Chapter 1

Introduction

1.0 Introduction

This chapter will be cover about introduction of the research. Section 1.1 discussed about the relationship between firm value and accounting information in Malaysia. Section 1.2 discussed about background of study. Section 1.3 discussed the scope and limitations of study. Section 1.4 described about problem statement of the study. Section 1.5 discussed about objective of study. Section 1.6 will be discussed about scope of study.

Overview of firm value and accounting information

Malaysia stock had a bad perform in 1997 it was signaling the currency crisis in the Asian region, our P.M. Dr. Mahathir Mohamad fighting the speculation against Ringgit Malaysia. MTEN and the imposition of selective exchange controls and pegging of Ringgit Malaysia brought back the confidence in financial markets. When 1999, Malaysia only showed a positive growth, that time Malaysia economy slowly become more and more stable.

Hence, in present research the relationship between accounting information presented in balance sheet, income statement, and cash flow statement and the value of the companies in stock market of Malaysia under consumer product sector has been investigated. In this research in the main hypotheses, the value of companies in stock market is dependent variable and accounting information is independent variable. Determining of companies value is a significant factor in investment process which is obtained by multiplying ending share value in the number of company’s outstanding shares. While in subsidiary hypotheses the value of companies is dependent variable and independent variable as: balance sheet data (assets, capital, liabilities and inventories), income statement data (revenues, costs, non-operating revenues, non operating costs, and tax), and operating cash flow data. Indicators investigated in this research are as follows: current ratio, quick ratio, ratio of total debt, ratio of fixed asset to net worth, return on asset, and return on equity.

The more investors use accounting information, it is expected that rational decisions are made. But the use of accounting information depends on several factors, some of which are related to users of the information and some others related to the quality and quantity of the information.

Overview of accounting information

There are four qualitative characteristics of accounting information that serve as the basis for decision making purposes in accounting which are relevance, reliability, comparability and consistency. These qualities make accounting information understandable and useful for decision and reporting purposes: the goal of financial reporting is to provide useful information to current and potential investors, creditors, and other users of accounting information (e.g., government, standard-setting bodies) to make investment, credit, and other decisions

Besides that, the role of accounting numbers in company valuation has been of fundamental interest to analysts, investors and researchers alike. Much accounting-based valuation has focused on analyzing historical and forecasted accounting numbers (Richardson & Tinaikar, 2004).An accounting number is deemed to be value-relevant if it is significantly related to stock price (the measure of value) (Beaver, 2002). There are four qualitative characteristics of accounting information that serve as the basis for decision making purposes in accounting which are relevance, reliability, comparability and consistency. These qualities make accounting information understandable and useful for decision and reporting purposes: the goal of financial reporting is to provide useful information to current and potential investors, creditors, and other users of accounting information (e.g., government, standard-setting bodies) to make investment, credit, and other decisions

1.3 Scope of study

This study will be emphasize on the time series data of the determinants of firm value which could obtained from the website of Bursa Malaysia under the sector of consumer product. The period covers 3 years from 2010 to 2012 by using yearly data in Malaysia. The companies chosen are 30 companies that under the sector of consumer product in Bursa Malaysia. This study focused on the relationship between stock price and accounting information in Malaysia. The representative of accounting information including balance sheet data, income statement data, and company’s operating cash flow.

1.4 Problem Statement

It is believed that the accounting information plays an important role in reflecting the stock price. Accordingly, this study aims to find out to what extent is the acceptance of accounting information in determining the price of stocks, in the context of firms listed in Bursa Malaysia KL.

Pirie and Smith (2008) have performed an empirical study based on the framework of Ohlson’s (1995) model, using the data of firms listed in FBM KLCI (it was known as Kuala Lumpur Stock Exchange during that time), covering 10 years from 1987 to 1996. The key variables in their model includes equity value, earning per share and the realize earning of next period, as the proxy of linear information dynamics. They conclude that two-way fixed effect model has the most significant explanatory power for the sample data from Malaysia market. Their model explained 74.56% of the variation in share price. The study conducted by Pirie and Smith (2008) covers the time period from 1987 to 1996, before the Asian financial crisis in 1997. The Malaysian securities market was seriously impacted by the Asian financial crisis. After the crisis, Malaysian securities exchange has implemented tighter controls on the listed firms to prevent the speculation activity and to ensure the listed firms are more versatile in facing the financial challenges. For example, the daily stock price variation is limited to 30% versus 50% before crisis. 5Also, no short selling is allowed after the crisis. The tighter controls may cause some shift on the atmosphere of Malaysia security market. Investors and fund managers may have different perception on the accounting variables that affect their investing decisions.

Research Question

The research questions in this study will be concentrated on:

There is a relationship between balance sheet data and companies’ value?

There is a relationship between income statement data and companies’ value?

There is a relationship between companies’ operating cash flow and their value?

Research Objectives

The research objectives in this study are:

To find the relationship of accounting information with the performance of stock market in Malaysia.

To examine the acceptance of selected accounting information with the performance of stock market under the sector of consumer product in Bursa Malaysia

Contribution of study

This research and study enhance the existing literature review about the relationship of accounting information and stock market performance in Malaysia. On the others hand, research also provided an updated data set of factors that affect stock market return in Malaysia.

Chapter 2

Literature Review

2.0 Introduction

This chapter will cover the literature review of the relationship of stock price and accounting information in different countries. Salvary (2007) pointed out that there have been many studies on stock evaluation using accounting information and most of the studies implicitly recognize the use of accounting information as a fundamental variable in stock price determination. Presumably accounting information underlies the fundamental valuation approach employed in the equity market.

2.1 The relationship between balance sheet data and stock price

A balance sheet is a financial statement at a fixed time. It provides a simple summary of what a business owns or is owed. It states what assets the business own and what it owes – liabilities, at a particular date. The balance sheet is used to show how the business is being funded and how those funds are being used.

We can use balance sheet data to fundamental analysis stock investment. Fundamental analysis involves determining a firm’s intrinsic value without reference to the price at which the firm’s equity trades on the stock market (Bauman, 1996). According to this approach, accounting information cause stock prices to change by capturing values toward which market prices drift (Francis and Schipper, 1999). It is not assumed that the market at all times reflects all available information, which means that this approach allows for an inefficient stock market.

RENATO DE C. T. RAPOSO and ADRIANO J. DE O. CRUZ (2002) researched that the relationship between stock prediction and fundamental analysis by using Neural Networks and Fuzzy Logic techniques. They explore that the network indicates if a trader would have to keep, sell or buy a stock using a combination of information extracted from balance sheets (released every three months) and market indicators. In the experiments, they concentrated on a segment of the Brazilian industry, companies from the textile sector. The results show that the network was able to deliver good results depending on the quality of the available data.

Gregory R. Duﬀee(2002) researched that the relationship between the balance sheet and stock price. He explore the theory of balance-sheet effects implies that cross-sectional, betas and book/market ratios should predict the strength of the return-volatility relation. These implications are supported in the data. The results also indicate that the well-known pattern of higher correlations among stock returns in down markets is in large part driven by a positive relation between market returns and idiosyncratic volatility.

2.2 The relationship between income statement data and stock price

The income statement is the financial statement that allows a business, as well as investors, to understand if a company is operating successfully. The income statement is a very important financial document when it comes to valuing securities or getting a feel for the creditworthiness of a company.

Income statement provided that current earnings may not reflect the future earnings growth of a firm has recently motivated researchers to directly incorporate information about future earnings, using analysts’ earnings forecasts, in a return/price-earnings relation. According to the results presented in Dechow et al. (1999) and Liu and Thomas (2000), the analysts’ earnings forecasts seem to have value relevance in the return/price-earnings relation. Both these studies make use of the residual income framework to model the relation between stock prices/returns and ac20 counting earnings. As stated by Kothari (2000), the natural step in studies investigating the returns-earnings relation is to compare the residual income model and its analytical extensions with simpler models, with and without analysts’ forecasts.

2.3 The relationships between company’s operating cash flow and stock price

In finance literature, stock price is believed to be linked to cash flow, and this is supported by the definition of stock and other stock valuation theories, such as efficient market hypothesis (Fama 1970). In spite of the well-accepted belief of the relationship between cash flow and stock price, there are some controversies about whether cash flow is a good value driver in terms of explaining the volatility of stock prices, when compared with other value drivers, such as earnings or dividends. From their research in 2001, Liu, Nissim and Thomas stated that earnings are better than cash flows in explaining the stock price, using the U.S. market data. This result also holds in international markets according to a later paper by Liu, Nissim and Thomas them (Liu, Nissim et al. 2007).

Besides that, Shiller (1981) had stated that stock price in the empirical world sometimes seems to be too volatile to align with this calculation. Among all the stock valuation models, the Efficient Market Hypothesis, the most famous model, will be discussed and the challenge brought by Shiller(1981) and other economists about this model will also be mentioned. Then, different opinions regarding the relationship between stock price and cash flow are presented.

