“ANALYSIS OF THE EFFECT OF INFLATION RATE, INTEREST...

“ANALYSIS OF THE EFFECT OF INFLATION RATE, INTEREST

RATE AND EXCHANGE RATE ON STOCK RETURN OF CONSUMER

GOODS AND PROPERTY AND REAL ESTATE SECTOR IN

INDONESIA STOCK EXCHANGE (IDX) 2006-2010”

Submitted by:

Ariningtyas Widyasnia Agustina

Student ID:107081101584

INTERNATIONAL UNDERGRADUATE PROGRAM

MANAGEMENT DEPARTMENT

FACULTY OF ECONOMICS AND BUSINESS

UIN SYARIF HIDAYATULLAH JAKARTA

2011

i

ACKNOWLEDGEMENT

My greatest gratitude to Allah SWT, the Grandest and Almighty, the Most

Gracious and the Most Merciful, for giving me the chance and ability to complete this

thesis,and for all the miracles He has granted to my life. May the talent You have

bestowed upon me will not got to waste. My greatest ggratitude to Prophet

Muhammad SAW for the teachings and love he has spread to all the creatures in this

whole universe. May we all always under his guidance.

First and foremost, I would like to thank to my thesis supervisors; Prof. Dr.

Ahmad Rodhoni, MM and Mr. Tirmidzi Taridi,MBA for their help, time, contribution

and effort in providing guidance and constructive suggestion to perform this study,

and for the understanding and support they had given.

I would also like to thank to the head of International Program, Faculty of

Economics and Business, UIN Syarif Hidayatullah Jakarta, Mr. Arief Mufraini, Lc.

M.Si, and his deputies Mr. Ahmad Dumiyati. Also, thanks to Kak Sugih, for his

patients in accomodating all of my administrative needs. Thank you.

This thesis will never be completed without the continous support and prays

from my friends, collagues, and people around me. Thank you for the people in my

current internship place, Bank Indonesia, the place where this thesis born. I would

also like to thank to my bestfriends in International Program Batch 4; Sukria, Andrea

Ardilla, Wike Vidya, Pramayassya, Fitra, Weldan, Muhammad Kharisma and all,

whose name can’t be mentioned one by one. Individually, thank you to Aprima Arta

for his continous support and understanding during my hardest time in making this

thesis.

Last, but not least, I dedicated this thesis to the greatest gift God has given to

my life, my family. My father, H. Sumarno, for his support and increadible patient,

you are the soul of our life. My mother, Hj. Sri Irawati, for her unlimited guidance

that always keeping me on the right track. My brother, Rifki Hidayat Rahkumulyo,

and my sister, Arindani Tri Ramadhani, for made my life so blessed and colorful

everyday. Thank you.

Ariningtyas W. Agustina

Jakarta, March 20 2011

ii

ABSTRAK

Penelitian ini bertujuan untuk menganalisis pengaruh suku bunga, inflasi, dan

nilai tukar terhadap tingkat pengembalian saham pada sektor properti dan real estate

serta sektor industry barang konsumsi periode 2006-2010. Penelitian ini

menggunakan model regresi linear berganda. Berdasarkan hasil penelitian, secara

parsial suku bunga berpengaruh signifikan negatif terhadap tingkat pengembalian

saham property dan real estate dan tingkat pengembalian saham barang konsumsi

.Inflasi tidak berpengaruh terhadap tingkat pengembalian saham property dan real

estate serta saham barang konsumsi. Nilai tukar berpengaruh signifikan negatif

terhadap tingkat pengembalian saham property dan real estate serta saham barang

konsumsi. Hasil penelitian juga menunjukkan bahwa suku bunga, inflasi, dan nilai

tukar berpengaruh lebih besar terhadap tingkat pengembalian saham property dan real

estate daripada saham barang konsumsi.

Kata Kunci: tingkat pengembalian saham, suku bunga, inflasi, nilai tukar.

iii

ABSTRACT

This research shows the effect of inflation rate, interest rate, and exchange

rate on stock return of property and real estate and consumer goods sector in period

2006-2010. A multiple regression model is applied to test the significance of

influence between the independent variable and the dependent variable. Based on the

result, partially interest rate has a negative effect to stock return of property and real

estate sector and consumer goods sector. Inflation has no effect to stock return of

property and real estate and consumer goods sector, while exchange rate has negative

effect to stock return of property and real estate sector. The result shows that the

inflation rate, exchange rate, and interest rate have a bigger effect on stock return of

property and real estate sector than consumer goods sector.

Keywords: stock return, interest rate, inflation, exchange rate.

iv

LIST OF CONTENTS

CHAPTER 1: INTRODUCTION __________________________________________ 1

A. Background ________________________________________________________ 1

B. Problem Formulation ________________________________________________ 6

C. Research Objectives _________________________________________________ 8

D. Usefullness of the Research ___________________________________________ 8

CHAPTER 2: LITERATURE REVIEW ____________________________________ 13

A. Fundamental of Capital Market _______________________________________ 13

1. History of Indonesian Capital Market _________________________________ 13

2. Capital Market Definition __________________________________________ 14

3. Capital Market Instrument __________________________________________ 15

B. Investment ________________________________________________________ 16

C. Stock Rate of Return ________________________________________________ 19

D. Risks ____________________________________________________________ 21

E. Inflation __________________________________________________________ 23

1. Relationship between inflation rate and stock return ______________________ 27

F. Interest Rate _______________________________________________________ 26

2.6.1 Relationship between interest rate and stock return ____________________ 27

2.6.2 SBI (Sertifikat Bank Indonesia) Rate _______________________________ 28

G. Exchange Rate ____________________________________________________ 30

H. Previous Researches ________________________________________________ 31

I. Research Framework and Hypotheses __________________________________ 37

v

CHAPTER 3: RESEARCH METHODOLOGY ______________________________ 40

A. Data Collection ____________________________________________________ 40

1. Unit of Analysis and Research Sampling _______________________________ 40

2. Types of Data ____________________________________________________ 41

B. Research Models ___________________________________________________ 41

3.3 Operational Variable _______________________________________________ 42

3.3.1 Stock’s Rate of Return __________________________________________ 42

3.3.2 Inflation Rate __________________________________________________ 43

3.3.2 Exchange Rate _________________________________________________ 44

3.3.2 Interest Rate ___________________________________________________ 45

3.4 Data Analysis Technique ____________________________________________ 45

3.4.1 Normality Test _________________________________________________ 46

3.4.1.1 Jarque-Bera Test of Normality _________________________________ 47

3.4.2 Classical Assumption Test _______________________________________ 48

3.4.2.1 Heteroscedastic Test _________________________________________ 50

3.4.2.2 Autocorrelation Test _________________________________________ 53

3.4.2.3 Multi-Collinearity Test _______________________________________ 54

3.4.3 Hypothesis Test ________________________________________________ 55

3.4.3.1 T Test ____________________________________________________ 55

3.4.3.2 F Test ____________________________________________________ 55

3.4.3.3 R Square (R2) Test __________________________________________ 56

3.4.4.4 Adjusted R Squared Test _____________________________________ 56

3.5 Research Design __________________________________________________ 57

CHAPTER 4: RESEARCH FINDINGS AND ANALYSIS _____________________ 58

4.1 Brief Introduction _________________________________________________ 58

vi

4.2 Descriptive Statistic ________________________________________________ 59

4.3 Normality Test ____________________________________________________ 60

4.4 Classical Assumption Test ___________________________________________ 62

4.4.1 Heteroscedasticity Test __________________________________________ 62

4.4.2 Auto-Correlation Test ___________________________________________ 64

4.4.3 Multi-Collinearity Test __________________________________________ 65

4.5 Multiple Linear Regression Model ____________________________________ 66

4.6 Hypothesis Testing ________________________________________________ 67

4.6.1 T-Test _______________________________________________________ 67

4.6.2 f Test ________________________________________________________ 81

4.6.3 R-Squared (R2) ________________________________________________ 81

4.6.4 Adjusted R-Squared ____________________________________________ 82

4.7 The comparison between macroeconomic factors’ influences towards the

stock’s return on consumer goods and property and real estate sector _________ 83

CHAPTER 5: CONCLUSION AND IMPLICATIONS ________________________ 84

5.1 Conclusion ______________________________________________________ 84

5.2 Implication of Study _______________________________________________ 85

5.2.1 For the Investor ________________________________________________ 85

5.2.1 For the Researcher ______________________________________________ 85

5.3 Limitation _______________________________________________________ 86

REFERENCES

APPENDIX I

APPENDIX I

vii

LIST OF FIGURES

2.1 Indonesia Interest Rate _____________________________________________ 29

2.2 Research Model ___________________________________________________ 39

3.1 Research Framework _______________________________________________ 57

4.1 Histogram Residual, Consumer Goods Sector ___________________________ 61

4.2 Histogram Residual, Property and Real Estate Sector _____________________ 61

4.3 Graph of Ex. Rate & Stock Return Movement 1 __________________________ 69

4.4 Graph of Inflation Rate & Stock Return Movement 1 _____________________ 71

4.5 Graph of SBI Rate & Stock Return Movement 1 _________________________ 73

4.6 Graph of Exchange Rate & Stock Return Movement 2 ____________________ 76

4.7 Graph of Inflation Rate & Stock Return Movement 2 _____________________ 78

4.8 Graph of SBI Rate & Stock Return Movement 2 _________________________ 80

viii

LIST OF TABLES

2.1 Indonesia Interest Rate _____________________________________________ 29

2.2 Research Model ___________________________________________________ 39

1.3 Research Objectives ________________________________________________ 8

1.4 Research Benefits __________________________________________________ 8

1.4 Research Benefits __________________________________________________ 8

4. Descriptive Statistics ________________________________________________ 59

4.2 Heteroscedasticity Test for Property and Real Estate Sector ________________ 63

4.3 Auto-Correlation Test for Consumer Goods Sector _______________________ 63

4.4 Auto-Correlation Test for Property and Real Estate Sector _________________ 64

4.5 Multi-Collinearity Test _____________________________________________ 65



4.8 Output Multiple Linear Regression Property and Real Estate Sector _________ 74

4.9 Comparison of Coefficient Regression _________________________________ 83

ix

LIST OF FORMULAS

2.1 Simple Returns ___________________________________________________ 19

2.2 Continously Compounded Return _____________________________________ 20

2.3 Consumer Price Index ______________________________________________ 24

2.4 Wholeseller Price Index _____________________________________________ 24

2.5 Inflation Rate _____________________________________________________ 25

2.6 Relationship between nominal and interest rate __________________________ 27

2.7 Capital Price Index Model ___________________________________________ 35

2.8 Multi Index Model _________________________________________________ 36

3.1 Model 1 _________________________________________________________ 41

3.2 Model 2 _________________________________________________________ 41

3.3 Continously Compounded Method ____________________________________ 42

3.4 Inflation Rate _____________________________________________________ 43

3.5 Change in Inflation Rate ____________________________________________ 43

3.6 Change in Exchange Rate ___________________________________________ 44

3.7 Change in Interest Rate _____________________________________________ 45

3.8 Jarque-Bera Test of Normality _______________________________________ 47

3.9 Zero Mean Value of Disturbance _____________________________________ 48

3.10 Homoscedasticity or equal variance of ui ______________________________ 49

3.11 No autocorrelation between the disturbances ___________________________ 49

x

3.12 Zero covariance between ui and Xi ____________________________________ 49

3.13 Regression Model ________________________________________________ 51

3.14 Auxiliary Regression Model ________________________________________ 51

3.15 Chi Square Distribution ____________________________________________ 52

3.16 R-Squared Test __________________________________________________ 56

“In the name of Allah, the Most Gracious, the Most Merciful....

All praise be to God alone, the Lord of all the world, the All-Merciful, the Ever Mercy-giving,

and the Master of the Day of Judgement.

You alone we worship, and You alone we ask for help.

Guide us on the right path,

The path of whom You have blessed, not of those who incur You anger, nor of those who go

astray.......”

(QS Al-Fathihah : 1-7)

