assets. Kamstra et al. (2001) use net income plus interest expenses divided by interest expenses to represent interest coverage, a debt ratio measured by total debt divided by total assets, profitability captured by the net income total assets ratio, and firm size measured as book value of firm assets. The financial ratios employed in these studies usually have a statistically significant intuitive effect. Specifically, in Kamstra et al. (2001) the debt ratio is negatively related and return on assets is positively related to credit ratings. The firm’s size equally significantly improves ratings, i.e. on average larger firms will have better ratings.

Bhojraj & Sengupta (2003) point out that a firm’s likelihood of default depends on the availability of credible information to evaluate the default risk and agency costs. Both of these are determined by governance mechanisms. Corporate governance is essentially the system by which firms are controlled and directed. Corporate governance is the focus of much research (Brown et al. (2011)). Here the choice of investing as a bondholder or a stockholder is one of the central issues (Shleifer & Vishny (1997)). The assessment of the rating agency reflects the view of a debt owner.

Rating Agencies declare that they employ a rating through-the-cycle as opposed to a more point-in-time perspective (e.g. Merton-type models (Gonzales et al. (2004)), to avoid short term business cycle effects while assessing the creditworthiness of corporations (Standard & Poor’s (2004)). But it is empirically observed that agency ratings and the business cycle do correlate (Amato & Furfine (2004), Kim & Sohn (2008), Feng et al. (2008)).

III. Methodology In this paper, we apply a similar methodology to that Dany Rogers et al. (2016). The historical data of listed firms was collected between 2012 and 2015. The data set was composed of 30 listed firms from the MSE. 2

The research methodology will be divided into four sections: the first one will treat the database and their main characteristics; the second one will present the model of the credit scoring and the definition of micro rating; the third one will detail the independent and dependent variables, with their proper explanations, justifications, authors and expected results; and in the fourth as last section, it will be highlighted econometric models and the analysis’ procedures, together with the search hypothesis.

Figure 1 shows the flow for analysis of the research data.

Figure 1. Analysis Flow of the Research Data (Source: Dany Rogers et al. (2016)) Credit Scoring Test

Data

Autocorrelation test

Estimation of the score equation

Identification test

Score calculation of each company in each year

Difference in Hansen and Sargan

Validation of the assumptions of the GMM

Firms with micro equal rating will be split into three equal parts

The firms that are in the upper and lower thirds will be considered with the imminence of reclassification in the rating

The firms that are in middle thirds will be considered WITHOUT imminence of reclassification in the rating

Estimation of regression models: dynamic panel

IV. Model specification The model specified in the paper has several independent variables that mostly applied in major working papers. For instance, financial coverage variables adopted in Blume et al. (1998), Kamstra et al. (2001) and capital structural variables in Kaplan and Urwitz (1979). We also identify macroeconomic variables that significantly influence on the capital market. However, we exclude issues about business cycle effects.

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For the variables that proxy a firm’s governance, it is impracticable to find so-called information from the MSE. Therefore, we also exclude governance variables in our model till a specified study carried out on finding these variables (as expecting, at the first quarter of 2017, this kind of study will be published).

The data processed in the model is a panel data collected from 30 listed firms’ financial ratios of the year 2012-2015. Let introduce the model: đ?‘Śđ?‘–đ?‘Ą = đ?›ź0 + đ?›˝1 đ??śđ??˝đ?‘–đ?‘Ą + đ?›˝2 đ??ˇđ?‘‡đ?‘–đ?‘Ą + đ?›˝3 đ??ˇđ??żđ?‘ƒ/đ??´đ?‘‡đ?‘–đ?‘Ą + đ?›˝4 đ??ˇđ?‘‡/đ?‘ƒđ??żđ?‘–đ?‘Ą + đ?›˝5 đ?‘…đ?‘‚đ??´đ?‘–đ?‘Ą + đ?›˝6 đ?‘€đ?‘‚đ?‘–đ?‘Ą + đ?›˝7 đ??¸đ??ľđ??źđ?‘‡đ??ˇđ??´đ?‘–đ?‘Ą + đ?›˝8 đ??´đ?‘‡đ??źđ?‘‰đ?‘‚đ?‘–đ?‘Ą + đ?‘˘đ?‘–đ?‘Ą (1) Where yit is a reclassification of the MSE as a dependent variable, independent variables described in Table 1. The linear equation 1 implied effect of financial variables on i firm’s at time t. These financial variables reasonably considered as internal effects. To specify external effects, the model shall comprise the macroeconomic variables. In this case, the equation 1 transformed as follow: đ?‘Ś ′ đ?‘–đ?‘Ą = đ?›ž0 + đ?œ‘1 đ?‘™đ?‘&#x;đ?‘–đ?‘Ą + đ?œ‘2 đ??šđ??¸đ?‘–đ?‘Ą + đ?œ‘3 đ??¸đ?‘‹đ?‘–đ?‘Ą + đ?œ‘4 đ?‘€2đ?‘–đ?‘Ą + đ?œ‘5 đ??¸đ?‘…đ?‘–đ?‘Ą + đ?œ‘6 đ??şđ?‘–đ?‘Ą + đ?œ‘7 đ??šđ?‘–đ?‘Ą + đ?œ‘8 đ??¸đ??ˇđ?‘–đ?‘Ą + đ?‘˘â€˛đ?‘–đ?‘Ą (2) In this case, these models combined for evaluating the credit rate: đ?‘Śđ?‘–đ?‘Ą = đ?›ź0 + ∑âˆ?đ?‘–=1,đ?‘Ą=1 đ?›˝đ?‘–đ?‘Ą đ?‘‹đ?‘–đ?‘Ą + đ?‘˘đ?‘–đ?‘Ą đ?‘Śâ€˛â€˛đ?‘–đ?‘Ą = đ?‘Śđ?‘–đ?‘Ą + đ?‘Śâ€˛đ?‘–đ?‘Ą or [ ] (3) đ?‘Śâ€˛đ?‘–đ?‘Ą = đ?›ž0 + ∑âˆ?đ?‘–=1,đ?‘Ą=1 đ?œ‘đ?‘–đ?‘Ą đ?‘‹đ?‘–đ?‘Ą + đ?‘˘â€˛đ?‘–đ?‘Ą Where đ?‘Śâ€˛â€˛đ?‘–đ?‘Ą is the combined form of equation 1 and 2. When applying panel data in the ordinary least square equation, it is critical to identify error terms. uit = Îźi + δit + Îťt + Îľit (4) We consider that the error term comprises Îźi (an unobserved error of i firm that to be consistent during the time), δit (an error that changing over time due to i firm’s own character), Îťt (despite characters, an error derived from events happened at the moment of time) and Îľit (other errors). In order to deal with these errors, we intuitively take advantage either random effect model or fixed effect model.