According to Ackert and Smith (1993), narrowly defined cash flow in the variance bounds tests leads to the rejection of efficient market hypothesis, and they proved that it is unable to reject the hypothesis of market efficiency when using a broader cash flow definition. Actually, within the finance literature, dividends include all cash distributions to shareholders. For example, in the seminal work of Miller and Modigliani (Miller and 15Modigliani 1961), cash distributed through share repurchases has the same economic role as ordinary cash dividends (Ackert and Smith 1993). Accounting information is hypothesised to be value if it conveys information that modifies investor expectations of the firms’ future cash flows, and ultimately causes the stock price to change (Scott, 2003).

Data and Methodology

3.0 Introduction

In this chapter, the data, variable, and methodology applied in this research will be further explained. Section 3.1 provides data description whereas Section 3.2 provides variables identification. The econometric methodology employed in this research study will elaborated in Section 3.3.

Econometrics analysis is used to carry out this study where the model is created and regressed accordingly. Econometric analysis can be measured the strength of relationship between dependent variable with all independent variables. Beside the regression model of study also can be used to predict and forecast.

Beside few tests are used to check on the significant of the model under hypothesis testing. Normality test is to determine normal distribution of data set used. Moreover, hypothesis testing is to check on the significance of each independent variable in the model to the dependent variable. While joint significant is test on significance of all independent variables together in the model to dependent variable. Autocorrelation mean the different period of error term have relationship. This kind of disturbance term can be detected by autocorrelation test. Other than discussion of methodology, the data collection for this research is also explained in this chapter.

3.1 Data Description

To study about the relationship between firm value and accounting information, the data of firm value and accounting information used in this research is yearly frequency. The sample period is spanning from 2010 to 2012. My yearly firm value and my accounting information are gets from the finance Yahoo’s website. For my dependent variable is 30 companies value under consumer product sector in Malaysia, whereas for my independent variable is accounting information which are balance sheet (ratio of total debt and fixed assets turnover ratio), income statement (return on asset and return on equity), and operating cash flow (current ratio, quick ratio).

Table 1: Description of sample data

Data

Formulas and data notations

Sample Period

Firm value

Firm value

Jan 2010 to Dec 2012

Balance sheet

ROTD, FATR

Jan 2010 to Dec 2012

Income statement

ROA, ROE

Jan 2010 to Dec 2012

Operating cash flow

CR, QR

Jan 2010 to Dec 2012

3.2 Variables Identification

Considering that availability of data in Malaysia, the following variables as below are included in this research:

3.2.1 Firm value

The data of firm value is collected from the 30 companies under the sector of consumer product in the stock market of Malaysia. Determining of companies value is a significant factor in investment process which is obtained by multiplying ending share value in the number of company’s outstanding shares.

3.2.2 Description of accounting information

Ratio on total debt

I used the balance sheet data to calculate the ratio on total debt and fixed asset turnover ratio. Ratio on total debt is the financial ratio that indicates the percentage of a company's assets that are provided via debt. High debt ratio shows an unhealthy condition of the company. In some conditions, company may perform better when they maintain the debt ratio at a healthy level.

Fixed asset turnover ratio

I used the balance sheet data to calculate the ratio on total debt and fixed asset turnover ratio. Fixed asset turnover ratio is the ratio that measures a company's ability to generate net sales from fixed-asset investments. A higher fixed-asset turnover ratio shows that the company has been more effective in using the investment in fixed assets to generate revenues.

Return on asset

I used the income statement data to calculate the return on asset and return on equity. Return on asset indicates that how profitable a company is relative to its total assets. Return on asset gives an idea as to how efficient management is at using its assets to generate earnings. A higher return on asset means that the company gets high profit and good performance.

Return on equity

I used the income statement data to calculate the return on asset and return on equity. Return on equity indicates that measures a corporation's profitability by revealing how much profit a company generates with the money shareholders have invested. If ROEs is between 15% and 20% considered as good.

Current ratio

I used the operating cash flow data to calculate the current ratio and quick ratio. Current ratio is the liquidity ratio that measures a company's ability to pay short-term debt with its short term asset. If the value of current ratio is less than 1, it indicates that the firm may have difficulty meeting current debt.

Quick ratio

I used the operating cash flow data to calculate the current ratio and quick ratio. Quick ratio is the liquidity ratio that measures a company’s ability to pay short term debt with its most liquidity short term asset (cash). A high value of quick ratio indicates that the company is high position of able to meet any short term debt.

3.3 Methodology

In order to model and evaluate the relationship among time series variables, the econometric tests computed in this study are based on a proper multivariate time series model, which is least square method and correlation analysis. It is noted that for the data analysis part as presented in chapter 4, firm value of 30 companies in stock market Malaysia is firm value and the accounting information which are balance sheet (ratio of total debt and fixed assets turnover ratio), income statement (return on asset and return on equity), and operating cash flow (current ratio, quick ratio).

Table 2: Symbol and Formula of Each Variable

ROTB = Total Debts/Total Assets

FATR = Sales/Fixed Assets

ROA = Net Income/Total Assets

ROE = Net Income/Total Shareholder’s equity

CR = Current Assets/Current Liabilities

QR = (Current Assets – Inventories)/Current Liabilities

All data was inserted into Microsoft Excel Sheet, then using Microsoft Excel Program to compute the rate of changes from month to month, the changes rate then will exported to E-views software for testing and investigation purposes. In this research, E-views software will be employed to conduct all the statistical analyses.

To examine the causality relationship between firm value and accounting information included in this research, the examination procedures and model specification (if any) were applied are briefly explained as below:

3.4 Test conducted in the Study

3.4.1 Descriptive Statistic Test

Descriptive statistic test carried out in E-views are mainly used to provide summary and quantitative explanation about data in a manageable structure. The descriptive statistics comprising mean, median, standard deviation, skewness, and kurtosis reveal basis features and characteristic of variables. Both mean and median whereas is the midpoint of a data series when the number are arranged in order. The purpose of estimated central tendency is to discover most representative single score of the entire group. Besides that, standard deviation is an accurate estimation of dispersion with the mean value. However, skewness estimates whether the data is symmetric in the data set. Nevertheless, kurtosis is a quantitative measure of peakedness is a distribution.

3.4.2 Correlation Analysis

Correlation analysis refers to the relationship between two random variables of statistical relationship involving dependence. This is a method to show the relationship either is negative or positive relationship; when the value of coefficient negative it means there is a negative relationship between these two variable, however when the value of coefficient is positive, it means there is a positive relationship between two variable. Regression analysis involves identifying the relationship between dependent variable and more than one independent variable.

3.4.3 Normality Test

Normality tests are used to test whether a data set is well-modeled by a normal distribution or not. It measured the fitness of a model to a set of data. The hypothesis for this test is as following:

H0: The data set is normally distributed.

Ha: The data set is not normally distributed.

Where:

= Jarque-Bera

S = skewness

K = kurtosis

n = number of observation

χ2 is compute from equation above. While the critical value of this test is distributed as with 2 degree of freedom is 5.99.

When χ2 value is larger than critical value, H0 will be rejected which mean the data set is not normally distributed. Else the data set is normally distributed while χ2 value is smaller than critical value.

3.4.4 Least Square Method

Least Square Method examines the relationship between independent variable and dependent variable. Besides that, Least Square method will show each independent variable how significant toward dependent variable. The least squares method is the most widely used procedure for developing estimates of the model parameters.

Yt = α0 + α1(X1)t-a + α2(X2)t-a + α3(X3) t-a + α4(X4) t-a + α5(X5) t-a …… + αn(Xn) t-a + εt

[t-stat(X1)] [t-stat(X2)] [t-stat(X3)] [t-stat(X4)] [t-stat(X5)] [t-stat(Xn)]

Where:

Yt = dependent variable at level

Xt-a = independent variable at t differences

α = coefficient

εt = error term

3.4.5 Heteroscedasticity Test

The error term has no constant variance when there is a heteroscedasticity problem. The heteroscedasticity problem is detected using the Heteroscedasticity White Test. Null hypothesis represents that there is no heteroscedasticity problem while alternative hypothesis represents that there is heteroscedasticity problem. In order to determine whether the error term has a constant variance, we compare the value obtained from the White Test (Observations*R-squared) with the chi-square critical value at the respective degree of freedom. Reject null hypothesis if the estimated value is greater than the critical value and vice versa. Heteroscedasticity problem can be solved using the Weighted Least Square method (WLS).

H0: The error term has constant variance (No heteroscedasticity).

HA: The error term has no constant variance (Heteroscedasticity).

3.4.6 Autocorrelation Test

Autocorrelation test is conducted to ensure that there is no serial correlation between the error terms of the sample data. For this test, the null hypothesis suggests that there is no autocorrelation between the error terms while the alternative hypothesis suggests that there is autocorrelation between the error terms. To test the autocorrelation problem, we compare Durbin Watson statistic to its optimal value, which is 2. There is no serial correlation between the error terms when the Durbin Watson statistic is close to 2. Autocorrelation problem can be solved by using the Generalized Least Square method (GLS).

H0: There is no autocorrelation between the error terms.

HA: There is autocorrelation between the error terms.