APPENDIX I

Consumer Goods Return

Date

Consumer

Goods Index

Price

Consumer

Goods Return

12/30/2005 280.928

1/31/2006 288.583 0.026884331

2/28/2006 292.458 0.013338327

3/31/2006 293.905 0.004935519

4/28/2006 328.354 0.110835709

5/31/2006 294.929 -0.107357645

6/30/2006 299.316 0.014765222

7/31/2006 313.201 0.045345285

8/31/2006 331.074 0.055496758

9/29/2006 343.543 0.03697037

10/31/2006 349.721 0.017823408

11/24/2006 347.347 -0.006811413

12/29/2006 392.458 0.122105245

1/31/2007 390.251 -0.005639403

2/28/2007 385.22 -0.012975522

3/30/2007 385.827 0.001574483

4/30/2007 391.795 0.015349661

5/31/2007 411.99 0.050260333

6/29/2007 324.964 -0.23728467

7/31/2007 453.843 0.334036916

8/31/2007 415.462 -0.088360169

9/28/2007 421.425 0.014250672

10/31/2007 429.804 0.019687465

11/30/2007 426.853 -0.006889599

12/28/2007 436.039 0.021291997

1/31/2008 438.132 0.004788546

2/29/2008 430.084 -0.018539696

3/31/2008 405.011 -0.060066311

4/30/2008 394.385 -0.026586638

5/30/2008 414.54 0.049841882

6/30/2008 398.285 -0.040001642

7/31/2008 390.81 -0.018946321

8/29/2008 396.007 0.013210379

9/29/2008 381.36 -0.037688077

10/31/2008 321.919 -0.16944385

11/28/2008 320.9 -0.003170413

12/30/2008 326.843 0.018350385

1/30/2009 337.847 0.033113197

2/27/2009 346.155 0.024293521

3/31/2009 351.269 0.01466566

4/30/2009 381.322 0.082091851

5/29/2009 433.732 0.128782669

6/30/2009 495.732 0.133608626

7/31/2009 591.202 0.176122294

8/31/2009 559.178 -0.055689904

9/30/2009 597.628 0.066500639

10/30/2009 575.396 -0.037909987

11/30/2009 625.728 0.083857272

12/30/2009 671.305 0.070307807

1/29/2010 699.783 0.041546708

2/26/2010 717.932 0.02560457

3/31/2010 738.141 0.027760006

4/30/2010 821.588 0.10710419

5/31/2010 889.807 0.079765532

6/30/2010 959.04 0.074928199

7/30/2010 1,001.30 0.043122649

8/31/2010 1,005.91 0.004595432

9/30/2010 1,176.56 0.15670104

10/29/2010 1,179.98 0.00290171

11/30/2010 1,070.01 -0.097826604

12/30/2010 1,094.65 0.022765686

Property and Real Estate Return

Date

Property and

Real Estate

Index Price

Property and

Real Estate

Return

12/29/2005 64.12

1/31/2006 70.484 0.094629406

2/28/2006 71.33 0.011931263

3/31/2006 79.331 0.106311977

4/28/2006 88.956 0.114512893

5/31/2006 81.352 -0.089356447

6/30/2006 77.433 -0.049372372

7/31/2006 77.942 0.006551914

8/31/2006 78.985 0.013293001

9/29/2006 83.775 0.058876673

10/31/2006 90.316 0.075179998

10/24/2006 90.467 0.001670511

12/29/2006 122.918 0.306532323

1/31/2007 123.101 0.00148769

2/28/2007 136.185 0.101009099

3/30/2007 143.243 0.050528233

4/30/2007 168.687 0.163502438

5/31/2007 201.037 0.175444044

6/29/2007 211.718 0.05176623

7/31/2007 247.47 0.156034162

8/31/2007 225.648 -0.0923131

9/28/2007 242.834 0.073401819

10/31/2007 247.309 0.018260485

11/30/2007 232.089 -0.063517648

12/28/2007 251.816 0.081577743

1/31/2008 229.563 -0.09252116

2/29/2008 229.517 -0.000200401

3/31/2008 195.603 -0.159890006

4/30/2008 177.721 -0.09587219

5/30/2008 184.272 0.036198022

6/30/2008 168.528 -0.089311019

7/31/2008 174.699 0.035962585

8/29/2008 164.414 -0.060676856

9/29/2008 142.421 -0.143600177

10/31/2008 101.346 -0.340247055

11/28/2008 105.632 0.041420951

12/30/2008 103.489 -0.020496029

1/30/2009 96.026 -0.074846339

2/27/2009 96.558 0.005524876

3/31/2009 99.742 0.032442988

4/30/2009 112.318 0.118747282

5/29/2009 130.986 0.153756313

6/30/2009 144.787 0.10017325

7/31/2009 159.975 0.099753856

8/31/2009 157.959 -0.012682047

9/30/2009 162.285 0.027018543

10/30/2009 153.985 -0.052498854

11/30/2009 143.635 -0.069579836

12/30/2009 146.8 0.021795757

1/29/2010 153.491 0.044570817

2/26/2010 150.231 -0.021467824

3/31/2010 166.378 0.102088199

4/30/2010 182.123 0.090419975

5/31/2010 154.504 -0.164462297

6/30/2010 163.384 0.055883272

7/30/2010 168.259 0.029401201

8/31/2010 170.904 0.015597536

9/30/2010 192.768 0.120385398

10/29/2010 202.413 0.048822771

11/30/2010 203.223 0.003993734

12/30/2010 203.097 -0.000620201

Data of Exchange Rate

Year Month Sell Buy Mid Change

2005 December 9879 9781 9830

2006 January 9442 9348 9395 -0.0443

February 9276 9184 9230 -0.0176

March 9120 9030 9075 -0.0168

April 8819 8731 8775 -0.0331

May 9266 9174 9220 0.05071

June 9347 9253 9300 0.00868

July 9115 9025 9070 -0.0247

August 9146 9054 9100 0.00331

September 9281 9189 9235 0.01484

October 9156 9064 9110 -0.0135

November 9211 9119 9165 0.00604

December 9065 8975 9020 -0.0158

2007 January 9135 9045 9090 0.00776

February 9206 9114 9160 0.0077

March 9164 9072 9118 -0.0046

April 9128 9038 9083 -0.0038

May 8872 8784 8828 -0.0281

June 9099 9009 9054 0.0256

July 9232 9140 9186 0.01458

August 9457 9363 9410 0.02438

September 9183 9091 9137 -0.029

October 9149 9057 9103 -0.0037

November 9423 9329 9376 0.02999

December 9466 9372 9419 0.00459

2008 January 9337 9245 9291 -0.0136

February 9096 9006 9051 -0.0258

March 9263 9171 9217 0.01834

April 9280 9188 9234 0.00184

May 9365 9271 9318 0.0091

June 9271 9179 9225 -0.01

July 9164 9072 9118 -0.0116

August 9199 9107 9153 0.00384

September 9425 9331 9378 0.02458

October 11050 10940 10995 0.17242

November 12212 12090 12151 0.10514

December 11005 10895 10950 -0.0988

2009 January 11412 11298 11355 0.03699

February 12040 11920 11980 0.05504

March 11633 11517 11575 -0.0338

April 10767 10659 10713 -0.0745

May 10392 10288 10340 -0.0348

June 10276 10174 10225 -0.0111

July 9970 9870 9920 -0.0298

August 10110 10010 10060 0.01411

September 9729 9633 9681 -0.0377

October 9593 9497 9545 -0.014

November 9527 9433 9480 -0.0068

December 9447 9353 9400 -0.0084

2010 January 9412 9318 9365 -0.0037

February 9382 9288 9335 -0.0032

March 9161 9069 9115 -0.0236

April 9057 8967 9012 -0.0113

May 9226 9134 9180 0.01864

June 9128 9038 9083 -0.0106

July 8997 8907 8952 -0.0144

August 9086 8996 9041 0.00994

September 8969 8879 8924 -0.0129

October 8973 8883 8928 0.00045

November 9058 8968 9013 0.00952

December 9036 8946 8991 -0.0024

Data of SBI Rate

Year Month

SBI

Rate Change

2005 December 0.1275

2006 January 0.1275 0

February 0.1274 -0.0008

March 0.1273 -0.0008

April 0.1274 0.00079

May 0.125 -0.0188

June 0.125 0

July 0.1225 -0.02

August 0.1175 -0.0408

September 0.1125 -0.0426

October 0.1075 -0.0444

November 0.1025 -0.0465

December 0.0975 -0.0488

2007 January 0.095 -0.0256

February 0.0925 -0.0263

March 0.09 -0.027

April 0.09 0

May 0.0875 -0.0278

June 0.085 -0.0286

July 0.0825 -0.0294

August 0.0825 0

September 0.0825 0

October 0.0825 0

November 0.0825 0

December 0.08 -0.0303

2008 January 0.08 0

February 0.0793 -0.0088

March 0.0796 0.00378

April 0.0799 0.00377

May 0.0831 0.04005

June 0.0873 0.05054

July 0.0923 0.05727

August 0.0928 0.00542

September 0.0971 0.04634

October 0.1098 0.13079

November 0.1124 0.02368

December 0.1083 -0.0365

2009 January 0.0977 -0.0979

February 0.0874 -0.1054

March 0.0821 -0.0606

April 0.0764 -0.0694

May 0.0725 -0.051

June 0.0695 -0.0414

July 0.0671 -0.0345

August 0.0658 -0.0194

September 0.0648 -0.0152

October 0.0649 0.00154

November 0.0647 -0.0031

December 0.0646 -0.0015

2010 January 0.0645 -0.0015

February 0.0641 -0.0062

March 0.0632 -0.014

April 0.0625 -0.0111

May 0.0637 0.0192

June 0.0629 -0.0126

July 0.067 0.06518

August 0.067 0

September 0.0645 -0.0373

October 0.0645 0

November 0.065 0.00775

December 0.063 -0.0308

Data of Inflation Rate

Year Month Inflation Change in Inflation

2005 December -0.04

2006 January 1.36 -35

February 0.58 -0.573529412

March 0.03 -0.948275862

April 0.05 0.666666667

May 0.37 6.4

June 0.45 0.216216216

July 0.45 0

August 0.33 -0.266666667

September 0.38 0.151515152

October 0.86 1.263157895

November 0.34 -0.604651163

December 1.21 2.558823529

2007 January 1.04 -0.140495868

February 0.62 -0.403846154

March 0.24 -0.612903226

April -0.16 -1.666666667

May 0.10 -1.625

June 0.23 1.3

July 0.72 2.130434783

August 0.75 0.041666667

September 0.80 0.066666667

October 0.79 -0.0125

November 0.18 -0.772151899

December 1.10 5.111111111

2008 January 1.77 0.609090909

February 0.65 -0.632768362

March 0.95 0.461538462

April 0.57 -0.4

May 1.41 1.473684211

June 2.46 0.744680851

July 1.37 -0.443089431

August 0.51 -0.627737226

September 0.97 0.901960784

October 0.45 -0.536082474

November 0.12 -0.733333333

December -0.04 -1.333333333

2009 January -0.07 0.75

February 0.21 -4

March 0.22 0.047619048

April -0.31 -2.409090909

May 0.04 -1.129032258

June 0.11 1.75

July 0.45 3.090909091

August 0.56 0.244444444

September 1.05 0.875

October 0.19 -0.819047619

November -0.03 -1.157894737

December 0.33 -12

2010 January 0.84 1.545454545

February 0.30 -0.642857143

March -0.14 -1.466666667

April 0.15 -2.071428571

May 0.29 0.933333333

June 0.97 2.344827586

July 1.57 0.618556701

August 0.76 -0.515923567

September 0.44 -0.421052632

October 0.06 -0.863636364

November 0.60 9

December 0.92 0.533333333

APPENDIX II

EVIEWS 5 OUTPUT (Consumer Goods Sector)

MULTIPLE REGRESSION

Dependent Variable: Y

Method: Least Squares

Date: 02/07/11 Time: 16:04

Sample: 2006M01 2010M12

Included observations: 60

Variable Coefficient Std. Error t-Statistic Prob.

C 0.016385 0.008014 2.044428 0.0456

X1 -0.693450 0.226342 -3.063726 0.0034

X2 3.36E-05 0.001505 0.022330 0.9823

X3 -0.483347 0.223922 -2.158550 0.0352

R-squared 0.286679 Mean dependent var 0.022268

Adjusted R-squared 0.248466 S.D. dependent var 0.067858

S.E. of regression 0.058827 Akaike info criterion -2.764084

Sum squared resid 0.193796 Schwarz criterion -2.624461

Log likelihood 86.92251 F-statistic 7.502025

Durbin-Watson stat 2.016865 Prob(F-statistic) 0.000263

AUTO CORRELATION TEST

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.150862 Probability 0.860328

Obs*R-squared 0.333385 Probability 0.846460

White Heteroskedasticity Test:



DESCRIPTIVE STATISTICS

Y C X1 X2 X3

Mean 0.022268 1.000000 -0.000831 -0.483316 -0.011012

Median 0.003819 1.000000 -0.003781 -0.076498 -0.007476

Maximum 0.193360 1.000000 0.172425 9.000000 0.130793

Minimum -0.169444 1.000000 -0.098840 -35.00000 -0.105425

Std. Dev. 0.067858 0.000000 0.037145 5.192304 0.036939

Skewness 0.352901 NA 1.717417 -5.028711 0.705108

Kurtosis 3.747713 NA 10.70772 34.36123 6.116995

Jarque-Bera 2.643079 NA 178.0175 2711.697 29.26092

Probability 0.266724 NA 0.000000 0.000000 0.000000

Sum 1.336056 60.00000 -0.049875 -28.99897 -0.660716

Sum Sq.

Dev. 0.271681 0.000000 0.081406 1590.641 0.080504

Observations 60 60 60 60 60

MULTI-COLLINEARITY TEST

Dependent Variable: X1


Date: 02/07/11 Time: 16:09

Sample: 2006M01 2010M12



C 0.003920 0.004661 0.841008 0.4039

X2 0.001321 0.000863 1.530626 0.1314

X3 0.373475 0.121341 3.077900 0.0032

R-squared 0.170209 Mean dependent var -0.000831








Date: 02/07/11 Time: 16:09

Sample: 2006M01 2010M12



C -0.601305 0.700757 -0.858079 0.3944

X1 29.87945 19.52107 1.530626 0.1314

X3 -12.97014 19.63019 -0.660724 0.5115



S.E. of regression 5.176819 Akaike info criterion 6.174965

Sum squared resid 1527.569 Schwarz criterion 6.279683

Log likelihood -182.2490 F-statistic 1.176746




Date: 02/07/11 Time: 16:09

Sample: 2006M01 2010M12



C -0.010978 0.004512 -2.432994 0.0181

X1 0.381592 0.123978 3.077900 0.0032

X2 -0.000586 0.000887 -0.660724 0.5115







0

1

2

3

4

5

6

7

8

9

-0.10 -0.05 -0.00 0.05 0.10 0.15

Series: Residuals

Sample 2006M01 2010M12

Observations 60

Mean -4.97e-18

Median -0.005805

Maximum 0.145997

Minimum -0.105790

Std. Dev. 0.057312

Skewness 0.487487

Kurtosis 2.684504

Jarque-Bera 2.625284

Probability 0.269108

EVIEWS 5 OUTPUT (Property and Real Estate Sector)

MULTIPLE REGRESSION



Date: 02/07/11 Time: 10:59

Sample: 2006M01 2010M12



C 0.008209 0.011062 0.742136 0.4611

X1 -1.065215 0.312405 -3.409723 0.0012

X2 -0.000461 0.002077 -0.221818 0.8253

X3 -0.898840 0.309064 -2.908261 0.0052







AUTO CORRELATION TEST




Test Equation:

Dependent Variable: RESID


Date: 02/07/11 Time: 10:57

Presample missing value lagged residuals set to zero.