In the random effects model, the individual-specific effect is a random variable that is uncorrelated with the explanatory variables. đ??¸[đ?‘?đ?‘– |đ?‘‹đ?‘– , đ?‘§đ?‘– ] = 0 (5) Equation 5 assumes that the individual-specific effect is a random variable that is uncorrelated with the explanatory variables of all past, current and future time periods of the same individual.

In the fixed effects model, the individual-specific effect is a random variable that is allowed to be correlated with the explanatory variables. đ??¸[đ?‘?đ?‘– |đ?‘‹đ?‘– , đ?‘§đ?‘– ] ≠0 (6) To identify what error correction model is appropriate in the model, we also take advantage of Hausmen test (null hypothesis will be đ??ť0 : đ?‘?đ?‘œđ?‘Ł(đ?‘Ľđ?‘–đ?‘— đ?œ‡đ?‘– ) = 0).

Table 1. Independent variables applied in the model Category Financial

Name

Operational Definition

CJ

Interest coverage = EBIT

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Expected Relationship Positive

Conceptual Definition Blume et al. (1998)

Coverage Indicators Capital Structure

Profitability

Size Macroeconomic

/ Financial expense

DT

Total debt / Total Asset

Negative / Positive

DLP/ AT

Noncurrent liabilities / Total asset

Negative / Positive

DLP/ PL

Noncurrent liabilities / Net worth

Negative

ROA

Return on Asset = Net profit / Total asset

Negative / Positive

MO

Operating margin = EBIT / Net Revenue

Positive / Negative

EBITDA ATIVO

EBITDA / Total asset Ln(Total Asset)

Positive Positive Positive / Negative Positive / Negative Positive / Negative Positive / Negative Negative / Positive Positive / Negative Positive / Negative

lr

Lending rate

FE

Foreign Investment / GDP

EX

Export / GDP

M2

Đœ2 / GDP

ER

The US dollar exchange rate (REER)

G

Economic growth

F

Economic freedom index

ED

Foreign debt / GDP

Negative / Positive

Blume et al. (1998), Kamstra et al. (2001), Damasceno et al.

(2008) Kaplan and Urwitz (1979), Blume et al.

(1998), Amato and Furfine (2004) Kaplan and Urwitz (1979) Kaplan and Urwitz (1979), Kamstra et al.

(2001), Damasceno et al. (2008) Blume et al. (1998), Amato and Furfine (2004) Kisgen (2010) Kisgen (2006)

Researchers’ own consideration Researchers’ own consideration Soh Wei Chee et al.

(2015) Soh Wei Chee et al.

(2015) Researchers’ own consideration Soh Wei Chee et al.

(2015) Soh Wei Chee et al.

(2015) Soh Wei Chee et al.

(2015), Researchers’ own consideration

Note. The expected relationship indicates the expected outcome of each variable in relation to the credit rating, the EBIT is equal to the Profit Before Interest and Taxes, the EBITDA is equal to the Profit Before Interest, Taxes, depreciation and amortization, the total debt is the sum of the current liability plus the noncurrent liability, lending rate, the US dollar REER, and foreign dedt/GDP.