Chapter 4 : Analysis of Data

4.1 Descriptive analysis

FIRM VALUE

ROA

ROE

ROTB

FATR

CR

QR

Mean

84002845

0.095359

0.291086

0.382058

2.603997

5.592852

2.377368

Median

77523925

0.042435

0.150559

0.364758

2.341294

2.532840

1.668854

Maximum

2.31E+08

1.899586

3.427709

0.936754

6.095522

78.84766

10.73968

Minimum

40000000

-0.12292

-0.46721

0.107617

0.238397

0.746506

0.203966

Std. Dev.

43364012

0.350559

0.694671

0.205352

1.810328

14.08324

2.510846

Skewness

1.477091

4.747953

3.263181

0.673724

0.525837

4.924007

1.937232

Kurtosis

5.605529

25.04936

15.13849

3.119593

2.109646

26.12092

6.126991

Jarque-Bera

19.39496

720.4331

237.4204

2.287395

2.373433

789.4504

30.98694

Probability

0.000061

0.000000

0.000000

0.318639

0.305222

0.000000

0.000000

Sum

2.52E+09

2.860760

8.732576

11.46175

78.11991

167.7856

71.32103

Sum Sq. Dev.

5.45E+16

3.563853

13.99446

1.222909

95.04133

5751.792

182.8261

Observations

30

30

30

30

30

30

30

Table 4.1 Descriptive Analysis

Table 4.1 shows the descriptive statistical table related with mean, median, maximum and minimum value, standard deviation, skewness and kurtosis of changes of the variables.

The mean annual growth of each variables are positive value. From the table, Firm Value is relatively high volatility among all. But, ROA is relatively lower volatility among all.

Skewness of each variable is positive but only have two variables are approximately zero and less than 1. Data is considered to be right skewed.

Kurtosis of each variable is shown in positive. Data is considered to be peaked distribution. According to the information has the highest kurtosis.

4.2 Correlation Matrix

FIRM VALUE

ROA

ROE

ROTB

FATR

CR

QR

FIRM VALUE

1

0.2326

0.4048

-0.3164

0.2278

0.0151

0.2742

ROA

0.2326

1

0.9316

-0.0849

0.0955

0.0615

0.3134

ROE

0.4048

0.9316

1

-0.1691

0.1696

0.0410

0.3215

ROTB

--0.3164

-0.0849

-0.1691

1

-0.4282

-0.2959

-0.5246

FATR

0.2278

0.0955

0.1696

-0.4282

1

0.3894

0.2831

CR

0.0151

0.0615

0.0410

-0.2959

0.3894

1

0.5944

QR

0.2742

0.3134

0.3215

-0.5246

0.2831

0.5944

1

Table 4.2 Correlation Matrix

Table 4.2 shows the correlation among all variables.

From the table 4.2, all the independent variables are positive correlation relations with the dependent variable expect the ROTB variable is negative correlation relation with the dependent variable. The relationship between the ROA and the ROE has the highest correlation relation. And the lowest correlation value is the relationship between the ROE and CR.

4.3 Normality Test

Table 4.3.1 Residual Graph

The consistency of the data can be studied through residual graph. When the range of value of Y-axis, which represent residual spread, is within 3 < x < -3, then data series is consistent. It is shown that the Y-axis of data series is outside the range of 3 < x < -3. According to residual graph shows the data series is not consistent.

Table 4.3.2 Jarque-Bera test

Jarque-Bera Test is used to examine whether the data is normally distributed. According to table regarding the critical value says that 2 degrees of freedom at 5% is 5.99.

It is stated that the value of Jarque-Bera is 1.241999. The value is smaller than the critical value. Hence, according to Jarque-Bera test, null hypothesis cannot be rejected. The data is normally distributed.

4.4 Ordinary Least Square Model

Dependent Variable: FIRM_VALUE

Method: Least Squares

Date: 01/06/13 Time: 14:16

Sample: 1 30

Included observations: 30

Variable

Coefficient

Std. Error

t-Statistic

Prob.

ROA

-1.24E+08

58770952

-2.108629

0.0461

ROE

77991414

30025357

2.597518

0.0161

ROTB

-20376020

44915041

-0.453657

0.6543

FATR

1716349.

4726515.

0.363132

0.7198

CR

-474682.3

688006.6

-0.689939

0.4971

QR

3578874.

4294620.

0.833339

0.4132

C

70580069

29202731

2.416900

0.0240

R-squared

0.374120

Mean dependent var

84002845

Adjusted R-squared

0.210847

S.D. dependent var

43364012

S.E. of regression

38522111

Akaike info criterion

37.97233

Sum squared resid

3.41E+16

Schwarz criterion

38.29927

Log likelihood

-562.5849

Hannan-Quinn criter.

38.07692

F-statistic

2.291376

Durbin-Watson stat

2.285871

Prob(F-statistic)

0.070163

Table 4.4 Ordinary Least Square Model

Firm Valuet=β0+β1ROAt-1+β2ROEt-1+β3ROTBt-1+β4FATR t-1+β5CR t-1+β6QR t-1+εt

Firm Valuet= 70580069 -1.24E+08ROAt-1+77991414ROEt-1-20376020ROTBt-1

[2.417] [-2.109]** [2.596] ** [-0.454]

+1716349FATR t-1-474682.3CR t-1+3578874QR t-1+29202731εt

[0.363] [-0.690][0.833]

R2 = 0.374 Adj. R2 = 0.211 DW = 2.286

** Significant at 5% level

Ordinary least square method determines the relationship between dependent variable and independent variables.

The R-Square value of the result is 0.374120. R-Square statistic indicates the "goodness of fit" of the model. It is the percentage of total variation in the dependent variables which explained by independent variables.

Statistical significance of model coefficients must be determined as the regression line of data may not fit the data points accurately. The coefficient estimate divided by the standard error equals to the t-statistics. The most important explanatory variable are ROA and ROE, with statistically significant with 95% confidence level.

Three of the variables, ROE, FATR, and QR are having positive relationship with Firm Value. Inversely, the others variables, ROA, ROTB, and CR are having negative relationship with Firm Value.

4.5Heteroskedasticity Test

Heteroskedasticity Test: White

F-statistic

0.893509

Prob. F(6,23)

0.5160

Obs*R-squared

5.670862

Prob. Chi-Square(6)

0.4611

Scaled explained SS

4.656976

Prob. Chi-Square(6)

0.5885

Test Equation:

Dependent Variable: RESID^2

Method: Least Squares

Date: 01/06/13 Time: 14:41

Sample: 1 30

Included observations: 30

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

9.07E+14

7.80E+14

1.163278

0.2566

ROA^2

1.09E+15

3.74E+15

0.290150

0.7743

ROE^2

-4.81E+14

1.15E+15

-0.419745

0.6786

ROTB^2

-9.06E+14

2.14E+15

-0.423423

0.6759

FATR^2

3.54E+13

3.91E+13

0.903861

0.3754

CR^2

-6.44E+11

4.01E+11

-1.606825

0.1217

QR^2

2.70E+13

1.72E+13

1.575699

0.1288

R-squared

0.189029

Mean dependent var

1.14E+15

Adjusted R-squared

-0.022529

S.D. dependent var

1.93E+15

S.E. of regression

1.96E+15

Akaike info criterion

73.45817

Sum squared resid

8.80E+31

Schwarz criterion

73.78511

Log likelihood

-1094.872

Hannan-Quinn criter.

73.56276

F-statistic

0.893509

Durbin-Watson stat

1.714339

Prob(F-statistic)

0.515977

Table 4.5Heteroskedasticity Test

Heteroskedasticity occurs when the variance of the unobservable error u is not constant. It acts an efficient estimator of re-weighting data correctly. Since the White test statistic has a probability(6,23) of 0.5160, which is greater than 5% critical value, we can reject null hypothesis that there is heteroscedasticity.

4.6 Autocorrelation Test

Breusch-Godfrey Serial Correlation LM Test:

F-statistic

0.850432

Prob. F(1,22)

0.3664

Obs*R-squared

1.116520

Prob. Chi-Square(1)

0.2907

Test Equation:

Dependent Variable: RESID

Method: Least Squares

Date: 01/06/13 Time: 14:47

Sample: 1 30

Included observations: 30

Presample missing value lagged residuals set to zero.

Variable

Coefficient

Std. Error

t-Statistic

Prob.

ROA

9328560.

59824411

0.155932

0.8775

ROE

-4730825.

30557157

-0.154819

0.8784

ROTB

4161718.

45287213

0.091896

0.9276

FATR

-322171.6

4754810.

-0.067757

0.9466

CR

-47100.84

692141.7

-0.068051

0.9464

QR

-185481.7

4313344.

-0.043002

0.9661

C

297537.2

29299925

0.010155

0.9920

RESID(-1)

-0.206862

0.224316

-0.922189

0.3664

R-squared

0.037217

Mean dependent var

-5.34E-09

Adjusted R-squared

-0.269123

S.D. dependent var

34306388

S.E. of regression

38647979

Akaike info criterion

38.00107

Sum squared resid

3.29E+16

Schwarz criterion

38.37472

Log likelihood

-562.0160

Hannan-Quinn criter.