C 0.000104 0.011158 0.009328 0.9926

X1 0.009653 0.322981 0.029888 0.9763

X2 -0.000342 0.002124 -0.161050 0.8727

X3 0.030717 0.315445 0.097378 0.9228

RESID(-1) 0.039112 0.138929 0.281525 0.7794

RESID(-2) 0.141898 0.137328 1.033283 0.3061

R-squared 0.021305 Mean dependent var 6.94E-19

Adjusted R-squared -0.069315 S.D. dependent var 0.079104








MULTI-COLLINEARITY TEST



Date: 02/07/11 Time: 11:01

Sample: 2006M01 2010M12



C 0.003920 0.004661 0.841008 0.4039

X2 0.001321 0.000863 1.530626 0.1314

X3 0.373475 0.121341 3.077900 0.0032









Date: 02/07/11 Time: 11:02

Sample: 2006M01 2010M12



C -0.601305 0.700757 -0.858079 0.3944

X1 29.87945 19.52107 1.530626 0.1314

X3 -12.97014 19.63019 -0.660724 0.5115



S.E. of regression 5.176819 Akaike info criterion 6.174965

Sum squared resid 1527.569 Schwarz criterion 6.279683

Log likelihood -182.2490 F-statistic 1.176746




Date: 02/07/11 Time: 11:02

Sample: 2006M01 2010M12



C -0.010978 0.004512 -2.432994 0.0181

X1 0.381592 0.123978 3.077900 0.0032

X2 -0.000586 0.000887 -0.660724 0.5115







Descriptive Statistics

Y X1 X2 X3

Mean 0.019215 -0.000831 -0.483316 -0.011012

Median 0.024407 -0.003781 -0.076498 -0.007476

Maximum 0.306532 0.172425 9.000000 0.130793

Minimum -0.340247 -0.098840 -35.00000 -0.105425

Std. Dev. 0.099661 0.037145 5.192304 0.036939

Skewness -0.521612 1.717417 -5.028711 0.705108

Kurtosis 5.198717 10.70772 34.36123 6.116995

Jarque-Bera 14.80669 178.0175 2711.697 29.26092

Probability 0.000609 0.000000 0.000000 0.000000

Sum 1.152927 -0.049875 -28.99897 -0.660716

Sum Sq. Dev. 0.586010 0.081406 1590.641 0.080504

Observations 60 60 60 60

Normality Test

0

2

4

6

8

10

12

14

-0.1 -0.0 0.1 0.2

Series: Residuals

Sample 2006M01 2010M12

Observations 60

Mean 6.94e-19

Median 0.001880

Maximum 0.238803

Minimum -0.167392

Std. Dev. 0.079104

Skewness 0.363828

Kurtosis 3.382237



1

CHAPTER 1

INTRODUCTION

A. Background

Investment is the commitment of money or capital to the purchase of

financial instruments or other assets so as to gain profitable returns in the form of

interest, income (dividends), or appreciation (capital gains) of the value of the

instrument (Sullivan,2003). It is related to saving or deferring consumption.

Investment is involved in many areas of the economy, such as business

management and finance no matter for households, firms, or governments.

Investment comes with the risk of the loss of the principal sum. The investment

that has not been thoroughly analyzed can be highly risky with respect to the

investment owner because the possibility of losing money is not within the

owner's control. The fundamental principle of investment‟s risk is the more risks

possible, the higher return we receive. The level of risk, especially in Indonesian

Capital Market is mainly affected by economics and political factors. Therefore,

to make an investment decision, investors need to be carefully analyzing the

investment‟s instrument they are most likely to choose. This analysis aims to

minimize the risk of the investment.

According to Bodi, Kane, and Marcus (2007, p.112) in their books “Investment”,

there are two types of risks that commonly faced by the investor when investing

http://en.wikipedia.org/wiki/Money

http://en.wikipedia.org/wiki/Interest

http://en.wikipedia.org/wiki/Income

http://en.wikipedia.org/wiki/Dividends

http://en.wikipedia.org/wiki/Capital_gains

http://en.wikipedia.org/wiki/Saving_%28money%29

http://en.wikipedia.org/wiki/Consumption_%28economics%29

http://en.wikipedia.org/wiki/Economy

http://en.wikipedia.org/wiki/Business_management


http://en.wikipedia.org/wiki/Finance

http://en.wiktionary.org/wiki/principal#Noun

2

their money; systematic and unsystematic risk. Interest rates, recession and wars

all represent sources of systematic risk because they affect the entire market and

cannot be avoided through diversification. While unsystematic risk is company or

industry specific risk that is inherent in each investment. Systematic risk can be

mitigated only by being hedged, while unsystematic risk can be reduced through

appropriate diversification.

Analyses of the financial markets are broadly divided between two schools of

thought; fundamental and technical analysis (Graham, et al: 1999). Fundamental

analysis is a method of evaluating the intrinsic value of a company, and hence the

share price, by researching and examining the corporate financial statements, the

business itself, industry outlook and so on. Conventional wisdom dictates that the

price of a stock that is trading for less than its intrinsic value should rise and the

price of a stock that is trading for higher than its intrinsic value should fall

(Chong, 2006). On the other hand, Technical analysis is a method of evaluating

future price of a stock based on statistical analysis of past behaviors of the stock.

Behaviors of stock include trading volume, moving averages, price trends and

other types of charts. Technical analysis assumes that the price chart frequently

anticipates news and other fundamental events before they become public

knowledge. It is used by market analysts to identify the beginning of an uptrend,

entry point, beginning of a downtrend and the exit point.

After the financial crisis hit Indonesia in 1998, the macroeconomic condition of

this country becomes unstable. The interest rate rose substantially and makes

3

investor prefers investing in the form of deposit rather than in stock market.

Inflation also rose and thus makes the exchange rate of Indonesian Rupiah to US

dollar dropped. On the article publish by daily news agency Reuters1, this

condition is likely to be happened again in 2008. This crisis is the effect resulted

from subprime mortgage case in US in 2007. The immediate cause or trigger of

the crisis was the Fed policy to decrease the interest rate which peaked in

approximately 2005–2006 (Lahart, 2007). Already-rising default rates on

"subprime" and adjustable rate mortgages (ARM) began to increase quickly

thereafter. As banks began to increasingly give out more loans to potential home

owners, the housing price also began to rise. In the optimistic terms the banks

would encourage the home owners to take on considerably high loans in the belief

they would be able to pay it back more quickly overlooking the interest rates.

Once the interest rates began to rise in mid 2007 the housing price started to drop

significantly in 2006 leading into 2007. In many states like California refinancing

became more difficult. As a result the number of foreclosed homes began to rise

as well.

The Global Financial Crisis brings a negative impact of other countries, and

Indonesia is one that is affected also. Based on the report publish by Bank

Indonesia, during this crisis, the exchange rates of Indonesian Rupiah to US dollar

1 Three top economists agree 2009 worst financial crisis since great depression;

risks increase if right steps are not taken. (February 29, 2009). Reuters.

Retrieved 2010-09-30, from Business Wire News database.

http://en.wikipedia.org/wiki/Subprime_lending

http://en.wikipedia.org/wiki/Adjustable_rate_mortgage

http://www.reuters.com/article/pressRelease/idUS193520+27-Feb-2009+BW20090227


http://en.wikipedia.org/wiki/Reuters

http://en.wikipedia.org/wiki/Business_Wire

4

decline significantly to Rp 12,000/ US$. On October 9-10 2008, many stocks in

Jakarta Stock Exchange are being suspended because of market‟s overreaction. It

seems like the Financial Global Crisis will harm the economic conditions of a

country, as well as its stock market.

The unstable macroeconomic conditions could affect the stock price in a stock

market. According to Bodi, Kane, and Marcus (2007 P.226) in their books

“Investment” the macroeconomic factors that affect the stock price are; interest

rate, inflation, and exchange rate. The stock price that is influenced by the cyclical

factors such as stock on property and real estate industries will be affected more

than the stock price on the other industries that is not influenced by cyclical

factors, such as consumption goods industries.

The interest rate influences the choice of investment choosed by investor. The

raise of interest rate makes investor tend to invest their money in the form of

deposit, rather than in stock market which has higher risk. The higher rate of

interest will bring a negative significant effect to the property and real estate

sector because it depends on the mortgage. In contradictory, if the interest rates

fell, and the mortgage also fell, the willingness of investor to buy property is

raised. This happens in United States when the Fed decreases their interest rate

and triggers the people to buy houses with lower mortgage. This condition makes

the performance of property and real estate sector increase, because the amount of

houses sold are also increased. However, when the Fed increases back the interest

rate, it will leads to the failure of payment from the creditor, and this condition

5

makes the performance of the property and real estate sector declining.

Fundamentally, the decreasing performance of a company will lead to the

decreasing of its stock price in the stock market.

Inflation, as an unpredictable macroeconomic indicator also influences the

performance of the stock in the market. Inflation is the raise of overall goods and

services in an economy over period of time (Mankiw, 2007). The raise of overall

goods and services will create the declining demand of secondary and tertiary

product, such as property and real estate, because the people will try to fulfill their

basic needs first. This makes the stock price on property and real estate sector will

be more affected than the stock price on consumption goods, theoretically.

The exchange rate of Indonesian Rupiah to US Dollar depreciated when the crisis

occurred. This condition caused a huge damage to major industries in Indonesia,

especially for those who finance their company by borrowing fund from foreign

country. They have to pay more for their debt and this will make the performance

of their company weaken and thus make their stock prices decrease. Exchange

rate also influences the foreign investor‟s decision to invest their money in

Indonesian stock market. Foreign investors are allowed to invest their money in

certain limitation. When the exchange rates change, foreign investor must rethink

their decision to invest carefully and this will influence the stock price as a whole.

As the condition described above, this research try to examine the macroeconomic

factors, especially interest rate, inflation, and exchange rate, that will influence the

stock performance, both for stock which influenced much by cyclical factors, and

6

stock which less influenced by cyclical factors. This research aims to help investor

to make the best decision regarding the choice of stocks when the economic

condition is unstable.

B. Problem Formulation

A country‟s capital market plays a significant role in determining the

growth of that particular country. There are many research conducted in various

countries, using various types of data to prove that theory, which later will be

explored on the next part of this research. But so far the result of this study is still

inconclusive. Every market is different and dynamic and that makes some

researches resulted in the opposite way of theory. The choice of method analysis

also influences the result of the research. Therefore, the writer desires to prove the

negative effect of macroeconomic factors, such as interest rate, inflation, and

exchange rate to the stock return in Indonesia‟s capital market.

This research chooses the property and real estate sector and consumer goods

sector as the object of research. The reason behind this choice is because both

sectors differ in terms of the level of cyclical factors‟ influence. Consumer goods

are become the sample of this research because this sector are less likely to be

influenced by cyclical factors so it can be a good comparator to the property and

real estate sector, which is considerably more influenced by cyclical factors.

Based on the two factors, we will see how macroeconomics variables affect the

performance of the stock on consumption goods sector and property and real

estate sectors.

7

Based on the consideration above, this research aims to answer these following

questions:

1. How the interest rate, inflation, and exchange rate are influences the

performance of a stock on consumption goods and property and real estate

sector in Indonesia stock exchange?

2. How much the influence of interest rate, inflation, and exchange rate

affected the performance of stock on consumption goods and property and

real estate sector in Indonesia stock exchange?

C. Research Objectives

The objectives of this research are:

1. To identify the influence of interest rate, inflation, and exchange rate to the

performance of stock on consumption goods sector and property and real

estate sector in Indonesia stock exchange.

2. To determine the extent to which interest rate, inflation and exchange rate

influence the performance of stock on consumption goods sector and

property and real estate sector in Indonesia stock exchange.

D. Usefullness of the Research

In general, this research is expected to provide the significant contribution to

investors especially the individual investors to have new insights regarding the

8

perfect time to invest in the capital market given a current macroeconomic

circumstances. This eventually will help investor in obtaining their investments‟ risks

manageable, which eventually will help in the improvement of the social economics

circumstances. Specifically, the benefits of research is diveded into three scope of

areas, which are:

1. For the Author

As for the author, this research is expected to enrich the author‟s

knowledge about investment in capital market, as well as macroeconomic

knowledges. This reserach is also expected to become a medium to

implement lessons learned during the author‟s study in the university.

2. For the Investor

As for the investor, this research attempts to help investor in increasing

their knowledge about macroeconomic condition before they choose to

invest in the capital market. By examining the condition, investors could

forecast their return in capital market. Thus, this is expected to help

investor minimize the risk of their investmrnt.

3. For academic purposes

This research aims to add the literature study for investor who wishes to

know the influence of macroeconomic factors, such as interest rate,

inflation, and exchange rate to the rate of return in consumption good

stocks and property and real estate stock. This study can be used as a

consideration before selecting the most appropriate stock for investor.

Also, This research aims to add understanding for other researcher who

wishes to do research in the same field of study.

9

CHAPTER 2

LITERATURE REVIEW

A. Fundamental of Capital Market

1. History of Indonesian Capital Market

The capital market in Indonesia has actually existed long before the

Independence of Indonesia. The first stock exchange in Indonesia was established

on 1912 in Batavia during the Dutch colonial era. At that time, the exchange was

established for the interest of the Dutch East Indies (VOC).

During that era, the capital market grew gradually, and even became inactive for a

period of time due to various conditions, such as the World War I and II, power

transition from the Dutch government to Indonesian government, etc.

Indonesian government reactivated its capital market in 1977, and it grew rapidly

ever since, along with the support of incentives and regulations issued by the

government.

The milestone of Indonesian capital market occurred in 2007, where Surabaya

Stock Exchange merged with Jakarta Stock Exchange and thus change the name

to become Indonesia Stock Exchange (IDX). This merger is expected to boost the

performance of Indonesian capital market in the future2.

2 www.bapepam.go.id

10

2. Capital Market Definition

The Capital Market is a market for several long-term investment

instruments which can be traded, whether in the form of debt or self-own capital

(equity) (Darmadji and Hendy, 2001). Capital market enables corporations to

issue the security in the form of certificate of indebtedness (bond) or the

certificate of ownership (stock). Through the existence of capital market, this

would be possible for the parties who has surplus of fund (lender) to move their

fund to another parties who are deficit in fund (borrower). The parties who have

the surplus of fund can invest their money to the parties who are deficit with the

expectation to get the return, while the issuer (in this case the corporate) can

utilize the fund for its interest to make its business growth without depending on

the operational activity of the corporate.

