Predicting Iran Cement Industry Performance by Using logistic regression

Technical Journal of Engineering and Applied Sciences Available online at www.tjeas.com 2013 TJEAS Journal-2013-3-1/77-82 ISSN 2051-0853 2013 TJEAS Predicting Iran Cement Industry Performance by Using logistic regression Vahide Hajihassani Young Researchers Club, Abhar Branch, Islamic Azad University, Abhar, Iran Corresponding author Email: v.hajihassani@yahoo.com ABSTRACT: The purpose of this study was to investigate predicting Iran Cement Industry performance in Tehran stock Exchange. Predicting performance based on profitability, liquidity, leverage financial were formulated using SPSS statistical software.financial data available on all 28 cement company were input to logistic regression to build the algorithms. the results indicate that variables(a,b,c,d,e,f,g,h) have significant effect on the performance. Keywords: profitability, liquidity, leverage financial, Predicting performance, logistic Regression. INTRODUCTION Logistic Regression, which is helpful for prediction of the presence or absence of a characteristic or outcome based on values of a set of predictor variables, is a multivariate analysis model (Lee, 2004). The applications of Logistic Regression have repeatedly been used in the area of corporate finance, banking and investments. Multivariate Discriminant Analysis has been used by many researchers for the default-prediction model; Altman, being the pioneer in this work in the year 1968 while Logistic Regression was used by the Ohlson to construct the default-prediction model in 1980. The early research on default prediction focuses on classifying firms as either defaulters or non-defaulters. Ohlson identifies that this assumption of default prediction is an equal payoff state. Clearly, misclassifying a defaulted firm as a non-defaulted firm will have repercussions that are more severe for an investor or a loan officer than the opposite case. As such, this research focuses on the ability of the models to accurately rank defaulted and non-defaulted firms based on their default probability. In predicting financial distress and bankruptcy which have been widely applied as the evaluation models providing credit-risk information, Logistic Regression was used by Ohlson (1980) which was then followed by several authors such as Zavgren (1985). Subsequently the same trend opted by Zmijewski (1984) for a Probit Analysis.hajihassani(2012) presented A Comparison of Financial Performance in Cement Sector in Iran. At present cement sector is an emerging market. The most important reason of this is the increase infrastructure investments. Also performance of finance system, and increase in investments will affect the cement demand.cement companies have wide impact on capital and credit markets of a country. statistical society is all 28 cement companies which accepted in Tehran Stock Exchange.This study presents comparison of financial performance for the period 2006 2009 by using financial ratios and measures of cement companies working in Iran.Financial ratios are divided into three main categories and measures including two indicators.this work concludes that the performance of cement companies on the basis of profitability ratio is different than on the basis of liquidity ratio, leverage financial.hajihassani(2012)presented Leverage Financial: An Analysis and its Impact on Profitability with Reference to Cement Companies in IranThe major objective of this paper is to analyze and understand the impact of leverage financial on the profitability of the companies. This paper investigates the relationship between (the Debt Ratios) and (the Return on Assets and Net Profit Margin Ratios). The study has been conducted during 1379 to 1388.In this study, all 28 Cement companies accepted in Tehran Stock Exchange are taken for analysis and hypothesis are examined with the help Correlation.The results suggest that the leverage financial and profitability are related and the leverage financial is having impact on the profitability of the firm. From the study it is found that there is a significant relationship between Debt and Return on Assets but correlation is negative.upadhyay and Bandyopadhyay and Dutta(2012) presented Forecasting Stock Performance in Indian Market using Multinomial Logistic Regression. The objective of this paper is to predict the outperforming stock with the help of Multinomial Logistic Regression (MLR). This paper uses financial ratios as usable selection criteria for determining performance in the stock market i.e. into three categories GOOD, AVERAGE and POOR based on the stock return and variance comparing with market return and variance. The sample of the study consists of 30 large market capitalization