38.12060

F-statistic

0.121490

Durbin-Watson stat

1.981457

Prob(F-statistic)

0.995935

Table 4.6 Autocorrelation Test

-no autocorrelation

Chapter 5

Discussion and Conclusion

5.0 Introduction

Section 5.2 will discuss about conclusion of this study, whereas Section 5.2 will discuss about the limitation of the study.

5.1 Conclusion

In every year ended, the company will provide the financial report showed about their company performance. Everybody can get the accounting information on the financial report. Based on the accounting information given, investor can evaluate the company firm value. The aim of this study was to examine whether the relationship and the causality relationship exists between firm value and accounting information. To achieve this main purpose, the yearly data period of 2010 to 2012 employed to examine the causality relationship among firm value and accounting information indicators. Before conducting correlation matrix, normality test, heteroskedasticity test, and autocorrelation test are carried out in order to fulfill the research result. Besides that, statistical tool such as descriptive statistic is also used to determine the basic characteristics and nature of variables tested in this study.

The purpose of this study is to verify the relationship between firm value of 30 companies under the sector of consumer product in the stock market of Malaysia and accounting information (CR, QR, ROE, ROA, FATR, and ROTB). To test the relationship Least Square method was applied in this study. There are some of the variable result match with the expected relationship, such as return on asset (ROA) and return on equity (ROE). Quick ratio, return on equity, and ratio on total debt have a negative relationship toward firm value, whereas current ratio, return on asset and fixed asset turnover ratio have a positive relationship toward firm value.

After the all test analysis, the accounting information plays an important role to find the company value. The result showed that, ROA and ROE are more significant than other CR, QR, FATR and ROTB indicator.

Firm value is a measurement the performance of 30 stocks under the consumer product sector in Malaysia. Investor may use the relationship of the variables to hedge their risk by comparing which sectors are moving more and in what direction. This could help investor to diversify their risk, when they diversify the risk; they may obtain low risk high return. This research studies the relationship of firm value and accounting information (CR, QR, ROA, ROE, FATR, and ROTB). Therefore Investor may link to the accounting information and business conditions to hedge their risk. Last but not least, the result of this research will be beneficial and useful information to investor when they know the exactly relationship between accounting information and firm value in Malaysia.

5.2 Limitation of the study

Since the yearly data over period of 2010-2012 only provide a total of 210 observations, the limitation of this study is lack of data to the most detail like monthly data. Besides that, there is lack of observation for research variables in Malaysia. Moreover, instead of using six financial indicators, more indicators should be taken into account in order to come out with a better econometric model. Likewise, the others financial indicators such as earning per share, ratio of goods to working capital, return on investment, assets turnover ratio, return on total capital, and return on owner’s equity. On the other hand, this econometric model should take in others country in ASEAN to know more about ASEAN country trend on affecting their stock performance, due to lack of data availability, these country doesn’t included in this research. Besides that, in reality, the historical information is not a good measurement, due to some others factor affect the historical information, and then the relationship direction will affected.

Appendix

Appendix 1: Descriptive Statistic

FIRM VALUE

ROA

ROE

ROTB

FATR

CR

QR

Mean

84002845

0.095359

0.291086

0.382058

2.603997

5.592852

2.377368

Median

77523925

0.042435

0.150559

0.364758

2.341294

2.532840

1.668854

Maximum

2.31E+08

1.899586

3.427709

0.936754

6.095522

78.84766

10.73968

Minimum

40000000

-0.12292

-0.46721

0.107617

0.238397

0.746506

0.203966

Std. Dev.

43364012

0.350559

0.694671

0.205352

1.810328

14.08324

2.510846

Skewness

1.477091

4.747953

3.263181

0.673724

0.525837

4.924007

1.937232

Kurtosis

5.605529

25.04936

15.13849

3.119593

2.109646

26.12092

6.126991

Jarque-Bera

19.39496

720.4331

237.4204

2.287395

2.373433

789.4504

30.98694

Probability

0.000061

0.000000

0.000000

0.318639

0.305222

0.000000

0.000000

Sum

2.52E+09

2.860760

8.732576

11.46175

78.11991

167.7856

71.32103

Sum Sq. Dev.

5.45E+16

3.563853

13.99446

1.222909

95.04133

5751.792

182.8261

Observations

30

30

30

30

30

30

30

Appendix 2: Correlation Matrix

FIRM VALUE

ROA

ROE

ROTB

FATR

CR

QR

FIRM VALUE

1

0.2326

0.4048

-0.3164

0.2278

0.0151

0.2742

ROA

0.2326

1

0.9316

-0.0849

0.0955

0.0615

0.3134

ROE

0.4048

0.9316

1

-0.1691

0.1696

0.0410

0.3215

ROTB

--0.3164

-0.0849

-0.1691

1

-0.4282

-0.2959

-0.5246

FATR

0.2278

0.0955

0.1696

-0.4282

1

0.3894

0.2831

CR

0.0151

0.0615

0.0410

-0.2959

0.3894

1

0.5944

QR

0.2742

0.3134

0.3215

-0.5246

0.2831

0.5944

1

Appendix 3: Residual Graph

Appendix 4: Jarque-Bera test

Appendix 5: Ordinary Least Square Model

Dependent Variable: FIRM_VALUE

Method: Least Squares

Date: 01/06/13 Time: 14:16

Sample: 1 30

Included observations: 30

Variable

Coefficient

Std. Error

t-Statistic

Prob.

ROA

-1.24E+08

58770952

-2.108629

0.0461

ROE

77991414

30025357

2.597518

0.0161

ROTB

-20376020

44915041

-0.453657

0.6543

FATR

1716349.

4726515.

0.363132

0.7198

CR

-474682.3

688006.6

-0.689939

0.4971

QR

3578874.

4294620.

0.833339

0.4132

C

70580069

29202731

2.416900

0.0240

R-squared

0.374120

Mean dependent var

84002845

Adjusted R-squared

0.210847

S.D. dependent var

43364012

S.E. of regression

38522111

Akaike info criterion

37.97233

Sum squared resid

3.41E+16

Schwarz criterion

38.29927

Log likelihood

-562.5849

Hannan-Quinn criter.

38.07692

F-statistic

2.291376

Durbin-Watson stat

2.285871

Prob(F-statistic)

0.070163

Appendix 6: Heteroskedasticity Test

Heteroskedasticity Test: White

F-statistic

0.893509

Prob. F(6,23)

0.5160

Obs*R-squared

5.670862

Prob. Chi-Square(6)

0.4611

Scaled explained SS

4.656976

Prob. Chi-Square(6)

0.5885

Test Equation:

Dependent Variable: RESID^2

Method: Least Squares

Date: 01/06/13 Time: 14:41

Sample: 1 30

Included observations: 30

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

9.07E+14

7.80E+14

1.163278

0.2566

ROA^2

1.09E+15

3.74E+15

0.290150

0.7743

ROE^2

-4.81E+14

1.15E+15

-0.419745

0.6786

ROTB^2

-9.06E+14

2.14E+15

-0.423423

0.6759

FATR^2

3.54E+13

3.91E+13

0.903861

0.3754

CR^2

-6.44E+11

4.01E+11

-1.606825

0.1217

QR^2

2.70E+13

1.72E+13

1.575699

0.1288

R-squared

0.189029

Mean dependent var

1.14E+15

Adjusted R-squared

-0.022529

S.D. dependent var

1.93E+15

S.E. of regression

1.96E+15

Akaike info criterion

73.45817

Sum squared resid

8.80E+31

Schwarz criterion

73.78511

Log likelihood

-1094.872

Hannan-Quinn criter.

73.56276

F-statistic

0.893509

Durbin-Watson stat

1.714339

Prob(F-statistic)

0.515977

Appendix 7: Autocorrelation Test

Breusch-Godfrey Serial Correlation LM Test:

F-statistic

0.850432

Prob. F(1,22)

0.3664

Obs*R-squared

1.116520

Prob. Chi-Square(1)

0.2907

Test Equation:

Dependent Variable: RESID

Method: Least Squares

Date: 01/06/13 Time: 14:47

Sample: 1 30

Included observations: 30

Presample missing value lagged residuals set to zero.

Variable

Coefficient

Std. Error

t-Statistic

Prob.

ROA

9328560.

59824411

0.155932

0.8775

ROE

-4730825.

30557157

-0.154819

0.8784

ROTB

4161718.

45287213

0.091896

0.9276

FATR

-322171.6

4754810.

-0.067757

0.9466

CR

-47100.84

692141.7

-0.068051

0.9464

QR

-185481.7

4313344.

-0.043002

0.9661

C

297537.2

29299925

0.010155

0.9920

RESID(-1)

-0.206862

0.224316

-0.922189

0.3664

R-squared

0.037217

Mean dependent var

-5.34E-09

Adjusted R-squared

-0.269123

S.D. dependent var

34306388

S.E. of regression

38647979

Akaike info criterion

38.00107

Sum squared resid

3.29E+16

Schwarz criterion

38.37472

Log likelihood

-562.0160

Hannan-Quinn criter.

