DETERMINANTS TAKEOVER TARGET

OF PUBLIC FIRMS IN INDONESIA 2009 - 2013

Submitted By :

Dawud Gede Wicaksono D.

12/343653/PEK/18069

MASTER OF MANAGEMENT PROGRAM

FACULTY OF ECONOMICS AND BUSINESS

UNIVERSITAS GADJAH MADA

2014

ii

SUMMARY

DETERMINANTS TAKEOVER TARGET

OF PUBLIC FIRMS IN INDONESIA 2009 - 2013

Submitted By :

Dawud Gede Wicaksono D.

12/343653/PEK/18069

Approved By :

Prof. Gudono, Ph.D., CMA

iii

TABLE OF CONTENTS

1. Title and Study Program Identification ............................................................... i

2. Approval of Supervisor ...................................................................................... ii

3. Table of Contents .............................................................................................. iii

4. Abstract .............................................................................................................. 1

5. Chapter 1: Introduction ...................................................................................... 2

6. Chapter 2: Literature Review ............................................................................. 2

7. Chapter 3: Research Methods ............................................................................ 4

8. Chapter 4: Results and Discussion ..................................................................... 6

9. Chapter 5: Conclusions, Recommendations and References ............................. 8

1

ABSTRACT

This study aims to examine financial variables that significantly determine

(determinants) public firm in Indonesia to become target in a takeover. Hypothesis

testing is done using binomial logistic regression (logit biner) model on 32 merged

& acquired firms and 32 non-merged and non-acquired firms listed on the

Indonesia Stock Exchange from 2009 to 2013.

The study concluded that ratio of return on capital employed, average

excess return, leverage ratio (debt to equity ratio), and Tobin's Q ratio showed

negative relation and statistically significant in determination of public firms to

become takeover targets. Meanwhile, sales growth, size of the firm's assets, and

dividend payout ratio are not significant factor related to takeover likelihood.

Results of logistic regression analysis also support market for corporate control

hypothesis works in a takeover event of a public listed firms in Indonesia.

Keywords: determinants, mergers and acquisitions, logit biner, hypothesis market

for corporate control

2

I. INTRODUCTION

1.1 Background

Empirical studies on determinants of mergers and acquisitions (M&A)

target firms in developing countries are still in early stages. Majority of research

adopt theories and models in developed countries. Findings by Barai and Mohanty

(2012) in India, Erdogan (2012) in Turkey, Lin et. al. (2012) in China, and

Rudiatmo (2012) in Indonesia show determinants of takeover target firms are

different than studies done in developing countries such as Cai et. al. (2011) in

Australia, Bhabra (2008) in the United States, and Powell (2004) in the UK.

Studies of takeover targets in the Indonesia context has been few and far

from adequate. This is surprising, since M&A is a growing phenomenon in

Indonesia. Previous studies show characteristics of the M&A target firms in each

countries are different. This prompted the author to conduct similar research.

1.2 Research Objectives

The purpose of this study is to examine the financial variables that

significantly determine (determinants) public firm in Indonesia to become target in

a takeover.

II. LITERATURE REVIEW

2.1 Accomplished Research

The discussion of this sub-chapter is divided into two things, namely: (a)

methodological issues, and (b) significant characteristics of target firms in earlier

studies

Previous studies show analysis are generally done in two methods,

discriminant analysis and logistic regression. Logistic model more preferable in

binary case (Pohar et. al., 2004), its prediction power no significantly different

between the two (Barnes, 2000), and logistic model doesn’t require normality

assumption to be met (Gudono, 2011). In addition to that, selection method of non-

targets is generally debated in various studies. Sampling by match number of

targets and non-targets (state-based) is preferred over using the entire samples of

firms in non-target group (choice-based). State-based method was chosen by

3

Palepu (1986), Sood & Kaur (2004), and Barai & Mohanty (2012), whereas Bhabra

(2008) and Powell (2004) used the last method. State-based is justified when

“noisy” data might have great influence to the samples.

Studies that use financial data to identify target firm are not new in the

developed countries. Similar research in United States, Britain, and Australia show

characteristics of target firm has a small asset size (Cudd & Duggal, 2000; Powell,

2004; Brar et al, 2009; Hamouda & Hamza, 2010), undervaluation (Brar et.al .,

2009; Cai et al, 2011), low financial leverage ratio (Cudd & Duggal, 2000), low

profitability (Barnes, 2000; Cai et al, 2011), and low sales growth (Bhabra, 2008;

Brar et al, 2009).