companies ratio of four years,which are actively traded in the Indian Stock Market. Using various financial ratios as the independent variables, this study investigates and determines the financial indicators that significantly affect the share performance by using Multi Logistic Regression Method. J. P. Arias and C. J. Pe rez(2008) presented A logistic regression-based pairwise comparison method to aggregate preferences. In a group decision making process, several individuals or a committee have the responsibility to choose the best alternative from a set. The problem addressed in this paper is how to aggregate personal preferences to arrive at an optimal group decision. New technologies allow individuals that may seldom or never meet to make group decisions. This paper proposes a methodology to obtain the group preference ordering in two steps. Firstly, each individual studies the problem isolated, and then, in a possibly virtual meeting, the group must agree on the preferences on some pairs of alternatives. Then, the group criterion is achieved by using a logistic regression model within the pairwise comparison framework proposed here.properties of the procedure are studied and two illustrative examples are presented. Research Qestion Wath is the effect of financial ratios on firms performance? Literature Review A wide variety of definitions of firm performance have also been proposed in the literature (Barney, 2002). Both accounting and market definitions have been used to study the relationship between corporate social responsibility and firm performance (Orlitzky, Schmidt, & Rynes, 2003). However, since most social responsibility scholars seek to understand the ways that socially responsible corporate activities can create or destroy shareholder wealth, market definitions of firm performance seem likely to be more appropriate than accounting definitions of firm performance in this context (Margolis & Walsh, 2001). Financial Ratio Financial ratios are practical indicators of a company s performance and financial condition. Financial ratios can be classified according to the information they provide(ertugrul, Karakasoglu,2009). The following types of ratios are frequently used and we have used these ratios in our application: profitability, liquidity, leverage financial. Table 1. Definition financial ratios Financial Definition ratios A This ratio measures how profitable company's sales are after all expanses, including taxes and interest, have been deducted. B this ratio is a final index to determine the quality and the efficiency of management in controlling the tasks of commercial C The current ratio is the ratio of current assets to current liabilities D the current ratio measures a company's ability to meet current liabilities out of existing current assets E The cash ratio is an indication of the company's ability to pay off its current liabilities if for some reason immediate payment were demanded F Debt ratio indicates what proportion of the company's assets is being financed through debt G Long-term Debts/ Shareholder's Equity H This ratio shows what proportions of the company's fixed assets are financed through shareholder's equity Source:Ertugrul and Karakasoglu (2009) Table 2. Formula financial ratios Financial ratios Definition A Net Profit Margin Ratio = Earning after taxes/sales B Return on Assets Ratio = (Net Profit/Assets)*100 C Current Ratio = Current Assets/Current Liabilities D Quick Ratio = (Current Assets-Inventories)/Current Liabilities E Cash Ratio = (Cash +Marketable Securities)/Current Liabilities F Debt Ratio = Total Debt/Total Assets G Long-term Debts to Shareholder's Equity Ratio =Long-term Debts/ Shareholder's Equity H Fixed Asset to Shareholder's Equity Ratio = Fixed Asset/ shareholder's Equity Source:Ertugrul and Karakasoglu (2009) logistic Regression Logistic regression is a regression technique employed to fit accident systems.logistic regression techniques have been used to model probabilistic systems to predict future events. These models are direct probability models that have no requirements on the distributions of the explanatory variables or predictors 78