38.12060

F-statistic

0.121490

Durbin-Watson stat

1.981457

Prob(F-statistic)

0.995935

Introduction

1.0 Introduction

This chapter will be cover about introduction of the research. Section 1.1 discussed about the relationship between firm value and accounting information in Malaysia. Section 1.2 discussed about background of study. Section 1.3 discussed the scope and limitations of study. Section 1.4 described about problem statement of the study. Section 1.5 discussed about objective of study. Section 1.6 will be discussed about scope of study.

Overview of firm value and accounting information

Malaysia stock had a bad perform in 1997 it was signaling the currency crisis in the Asian region, our P.M. Dr. Mahathir Mohamad fighting the speculation against Ringgit Malaysia. MTEN and the imposition of selective exchange controls and pegging of Ringgit Malaysia brought back the confidence in financial markets. When 1999, Malaysia only showed a positive growth, that time Malaysia economy slowly become more and more stable.

Hence, in present research the relationship between accounting information presented in balance sheet, income statement, and cash flow statement and the value of the companies in stock market of Malaysia under consumer product sector has been investigated. In this research in the main hypotheses, the value of companies in stock market is dependent variable and accounting information is independent variable. Determining of companies value is a significant factor in investment process which is obtained by multiplying ending share value in the number of company’s outstanding shares. While in subsidiary hypotheses the value of companies is dependent variable and independent variable as: balance sheet data (assets, capital, liabilities and inventories), income statement data (revenues, costs, non-operating revenues, non operating costs, and tax), and operating cash flow data. Indicators investigated in this research are as follows: current ratio, quick ratio, ratio of total debt, ratio of fixed asset to net worth, return on asset, and return on equity.

The more investors use accounting information, it is expected that rational decisions are made. But the use of accounting information depends on several factors, some of which are related to users of the information and some others related to the quality and quantity of the information.

Overview of accounting information

There are four qualitative characteristics of accounting information that serve as the basis for decision making purposes in accounting which are relevance, reliability, comparability and consistency. These qualities make accounting information understandable and useful for decision and reporting purposes: the goal of financial reporting is to provide useful information to current and potential investors, creditors, and other users of accounting information (e.g., government, standard-setting bodies) to make investment, credit, and other decisions

Besides that, the role of accounting numbers in company valuation has been of fundamental interest to analysts, investors and researchers alike. Much accounting-based valuation has focused on analyzing historical and forecasted accounting numbers (Richardson & Tinaikar, 2004).An accounting number is deemed to be value-relevant if it is significantly related to stock price (the measure of value) (Beaver, 2002). There are four qualitative characteristics of accounting information that serve as the basis for decision making purposes in accounting which are relevance, reliability, comparability and consistency. These qualities make accounting information understandable and useful for decision and reporting purposes: the goal of financial reporting is to provide useful information to current and potential investors, creditors, and other users of accounting information (e.g., government, standard-setting bodies) to make investment, credit, and other decisions

1.3 Scope of study

This study will be emphasize on the time series data of the determinants of firm value which could obtained from the website of Bursa Malaysia under the sector of consumer product. The period covers 3 years from 2010 to 2012 by using yearly data in Malaysia. The companies chosen are 30 companies that under the sector of consumer product in Bursa Malaysia. This study focused on the relationship between stock price and accounting information in Malaysia. The representative of accounting information including balance sheet data, income statement data, and company’s operating cash flow.

1.4 Problem Statement

It is believed that the accounting information plays an important role in reflecting the stock price. Accordingly, this study aims to find out to what extent is the acceptance of accounting information in determining the price of stocks, in the context of firms listed in Bursa Malaysia KL.

Pirie and Smith (2008) have performed an empirical study based on the framework of Ohlson’s (1995) model, using the data of firms listed in FBM KLCI (it was known as Kuala Lumpur Stock Exchange during that time), covering 10 years from 1987 to 1996. The key variables in their model includes equity value, earning per share and the realize earning of next period, as the proxy of linear information dynamics. They conclude that two-way fixed effect model has the most significant explanatory power for the sample data from Malaysia market. Their model explained 74.56% of the variation in share price. The study conducted by Pirie and Smith (2008) covers the time period from 1987 to 1996, before the Asian financial crisis in 1997. The Malaysian securities market was seriously impacted by the Asian financial crisis. After the crisis, Malaysian securities exchange has implemented tighter controls on the listed firms to prevent the speculation activity and to ensure the listed firms are more versatile in facing the financial challenges. For example, the daily stock price variation is limited to 30% versus 50% before crisis. 5Also, no short selling is allowed after the crisis. The tighter controls may cause some shift on the atmosphere of Malaysia security market. Investors and fund managers may have different perception on the accounting variables that affect their investing decisions.

Research Question

The research questions in this study will be concentrated on:

There is a relationship between balance sheet data and companies’ value?

There is a relationship between income statement data and companies’ value?

There is a relationship between companies’ operating cash flow and their value?

Research Objectives

The research objectives in this study are:

To find the relationship of accounting information with the performance of stock market in Malaysia.

To examine the acceptance of selected accounting information with the performance of stock market under the sector of consumer product in Bursa Malaysia

Contribution of study

This research and study enhance the existing literature review about the relationship of accounting information and stock market performance in Malaysia. On the others hand, research also provided an updated data set of factors that affect stock market return in Malaysia.

Chapter 2

Literature Review

2.0 Introduction

This chapter will cover the literature review of the relationship of stock price and accounting information in different countries. Salvary (2007) pointed out that there have been many studies on stock evaluation using accounting information and most of the studies implicitly recognize the use of accounting information as a fundamental variable in stock price determination. Presumably accounting information underlies the fundamental valuation approach employed in the equity market.

2.1 The relationship between balance sheet data and stock price

A balance sheet is a financial statement at a fixed time. It provides a simple summary of what a business owns or is owed. It states what assets the business own and what it owes – liabilities, at a particular date. The balance sheet is used to show how the business is being funded and how those funds are being used.

We can use balance sheet data to fundamental analysis stock investment. Fundamental analysis involves determining a firm’s intrinsic value without reference to the price at which the firm’s equity trades on the stock market (Bauman, 1996). According to this approach, accounting information cause stock prices to change by capturing values toward which market prices drift (Francis and Schipper, 1999). It is not assumed that the market at all times reflects all available information, which means that this approach allows for an inefficient stock market.

RENATO DE C. T. RAPOSO and ADRIANO J. DE O. CRUZ (2002) researched that the relationship between stock prediction and fundamental analysis by using Neural Networks and Fuzzy Logic techniques. They explore that the network indicates if a trader would have to keep, sell or buy a stock using a combination of information extracted from balance sheets (released every three months) and market indicators. In the experiments, they concentrated on a segment of the Brazilian industry, companies from the textile sector. The results show that the network was able to deliver good results depending on the quality of the available data.

Gregory R. Duﬀee(2002) researched that the relationship between the balance sheet and stock price. He explore the theory of balance-sheet effects implies that cross-sectional, betas and book/market ratios should predict the strength of the return-volatility relation. These implications are supported in the data. The results also indicate that the well-known pattern of higher correlations among stock returns in down markets is in large part driven by a positive relation between market returns and idiosyncratic volatility.

2.2 The relationship between income statement data and stock price

The income statement is the financial statement that allows a business, as well as investors, to understand if a company is operating successfully. The income statement is a very important financial document when it comes to valuing securities or getting a feel for the creditworthiness of a company.

Income statement provided that current earnings may not reflect the future earnings growth of a firm has recently motivated researchers to directly incorporate information about future earnings, using analysts’ earnings forecasts, in a return/price-earnings relation. According to the results presented in Dechow et al. (1999) and Liu and Thomas (2000), the analysts’ earnings forecasts seem to have value relevance in the return/price-earnings relation. Both these studies make use of the residual income framework to model the relation between stock prices/returns and ac20 counting earnings. As stated by Kothari (2000), the natural step in studies investigating the returns-earnings relation is to compare the residual income model and its analytical extensions with simpler models, with and without analysts’ forecasts.

2.3 The relationships between company’s operating cash flow and stock price

In finance literature, stock price is believed to be linked to cash flow, and this is supported by the definition of stock and other stock valuation theories, such as efficient market hypothesis (Fama 1970). In spite of the well-accepted belief of the relationship between cash flow and stock price, there are some controversies about whether cash flow is a good value driver in terms of explaining the volatility of stock prices, when compared with other value drivers, such as earnings or dividends. From their research in 2001, Liu, Nissim and Thomas stated that earnings are better than cash flows in explaining the stock price, using the U.S. market data. This result also holds in international markets according to a later paper by Liu, Nissim and Thomas them (Liu, Nissim et al. 2007).

Besides that, Shiller (1981) had stated that stock price in the empirical world sometimes seems to be too volatile to align with this calculation. Among all the stock valuation models, the Efficient Market Hypothesis, the most famous model, will be discussed and the challenge brought by Shiller(1981) and other economists about this model will also be mentioned. Then, different opinions regarding the relationship between stock price and cash flow are presented.