Meanwhile, research in India shows target firms has the characteristics of high

market to book value, high sales growth (Sood & Kaur, 2004; Barai & Mohanty).

In China, findings of Lin et al (2012) reveals the target firms tend to be a great

asset to the low profitability, while Rudiatmo (2012) in Indonesia concluded size of

asset does not affect the takeover. Despite studies in developing countries are still

new and adopt theories and models in developed countries, these studies show

characteristics of M&A target firms in each countries are different

2.2 Research Hypothesis

Associated with the research questions, the following research hypothesis

was formulated:

Palepu (1986) mentions the acquisition is a mechanism to replace the failed

management to maximize the value of the firm. Management who were failed to

maximize their value will be replaced by more efficient ones (Powell, 2004).

Thus, the hypothesis (H1) is : There is a negative relation between the management

efficiency with possible takeover.

Firms with a lower market value relative to the replacement value of assets

considered attractive by acquirer. The lower the value of Tobin's Q, the more

potential for acquisition.

Thus, the hypothesis (H2) is : There is a negative relation between Tobin's Q with

possible takeover .

4

Findings in India by Barai & Mohanty (2012), Kumar & Rajib (2007) show

growing firms considered attractive by acquirer.

Thus, the hypothesis (H3) is : There is a positive relation between sales growth

with possible takeover.

Firms with low debt capacity is less likely to default in the future, hence

firms with low financial leverage ratio is seen as attractive targets.

Thus, the hypothesis (H4) is : There is a negative relation between leverage ratio

with possible takeover.

It is generally known that chances of being acquired decreases with

increasing size of the asset. Larger firms size redeemed at more expensive cost than

small ones.

Thus, the hypothesis (H5) is : There is a relation between the asset size with

possible takeover.

According to the free cash flow (FCF) hypothesis (Jensen, 1986), managers

prefer to invest excess cash rather than pay dividends. However, a low dividend

payout is not favored by shareholders. Low payout ratio also shows firm has little

FCF, which means no ammunition for anti-takeover strategy.

Thus the hypothesis (H6) is : There is a negative relation between the dividend

payout ratio with possible takeover.

III. RESEARCH METHODS

3.1 Data Collection Methods

Sources of data are secondary data from public firms’ financial performance

reports issued by the Indonesia Stock Exchange from 2009 to 2013. The sampling

method used in this research is purposive sampling and state-based. Defined

criteria are: non-bank, non-state-owned firms, listed on Stock Exchange during

period of January 2009 till December 2013; have complete financial statements and

stock trading data during three years prior to the acquisition; and a change in

ownership above 51%. Meanwhile, the same amount of non-target samples are

selected using random sampling in same sub-industry group with target firms.

5

3.2 Data Analysis Methods

Analysis was performed using descriptive statistics and binary logit

regression. This study basically compares the financial characteristics of takeover

and non-takeover firms, dependent variable is dichotomous dummy variable with

value 1 as “target” and 0 for “non-target”.

Research model is estimated using a binomial logistic regression as follows:

...........(1)

Whereby: P = takeover probability

βi = parameter estimates for the independent variable-i

Xi = independent variables tested

After the stage of estimating the model, the ability of each model to predict

takeover was tested for each firm in the sample. The “odds” of acquisition is likely

occurred or odds ratio is defined as probability of "success" divided by probability

of "failed". Odds ratio is calculated using the following formula

)exp(-1

ratio ii xP(x)

P(x)Odds

...........(2)

Independent variables tested in the study include:

Table 1: Independent variables

No Hypothesis Variable Definition Expect

ations

Variable Construction

1 Management

inefficiency

ROCE Return on

Capital

Employed

-

Debt)Current -Asset Total(

EBITROCE

2 Management

inefficiency

AER Average

Excess

Return

-

n

returnreturn

AER i

iindexistock

3

1

)()(

3 Under-

valuation

APQ Approx.

Tobin’s Q

-

TotalAsset

DEBTpreferenMarketEqAP

. Q

4 Growth GRSALES Sales

Growth

+

n

salesGRSALES

growth

kk XXXxP

xP

...

)(1

)(log 2211

6

5 Financial

leverage

DER Debt to

equity ratio

-

Equity

DebtDER

6 Asset size SIZE Log (asset

size)

-/+ SIZE = Log (Total Asset)

7 Cash flow

payout

PAYR Dividend

payout

ratio

-

EPS

dividend PAYR

IV. RESULTS AND DISCUSSION

Number of samples obtained under the criteria in (III) is a total of 64 firms

(target and non-target) dispersed into nine industry sectors in Indonesia Stock

Exchange which can be seen in Table 2 below.