Capital market has many advantages which can be categorized as economic

advantage and financial advantage. On the economic side, capital market is

expected to boost economic activity because capital market is becoming a funding

alternative for the corporations so that the corporations can operate in the higher

scale which eventually can increase its revenue and prosperity of the society.

While in the financial side, capital market is expected to become the source of

financing (long term) for the corporations and enable investor to diversify its

investment.

3. Capital Market Instrument

Stock exchange instrument consist of promissory notes that can be traded

through the stock exchange. Capital market instruments which currently traded at

11

Indonesia Stock Exchange (IDX) are classified into three types; stocks, bonds, and

rights3.

a) Stock

The capital stock of a business entity represents the original capital paid

into or invested in the business by its founders. Stock typically takes the

form of shares of either common stock or preferred stock. As a unit of

ownership, common stock typically carries voting rights that can be

exercised in corporate decisions. They include the right to receive dividend

payments typically from earnings, if authorized by the board of directors

and the power to sell the stock (liquidity rights) and realize capital gains

on public trading markets or in private transactions. On the other hand,

preferred stock differs from common stock in that it typically does not

carry voting rights but is legally entitled to receive a certain level of

dividend payments before any dividends can be issued to other

shareholders.

b) Rights

Rights are the right given to the old shareholder to buy new additional

shares issued by a particular company.

3 www.idx.co.id

http://en.wikipedia.org/wiki/Common_stock

http://en.wikipedia.org/wiki/Preferred_stock

http://en.wikipedia.org/wiki/Dividend

12

c) Bond

Bond is a debt security, in which the authorized issuer owes the holders a

debt and, depending on the terms of the bonds, is obliged to pay interest

(the coupon) and/or repay the principal at later date, termed maturity. A

bond is a formal contract to repay borrowed money with interest at fixed

intervals.

B. Investment

Investment is putting money into something with the hope of profit. More

specifically, investment is the commitment of money or capital to the purchase of

financial instruments or other assets so as to gain profitable returns in the form of

interest, income (dividends), or capital gain appreciation of the value of the

instrument (Sullivan, 2003). It is related to saving or deferring consumption.

Investment is involved in many areas of the economy, such as business

management and finance no matter for households, firms, or governments. An

investment involves the choice by an individual or an organization, such as a

pension fund, after some analysis or thought, to place or lend money in a vehicle,

instrument or asset, such as property, commodity, stock, bond, financial

derivatives (e.g. futures or options), or the foreign asset denominated in foreign

currency, that has certain level of risk and provides the possibility of generating

returns over a period of time (Graham et al., 1991).

Investor usually assesses their risk before making an investment decision. They

assess the expected profitability return they will earn from the investment. In the

practice, not all of the expected returns in the future match the prediction of the



http://en.wikipedia.org/wiki/Income

http://en.wikipedia.org/wiki/Dividends

http://en.wikipedia.org/wiki/Saving_%28money%29

http://en.wikipedia.org/wiki/Consumption_%28economics%29




http://en.wikipedia.org/wiki/Finance

http://en.wikipedia.org/wiki/Real_estate

http://en.wikipedia.org/wiki/Commodity

http://en.wikipedia.org/wiki/Stock

http://en.wikipedia.org/wiki/Bond_%28finance%29

http://en.wikipedia.org/wiki/Financial_derivatives

http://en.wikipedia.org/wiki/Financial_derivatives

http://en.wikipedia.org/wiki/Futures_contract

http://en.wikipedia.org/wiki/Option_%28finance%29

13

investor. There are many factors that make investment‟s return somehow

unpredictable. Because of these factors, investor needs to make a comprehensive

analysis before they make an investment decision to minimize the risks. The

analysis could be a fundamental analysis or technical analysis.

Generally, there are two different styles and types of investors that exist in the

stock market; investors use the stock market to build their investment portfolio so

that they can see a long term profit that takes place over a long period of time, and

investor who just using the stock market to make money quickly for a short period

of time, which is called a "trader". The first type of investor has a short term

orientation while the second type has a long term orientation.

Investor in capital market must analyze the factors that could influence the change

of stock price. Stock prices definitely change over the course of time. Some can

increase rapidly and make investors a fortune, whereas others can lose a lot of

value quickly and bankrupt investors. Stock prices change because of the

economics of market forces, and the supply and demand for the stock (Coleman,

2006). This is all based on personal perception. If people think that a company

will do better in the future, this will raise the demand and price of the stock, and if

they think a company will do worse, this will lower the demand and price of the

stock.

The change of stock price is influenced by many factors. Some studies have

concluded that company fundamentals such as earning and valuation multiple are

major factors that affect stock prices. Other indicated that inflation, economic

conditions, investor behavior, the behavior of the market and liquidity, are the

14

most influencing factors of stock prices (Bodie, 2007). News or information also

affects the change in stock price. Positive news about a company can increase

buying interest in the market while a negative press release can ruin the prospect

of a stock. Nevertheless, investor could not only rely on the news when

attempting to predict stock price. It is the overall performance of the company

that matters more than news. It is always wise to take a wait and watch policy in a

volatile market or when there is mixed reaction about a particular stock.

In capital market, there is term named „market efficiency concept‟ which

mentions the degree to which stock prices reflect all available and relevant

information. In 1900, Louis Bachelier made an important contribution to the

formalism of classical economics with a theory that says trading strategies based

only on observed price changes cannot succeed. Markets are moved by news and

since, by definition, news cannot be predicted (or it would not be news), price

movement cannot be anticipated. Consequently, price data are not linked and price

series follow a geometric Brownian random walk, whereby market prices are log-

normal distributed, i.e. the differences of the logarithms of prices are Gaussian

distributed.

There are three categories of market efficiency concept (Elton, et al: 2007)

1. Weak Form Efficiency: In this form of market efficiency, no investor can

earn excess returns by developing trading rules based solely on historical

price or return information.

15

2. Semi-Strong Form Efficiency: In this form of market efficiency, no

investor can earn excess returns from using trading rules based on any

publicly available information.

3. Strong Form Efficiency: In this form of efficiency, no investor can earn

excess returns using any information whether publicly available or not.

As every casual follower of financial news knows, stock prices rise and fall in

response to earnings and revenues (Yee, 2001). Positive information regarding

expected Earnings per Share (EPS) will boost the stock price and vice versa.

C. Stock Rate of Return

Capital Market Theory explains the behavior of investor in making an

investment decision. Investor will consider two most important factors when

making an investment decision; risk and return. A stock‟s rate of return is gained

from the dividend paid annually or capital gain or margin from the purchase-sell

activity.

Chris Brooks (2002 p.154) in his book, Introductory of Econometrics for Finance

explain two methods in calculating return gained from capital gain margin; simple

return and continuously compounded returns. The formula of each method is as

follows:

Simple returns:

(2.1)

16

Continuously compounded return

(2.2)

Where,

Rt = Simple returns on t period

rt = Continuously compounded returns on t period

Pt = Stock Price on t period

Pt-1 = Stock Price on t-1 period

Investor will make an investment decision to buy a particular stock if a stock‟s

rate of return forecasted to be increased in the future. In this condition, investor

will gained more return than they‟re required rate of return. When a stock‟s return

is predicted to up, it will increase the demand from investor to that particular stock

and automatically increase the stock price. On the other hand, when a stock‟s

return is predicted to decrease, it will also decrease the demand from investor to

that particular stock and resulted in the decrease of stock price as well. Investor

will tend to sell the stocks which not meet their required rate of return. Here we

can see a linear correlation between a stock‟s rate of return and stock‟s price.

D. Risks

Despite considering a stock‟s return, investor who wishes to invest their

money on capital market must also define the risk of the stock. In one definition,

"risks" are simply future issues that can be avoided or mitigated, rather than

17

present problems that must be immediately addressed (Hubbard, 2009). While

others said that risk is the uncertainty about rate of return in the future (Bodie et

al: 2007). Risks that faced by investors are strongly related to the fluctuation of

stock price. According to Yan Yee Chong (2006, p. 75) in his books Investment

Risk Management, risks are classified into several types:

a. Business Risk

Business risk is the potential for loss of value through competition,

mismanagement, and financial insolvency. There are a number of

industries that are predisposed to higher levels of business risk (airlines,

railroads, steel, etc).

b. Credit and Country Risk

Credit risk is the risk that firm unable to deal with its obligations, and

therefore the asset will become unprofitable. Country risk refers to the risk

that a country will change the rules under which its financial system

operates in some way that affects that country‟s native financial instrument

and assets; country risk is also known as political risk.

c. Interest

The raise of interest rate will decrease the demand of stocks investment

because people will prefer to invest in the form of deposits. This resulted

in the decreasing of stock price.

d. Market Risk

Market risk is risk associated with daily fluctuations in stock price. Market

risk is also referred to as volatility; assets with high volatility (market risk)

18

are likely to fluctuate greatly in stock price, whereas assets with low

volatility are more immune to fast, large price changes. Volatility is

important in the stock world for a variety of reasons. The more volatile a

stock, the more potential for profit there may exist (which is why some

investors focus on identifying growth stocks, which have the capability for

explosive growth), but at the same time, there is also the possibility of

dramatic loss. The less volatile the investment, the less on average the

return will be to that investment.

e. Liquidity Risk

The final type of risk associated with stock market transactions is liquidity

risk. Liquidity risk refers to risk that the stock will not be able to be traded

fast enough to avoid or loss or capitalize on a potential profit. Liquidity

risk can be avoided by making sure the daily volume of share trading is

above a certain level.

Generally, risk is calculated by the deviation of the actual return from the required

rate of return (expected return) as a result of success/failure of an investment. Risk

of a stock can be measured by its variance or standard deviation. With the risk of

uncertainty, investors not only need to calculate the return of a stock but also the

risks associated with it.

There are two types of risks that commonly faced by the investor when investing

their money; systematic and unsystematic risk. Interest rates, recession and wars

all represent sources of systematic risk because they affect the entire market and

cannot be avoided through diversification. While unsystematic risk is company or

19

industry specific risk that is inherent in each investment. Systematic risk can be

mitigated only by being hedged, while the amount of unsystematic risk can be

reduced through appropriate diversification.

E. Inflation

In economics, inflation is a rise in the general level of prices of goods and

services in an economy over a period of time (Blanchard, 2000). The rise of

general prices of goods and services does not merely caused inflation if it is only

happen in a moment of time. Therefore, inflation is at least measured by monthly

basis. If in one month the rise of general price of goods and services are

continuously, a country can be claimed to have an increase in inflation rate.

Inflation rate is the percentage change in the price index from the preceding

period (Mankiw, 2007). Another longer period to measure inflation is by yearly,

or quarter-yearly basis. Still According to Mankiw in his books, Theory of

Macroeconomic, there are several macroeconomic indicators that can be used to

measure an inflation rate within a period;

a. Consumer Price Index (CPI)

The consumer price index is a measure of the overall costs of the goods

and services brought by a typical consumer. Inflation rate can be measured

by applying the following formula:

(2.3)

b. Wholesale Price Index

http://en.wikipedia.org/wiki/Economics

http://en.wikipedia.org/wiki/Price_level


20

If consumer price index measure inflation rate from the perspective of the

consumers, wholesale price index measure inflation rate from the

perspective of the producers. Wholesale price index measures the cost of a

basket of goods and services. Inflation rate can be measured by applying

the following formula:

(2.4)

c. GDP Deflator

Two previously discussed indicators have some limitation in measuring

inflation rate. Both indicators only measure some goods and services in

some cities, and it does not reflect the overall goods and services produced

and consumed within a country. Therefore, economist uses GDP deflator

to measure inflation rate more precisely. GDP Deflator is the ratio of

nominal GDP to real GDP. Because nominal GDP is current output valued

at current prices and real GDP is current output valued at base-year prices,

GDP deflator reflects the current level of prices relatives to the level of

prices in the base year. Interest rate calculation using GDP deflator can be

measured as follows:

21

(2.5)

Theoretically, inflation has a negative relationship with the stock return. This

phenomenon is caused by:

a. Cost-push inflation

When the cost of production increases, a company‟s ability to fulfill the

demand from the customer is decrease. This will cause an aggregate

supply. Aggregate supply is the total volume of goods and services

produced by an economy at a given price level. When there is a decrease

in the aggregate supply of goods and services stemming from an increase

in the cost of production, cost-push inflation occurred. Cost-push inflation

basically means that prices have been “pushed up” by increases in costs of

any of the four factors of production (labor, capital, land or

entrepreneurship) when companies are already running at full production

capacity. With higher production costs and productivity maximized,

companies cannot maintain profit margins by producing the same amounts

of goods and services. As a result, the increased costs are passed on to

consumers, causing a rise in the general price level (inflation).

b. Demand-pull inflation

Demand-pull inflation occurs when there is an increase in aggregate

demand, categorized by the four sections of the macroeconomic:

households, businesses, governments and foreign buyers. When these four

http://www.investopedia.com/terms/m/macroeconomics.asp

22

sectors concurrently want to purchase more output than the economy can

produce, they compete to purchase limited amounts of goods and services.

1. Relationship between inflation and stock return

Inflation influences the purchasing power of the individuals in some

extend. Brealey and Meyer (2000) exposes that inflation has a negative

inlfluences to the stock performance in capital market. When inflation rate

increase, the price of overall goods and services increase as well. Thus will make

individuals loose their ability to invest in capital market. They prefer to fulfill

their basic needs first before making an investment. The capital matket will suffer

because of this condition. The price of overall stocks in the capital market will

decrease and thus make the return of the stock decrease as well.

F. Interest Rate

According to Mankiw (2007, p. 297), an interest rate is the rate at which

interest is paid by a borrower for the use of money that they borrow from a lender.