According to Ackert and Smith (1993), narrowly defined cash flow in the variance bounds tests leads to the rejection of efficient market hypothesis, and they proved that it is unable to reject the hypothesis of market efficiency when using a broader cash flow definition. Actually, within the finance literature, dividends include all cash distributions to shareholders. For example, in the seminal work of Miller and Modigliani (Miller and 15Modigliani 1961), cash distributed through share repurchases has the same economic role as ordinary cash dividends (Ackert and Smith 1993). Accounting information is hypothesised to be value if it conveys information that modifies investor expectations of the firms’ future cash flows, and ultimately causes the stock price to change (Scott, 2003).

Data and Methodology

3.0 Introduction

In this chapter, the data, variable, and methodology applied in this research will be further explained. Section 3.1 provides data description whereas Section 3.2 provides variables identification. The econometric methodology employed in this research study will elaborated in Section 3.3.

Econometrics analysis is used to carry out this study where the model is created and regressed accordingly. Econometric analysis can be measured the strength of relationship between dependent variable with all independent variables. Beside the regression model of study also can be used to predict and forecast.

Beside few tests are used to check on the significant of the model under hypothesis testing. Normality test is to determine normal distribution of data set used. Moreover, hypothesis testing is to check on the significance of each independent variable in the model to the dependent variable. While joint significant is test on significance of all independent variables together in the model to dependent variable. Autocorrelation mean the different period of error term have relationship. This kind of disturbance term can be detected by autocorrelation test. Other than discussion of methodology, the data collection for this research is also explained in this chapter.

3.1 Data Description

To study about the relationship between firm value and accounting information, the data of firm value and accounting information used in this research is yearly frequency. The sample period is spanning from 2010 to 2012. My yearly firm value and my accounting information are gets from the finance Yahoo’s website. For my dependent variable is 30 companies value under consumer product sector in Malaysia, whereas for my independent variable is accounting information which are balance sheet (ratio of total debt and fixed assets turnover ratio), income statement (return on asset and return on equity), and operating cash flow (current ratio, quick ratio).

Table 1: Description of sample data

Data

Formulas and data notations

Sample Period

Firm value

Firm value

Jan 2010 to Dec 2012

Balance sheet

ROTD, FATR

Jan 2010 to Dec 2012

Income statement

ROA, ROE

Jan 2010 to Dec 2012

Operating cash flow

CR, QR

Jan 2010 to Dec 2012

3.2 Variables Identification

Considering that availability of data in Malaysia, the following variables as below are included in this research:

3.2.1 Firm value

The data of firm value is collected from the 30 companies under the sector of consumer product in the stock market of Malaysia. Determining of companies value is a significant factor in investment process which is obtained by multiplying ending share value in the number of company’s outstanding shares.

3.2.2 Description of accounting information

Ratio on total debt

I used the balance sheet data to calculate the ratio on total debt and fixed asset turnover ratio. Ratio on total debt is the financial ratio that indicates the percentage of a company's assets that are provided via debt. High debt ratio shows an unhealthy condition of the company. In some conditions, company may perform better when they maintain the debt ratio at a healthy level.

Fixed asset turnover ratio

I used the balance sheet data to calculate the ratio on total debt and fixed asset turnover ratio. Fixed asset turnover ratio is the ratio that measures a company's ability to generate net sales from fixed-asset investments. A higher fixed-asset turnover ratio shows that the company has been more effective in using the investment in fixed assets to generate revenues.

Return on asset

I used the income statement data to calculate the return on asset and return on equity. Return on asset indicates that how profitable a company is relative to its total assets. Return on asset gives an idea as to how efficient management is at using its assets to generate earnings. A higher return on asset means that the company gets high profit and good performance.

Return on equity

I used the income statement data to calculate the return on asset and return on equity. Return on equity indicates that measures a corporation's profitability by revealing how much profit a company generates with the money shareholders have invested. If ROEs is between 15% and 20% considered as good.

Current ratio

I used the operating cash flow data to calculate the current ratio and quick ratio. Current ratio is the liquidity ratio that measures a company's ability to pay short-term debt with its short term asset. If the value of current ratio is less than 1, it indicates that the firm may have difficulty meeting current debt.

Quick ratio

I used the operating cash flow data to calculate the current ratio and quick ratio. Quick ratio is the liquidity ratio that measures a company’s ability to pay short term debt with its most liquidity short term asset (cash). A high value of quick ratio indicates that the company is high position of able to meet any short term debt.

3.3 Methodology

In order to model and evaluate the relationship among time series variables, the econometric tests computed in this study are based on a proper multivariate time series model, which is least square method and correlation analysis. It is noted that for the data analysis part as presented in chapter 4, firm value of 30 companies in stock market Malaysia is firm value and the accounting information which are balance sheet (ratio of total debt and fixed assets turnover ratio), income statement (return on asset and return on equity), and operating cash flow (current ratio, quick ratio).

Table 2: Symbol and Formula of Each Variable

ROTB = Total Debts/Total Assets

FATR = Sales/Fixed Assets

ROA = Net Income/Total Assets

ROE = Net Income/Total Shareholder’s equity

CR = Current Assets/Current Liabilities

QR = (Current Assets – Inventories)/Current Liabilities

All data was inserted into Microsoft Excel Sheet, then using Microsoft Excel Program to compute the rate of changes from month to month, the changes rate then will exported to E-views software for testing and investigation purposes. In this research, E-views software will be employed to conduct all the statistical analyses.

To examine the causality relationship between firm value and accounting information included in this research, the examination procedures and model specification (if any) were applied are briefly explained as below:

3.4 Test conducted in the Study

3.4.1 Descriptive Statistic Test

Descriptive statistic test carried out in E-views are mainly used to provide summary and quantitative explanation about data in a manageable structure. The descriptive statistics comprising mean, median, standard deviation, skewness, and kurtosis reveal basis features and characteristic of variables. Both mean and median whereas is the midpoint of a data series when the number are arranged in order. The purpose of estimated central tendency is to discover most representative single score of the entire group. Besides that, standard deviation is an accurate estimation of dispersion with the mean value. However, skewness estimates whether the data is symmetric in the data set. Nevertheless, kurtosis is a quantitative measure of peakedness is a distribution.

3.4.2 Correlation Analysis

Correlation analysis refers to the relationship between two random variables of statistical relationship involving dependence. This is a method to show the relationship either is negative or positive relationship; when the value of coefficient negative it means there is a negative relationship between these two variable, however when the value of coefficient is positive, it means there is a positive relationship between two variable. Regression analysis involves identifying the relationship between dependent variable and more than one independent variable.

3.4.3 Normality Test

Normality tests are used to test whether a data set is well-modeled by a normal distribution or not. It measured the fitness of a model to a set of data. The hypothesis for this test is as following:

H0: The data set is normally distributed.

Ha: The data set is not normally distributed.

Where:

= Jarque-Bera

S = skewness

K = kurtosis

n = number of observation

χ2 is compute from equation above. While the critical value of this test is distributed as with 2 degree of freedom is 5.99.

When χ2 value is larger than critical value, H0 will be rejected which mean the data set is not normally distributed. Else the data set is normally distributed while χ2 value is smaller than critical value.

3.4.4 Least Square Method

Least Square Method examines the relationship between independent variable and dependent variable. Besides that, Least Square method will show each independent variable how significant toward dependent variable. The least squares method is the most widely used procedure for developing estimates of the model parameters.

Yt = α0 + α1(X1)t-a + α2(X2)t-a + α3(X3) t-a + α4(X4) t-a + α5(X5) t-a …… + αn(Xn) t-a + εt

[t-stat(X1)] [t-stat(X2)] [t-stat(X3)] [t-stat(X4)] [t-stat(X5)] [t-stat(Xn)]

Where:

Yt = dependent variable at level

Xt-a = independent variable at t differences

α = coefficient

εt = error term

3.4.5 Heteroscedasticity Test

The error term has no constant variance when there is a heteroscedasticity problem. The heteroscedasticity problem is detected using the Heteroscedasticity White Test. Null hypothesis represents that there is no heteroscedasticity problem while alternative hypothesis represents that there is heteroscedasticity problem. In order to determine whether the error term has a constant variance, we compare the value obtained from the White Test (Observations*R-squared) with the chi-square critical value at the respective degree of freedom. Reject null hypothesis if the estimated value is greater than the critical value and vice versa. Heteroscedasticity problem can be solved using the Weighted Least Square method (WLS).

H0: The error term has constant variance (No heteroscedasticity).

HA: The error term has no constant variance (Heteroscedasticity).

3.4.6 Autocorrelation Test

Autocorrelation test is conducted to ensure that there is no serial correlation between the error terms of the sample data. For this test, the null hypothesis suggests that there is no autocorrelation between the error terms while the alternative hypothesis suggests that there is autocorrelation between the error terms. To test the autocorrelation problem, we compare Durbin Watson statistic to its optimal value, which is 2. There is no serial correlation between the error terms when the Durbin Watson statistic is close to 2. Autocorrelation problem can be solved by using the Generalized Least Square method (GLS).

H0: There is no autocorrelation between the error terms.

HA: There is autocorrelation between the error terms.