Table 2: Sample Distribution Target Firms Based on Industries

No. Industry Total Percentage

1 Agriculture 1 3,13%

2 Mining 2 6,25%

3 Basic Industry & Chemicals 7 21,87%

4 Miscellaneous Industries 2 6,25%

5 Consumer Goods Industry 2 6,25%

6 Property, Real Estate, & Building Construction 4 12,50%

7 Infrastructure, Utilities, & Transportation 2 6,25%

8 Finance (leasing) 2 6,25%

9 Trade, Service & Investment 10 31,25%

TOTAL 32

Results of the descriptive statistics in Table 3 shows the efficiency of the

target group, as measured by the ROCE variable lower than non-targets. AER,

APQ, and DER have higher values on non-target group compared to target group.

It shows target firm has a tendency of low excess return, low Tobin's Q ratio and

low financial leverage ratio.

Table 3: Sample Descriptive Statistics Target Non-Target

Variable Mean Std. Dev. Mean Std. Dev.

ROCE 0,04 0,21 0,18 0,25

AER 1,22 67,28 23,67 70,19

APQ 0,65 0,78 1,08 0,85

GRSALES 31,30 129,22 34,76 52,72

DER 2,70 4,29 3,40 5,14

SIZE 5,57 0,86 5,81 0,63

PAYR 0,11 0,22 0,46 1,76

7

Logit biner models were tested using Wald statistic test, Wald scores follow

a Chi-square distribution with df = 1, α = 5%. Variable was concluded significant if

Z ≥ χ2 (1, α) or statistically can also be determined by comparing the significance

(p-value) with degree of confidence (α = 5%).

The test results are shown in Table 4.

Table 4: Logit Biner Model Parameter Estimation

Hypothesis Variable Expectations Coefficients Exp(B)

Constant Const

3,649

sig 0,193

Management inefficiency ROCE -

-4,592 0,010

sig 0,023*

Management inefficiency AER -

-0,010 0,990

sig 0,037*

Undervaluation APQ -

-0,634 0,530

sig 0,088**

Growth GRSALES -

-0,001 0,999

sig 0,708

Financial Leverage DER -

-0,141 0,869

sig 0,046*

Asset size SIZE -/+

-0,348 0,706

sig 0,470

Cashflow payout PAYR -

-0,274 0,760

sig 0,593

R2 Nagelkerke 0,344

Likelihood Ratio 19,066

*. Sig. at α=5%; **. Sig. at α=10%

Table 4 shows characteristics of ROCE (return on capital employed), low

AER (average excess return), Tobin's Q ratio and financial leverage ratio (DER)

has a negative and significant relation in determining firms to become takeover

targets. Meanwhile, growth rate of sales, asset size, and dividend payout ratio has

no effect on takeover events.

Negative value of coefficient showed firms with inefficient management

(low ROCE) resulted in a decrease in stock price (low AER) with undervaluation

assessment (low APQ) in the eyes of the market. Low dividend payout ratio (high

FCF) but managed by inefficient management will be disciplined by market

8

through acquisition mechanisms. Thus, hypothesis testing results show market for

corporate control work on public firms takeover events in Indonesian.

Model summary of logit biner model indicated by Nagelkerke R2 value is

equal to 0.344. It can be interpreted that as many as 34.4% of variation in the

dependent variable is explained by variables while the remaining 65.6% is

explained by other causes. The low degree of explanatory is caused partly by

highly diverse M&A motives and cannot be explained entirely by the model, model

ignores characteristics of the acquirers and other external influences that can affect

M&A in certain industry.

Odds ratio of takeover likelihood [table 4, column Exp(B)]. is as follow:

odds of firms likely acquired is increase 0.1 times for every 1 unit of return of

capital employed decrease; odds of firms likely acquired is increase 0.99 times for

every 1% of average excess return decrease; odds of firms likely acquired is

increase 0.53 times for every 1 unit of Tobin’s Q ratio decrease; and odds of firms

likely acquired is increase 0.869 times for every 1 unit of debt equity ratio

decrease.

V. CONCLUSIONS, RECOMMENDATIONS, AND REFERENCES

5.1 Conclusions

Based on results of analysis described in previous chapter; market for

corporate control hypothesis works on takeover public firms events in Indonesia, as

evidenced by proving hypothesis (H1) about management inefficiencies.