It is consist of two types, real and nominal interest rate. In finance and economics

nominal interest rate or nominal rate of interest refers to the rate of interest before

adjustment for inflation (in contrast with the real interest rate); or, for interest

rates "as stated" without adjustment for the full effect of compounding (also

referred to as the nominal annual rate). An interest rate is called nominal if the

frequency of compounding (e.g. a month) is not identical to the basic time unit

(normally a year). The "real interest rate" is approximately the nominal interest



http://en.wikipedia.org/wiki/Lender


http://en.wikipedia.org/wiki/Inflation

http://en.wikipedia.org/wiki/Real_interest_rate

http://en.wikipedia.org/wiki/Compound_interest

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23

rate minus the inflation rate. It is the rate of interest an investor expects to receive

after subtracting inflation.

The relationship between real and nominal interest rates can be described in the

equation (Brealey and Meyer, 2000)

( )( ) ( )

(2.6)

Where r is the real interest rate, i is the inflation rate, and R is the nominal interest

rate.

1. Relationship between interest rate and stock return

Interest rate are important variables in for macroeconomic to understand

because they link the economy of the present and the economy of the future

through their effects on saving and investment (Mankiw, 2007 p.369). Keynes

reanalyzed about the effect of interest rate on investment decision. The increase of

interest rate could increase the exchange rate, but this can make the price of stock

decrease. Keynes also stated the negative relationship between interest rate and

stock price. This happen because if the interest rate increases, people tend to

invest their money in the form of deposit, and thus make investment in capital

market weakened.

The relationship between interest rate and investment decision also explained by

Fisher in his Theory of Investment(1930). Through the IS and LM curve, if the

http://en.wikipedia.org/wiki/Nominal_interest_rate

http://en.wikipedia.org/wiki/Inflation

24

interest rate increase, the cost of investment will increase and thus make

company‟s profit decrease. A decrease in profit will make a dividend for the

stockholder decrease too, which affected the stock price to be decreasing as well.

2. SBI (Bank Indonesia Certificate) Rate

In the past two centuries, interest rates have been variously set either by

national governments or central banks. In Indonesia, the interest rate is set by the

Central Bank of Indonesia, namely BI rate. The SBI Rate is announced by the

Board of Governors of Bank Indonesia in each monthly Board of Governors

Meeting. It is implemented in the Bank Indonesia monetary operations conducted

by means of liquidity management on the money market through SBI Rate to

achieve the monetary policy operational target.

The monetary policy operational target is reflected in movement in the Interbank

Overnight (O/N) Rate. It is then expected that bank deposit rates will track the

movement in interbank rates, with bank lending rates following suit.

While other factors in the economy are also taken into account, Bank Indonesia

will normally raise the BI Rate if future inflation is forecasted ahead of the

established inflation target. Conversely, Bank Indonesia will lower the BI Rate if

future inflation is predicted below the inflation target.

The benchmark interest rate in Indonesia was last reported at 6.50 percent. In

Indonesia the interest rate decisions are taken by The Central Bank of Republic of

Indonesia. The official interest rate is the Discount rate. This is the rate at which

http://www.bi.go.id/web/en/Ruang+Media/Agenda+Kegiatan+BI/Rapat+Dewan+Gubernur/

http://www.bi.go.id/web/en/Ruang+Media/Agenda+Kegiatan+BI/Rapat+Dewan+Gubernur/

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central banks lend or discount eligible paper for deposit money banks, typically

shown on an end-of-period basis. From 2005 until 2010, Indonesia's average

interest rate was 8.76 percent reaching an historical high of 12.75 percent in

December of 2005 and a record low of 6.50 percent in August of 2009. The graph

below represents the interest rate in Indonesia from December 2005 to August

2009.

Figure 2.1 (Indonesia Interest Rate)

G. Exchange Rate

According to Jeff Madura (2007, p.78), exchange rate between two

countries specifies how much one currency is worth in terms of the other. It is the

value of a foreign nation‟s currency in terms of the home nation‟s currency.

According to traditional approach, exchange rates lead stock prices. This approach

states that stock price is expected to lead exchange rate with a negative correlation

26

since a decrease in stock prices reduces domestic wealth, which leads to lower

domestic money demand and interest rates (Aydemir and Demirhan, 2009). Also,

the decrease in domestic stock prices leads foreign investors to lower demand for

domestic assets and domestic currency. These shifts in demand and supply of

currencies cause capital outflows and the depreciation of domestic currency. On

the other hand, when stock prices rise, foreign investors become willing to invest

in a country‟s equity securities. Thus, they will get benefit from international

diversification. This situation will lead to capital inflows and a currency

appreciation (Granger et al. 2000;Pan et al. 2007).

Exchange rates can affect stock prices not only for multinational and export

oriented firms but also for domestic firms. For a multinational company, changes

in exchange rates will result in both an immediate change in value of its foreign

operations and a continuing change in the profitability of its foreign operations

reflected in successive income statements. Therefore, the changes in economic

value of firm‟s foreign operations may influence stock prices. Domestic firms can

also be influenced by changes in exchange rates since they may import a part of

their inputs and export their outputs. For example, a devaluation of its currency

makes imported inputs more expensive and exported outputs cheaper for a firm.

Thus, devaluation will make positive effect for export firms (Aggarwal, 1981) and

increase the income of these firms, consequently, boosting the average level of

stock prices (Wu, 2000).

1. Relationship between exchang rate and stock return

27

Stock return will get effected by the fluctuation of exchange rate in a

country (Wu, 2000). When the exchange rate fluctuates, foreign investor

will get atrratcted to invest their money in capital market. Thus will make

the price of stock corrected.

H. Previous Research

In 2004, Maysami, et al. made a research about relationship between

selected macroeconomic variables and the Singapore Stock Market Index (STI),

as well as with various Singapore Exchange Sector Indices-the finance index, the

property index, and the hotel index. This research used VCEM model proposed by

Johansen(1990) which allows for testing cointegration in a whole system of

equations in one stop, without requiring a specific variable to be normalized. The

research‟s data is the monthly time-series which is obtained from the

PublicAccess Time-Series system, an online service by the Singapore Department

of Statistics. And the SES All-S Equities indicies figures are obtained from the

Singapore Statistics published by the Singapore Department of Statistics. The

study concludes that the Singapore‟s stock market and the property index form

cointegrating relationship with changes in the short and long-term interest rates,

industrial production, price levels, exchange rate and money supply

(macroeconomics variables).

Gay (2008) conducted a research about effect of macroeconomic variables on

stock market return for four emerging economies: Brazil, Russia, India and China.

The Box-Jenkins ARIMA model used to describe the relationship will use the

28

moving-averages at the one-month MA(1), three-month MA(3), six-month

MA(6), and twelve-month MA(12) for the lagged dependent of stock market price

and the two intervening variables of exchange rate and oil price. Available

monthly data for stock market price index, exchange rate, and oil price between

1999:03 to 2006:06 for Brazil, Russia, India, and China from the Organization for

Economic Cooperation and Development (OECD) is used in this study, which

will provide 90 observations per variable for each BRIC for a total of 1,080

observations. This study concludes that no significant relationship was found

between respective exchange rate and oil price (Macroeconomic variables) on the

stock market index prices of either BRIC country, this may be due to the influence

other domestic and international macroeconomic factors on stock market returns,

warranting further research. Also, there was no significant relationship found

between present and past stock market returns, suggesting the markets of Brazil,

Russia, India, and China exhibit the weak-form of market efficiency.

Wan Mahmood (2009) from Universiti Teknologi Mara Trengganu, Malaysia,

examine the dynamics relationship between stock prices and economic variables

in six Asian-Pacific selected countries of Malaysia, Korea, Thailand, Hong Kong,

Japan, and Australia. The monthly data on stock price indices, foreign exchange

rates, consumer price index and industrial production index that spans from

January 1993 to December 2002 are used. This study performed two cointegration

tests of Engle and Granger (1987) and Johansen and Juselius as his method. The

results indicate the existing of a long run equilibrium relationship between and

among variables in only four countries, i.e., Japan, Korea, Hong Kong and

29

Australia. As for short run relationship, all countries except for Hong Kong and

Thailand show some interactions. The Hong Kong shows relationship only

between exchange rate and stock price while the Thailand reports significant

interaction only between output and stock prices. An accurate estimation of these

relationships enables investors to make effective investment decisions. At the

same time, it helps policy makers in designing policies to encourage more capital

inflows into the respective countries‟ capital market.

Pakistani scholar, Ahmad et al. (2010) performed a study that examines the

relationship between stock return, interest rate and exchange rates in Pakistani

economy. For this, the data of short term interest rate, exchange rate (Rs/US $)

and stock market returns (KSE-100) over the period of 1998-2009 is collected. A

multiple regression model is applied to test the significance of change in interest

rate and exchange on stock returns. The results show that both the change in

interest rate and change in exchange rate has a significant impact on stock returns

over the sample period.

Kandir (2008) investigates the role of macroeconomic factors in explaining

Turkish stock returns. A macroeconomic factor model is employed for the period

that spans from July 1997 to June 2005. Macroeconomic variables used in this

study are, growth rate of industrial production index, change in consumer price

index, growth rate of narrowly defined money supply, change in exchange rate,

interest rate, growth rate of international crude oil price and return on the MSCI

World Equity Index. This study uses data for all non-financial firms listed on the

ISE. The analysis is based on stock portfolios rather than single stocks. In

30

portfolio construction, four criteria are used: market equity, the book-to-market

equity, the earnings-to-price equity and the leverage ratio. A multiple regression

model is designed to test the relationship between the stock portfolio returns and

seven macroeconomic factors. Empirical findings reveal that exchange rate,

interest rate and world market return seem to affect all of the portfolio returns,

while inflation rate is significant for only three of the twelve portfolios. On the

other hand, industrial production, money supply and oil prices do not appear to

have any significant affect on stock returns.

Meanwhile, In Indonesia, there was Handoko (2003) who examines a few of

macro economics variables which are influencing the rate of return of stocks

portfolios from all public companies in consumer goods industry which are

already listing at Jakarta Stocks Exchange. The model in building a portfolio is

based on market share with Ordinary Least Square (OLS) analyzing method is

using to test the six-independent variables simultaneously which are predicted

influencing the rate of return in every portfolio, which are Stock Price Index in

Jakarta Stocks Exchange, non-gas and petroleum export, the currency of Rupiah,

rate of interest, price of gold and Consumer Price Index in food industry. The

sample uses in this study is taking from all go-public companies in consumer

goods industry during January 2000 -December 2003 period of time and gets 48

observations. The analysis result is showing that not all the independent variables

which are predicted having a significant influence through the rate of return of

stock portfolio.

31

Another research conducted by Softameiono (2007) proposed there is relationship

between macroeconomics variable; inflation, interest rate, and exchange rate, and

stock return in banking sector. The data is gathered from Jakarta Stock Exchange

which are the stock returns in Banking sector listed in two effects from January

2002 to February 2004. The method used to analyze the data is regression of panel

data with random effect. Besides, the T test, F test, and Determination test is also

employed. The result is macroeconomic variables (interest rate, inflation and

exchange rate) give significance influence to the stocks return in Indonesia Stock

Exchange. The most significant variable is exchange rate.

I. Research Framework and Hypotheses

a. Inflation and Stock’s Return

Theoretically, inflation and a stock‟s rate of return have a negative relationship. If

an inflation rate increased, a stock‟s rate of return will decrease and vice versa.

Based on the theories and previous studies, we formulate hypothesis as follow:

H0 : There is no relationship between inflation rate and stock‟s rate of return

H1 : There is a negative relationship between inflation rate and stock‟s rate

b. Interest Rate and Stock’s Return

Theoretically, interest rate and a stock‟s rate of return have a negative

relationship. If an interest rate increased, a stock‟s rate of return will decrease and

32

vice versa. Based on the theories and previous studies, we formulate hypothesis as

follow:

H0 : There is no relationship between interest rate and stock‟s rate of

return.

H1 : There is a negative relationship between interest rate and stock‟s rate

of return.

c. Exchange Rate and Stock’s Return

Theoretically, exchange rate and a stock‟s rate of return have a negative

relationship. If an exchange rate increased, a stock‟s rate of return will decrease

and vice versa. Based on the theories and previous studies, we formulate

hypothesis as follow:

H0 : There is no relationship between exchange rate and stock‟s rate of

return.

H1 : There is a negative relationship between exchange rate and stock‟s

rate of return.

Based on the previous study, there is no fixed explanation about what

macroeconomic factors that influence the stock‟s rate of return. Therefore, this

research aims to investigate the specific macroeconomic factors that influence the

stock‟s rate of return with developing multi index model as follows:

33

Figure 2.2 (Research Model)

Interest Rate

Stock‟s

Rate of

Return

Inflation

Exchange Rate

Dependent

Variable

Independent

Variable

34

CHAPTER 3

RESEARCH METHODOLOGY

A. Data Collection

1. Unit of Analysis and Research Sampling

The unit of analysis refers to the level of aggregation of the data collected

during the subsequent data analysis stage (Cooper et al., 2006). This research

focusing on the influence of selected macroeconomic variables on stock‟s rate of

return. The unit of analysis for this study is the market sector indices listed in

Indonesia Stock Exchange (IDX). The market sector indices in Indonesia Stock

Exchange (IDX) is divided into 10 sectors; mining, agricultural, consumer goods,

miscellaneous industry, manufacturing, infrastructure and transportation, finance,

trade investment and services, chemical basic industry and property and real estate

The sample of this research is the market sector indices on consumer goods and

property and real estate sector during the period 2006-2010. Both categories are

chosen because each category have different characteristic than others. The

market sector indicies on consumer goods sector is less likely to be affected by the

cyclical factor (in this research, we examine 3 macroeconomic variable) compared

with the market sector indices on property and real estate sector. From that

sample, the writer able to calculate the stock‟s return during the period stated.

35

2. Types of Data

This research uses a secondary type of data, which is obtained from certain

sources. The independent variable data, which are inflation rate and exchange

rate of Indonesian Rupiah to US Dollar is obtained from Indonesia‟s Economic

and Financial Statistic published by Bank of Indonesia while the data of inflation

rate is gathered from National Statistical Bureau (BPS).