Chapter 4 : Analysis of Data

4.1 Descriptive analysis

FIRM VALUE

ROA

ROE

ROTB

FATR

CR

QR

Mean

84002845

0.095359

0.291086

0.382058

2.603997

5.592852

2.377368

Median

77523925

0.042435

0.150559

0.364758

2.341294

2.532840

1.668854

Maximum

2.31E+08

1.899586

3.427709

0.936754

6.095522

78.84766

10.73968

Minimum

40000000

-0.12292

-0.46721

0.107617

0.238397

0.746506

0.203966

Std. Dev.

43364012

0.350559

0.694671

0.205352

1.810328

14.08324

2.510846

Skewness

1.477091

4.747953

3.263181

0.673724

0.525837

4.924007

1.937232

Kurtosis

5.605529

25.04936

15.13849

3.119593

2.109646

26.12092

6.126991

Jarque-Bera

19.39496

720.4331

237.4204

2.287395

2.373433

789.4504

30.98694

Probability

0.000061

0.000000

0.000000

0.318639

0.305222

0.000000

0.000000

Sum

2.52E+09

2.860760

8.732576

11.46175

78.11991

167.7856

71.32103

Sum Sq. Dev.

5.45E+16

3.563853

13.99446

1.222909

95.04133

5751.792

182.8261

Observations

30

30

30

30

30

30

30

Table 4.1 Descriptive Analysis

Table 4.1 shows the descriptive statistical table related with mean, median, maximum and minimum value, standard deviation, skewness and kurtosis of changes of the variables.

The mean annual growth of each variables are positive value. From the table, Firm Value is relatively high volatility among all. But, ROA is relatively lower volatility among all.

Skewness of each variable is positive but only have two variables are approximately zero and less than 1. Data is considered to be right skewed.

Kurtosis of each variable is shown in positive. Data is considered to be peaked distribution. According to the information has the highest kurtosis.

4.2 Correlation Matrix

FIRM VALUE

ROA

ROE

ROTB

FATR

CR

QR

FIRM VALUE

1

0.2326

0.4048

-0.3164

0.2278

0.0151

0.2742

ROA

0.2326

1

0.9316

-0.0849

0.0955

0.0615

0.3134

ROE

0.4048

0.9316

1

-0.1691

0.1696

0.0410

0.3215

ROTB

--0.3164

-0.0849

-0.1691

1

-0.4282

-0.2959

-0.5246

FATR

0.2278

0.0955

0.1696

-0.4282

1

0.3894

0.2831

CR

0.0151

0.0615

0.0410

-0.2959

0.3894

1

0.5944

QR

0.2742

0.3134

0.3215

-0.5246

0.2831

0.5944

1

Table 4.2 Correlation Matrix

Table 4.2 shows the correlation among all variables.

From the table 4.2, all the independent variables are positive correlation relations with the dependent variable expect the ROTB variable is negative correlation relation with the dependent variable. The relationship between the ROA and the ROE has the highest correlation relation. And the lowest correlation value is the relationship between the ROE and CR.

4.3 Normality Test

Table 4.3.1 Residual Graph

The consistency of the data can be studied through residual graph. When the range of value of Y-axis, which represent residual spread, is within 3 < x < -3, then data series is consistent. It is shown that the Y-axis of data series is outside the range of 3 < x < -3. According to residual graph shows the data series is not consistent.

Table 4.3.2 Jarque-Bera test

Jarque-Bera Test is used to examine whether the data is normally distributed. According to table regarding the critical value says that 2 degrees of freedom at 5% is 5.99.

It is stated that the value of Jarque-Bera is 1.241999. The value is smaller than the critical value. Hence, according to Jarque-Bera test, null hypothesis cannot be rejected. The data is normally distributed.

4.4 Ordinary Least Square Model

Dependent Variable: FIRM_VALUE

Method: Least Squares

Date: 01/06/13 Time: 14:16

Sample: 1 30

Included observations: 30

Variable

Coefficient

Std. Error

t-Statistic

Prob.

ROA

-1.24E+08

58770952

-2.108629

0.0461

ROE

77991414

30025357

2.597518

0.0161

ROTB

-20376020

44915041

-0.453657

0.6543

FATR

1716349.

4726515.

0.363132

0.7198

CR

-474682.3

688006.6

-0.689939

0.4971

QR

3578874.

4294620.

0.833339

0.4132

C

70580069

29202731

2.416900

0.0240

R-squared

0.374120

Mean dependent var

84002845

Adjusted R-squared

0.210847

S.D. dependent var

43364012

S.E. of regression

38522111

Akaike info criterion

37.97233

Sum squared resid

3.41E+16

Schwarz criterion

38.29927

Log likelihood

-562.5849

Hannan-Quinn criter.

38.07692

F-statistic

2.291376

Durbin-Watson stat

2.285871

Prob(F-statistic)

0.070163

Table 4.4 Ordinary Least Square Model

Firm Valuet=β0+β1ROAt-1+β2ROEt-1+β3ROTBt-1+β4FATR t-1+β5CR t-1+β6QR t-1+εt

Firm Valuet= 70580069 -1.24E+08ROAt-1+77991414ROEt-1-20376020ROTBt-1

[2.417] [-2.109]** [2.596] ** [-0.454]

+1716349FATR t-1-474682.3CR t-1+3578874QR t-1+29202731εt

[0.363] [-0.690][0.833]

R2 = 0.374 Adj. R2 = 0.211 DW = 2.286

** Significant at 5% level

Ordinary least square method determines the relationship between dependent variable and independent variables.

The R-Square value of the result is 0.374120. R-Square statistic indicates the "goodness of fit" of the model. It is the percentage of total variation in the dependent variables which explained by independent variables.

Statistical significance of model coefficients must be determined as the regression line of data may not fit the data points accurately. The coefficient estimate divided by the standard error equals to the t-statistics. The most important explanatory variable are ROA and ROE, with statistically significant with 95% confidence level.

Three of the variables, ROE, FATR, and QR are having positive relationship with Firm Value. Inversely, the others variables, ROA, ROTB, and CR are having negative relationship with Firm Value.

4.5Heteroskedasticity Test

Heteroskedasticity Test: White

F-statistic

0.893509

Prob. F(6,23)

0.5160

Obs*R-squared

5.670862

Prob. Chi-Square(6)

0.4611

Scaled explained SS

4.656976

Prob. Chi-Square(6)

0.5885

Test Equation:

Dependent Variable: RESID^2

Method: Least Squares

Date: 01/06/13 Time: 14:41

Sample: 1 30

Included observations: 30

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

9.07E+14

7.80E+14

1.163278

0.2566

ROA^2

1.09E+15

3.74E+15

0.290150

0.7743

ROE^2

-4.81E+14

1.15E+15

-0.419745

0.6786

ROTB^2

-9.06E+14

2.14E+15

-0.423423

0.6759

FATR^2

3.54E+13

3.91E+13

0.903861

0.3754

CR^2

-6.44E+11

4.01E+11

-1.606825

0.1217

QR^2

2.70E+13

1.72E+13

1.575699

0.1288

R-squared

0.189029

Mean dependent var

1.14E+15

Adjusted R-squared

-0.022529

S.D. dependent var

1.93E+15

S.E. of regression

1.96E+15

Akaike info criterion

73.45817

Sum squared resid

8.80E+31

Schwarz criterion

73.78511

Log likelihood

-1094.872

Hannan-Quinn criter.

73.56276

F-statistic

0.893509

Durbin-Watson stat

1.714339

Prob(F-statistic)

0.515977

Table 4.5Heteroskedasticity Test

Heteroskedasticity occurs when the variance of the unobservable error u is not constant. It acts an efficient estimator of re-weighting data correctly. Since the White test statistic has a probability(6,23) of 0.5160, which is greater than 5% critical value, we can reject null hypothesis that there is heteroscedasticity.

4.6 Autocorrelation Test

Breusch-Godfrey Serial Correlation LM Test:

F-statistic

0.850432

Prob. F(1,22)

0.3664

Obs*R-squared

1.116520

Prob. Chi-Square(1)

0.2907

Test Equation:

Dependent Variable: RESID

Method: Least Squares

Date: 01/06/13 Time: 14:47

Sample: 1 30

Included observations: 30

Presample missing value lagged residuals set to zero.

Variable

Coefficient

Std. Error

t-Statistic

Prob.

ROA

9328560.

59824411

0.155932

0.8775

ROE

-4730825.

30557157

-0.154819

0.8784

ROTB

4161718.

45287213

0.091896

0.9276

FATR

-322171.6

4754810.

-0.067757

0.9466

CR

-47100.84

692141.7

-0.068051

0.9464

QR

-185481.7

4313344.

-0.043002

0.9661

C

297537.2

29299925

0.010155

0.9920

RESID(-1)

-0.206862

0.224316

-0.922189

0.3664

R-squared

0.037217

Mean dependent var

-5.34E-09

Adjusted R-squared

-0.269123

S.D. dependent var

34306388

S.E. of regression

38647979

Akaike info criterion

38.00107

Sum squared resid

3.29E+16

Schwarz criterion

38.37472

Log likelihood

-562.0160

Hannan-Quinn criter.