Most of papers published in this field used the classification of the ratings agencies as a dependent variable for finding determinants of corporate credit rating. In our case, rating agencies has yet to evaluate ratings among listed firms. However, the MSE evaluates its listed firms’ capitalization and net worth and classified them into three rates which is our �′′�� in the model. This classification is very large-scaled, but there is no reliable different bases. We do hope results from the paper can contribute in the development of the MSE scoring system. The dependent variable �′′�� takes its value from each firm’s growth in the net worth or total assets. Otherwise, the �′′�� variable contain a firm’s information of net worth, retain earning, and effects of revaluation.

Table 2. Dependent variable Variable �′′��

International agencies of risk classification (similar to)

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S&P

Moody’s

Fitch

1

A

A2

A

2

B

B2

B

3

CC

Ca

CC

V. Results from the Initial-Stage Model for Rating Since panel data applied in this study, we hypothesis all variables have unit roots meaning that they are non-stationary thus transforming them into the first differenced form. According to the Table 3, score_2 variable shows an excellent fit with a higher statistical significance rather than score_1 variable. It means that a i firm’s internal information about retained earnings and share capital change proxy a credit rating of the i firm more effectively.

Table 3. Summary of the initial-stage model results Dependent variable: D(Score_1) – total asset growth, D(Score_2) – Net worth growth Method: Panel EGLS Sample (adjusted): 2012-2015 Cross-section included: Total panel observations: Variable Score_1, Panel EGLS Score_2, Panel EGLS (Cross section random effects) (Cross section fixed effects) Coefficient t – Statistic Coefficient t – Statistic C 49.04574 ***1.977183 68.13133 *5.605723 D(ATIVO) 29.72477 *3.677883 11.12562 *2.252804 D(CJ) 0.198427 1.058721 0.146185 0.912788 D(DLP_AT) 9.813345 0.058187 285.8731 *2.429476 D(DLP_PL) 1.860786 0.080765 -55.94940 *-2.606438 D(DT) 1.603962 0.014945 -265.5267 *-2.768610 D(EBITDA) 2205.105 *4.101739 3388.723 *11.97113 D(MO) -0.176875 -0.575239 -0.212616 -0.862030 D(ROA) -2229.313 *-4.138942 -3370.085 *-11.86646 0.525 0.946 đ?‘šđ?&#x;? Ě…đ?&#x;?

0.441 0.864 � Hausman Tests (prob. Chi-Sq(8)) 0.394 0.034 Omitted Random effects – Lagrange 0.004 multiplier test (Breusch-Pagan) Redundant Fixed effects test – 0.295 likelihood ratio Note: *, ** and *** refers to 1%, 5% and 10% probability respectively.

0.000 0.000

As results of the initial-stage model, a firm’ size (ATIVO), noncurrent liabilities / total asset (DLP_AT), noncurrent liabilities / net worth (DLP_PL), total debt / total Asset (DT), EBITA and return on assets can be determinants of corporate credit rating. Relationship between dependent and independent variables are correlated in a similar way with expected results. EBIT / Financial expense (CJ) and EBIT / Net Revenue (MO) have no empirical evidence to having effects on the corporate credit rating in accordance with our findings. These variables were applied broadly in the Latin American countries. Therefore, ineffectiveness of these variables might depend on the quite different financial structure between Mongolia and Latin American region.

The model estimation based on macroeconomic factors shows poor relationships among đ?‘Ś ′ đ?‘–đ?‘Ą and ∑âˆ?đ?‘–=1,đ?‘Ą=1 đ?œ‘đ?‘–đ?‘Ą đ?‘‹đ?‘–đ?‘Ą econometrically. In this case, we assume that all forms 6

of dependent variables cannot capture macroeconomic events over the period of time so that the model shall be expanded to where it captures these events. Otherwise, the sample horizon will be extend to capture a whole business cycle or two decades at least. Unfortunately, we cannot carry out this session of macroeconomic effects in the paper due to the lack of information.

VI. Conclusion In the paper, we principally focus on finding determinants of corporate credit rating in the case of Mongolian Stock Exchange. To sum up major findings, variables presented in the equation of đ?‘Śđ?‘–đ?‘Ą can be effective determinants for corporate credit rating excluding Interest coverage and operating margin ratios. For a set of macroeconomic variables, we did not find out any empirical evidence that macroeconomic effects (external effects) could influence on the rating. However, it is critically important to extend sample size, and to calibrate macroeconomic data set for estimating accurately.

Based on our major findings, the paper provides various policy implications: (1) due to corporate size has significant influence on the credit rating, the MSE shall classifies its listed firms by categories of small, medium, and big (2) short-term debt increase in total liabilities and equity reduces corporate credit rating simultaneously, therefore management team of listed firms should seek to increase non-debt sources (share capital, retained earnings etc.). Theoretical framework requires management to avoid higher-costed financing in equity securities, but it would be a wise decision to issue equity securities in the practical case of the MSE. References Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance , 23(4): 589 - 609.

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Gonzales, F., Haas, F., Johannes, R., Persson, M., Toledo, L., Violi, R., . . . Wieland, M. (2004). Market dynamics associated with credit ratings: a literature review.

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