(Harrell, 2001). If p is the probability that a binary response variable Y = 1 when input variable X= x, then the logistic response function is modeled as: P= P( ) = This function represents an s-shaped curve and is non-linear. Here, β is the coefficient of the predictor or input variable x used in a regression equation. A simplified version of this function can accommodate for multiple input variables and is linear. This function is called the logistic regression function and is superior to the logistic response function (Chatterjee & Hadi, 2006): P= P( )= This equation calculates the probability of the response variable to be 1, given multiple predictor variables. This model is still non-linear, and is transformed into linearity by using the logit response function. The equation for logistic response function then becomes: The term sides, Log( ) in the above equation is called as the odds ratio of the event. Taking the natural logarithm on both Since, L.H.S. is a function of,, : g( )= The above equation is linear and can be used to determine relationships between variables of interest.some studies have used logistic regression modeling to determine the relationship between crash severity and factors which cause crashes. Dissanayake and Lu (2002) determined that presence of certain input variables such as influence of alcohol, point of impact and lack of judgment increase the probability of a crash occurring with higher severity. A variable contributing to a crash was coded 1, else 0. Severity was rated as a non-incapacitating or an incapacitating injury and coded 0 or 1 respectively. P(Y=1) was the probability that a crash occurs which results in an incapacitating injury.as discussed earlier, Li and Bai (2008) used Crash Severity Index (CSI) as a measure of incident severity. They have used logistic regression as a basis of modeling their system. Their approach is similar to that used by Dissanayake and Lu (2002). This technique has been used to estimate the influences of driver, highway, and environmental factors on run-off-road crashes (McGinnis, Wissinger, Kelly, & Acuna, 1999). It has been used to determine the personal and behavioral predictors of automobile crash and injury severity (Kim, Nitz, Richardson, & Li, 2000). Chang and Yeh (2006) used this technique to identify the most contributing risk factors for motorcyclist fatalities. In these studies, the authors have used the environmental and behavioral causes of the incidences as predictors. In linear regression, the relationship between two variables is in the form of: Y= α+βx In the above equation, α is known as the intercept, or the value of Y when X= 0. β is known as the slope or the change in Y when X increases by one unit. The method used for estimating the values of α and β is known as ordinary least-squares regression (OLS). This method produces the estimates of all the above terms as well as an error term ej. The error term is the difference between the estimate of X and Y for case j. The predicted values of the dependent variable Y are well within the range of possible values of Y (Menard, 2001). When the dependent variable is dichotomous, it can carry only two values, 0 or 1. Since the variable is coded in a binary manner, the mean of the predicted values of the dichotomous variable lies between 0 and 1. Hence, the mean of this variable can be interpreted as a function of the probability that a selected case will fall into the higher of the two categories for this variable (Menard, 2001). When the dependent variable is dichotomous and OLS regression is used to estimate the terms, the predicted values of this dependent variable can exceed 1 or can be less than 0. The values of probability always lie between 0 and 1, and OLS regression predicts values of the dependent variable that do not fall in this range. In logistic regression analysis, the interest is not to directly predict the intrinsic value of the dependent variable Y but to determine the probability that an event will occur. Y = 1 indicates that the event has occurred, and P(Y=1) indicates the probability that it will occur. The problem of having predicted values exceed 1 or be less than zero can be avoided utilizing the concept of odds ratio discussed earlier. An odds ratio is the ratio of Y = 1 to Y 1. For example, if the odds ratio is 1.59, then it indicates that an observation is 1.59 times more likely to fall in a category Y = 1 than Y = 0. Odds ratios cannot 79

have values less than zero, but can have values more than one. Hence, OLS cannot be used to determine the probability that an accident will occur with a particular level of severity.(kavade,2009). Metodology Formulation In first,calculated firms financial ratios then all 28 cement firms which accepted in Tehran Stock Exchang be ranking based on your financial ratios and then predicting of the performance with lojistic regression in 2006 to 2009. Financial Ratios Logistic Regression PREDICTING Performance Figure1. Conceptual model RESULTS The study concludes that each investment bank has a different conclusions based on each financial ratio related to profitability ratios, liquidity ratio, leverage financial ratio. a. Based on Net Profit Margin, Shomal cement firm is the first, Khazar cement firm is the twenty eight. b. Based on Return on Assets Ratio, Shomal cement company is the first, Gharb cement firm is the twenty eight. c. Based on Current Ratio, Ghuen cement firm is the first, Bojnoord