B. Research Models

This research uses multiple linear regression models as follow:

Model 1

(3.1)

Model 2

(3.2)

where,

Ri = Stock return of property and real estate sector

Rj = Stock return of consumer goods sector

α = Intercept

β = Regression coefficient

IR = the Indonesia Interest Rate (SBI) change

INFLATION = the inflation rate change

36

EXCHANGE RATE = the change in exchange rate of Indonesian Rupiah to

US Dollar

C. Operational Variable

Operational variable is a statement of the specific dimensions and

elements through which a concept will become measurable (Sekaran, 2006).There

are two kinds of variable, which are independent variable and dependent variable,

which we turn into certain dimension and definition.

The operational definition of each variable is as follow:

1. Stock’s Rate of Return

In calculating a stock‟s rate of return, the writer uses continuous

compounding method as follow:

[

]

(3.3)

Where,

rt = Continuously compounded returns on t period

Pt = Stock Price on t period

Pt-1 = Stock Price on t-1 period

From the formula above, we can see that a stock‟s rate of return can be

seen by the margin of change in stock‟s price. The data of stock price used is the

closing price which gathered monthly during December 2005- December 2010.

37

2. Inflation Rate

The inflation rate data used in this research calculated from change in

monthly inflation rate based on Consumer Price Index (CPI). Inflation per month

can be calculated as follows:

(3.4)

After we calculate the monthly inflation rate, then to measure the change in

inflation rate we can use this following formula:

(3.5)

Where,

INFLATIONt = Change in inflation rate during t period

INFt = Inflation on t period

INFt-1 = Inflation on t-1 period

3. Exchange Rate

This research uses exchange rate of Indonesian Rupiah to US Dollar. The

exchange rate uses is the mid-point between buy and write price.

38

(3.6)

Where,

EXRATEt = Change in exchange rate of Indonesian Rupiah to US

Dollar in a t period

ERt = Exchange rate of Indonesian Rupiah to US Dollar in t

period

ERt-1 = Exchange rate of Indonesian Rupiah to US Dollar in t-1

period

This data is gained from the exchange rate of Indonesian Rupiah to US Dollar in

monthly basis during December 2005-December 2010 published by Bank of

Indonesia.

4. Interest Rate

In this research, interest rate refers to Bank Indonesia Certificate (SBI)

Rate on monthly basis. The operational definition used in this research is the

change in SBI rate. Change in Indonesia Interest Rate is defined as follows:

(3.7)

Where,

∆IRATE = Change in interest rate (SBI rate) on t period

39

IRt = Interest rate (SBI rate) on t period

IRt-1 = Interest rate (SBI rate) on t-1 period

D. Data Analysis Technique

Regression analysis will be used to test hypotheses formulated for this

study. Three variables (inflation, interest rate, and exchange rate) were entered.

Multiple regressions will determine the significant relationship between

dependent and independent variables, the direction of the relationship, the degree

of the relationship and strength of the relationship (Sekaran, 2006). Multiple

regression are most sophisticated extension of correlation and are used to explore

the predict ability of a set of independent variables on dependent variable (Pallant,

2001). Three hypotheses generated. From the hypothesis it gives direction to

assess the statistical relationship between the dependent and independent

variables. The convention of P value has been set as of 5% i.e. 0.05 used as

evidence of a statistical association between the dependent and independent

variables.

To gather the best model of research, researcher must perform other pre-tests. The

test are: normality test, assumption test (heteroscedasticity test, auto-correlation

test, multi-collinearity test), and hypothesis test.

1. Normality Test

In statistics, normality tests are used to determine whether a data set is

well-modeled by a normal distribution or not, or to compute how likely an

http://en.wikipedia.org/wiki/Statistics

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40

underlying random variable is to be normally distributed. An informal approach to

testing normality is to compare a histogram of the residuals to a normal

probability curve. The actual distribution of the residuals (the histogram) should

be bell-shaped and resemble the normal distribution.

There are certain methods to detect whether data is normally distributed or not.

The methods are using Histogram of Residual, Normal Probability Plot, and

Jarque-Bera Test. In this research, researchers want to use a method proposed by

Jarque Bera or commonly known as Jarque Bera Test to gather the most accurate

model of data.

a. Jarque-Bera Test of Normality

The JB test of normality is an asymptotic, or large-sample, test. It is also based on

the OLS residuals (Gujarati, 2004). This test first computes the skewness and

kurtosis measures of the OLS residuals and uses the following test statistic:

[

( )

]

(3.8)

Where,

n = sample size

S = skewness coefficient

K = kurtosis coefficient

For a normally distributed variable, S=0 and K=3. Therefore, the JB test of

normality is a test of the joint hypothesis that S and K are 0 and 3, respectively. In

that case the value of the JB statistic is expected to be 0.

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41

Regarding this, the hypothesis of Jarque-Bera Test is described as follows:

H0 : Data is not normally distributed

Ha : Data is normally distributed

To detect whether the variable is normally distributed or not, one can compare the

value of Jarque Bera statistic with the value of Jarque Bera table (X2)., as follows:

a. If JB Statistic > X2, the data is not normally distributed, and thus we do

not reject H0.

b. If JB Statistic < X2 , the data is normally distributed, and thus we reject H0.

2. Classical Assumption Test

The Gaussian, standard, or classical linear regression model (CLRM),

which is the cornerstone of most econometric theory, makes 10 assumptions

underlying of Ordinary Least Square method (Gujarati, 2004, p.65). This research

will focus on its 6 basic assumption in context of the two-variable regression

model.

Assumption 1 : Linear Regression Model. The regression model is linear in

the parameters

Assumption 2 : X values (The independent variable) are fixed in repeated

sampling. Values taken by the regressor X are considered fixed

in repeated samples. More technically, X is assumed to be

nonstochastic.

Assumption3 : Zero mean value of disturbance ui. Given the value of X, the

mean, or expected, value of the random disturbance term ui is

42

zero. Technically, the conditional mean value of ui is zero.

Symbolically, we have

( | )

(3.9)

Assumption 4 : Homoscedasticity or equal variance of ui. Given the value

of X, the variance of ui is the same for all observations. That is,

the conditional variances of ui are identical. Symbolically, we

have

( | )

(3.10)

Assumption 5 : No autocorrelation between the disturbances. Given any

two X values, Xi and Xj (i≠ j), the correlation between any two

ui and uj (i≠j) is zero. Symbolically, we have

( | )

(3.11)

Assumption 6 : Zero covariance between ui and Xi, or E(uiXi) = 0. By

Assumption,

( )

(3.12)

43

As noted earlier, given the assumptions of the classical linear regression model,

the least-squares estimates possess some ideal or optimum properties. These

properties are contained in the well-known Gauss–Markov theorem. To

understand this theorem, we need to consider the best linear unbiasedness

property of an estimator. The OLS estimator is said to be a best linear unbiased

estimator (BLUE) if the following hold (Brooks, 2002):

1. It is linear, that is, a linear function of a random variable, such as the

dependent variable Y in the regression model.

2. It is unbiased, that is, its average or expected value, E(β2), is equal to the

true value, β2.

3. It has minimum variance in the class of all such linear unbiased estimators;

an unbiased estimator with the least variance is known as an efficient

estimator.

The classical assumption test needed to ensure that the regression model is the

best estimator or BLUE. The classical assumption test also used to detect any

mislead of the classical linear model. The test used are Heteroscedastic Test,

Auto-Correlation Test, and Multi-Colinearity Test.

a. Heteroscedastic Test

One of the important assumptions of the classical linear regression model

is that the variance of each disturbance term ui, conditional on the chosen values

of the explanatory variables, is some constant number equal to σ2. This is the

assumption of homoscedasticity, or equal (homo) spread (scedasticity). When the

44

variance of each disturbance ui is not constant however, there is heteroscedasticity

on that variance. Still according to Gujarati in his book Basic Econometric, the

consequences of heteroscedasticity in the regression model is as follows:

1. The estimator produced will still be consistent, but that estimator will be

no longer efficient. Meaning that, there are variance that has little error

than the estimator produced in the regression model that contains

heteroscedasticity.

2. Estimator produced from the regression linear model is no longer have an

accurate heteroscedastic. This will cause the hypothesis testing become not

accurate.

In short, if we persist in using the usual testing procedures despite

heteroscedasticity, whatever conclusion we draw or influences we make may be

very misleading.

To detect whether heteroscedasticity is present in the data, researcher will conduct

a formal test using White Test method. The reason why researcher use White‟s

General Heteroscedasticity Test is because it does not rely on the normality

assumption and easy to implement. The White test proceeds as follows:

Step 1. Given the data, we estimate regression model and obtain the residuals,ui

(3.13)

Step 2. We then run the following (auxiliary) regression:

45

(3.14)

Step 3. Formulate the Hypothesis Test

H0 = There is no heteroscedastic

Ha = There is heteroscedastic

Under the null hypothesis that there is no heteroscedasticity, it can be shown that

sample size (n) times the R2 obtained from the auxiliary regression asymptotically

follows the chi-square distribution with df equal to the number of regressors

(excluding the constant term) in the auxiliary regression. That is,

(3.15)

Step 4. If the chi-square value obtained in (3.15) exceeds the critical chi-square

value at the chosen level of significance, the conclusion is that there is

heteroscedasticity. If it does not exceed the critical chi-square value, there is no

heteroscedasticity, which is to say that in the auxiliary regression (3.14).

If heteroscedasticity truly exist, one can use the Generelized Square

Method or White Test method. White Test method developed heteroscedasticity-

corrected standard error. Software Eviews 5 has already provided White method

to overcome heteroscedastic.

46

b. Autocorrelation Test

The term autocorrelation may be defined as correlation between members

of series of observations ordered in time [as in time series data] or space [as in

cross-sectional data] (Gujarati, 2004, p.442). In the fifth assumption of the

classical linear regression model, it assumes that such autocorrelation does not

exist in the disturbance uj,. The classical model assumes that the disturbance term

relating to any observation is not influenced by the disturbance term relating to

any other observation.

As in the case of heteroscedasticity, in the presence of autocorrelation the OLS

estimators are still linear unbiased as well as consistent and asymptotically

normally distributed, but they are no longer efficient (i.e., minimum variance).

Therefore, the usual t and F tests of significance are no longer valid, and if

applied, are likely to give seriously misleading conclusions about the statistical

significance of the estimated regression coefficients.

To detect autocorrelation, the writer use The Breusch-Godfrey (BG) Test in the

application software EViews 5. BG Test, which is also known as Lagrange-

Multiplier LM Test involves the following hypothesis:

Ho: There is auto-correlation

H1: There is no auto-correlation

After we run the test, we can analyze the result by comparing the value of Obs*R-

squared, which comes from the coefficient determination (R squared multiple

47

with the number of observation), and the value of the probability with the

significant value (α), as follows:

If the Probability > α = 5%, there is no auto-correlation, thus we reject Ho.

If the Probability < α = 5%, there is auto-correlation, thus we failed to

reject H0

c. Multi-Collinearity Test

Estimator that has BLUE characteristic supposed to be not contains

multicollinearity. Since multicollinearity is essentially a sample phenomenon,

arising out of the largely nonexperimental data collected in most social sciences,

there is no one unique method of detecting it or measuring its strength. In this

research, the writer will detect the existence of multi-collinearity by using

software Eviews 5. The method that will be used is through the examination of

partial correlation. This method is developed by Farrar and Glauber. They

suggested that in examining the existence of multicollinearity one should look at

the partial correlation coefficient (Gujarati, 2004). Thus, in the regression of Y on

X2, X3, and X4, a finding that R2 1.234 is very high but r 2 12.34, r 2 13.24, and r 2 14.23 are

comparatively low may suggest that the variables X1, X2, and X3 are highly

intercorrelated and that at least one of these variables is superfluous.

48

3. Hypothesis Test

a. t- test

According Bhuono Theories (2005) if t test > t table therefore Ho rejected

and Ha accepted, that means independent variables partially as influence

significantly toward dependent variable. If t test < t table therefore Ho accepted

and Ha rejected, that means independent variable partially has no influence

significantly toward dependent variable. Level of significant use amount 5% or

(α) 0.05. Based on the theory above, so the test for each hypothesis is as follow:

a. Hypothesis related to interest rate

H0 : β1 ≥ 0

H1 : β1 < 0

b. Hypothesis related to inflation

H0 : β2 ≥ 0

H1 : β2 < 0

c. Hypothesis related to exchange rate

H0 : β3 ≥ 0

H1 : β3 < 0

b. F test

The function of Ftest is to see and understand the influence of both

independent variables toward dependent variables. Steps of this test:

a. Create the hypothesis formulation

HO: β1= β2= β3= 0, There was no influence that is significant from the

independent variable (X) together against the dependent variable (Y).

49

HA: β1≠ β2 ≠ β3≠ 0, There was influence that is significant from the

independent variable (X) together against the dependent variable (Y)

b. Determine the level of the significant of 5%

c. R Square (R2) Test

We know that one of the measures of goodness of fit of a regression model

is R2, which is defined as:

(3.16)

R2, thus defined, of necessity lies between 0 and 1. The closer it is to 1, the better

is the fit.

d. Adjusted R Squared Test

The value of adjusted R Squared always smaller than the value of R

Squared. Adjusted R Squared penalizes for adding more regressors. Unlike R2,

adjusted R2 will increase only if the absolute t value of the added variable is

greater than 1. The closer it is to 1, the better is the fit. This means that the

independent variable used could explain almost 100% of the variance in the

dependent variable.