Hypothesis market for corporate control mentions that there is a positive relation

between efficiency of management and the firm's stock price. Return on capital

employed (ROCE) variable is a measure of efficiency while average excess return

variabel as a proxy for firm's rate of return, showed a significant negative relation

against possible takeover of the firm. Furthermore, acquirers tend to choose firms

which bears characteristics of low return on capital employed, low value of average

excess return, low Tobin's Q ratio, low DER ratio, low sales growth, small asset

size and low dividend payout ratio.

9

5.2 Suggestions

1. For academics

First, non-target sample in this study was not controlled by the control

variables. In following studies, the use of other control variables which do not

affect the research model should be considered. Secondly, this study did not

include external factors that can influence events such as M&A trend in certain

industry. Third, this study aims to find determinants that affect public firms to

become takeover targets. This finding can be followed up by making a prediction

model of takeover targets in Indonesia.

2. For management

This study is expected to provide input for management to (a) conduct

M&A strategies by looking at the characteristics of potential target firms for

takeover strategis, and (2) detect and anticipate if the firm is considered as takeover

targets.

3. For investors

In some previous studies, accuracy of predictive models is still low.

However, the investor has the opportunity to gain cumulative abnormal return if

they can anticipate a takeover events and choosing potensial firms into stock

portfolio.

5.3 References

Barai, P, and Mohanty, P. 2012. “Predicting Acquisitions in India”. VIKALPA, Vol.

37, No. 3, 29-49.

Barnes, P. 2000. “The Identification Of U.K. Takeover Targets Using Published

Historical Cost Accounting Data. Some Empirical Evidence Comparing

Logit With Linear Discriminant Analysis And Raw Financial Ratios With

Industry Relative Ratios”. International Review of Financial Analysis, Vol.

9, No. 2, 147-62.

Bhabra, G. S. 2008. “Potential Targets : Analysis of Stock Price Reactions to

Acquisition Program Announcements”. J Econ Finan, Vol. 32, pp. 158–

175.

Brar G., Giamouridis, D., and Liodakis, M. 2009. “Predicting European Takeover

Targets”. European Financial Management, Vol. 15, No.2, 430–50.

10

Cai, S. W., Balachandran, B., and Dempsey, M. 2011. “The financial profiles of

takeover target firms and their takeover predictability: Australian

evidence”. Corporate Ownership and Control, Vol. 8, No. 3.

http://ssrn.com/abstract=1884877

Cudd, M., and Duggal, M. 2000. “Industry Distributional Characteristics of

Financial Ratios: An Acquisition Theory Application”. The Financial

Review, No. 41, 105-20.

Erdogan. A. I. 2012. “The Determinants of Mergers and Acquisitions: Evidence

from Turkey”. International Journal of Economics and Finance, Vol. 4,

No. 4, 72-7

Gudono. (2011). Analisis Data Multivariat. Yogyakarta : BPFE

Hamouda, Z. and Hamza, T. 2010. “Predicting French Takeover Targets: New

Empirical Evidence”. Working Paper. http://ssrn.com/abstract=1575983.

Lin, C. J., Chang, W. S., and Lu, Y. C. 2012. “Do Common Characteristics Exist

for Mergers and Acquisition Targets Listed in the Chinese Stock Market?”

dalam: Asian FA 2012 International Conference Program, accessed at

June 25, 2014 from http://asianfa2012.mcu.edu.tw/fullpaper/10119.pdf.

Powell, R. G. 2004. “Takeover Prediction Models and Portfolio Strategies: A

Multinomial Approach”. Multinational Finance Journal, Vol. 8, No. 1, 35-

7.

Palepu, K. G. 1986. “Predicting Takeover Targets: A Methodological And

Empirical Analysis”. Journal of Accounting & Economics, Vol. 8, No. 1, 3-

35.

Pohar, M., Blas, M., and Turk, S. 2004. "Comparison of Logistic Regression and

Linear Discriminant Analysis: A Simulation Study". Metodološki zvezki,

Vol. 1, No. 1, 2004, pp. 143-161

Rudiatmo, I. (2012). Pengaruh Mekanisme Corporate Governance Dan

Karakteristik Perusahaan Terhadap Kemungkinan Menjadi Target Akuisisi

(Tesis, Fakultas Ekonomi Program MM, Universitas Indonesia, 2012).

Jakarta : Universitas indonesia.

Sood, G. S., and Kaur, S. 2004. “Predicting Corporate Takeovers in India: An

Empirical Analysis”. The Journal of Business Perspective, Vol. 8, pp. 57.