cement firm is the twenty eight. d. Based on Quick Ratio, Ghuen cement firm is the first and Shuhrood cement firm is the twenty eight. e. Based on Cash Ratio, Ghuen cement firm is the first and Bojnoord cement firm is the twenty eight. f. Based on Debt Ratio, Khush cement firm is the first and Hegmatan cement firm is the twenty eight. g. Based on Long-term Debts to Shareholder's Equity Ratio, Khazar cement firm is the first in rank and Fars and khoozestan cement firm is the twenty eight. h. Based on Fixed Asset to Shareholder's Equity Ratio, Khazar cement firm is the first in rank and Fars and khoozestan cement firm is the twenty eight. Finally, this effort highlighted important information useful for managers about the activities that may increase the financial performance of cement firms in Iran. On the other hand, it is also pertinent to mention that the objective of this study is purely for academic purposes and authors intention is not to make any ranking of cement firms in Iran and nor to give any guidelines for investment purposes. Therefore, it is recommended that for financial decision making, financial analysis of all the cement firms with data over a reasonable period of time must be considered. The table1 shows the final ranking of cment companies based on financial ratios. cement Table 1. Ranks of Cement firms based on Financial Ratios Ratios A B C D E F G H Orumie 4 15 12 11 8 15 15 9 Tehran 14 19 19 12 22 16 17 18 Khazar 28 16 27 27 25 2 1 1 Sepahan 11 21 13 9 9 13 14 11 Shuhrood 9 4 8 28 26 5 6 7 Shomal 1 1 22 19 20 19 12 13 Soofian 15 2 26 21 13 10 8 24 Shargh 3 23 20 13 16 9 7 26 Gharb 25 28 21 20 18 3 4 6 Fars and khoozestan 2 22 9 6 15 27 28 28 Ghuen 5 3 1 1 1 24 23 23 Muzandarun 7 25 25 25 27 4 2 4 Kermun 8 12 14 10 17 23 27 22 Kordestun 16 9 2 2 7 25 10 27 80

Kuroon 10 17 17 15 24 12 9 25 Dorood 26 18 18 17 11 7 13 5 Dashtestan 6 10 5 3 6 26 26 19 Ilum 19 26 15 22 21 8 3 2 Sefid Neyriz 21 11 3 8 5 21 19 16 Fars 20 13 24 23 23 11 18 8 Durub 17 20 16 18 14 17 16 12 Esfehan 12 7 6 5 2 18 21 20 Ardabil and Ahak Azar shahr 13 6 7 7 4 22 22 17 Bojnoord 22 27 28 26 28 6 5 3 Hegmatan 23 24 23 24 19 28 11 10 Hormozgan 27 5 4 4 3 20 20 21 Behbahun 24 8 10 16 10 14 25 15 Khush 18 14 11 14 12 1 24 14 Based on the analysis result, the following logistic regression model has been developed: Log( ) where p represents the probability that an predicting performance. Hence, P= P( )= Table 2. Results of the logistic regression analysis B df P value Step 1 a VAR A -30.019 1 VAR B -91.837 1 VAR C -75.310 1 VAR D -41.528 1 VAR E -236.939 1 VAR F -240.626 1 VAR G -15.327 1 VAR H 33.979 1 Constant 280.770 1 The goal of logistic regression is to correctly predict the category of outcome for individual cases using the most parsimonious model. To accomplish this goal, a model is created that includes all predictor variables that are useful in predicting the response variable. Logistic regression is often used for two purposes. The first is the prediction of group membership. The second is to provide knowledge of the relationships and strengths among the variables. As shown in Table 2, the results indicate that variables(a,b,c,d,e,f,g,h) have significant effect on the performance firms. REFERENCES Arias, N and C. J. Pe rez,j.m. 2008. A logistic regression-based pairwise comparison method to aggregate preferences. Group Decis Negot,17:237 247. Barney, J. B.2002. Gaining and sustaining competitive advantage (2nd ed.). Upper Saddle River, NJ: Prentice-Hall. Chang, H. and Yeh, T. 2006. Risk factors to driver fatalities in single-vehicle crashes: Comparisons between nonmotorcycle drivers and motorcyclists. Journal of Transportation Engineering, 132(3):227 236. Chatterjee, H and Hadi, A. 2006. Regression analysis by example. Hoboken, NJ: Wiley. Dissanayake, S., & Lu, J. 2002. Analysis of severity of young driver crashes: Sequential binary logistic regression modeling. Transportation Research Record, 1784, 108-114. Ertugrul, I, Karakasoglu, N. 2009. Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert systems with applications, 3(6):702-715. Hajihassani,V. 2012. A Comparison of Financial Performance in Cement Sector in Iran. Inventi Rapid: Microfinance & Banking, 4:1-8. Hajihassani,V.2012. Leverage Financial: An Analysis and its Impact on Profitability with Reference to Cement Companies in Iran. Inventi Rapid: Start Ups,4:1-3. Harrell., F.E. Jr. 2001. Regression modeling strategies-with application to linear models, logistic regression, and survival analysis. Springer-Verlag Inc, New York, 215 221. Kavade,H. 2009. A Lojistic Regression Model to Predict Incident Severity Using the Human Factors Analysis and Classification System. the Graduate School of Clemson University,p91. 81

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