50

E. Research Design

Figure 3.1 Research Framework

Sampling Process

Dependent Variable Independent Variable

Sectoral market return

indicies

Macroeconomic

factors(inflation,SBI

rate,exchange rate

Calculate based on

operational variable

definition

Calculate based on

operational variable

definition

𝑅𝑖 𝛼𝑖 𝛽𝑖 𝐼𝑅 𝛽𝑖 𝐼𝑁𝐹𝐿𝐴𝑇𝐼𝑂𝑁 𝛽𝑖 𝐸𝑋𝐶𝐻𝐴𝑁𝐺𝐸 𝑅𝐴𝑇𝐸 𝑒𝑖

𝑅𝑗 𝛼𝑗 𝛽𝑗 𝐼𝑅 𝛽𝑗 𝐼𝑁𝐹𝐿𝐴𝑇𝐼𝑂𝑁 𝛽𝑗 𝐸𝑋𝐶𝐻𝐴𝑁𝐺𝐸 𝑅𝐴𝑇𝐸 𝑒𝑗

Regression Model

Model 1 :

Model 2 :

Regression model test

Hypothesis test

Analysis

Conclusion

51

CHAPTER 4

RESEARCH FINDINGS AND ANALYSIS

A. Brief Introduction

This chapter will present the process of data processing through the

statistical tools as well as the analysis of the findings. The data will be processed

through the statistical software Eviews 5 using the multiple regression analysis

model. The model will be tested using the OLS (Originally Least Squared)

Method before conducting the multiple regression analysis model. To ensure that

the model has the characteristic BLUE (Best Linear Unbiased Estimator), before

testing the significance of the model, we conduct the classical assumption test

which consists of normality test, heteroscedasticity test, multi-collinearity test,

and autocorrelation test. If the model is passing those classical assumption tests,

thus we assume that the model has BLUE characteristic and we will be ready to

test the significance of the model. But if the model has not passed yet, we will do

some remedial actions accordingly. After achieving a model that has BLUE

characteristic, the writer can interpret the result and make the analyses as well as

compare it with the theoretical assumption.

52

B. Descriptive Statistics

The descriptive statistics that will be used in this research is as follows:

CG P&R EXRATE INLATION SBI

Mean 0.022268 0.019215 -0.000831 -0.483316 -0.011012

Median 0.003819 0.024407 -0.003781 -0.076498 -0.007476

Maximum 0.193360 0.306532 0.172425 9.000000 0.130793

Minimum -0.169444 -0.340247 -0.098840 -35.00000 -0.105425

Std. Dev. 0.067858 0.099661 0.037145 5.192304 0.036939

Skewness 0.352901 -0.521612 1.717417 -5.028711 0.705108

Kurtosis 3.747713 5.198717 10.70772 34.36123 6.116995

Jarque-Bera 2.643079 14.80669 178.0175 2711.697 29.26092

Probability 0.266724 0.609000 0.000000 0.000000 0.000000

Sum 1.336056 1.152927 -0.049875 -28.99897 -0.660716

SumSq.

Dev. 0.271681 0.586010 0.081406 1590.641 0.080504

Observations 60 60 60 60 60

Table 4.1

Where,

CG : Return in Consumer Goods Sector

P&R : Return in Property and Real Estate Sector

EXRATE : Change in Exchange Rate (Monthly)

INFLATION : Change in Inflation Rate (Monthly)

SBI : Change in SBI Rate (Monthly)

53

Explanation of Table 4.1:

1. The dependent variable of the stocks rate of return in consumer goods

sector has an average return of 0.022268. The maximum return of

consumer goods sector during the period of observation is 0.193360, while

the minimum return is -0.169444 with the 6.785 % standard deviation. In

the meantime, the dependent variable of the stocks rate of return in

property and real estate sector has an average return of 0.019215. The

maximum return of property and real estate sector during the period of

observation is 0.306532, while the minimum return is -0.340247with the

9.9661 % standard deviation.

2. As for the independent variable, the change in exchange rate during the

period of the observation has an average change of -0.000831. The

maximum change is 0.172425 and the minimum change is -0.098840 with

3.7145% standard deviation. Another variable, which is change in inflation

rate, has an average change of -0.483316. The maximum change is

9.000000 and the minimum change is -35.00000with 519.2304% standard

deviation. And as the last independent variable, which is change in SBI

rate, it has the average change of -0.011012. The maximum change is

0.130793 and the minimum change is -0.105425 with 3.6939% standard

deviation.

C. Normality Test

In this research, the writer uses the normality test of Jarque Bera using statistical

software Eviews 5. Normality test is used to test whether the residual of the model

54

used in the research is normally distributed or not. The diagram below present the

result of normality test using histogram residual/Jarque-Bera method for the

consumer goods sector:

Figure 4.1 (Histogram Residual, Consumer Goods Sector)

Figure 4.2 (Histogram Residual, Property and Real Estate Sector)

0

1

2

3

4

5

6

7

8

9

-0.10 -0.05 -0.00 0.05 0.10 0.15

Series: Residuals

Sample 2006M01 2010M12

Observations 60

Mean -4.97e-18

Median -0.005805

Maximum 0.145997

Minimum -0.105790

Std. Dev. 0.057312

Skewness 0.487487

Kurtosis 2.684504



0

2

4

6

8

10

12

14

-0.1 -0.0 0.1 0.2

Series: Residuals

Sample 2006M01 2010M12

Observations 60

Mean 6.94e-19

Median 0.001880

Maximum 0.238803

Minimum -0.167392

Std. Dev. 0.079104

Skewness 0.363828

Kurtosis 3.382237



55

From the Figure 4.1 we can see that the probability of Jarque Bera of the

consumer goods sector is bigger than α ( 0.269108 > 0.05). Thus, we failed to

reject the Null Hypothesis and that means the residual of the model is normally

distributed.

From the Figure 4.2 we can see that the probability of Jarque Bera of the property

and real estate sector is bigger than α ( 0.429778 > 0.05). Thus, we failed to reject

the Null Hypothesis and that means the residual of the model is normally

distributed.

D. Classical Assumption Test

In order to achieve the model that has BLUE characteristic, the writer

needs to conduct the classical assumption test before doing the regression

analysis. If in this test we find that the model is not resulting in BLUE

characteristic we will use the remedial measurement accordingly.

1. Heteroscedasticity Test

One of the important assumptions of the classical linear regression model

is that the variance of each disturbance, conditional on the chosen values of the

explanatory variables, is somewhat constant. This is the assumption of

homoscedasticity.

To detect whether heteroscedasticity is present in the data, the writer will conduct

a formal test using White Test method. The reason why we use White‟s General

Heteroscedasticity Test is because it does not rely on the normality assumption

and easy to implement.

56

After using the statistical software Eviews 5, the writer find the result of White

Heteroscedasticity Test- No cross terms, as follows:




Table 4.2 (Heteroscedasticity Test for Consumer Goods Sector)

Table 4.2 presents the result of the White Heteroscedasticity test on consumer

goods sector using statistical software Eviews 5. The result of the test provides the

proof that the data we are using is free from heteroscedastic problem

(homoscedastic). From the test above, we can see that the probability of Obs*R-

squared is bigger than our significant value (0.088076 > 0.05) and thus we can

reject the null hypothesis.




Table 4.3 (Heteroscedasticity Test for Property and Real Estate Sector)

57

Table 4.3 presents the result of the White Heteroscedasticity test on property and

real estate sector using statistical software Eviews 5. The result of the test

provides the proof that the data we are using is free from heteroscedastic problem

(homoscedastic). From the test above, we can see that the probability of Obs*R-

squared is bigger than our significant value (0750581 > 0.05) and thus we can

reject the null hypothesis.

2. Auto-Correlation Test

As in the case of heteroscedasticity, in the presence of autocorrelation the

OLS estimators are still linear unbiased as well as consistent and asymptotically

normally distributed, but they are no longer efficient (i.e., minimum variance).

To detect autocorrelation, the writer use The Breusch-Godfrey (BG) Test, which

is also known as Lagrange-Multiplier LM Test in the application software EViews

5. The results are presented in tables below:




Table 4.4 (Auto-Correlation Test for Consumer Goods Sector)




Table 4.5 (Auto-Correlation Test for Property and Real Estate Sector)

58

From the Table 4.4 we find that the probability Obs*R-squared from the data of

consumer goods sector is bigger than sig. value α (0.846460 > 0.05), thus we can

conclude that auto-correlation is not existed in our data. The same result happens

in the property and real estate sector. As if presented in Table 4.5, the probability

Obs*R-squared is bigger than sig. value α (0.527740> 0.05), thus we can reject

the null hypothesis and conclude that there is no auto-correlation in our data.

3. Multi-Collinearity Test

R-Squared

Y

R-Squared

X1

R-Squared

X2

R-Squared

X3

Consumer Goods 0.286679 0.170209 0.039652 0.142669

Property and Real Estate 0.369993 0.170209 0.039652 0.142669

Table 4.6 (Multi-Collinearity Test)

When examining the existence of multi-collinearity, one should look at the

partial correlation coefficient (Gujarati, 2004). The table above describes the

summary of the linear regression, given each variable to be the dependent

variable. For the consumer goods sector, the R-Squared of the Dependent Variable

(Y) is bigger than its independent variables (y=28.6667 % > x1=17.0209% >

x2=3.9652% > x3=14.26%), and thus we reject H0 and assume that there is no

multi-collinearity existed in the data. The same thing goes for Property and Real

Estate Sector, where the obtained R-squared Y is 36.99%, which is bigger than its

independent variables. And thus, it is confirmed that multi-collinearity doesn‟t

exists as in the property and real estate as well.

59

E. Multiple Linear Regression Model

Several classical assumption tests have done through the model to ensure

that the model is a good estimator. In this research, the writer use the multiple

linear regression model to estimate the influence of the inflation rate, exchange

rate and SBI rate to the return of stocks in consumer goods and property and real

estate sector.

In order to achieve that, the writers use the statistical software Eviews 5 which is

resulted in models as follow:

4.1 Model of Consumer Goods Sector

4.2 Model of Property and Real Estate Sector

F. Hypothesis Testing

1. T-Test

The model above is obtained from the T-statistic Test using the multiple

linear regression models. The purpose of using this model is to estimate the

influence of each independent variable to the dependent variable respectively. T-

test, which is conducted using the statistical software Eviews 5 is resulted in the

output as follows:

RCG = 0.016385 -0.693450 EXRATE + 3.36E-05 INF - 0.483347 SBI + e

RPROP = 0.008209 - 1.065215 EXRATE - 0.000461 INF - 0.898840 SBI + e

60

Table 4.7 Multi-Collinearity Test



Date: 02/07/11 Time: 16:04

Sample: 2006M01 2010M12



C 0.016385 0.008014 2.044428 0.0456

X1 -0.693450 0.226342 -3.063726 0.0034

X2 3.36E-05 0.001505 0.022330 0.9823

X3 -0.483347 0.223922 -2.158550 0.0352







Where:

X1 = Exchange Rate

X2 = Inflation Rate

X3 = SBI Rate

From the Table 4.7 above, we can see that at the 95% confidence level (α = 0.05),

exchange rate and SBI rate partially has negative effect to the stock‟s return on

consumer goods sector. The probability value of the T-statistic is way below the

significance value (0.05) and thus makes us reject the Null Hypothesis and

conclude that there is a negative relationship between exchange rate and SBI rate

to the stock return on consumer goods. Inflation does not influence the return of

61

stock on consumer goods sector as it represented on the probability value of its T-

statistic, which is beyond α = 0.05. The interpretation of the intercept coefficient

and the independent variable is as follows:

The intercept coefficient of the output is 0.016385. This means if there is

no change in the rate of the independent variable (exchange rate, inflation,

and SBI rate), the stock‟s return on consumer goods would increase by

1.6385 point, based on the output.

The coefficient of exchange rate is -0.693450, which means that exchange

rate has a negative relationship with the stock‟s return on consumer goods.

If the change in exchange rate increases 1 point, the stock‟s return on

consumer goods sector would decrease by 0.693450 points, given the

remaining variables are constant. The graph below presents the graph

about the movement of exchange rate as compared to the stock‟s return in

consumer goods during the period 2006-2010.

Figure 4.3 Graph of Ex. Rate & Stock Return Movement (Cons.

Goods)

-0,2

-0,15

-0,1

-0,05

0

0,05

0,1

0,15

0,2

0,25

Stock's return

exchange rate

62

The negative relationship between the exchange rate and the stock‟s return

is matched with the result provided in the previous researches. This could

be explained by the following analyses:

1. When the domestic currency depreciates, we need more Rupiah to

buy US Dollar. This condition make corporations, especially MNC

that operates using foreign currency, needs more money to perform

its operation. This condition could lead to the decrease of the

company‟s profit and eventually lead their equity price falls.

Stockholders are also suffering because the fall of the stock‟s price

could lead to the decrease of their stock‟s return (capital loss).

2. Exchange rates can affect stock prices not only for multinational

and export oriented firms but also for domestic firms. Domestic

firms can also be influenced by changes in exchange rates since

they may import a part of their inputs and export their outputs. For

example, a devaluation of its currency makes imported inputs more

expensive and exported outputs cheaper for a firm. Thus,

devaluation will make positive effect for export firms (Aggarwal,

1981) and increase the income of these firms, consequently,

boosting the average level of stock prices (Wu, 2000).

3. The sample of this research is the stock‟s return during 2006-2010.

The reason behind the selection of this period is because the writer

intends to provide the latest data and also examine whether any

63

different symptoms occur since the world is being hit by the Global

Financial Crisis during the year 2007. The result shows that there is

no significant effect to the stock‟s return although the overall

prices of the stocks in consumer goods are decreasing. But overall,

Indonesia, learned from the previous Asian Financial Crisis in

1997 able to recover quickly from this crisis and thus make us

survive from the collapse in financial market.

The coefficient of inflation rate is 3.36E-05 which means that inflation has

a positive relationship to the stock‟s return. If there is a change in inflation

by 1 point, the stock‟s return on consumer goods sector would decrease by

3.36E-05 points, given the remaining variables are constant. The output of

the research shows that inflation does not influence the stock‟s return

significantly. Figure below present the graph about the movement of

inflation rate as compared to the stock‟s return in consumer goods during

the period 2006-2010.

Figure 4.4 Graph of Inflation Rate & Stock Return Movement (Cons.

Goods)

-40

-30

-20

-10

0

10

20

Stock's Return

Inflation

64

This result is somewhat contradicted to the result of the previous research

due to the following analyses:

1. Indonesia has a large number of populations. The productivity ages

of average Indonesian are 24 years old. This could be very

potential to increase the domestic consumption. As the domestic

consumption always show a positive trend, the performance of the

issuers in this sector does not significantly affected by the change

in inflation.