38.12060

F-statistic

0.121490

Durbin-Watson stat

1.981457

Prob(F-statistic)

0.995935

Table 4.6 Autocorrelation Test

-no autocorrelation

Chapter 5

Discussion and Conclusion

5.0 Introduction

Section 5.2 will discuss about conclusion of this study, whereas Section 5.2 will discuss about the limitation of the study.

5.1 Conclusion

In every year ended, the company will provide the financial report showed about their company performance. Everybody can get the accounting information on the financial report. Based on the accounting information given, investor can evaluate the company firm value. The aim of this study was to examine whether the relationship and the causality relationship exists between firm value and accounting information. To achieve this main purpose, the yearly data period of 2010 to 2012 employed to examine the causality relationship among firm value and accounting information indicators. Before conducting correlation matrix, normality test, heteroskedasticity test, and autocorrelation test are carried out in order to fulfill the research result. Besides that, statistical tool such as descriptive statistic is also used to determine the basic characteristics and nature of variables tested in this study.

The purpose of this study is to verify the relationship between firm value of 30 companies under the sector of consumer product in the stock market of Malaysia and accounting information (CR, QR, ROE, ROA, FATR, and ROTB). To test the relationship Least Square method was applied in this study. There are some of the variable result match with the expected relationship, such as return on asset (ROA) and return on equity (ROE). Quick ratio, return on equity, and ratio on total debt have a negative relationship toward firm value, whereas current ratio, return on asset and fixed asset turnover ratio have a positive relationship toward firm value.

After the all test analysis, the accounting information plays an important role to find the company value. The result showed that, ROA and ROE are more significant than other CR, QR, FATR and ROTB indicator.

Firm value is a measurement the performance of 30 stocks under the consumer product sector in Malaysia. Investor may use the relationship of the variables to hedge their risk by comparing which sectors are moving more and in what direction. This could help investor to diversify their risk, when they diversify the risk; they may obtain low risk high return. This research studies the relationship of firm value and accounting information (CR, QR, ROA, ROE, FATR, and ROTB). Therefore Investor may link to the accounting information and business conditions to hedge their risk. Last but not least, the result of this research will be beneficial and useful information to investor when they know the exactly relationship between accounting information and firm value in Malaysia.

5.2 Limitation of the study

Since the yearly data over period of 2010-2012 only provide a total of 210 observations, the limitation of this study is lack of data to the most detail like monthly data. Besides that, there is lack of observation for research variables in Malaysia. Moreover, instead of using six financial indicators, more indicators should be taken into account in order to come out with a better econometric model. Likewise, the others financial indicators such as earning per share, ratio of goods to working capital, return on investment, assets turnover ratio, return on total capital, and return on owner’s equity. On the other hand, this econometric model should take in others country in ASEAN to know more about ASEAN country trend on affecting their stock performance, due to lack of data availability, these country doesn’t included in this research. Besides that, in reality, the historical information is not a good measurement, due to some others factor affect the historical information, and then the relationship direction will affected.

Appendix

Appendix 1: Descriptive Statistic

FIRM VALUE

ROA

ROE

ROTB

FATR

CR

QR

Mean

84002845

0.095359

0.291086

0.382058

2.603997

5.592852

2.377368

Median

77523925

0.042435

0.150559

0.364758

2.341294

2.532840

1.668854

Maximum

2.31E+08

1.899586

3.427709

0.936754

6.095522

78.84766

10.73968

Minimum

40000000

-0.12292

-0.46721

0.107617

0.238397

0.746506

0.203966

Std. Dev.

43364012

0.350559

0.694671

0.205352

1.810328

14.08324

2.510846

Skewness

1.477091

4.747953

3.263181

0.673724

0.525837

4.924007

1.937232

Kurtosis

5.605529

25.04936

15.13849

3.119593

2.109646

26.12092

6.126991

Jarque-Bera

19.39496

720.4331

237.4204

2.287395

2.373433

789.4504

30.98694

Probability

0.000061

0.000000

0.000000

0.318639

0.305222

0.000000

0.000000

Sum

2.52E+09

2.860760

8.732576

11.46175

78.11991

167.7856

71.32103

Sum Sq. Dev.

5.45E+16

3.563853

13.99446

1.222909

95.04133

5751.792

182.8261

Observations

30

30

30

30

30

30

30

Appendix 2: Correlation Matrix

FIRM VALUE

ROA

ROE

ROTB

FATR

CR

QR

FIRM VALUE

1

0.2326

0.4048

-0.3164

0.2278

0.0151

0.2742

ROA

0.2326

1

0.9316

-0.0849

0.0955

0.0615

0.3134

ROE

0.4048

0.9316

1

-0.1691

0.1696

0.0410

0.3215

ROTB

--0.3164

-0.0849

-0.1691

1

-0.4282

-0.2959

-0.5246

FATR

0.2278

0.0955

0.1696

-0.4282

1

0.3894

0.2831

CR

0.0151

0.0615

0.0410

-0.2959

0.3894

1

0.5944

QR

0.2742

0.3134

0.3215

-0.5246

0.2831

0.5944

1

Appendix 3: Residual Graph

Appendix 4: Jarque-Bera test

Appendix 5: Ordinary Least Square Model

Dependent Variable: FIRM_VALUE

Method: Least Squares

Date: 01/06/13 Time: 14:16

Sample: 1 30

Included observations: 30

Variable

Coefficient

Std. Error

t-Statistic

Prob.

ROA

-1.24E+08

58770952

-2.108629

0.0461

ROE

77991414

30025357

2.597518

0.0161

ROTB

-20376020

44915041

-0.453657

0.6543

FATR

1716349.

4726515.

0.363132

0.7198

CR

-474682.3

688006.6

-0.689939

0.4971

QR

3578874.

4294620.

0.833339

0.4132

C

70580069

29202731

2.416900

0.0240

R-squared

0.374120

Mean dependent var

84002845

Adjusted R-squared

0.210847

S.D. dependent var

43364012

S.E. of regression

38522111

Akaike info criterion

37.97233

Sum squared resid

3.41E+16

Schwarz criterion

38.29927

Log likelihood

-562.5849

Hannan-Quinn criter.

38.07692

F-statistic

2.291376

Durbin-Watson stat

2.285871

Prob(F-statistic)

0.070163

Appendix 6: Heteroskedasticity Test

Heteroskedasticity Test: White

F-statistic

0.893509

Prob. F(6,23)

0.5160

Obs*R-squared

5.670862

Prob. Chi-Square(6)

0.4611

Scaled explained SS

4.656976

Prob. Chi-Square(6)

0.5885

Test Equation:

Dependent Variable: RESID^2

Method: Least Squares

Date: 01/06/13 Time: 14:41

Sample: 1 30

Included observations: 30

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

9.07E+14

7.80E+14

1.163278

0.2566

ROA^2

1.09E+15

3.74E+15

0.290150

0.7743

ROE^2

-4.81E+14

1.15E+15

-0.419745

0.6786

ROTB^2

-9.06E+14

2.14E+15

-0.423423

0.6759

FATR^2

3.54E+13

3.91E+13

0.903861

0.3754

CR^2

-6.44E+11

4.01E+11

-1.606825

0.1217

QR^2

2.70E+13

1.72E+13

1.575699

0.1288

R-squared

0.189029

Mean dependent var

1.14E+15

Adjusted R-squared

-0.022529

S.D. dependent var

1.93E+15

S.E. of regression

1.96E+15

Akaike info criterion

73.45817

Sum squared resid

8.80E+31

Schwarz criterion

73.78511

Log likelihood

-1094.872

Hannan-Quinn criter.

73.56276

F-statistic

0.893509

Durbin-Watson stat

1.714339

Prob(F-statistic)

0.515977

Appendix 7: Autocorrelation Test

Breusch-Godfrey Serial Correlation LM Test:

F-statistic

0.850432

Prob. F(1,22)

0.3664

Obs*R-squared

1.116520

Prob. Chi-Square(1)

0.2907

Test Equation:

Dependent Variable: RESID

Method: Least Squares

Date: 01/06/13 Time: 14:47

Sample: 1 30

Included observations: 30

Presample missing value lagged residuals set to zero.

Variable

Coefficient

Std. Error

t-Statistic

Prob.

ROA

9328560.

59824411

0.155932

0.8775

ROE

-4730825.

30557157

-0.154819

0.8784

ROTB

4161718.

45287213

0.091896

0.9276

FATR

-322171.6

4754810.

-0.067757

0.9466

CR

-47100.84

692141.7

-0.068051

0.9464

QR

-185481.7

4313344.

-0.043002

0.9661

C

297537.2

29299925

0.010155

0.9920

RESID(-1)

-0.206862

0.224316

-0.922189

0.3664

R-squared

0.037217

Mean dependent var

-5.34E-09

Adjusted R-squared

-0.269123

S.D. dependent var

34306388

S.E. of regression

38647979

Akaike info criterion

38.00107

Sum squared resid

3.29E+16

Schwarz criterion

38.37472

Log likelihood

-562.0160

Hannan-Quinn criter.

38.12060

F-statistic

0.121490

Durbin-Watson stat

1.981457

Prob(F-statistic)

0.995935