2. Consumer goods sector indices are proven to be the most

insensitive index among others in Indonesia Stock Exchange

(IDX). This sector is less likely to be influenced by the instability

of macroeconomic condition, especially inflation. Thus, investors

are having a high confidence in choosing the consumer goods

sector to their portfolio. This make the stock‟s return in consumer

goods sector is relatively stable regardless the influence of the

inflation rate.

3. Consumer goods sector, leads by the stable issuers in the IDX such

as Unilever Tbk., Indofood Tbk., and Gudang Garam Tbk., are

widely-known as stocks with relatively high and stable price. And

thus, inflation does not influence the performance of the stocks in

this sector.

The coefficient of SBI rate is -0.483347 which means that SBI rate has a

negative relationship with the stock‟s return on consumer goods. If the

65

change in SBI rate increases by 1 point, the stock‟s return on consumer

goods sector would decrease by -0.483347 points, given the remaining

variables are constant. The graph below presents the graph about the

movement of SBI rate as compared to the stock‟s return in consumer

goods during the period 2006-2010.

Figure 4.5 Graph of SBI Rate & Stock Return Movement (Cons.

Goods)

The negative relationship between the SBI rate and the stock‟s return is

matched with the result provided in the previous researches. This could be

explained by the following analyses:

1. SBI rate is determined by Bank Indonesia in accordance to the

inflation rate. BI would increase its SBI rate if the inflation rate is

predicted to be increased. As we stated in the theoretical

framework, if the SBI rate is increasing, investor would prefer to

invest their money in the bank, to gain better interest. This could

-0,4

-0,2

0

0,2

0,4

0,6

0,8

Stock's return

SBI rate

66

influence the investor preference in investing in capital market, and

make the price of stock in the capital market decreased.

2. As if for the consumer goods, the influence of the SBI rate is

relatively insignificant, since as previously mentioned this sector is

not sensitive to the change in macroeconomic factors. If there is a

decrease in the stock‟s return since the SBI rate is increasing, the

change is not too significant.

Table 4.8 Output Multiple Linear Regression Property and Real Estate

Sector



Date: 02/07/11 Time: 10:59

Sample: 2006M01 2010M12



C 0.008209 0.011062 0.742136 0.4611

X1 -1.065215 0.312405 -3.409723 0.0012

X2 -0.000461 0.002077 -0.221818 0.8253

X3 -0.898840 0.309064 -2.908261 0.0052







Where:

67

X1 = Exchange Rate

X2 = Inflation Rate

X3 = SBI Rate

From the Table 4.7 above, we can see that at the 95% confidence level (α = 0.05),

exchange rate and SBI rate partially has negative effect to the stock‟s return on

property and real estate sector. The probability value of the T-statistic is way

below the significance value (0.05) and thus makes us reject the Null Hypothesis

and conclude that there is a negative relationship between exchange rate and SBI

rate to the stock return on consumer goods. Inflation does not influence the return

of stock on consumer goods sector as it represented on the probability value of its

T-statistic, which is beyond α = 0.05. The interpretation of the intercept

coefficient and the independent variable is as follows:

The intercept coefficient of the output is 0.008209. This means if there is

no change in the rate of the independent variable (exchange rate, inflation,

and SBI rate), the stock‟s return on property and real estate would increase

by 0.008209point, based on the output.

The coefficient of exchange rate is -1.065215, which means that exchange

rate has a negative relationship with the stock‟s return on property and real

estate sector. If the change in exchange rate increases 1 point, the stock‟s

return on property and real estate sector would decrease by -1.065215

points, given the remaining variables are constant. The graph below

presents the graph about the movement of exchange rate as compared to

the stock‟s return in property and real estate during the period 2006-2010.

68

Figure 4.6 Graph of Exchange Rate & Stock Return Movement (Prop

& Est)

The negative relationship between the exchange rate and the stock‟s return

is matched with the result provided in the previous researches. This could

be explained by the following analyses:

1. The movement of Indonesian Rupiah to US Dollar affected the

stock price especially for the perspective of foreign investor. The

possession of foreign stocks in IDX is recorded to be IDR 129.8

billion as for March 2010. The depreciation of Rupiah will attract

foreign investor to invest more money in the possession of stocks

in IDX. This will make the price of stock in IDX tend to be

corrected. The more flow of capital coming to the country, the

more beneficial it is for the exchange rate movement. Rupiah will

appreciate as many investor injected their money in the domestic

investment and need more Rupiah to do so.

-0,4

-0,2

0

0,2

0,4

Stock's Return

Exchange Rate

69

2. When Rupiah depreciates to US Dollar, theoretically, the price of

the stocks will decrease too. The exchange rate influences the

investor power to make an investment. The depreciation of Rupiah

to US Dollar generally will increase the price of daily needs and

thus make the investor‟s ability to make investment weakened.

3. As for the Company‟s perspective, the depreciation of Rupiah will

make company, especially MNCs who have to pay their debt in

other currencies, must spend more Rupiah to pay their debt. This

will make their performance decreasing and lead to the drop of

their stock price, and eventually, returns gained by their

shareholders.

The coefficient of inflation rate is -0.000461 which means that inflation

has a positive relationship to the stock‟s return. If there is a change in

inflation by 1 point, the stock‟s return on consumer goods sector would

decrease by -0.000461 points, given the remaining variables are constant.

The output of the research shows that inflation does not influence the

stock‟s return significantly. Figure below present the graph about the

movement of inflation rate as compared to the stock‟s return in property

and real estate sector during the period 2006-2010.

70

Figure 4.7 Graph of Inflation Rate & Stock Return Movement (Prop

& Est)

This result is somewhat contradicted to the result of the previous research

due to the following analyses:

1. Inflation is mainly caused by two reasons: the increase of the

demand by the customer, while the supply provided is stable, or

the raise of overall cost of production. In 2008, The booming of the

property and real estate sector are achieving its peak. This

condition happens during the period of 2006-2010. This also

become one of the consideration of the writer in choosing the

period of study. Major player in this sector, such as PT. Alam

Sutera Tbk., PT. Summareccon Agung start their Initial Public

Offering (IPO) in the mid of 2008. This marked as a huge step in

the property and real estate sector. The positive coefficiet of this

variable indicates that there is a increase in the demand of property

and real estate especially in the 2006-2010 period. Thus, make the

price of the stocks in this sector increasing as well.

-0,4

-0,2

0

0,2

0,4

Stock's Return

Exchange Rate

71

2. The result provides that inflation is not significantly influence the

return of the stock in property and real estate sector. As we know

that, the world is being hit by the global financial crisis in 20007.

The crisisi is mostly caused by the mortgage crisis in USA, which

is spreading to all over he world, especially in the world major

leading economies. This condition is supposed to be affect the

share price. However, the result provide the contradictory. The

writer suspect this symptom is happen because Indonesia, as a big

economy is relatively stable towards the economic imbalances in

the world major economies such as USA and Japan. Indonesia had

learned from the Asian Financial Crisis in 1997, so that the central

banks, government and related institution had formulated the better

monetary tools and instrument to prevent the collapse of financeial

market. And thus, altough during the period of 20006-2010

inflation is relatively high, this condition does nt bring significant

influence to the performance of the company in stock market.

The coefficient of SBI rate is -0.898840 which means that SBI rate has a

negative relationship with the stock‟s return on property and real estate. If

the change in SBI rate increases by 1 point, the stock‟s return on property

and real estate sector would decrease by -0.898840 points, given the

remaining variables are constant. The graph below presents the graph

about the movement of SBI rate as compared to the stock‟s return in

property and real estate sector during the period 2006-2010.

72

Figure 4.8 Graph of SBI Rate & Stock Return Movement (Prop &

Est)

The negative relationship between the SBI rate and the stock‟s return is

matched with the result provided in the previous researches. The result of

this research is matched with the theory proposed by Keynes regarding the

relationship between interest rate and the stock return. The raise of interest

rate will attract the investor to invest their money in the form of deposit

rather that in the capital market. And this will make the price of the stocks

decreases. On the other hand, the result of this research is also matched

with the theory proposed by Fisher about the relationship between interest

rate ad stock return to the IS and LM curve. This condition is also relate to

the mortgage system. When the interest rate increase, consumer needs to

pay higher for the purchase of property with the mortgage system and this

will make the demand of the property decaresing and eventually make the

share price decrease as well.

2. F Test

The function of F test is to see and understand the influence of both

independent variables toward dependent variables. On table 4.7 we can see that the

probability of F-statistic in the property and real estate sector is 0.000263, which is

-0,5

0

0,5

1

Stock Return

SBI

73

less that its sig. value (α = 0.05), and thus we reject the Null Hypothesis which

means that the independent variables (the change in exchange rate, inflation rate,

and SBI rate) have a significant influence together against the dependent variables

(stock‟s return). In the mean time, the writer also found that the probability of F-

statistic in the consumer goods sector is 0.000009, as it stated in the table 4.8. This

means that we can reject the Null Hypothesis as the probability F-statistic is less

than its significant value. Thus, we conclude that the independent variables (the

change in exchange rate, inflation rate, and SBI rate) have a significant influence

together against the dependent variables (stock‟s return) in the property and real

estate sector.

Based on the coefficient value, we can conclude that exchange rate is the most

influential independent variable towards the stock‟s return in both consumer

goods and property and real estate sector.

3. R-Squared (R2)

R-Squared describe how big the influence of the independent variables

together against the dependent variable. On table 4.7, we can see that the obtained

R-squared 0.286679. This means that the independent variables which consist of

the change in exchange rate, inflation rate and SBI rate could explain 28.6679 %

of the stock‟s return in property and real estate sector, while the remaining are

explained by others variables. While on table 4.8, we can see that the obtained R-

squared is 0.369993. This means that the independent variables which consist of

the change in exchange rate, inflation rate and SBI rate could explain 36.9993 %

74

of the stock‟s return in property and real estate sector, while the remaining are

explained by others variables.

4. Adjusted R-Squared

The adjusted R-squared explain how far the independent variables could

explain the variance of the dependent variable. The more close it to 1, the more

better the ability of the independent variable to explain its dependent. The value of

adjusted R-squared in the consumer goods sector is 0.248466, which describes

that 24.846 % of the variance in dependent variables could be explained by the

independent variables. . The value of adjusted R-squared in the property and real

estate sector is 0.336243, which describes that 33.6243 % of the variance in

dependent variables could be explained by the independent variables. The value of

adjusted R-squared in property and real estate sector is bigger than in consumer

goods sector thus concludes that the independent variables are having more

influence towards the property and real estate sector rather than consumer goods

sector. This proves the theory that has been previously mentioned in Chapter 2.

G. The Comparison between Macroeconomic Factors’ Influences Towards

The Stock’s Return on Consumer Goods and Property and Real Estate

Sector

Coefficient

Regression

Consumer Goods Sector Property and Real Estate

Sector

Exchange Rate -0.693450 -1.065215

Inflation Rate 3.36E-05 -0.000461

75

SBI Rate -0.483347 -0.898840

Table 4.9 Comparison of Coefficient Regression between Consumer Goods

and Property and Real Estate Sector

Based on the above table, the writer concludes that, the change in SBI rate

influences the property and real estate sector more than consumer goods. As a

cyclical factor, this sector is more sensitive to the change in interest rate. When

inflation occurred, and the price of overall goods and services increase, consumer

would prefer to buy the primary goods rather than secondary. Therefore, the

consumer goods sector is more resistant to the change in inflation rate rather than

property and real estate sector.

The change in exchange rate also influences the property and real estate more than

the consumer goods sector. From the three variables above, the property and real

estate sector seems to be influenced more by the fluctuation of macroeconomic

variables, proven by the result of the statistical test.

76

CHAPTER 5

CONCLUSION AND IMPLICATIONS

A. Conclusion

This research, which is conducted using the monthly basis data of stock‟s return

on consumer goods and property and real estate sector in 2006-2010 period and

using the multiple linear regression model in statistical software Eviews 5,

provide conclusions as follows:

1. Firstly, based on the result in the regression, at the 0.05 significance level,

the exchange rate independent variable significantly has a negative

influence to the stock‟s return on both sector; consumer goods and

property and real estate sector. Based on the result in the regression, at the

0.05 significance level, the inflation rate independent variable significantly

has no influence to the stock‟s return in both sectors. Based on the result in

the regression, at the 0.05 significance level, the interest rate independent

variable significantly has a negative influence to the stock‟s return in both

sectors

2. The independent variables; exchange rate, inflation rate, and the interest

rate could influence the stock‟s return on the property and real estate

sector by 33.6243 % and the consumer goods sector by 24.8466 % at the

0.05 significance level.

77

B. Implication of Study

1. For the Investor

As for the investor, this researcher is expected to add information for the investor

as their considerations when choosing investment, especially in the capital market.

Investors must aware of the macroeconomic conditions in a country which could

influence the performance of the stocks before deciding to choose the investment

instrument in order to minimize risks. When a country‟s economic condition seem

to be unstable, investors must be aware of their choice of investment especially in

the capital market.

2. For the Business Player

The business player must carefully examined the real macroeconomic condition

before they decided to do their Initial Public Offering (IPO) in the capital market.

Business player must be able to forecast the macroeconomic condition which will

effect the performance of the stock price of their company. When economic is

doing good, the stock price will get the positive influence and vice versa.

3. For the Researcher

As for the researcher, this research is expected to add literatures study in these

topics. However, the writer suggest the future researcher to complete this research

by using more sectors as sample to get the better model of how those

macroeconomic variables influence the capital market as a whole.

78

C. Limitation

The writer realizes that there are still many limitation of this study. This

research is not using the individual samples of a company, and so the results of

this research describe a general condition of a sector, and not focusing on the

companies on that sector specifically. This research only highlights the

macroeconomic factors that influence the return, and not focusing on the micro

factors of company individually.

This research is also only using 2 sectors, as a comparison of one to each other. If

this research use more sectors, perhaps this research could provide a better

description of the influence of macroeconomic factors (exchange rate, inflation

rate, and SBI rate) to the performance of the stock in Indonesian capital market.

At the end, the writer wishes that the lack of this research could be improved by

another researcher in the future.

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