Ppt 0000007

A QUANTITATIVE STUDY OF P/E RATIO IN THE MALAYSIAN MARKET

A QUANTITATIVE STUDY OF P/E RATIO IN THE MALAYSIAN MARKET

PRESENTERS: NORHAFIZAH BINTI AHMAD (2009475496)

ROSMIZIE BIN MOHAMED (2009690284)

Outline PresentationOutline Presentation

• Chapter 1 - Introduction

• Chapter 2 – Literature Review

• Chapter 3 – Research Methodology

• Chapter 4 – Results

• Chapter 5 – Conclusion &

Recommendation

BACKGROUND OF THE STUDYBACKGROUND OF THE STUDY

• P/E ratio of individual stocks has the ability to explain cross sectional variation in stock return (Fama and French:1992)

• P/E ratio have been used by investors and stock analysis in determine the individual stocks are price accordingly.

• There are not many research doing on the determination factors of P/E ratio especially in Malaysia

cont….cont….

• Aim of this research is to determine the most factors that effects the price earning ratio in Malaysia

• The independent variable was divide into two major scope that are macro variables (effects on whole industries) and micro variables (effects each industries-individually)

PROBLEM STATEMENTPROBLEM STATEMENT

• Measure the relationship between Independent variable (Macro and Micro) and Dependent Variables (Price Earning Ratio) in the Malaysian market

• Examine which of the variables give impacts to each industries in Malaysia.

OBJECTIVE OF THE STUDYOBJECTIVE OF THE STUDY

• Do macro factors gave an impact on P/E Ratio?

• Do micro factors gave an impact on P/E Ratio?

• Is there have determinants differs across industries in Malaysia?

LIMITATIONSLIMITATIONS

• Time Constraints

• Data analysis-basis

• Accuracy and Reliability of data

CHAPTER 2:LITERATURE REVIEW

CHAPTER 2:LITERATURE REVIEW

Dependent Variable (P/E Ratio)

Dependent Variable (P/E Ratio)

Supported/Related Studies

Statements

Basu (1983) •P/E Ratio can be used to construct portfolios which outperform the market.•P/E Ratio is an indicator of mispriced stocks

Ohlson (1983) Employs analytical framework to relate market price of equity to capitalized earnings

Beaver and Moose (1978),Zarowin (1990) Investigate variables that may explain some of the cross sectional variation in P/E Ratio among US firms

Beaver and Moose (1978), The differences in expected growth are commonly offered as major explanations for differences in P/E Ratio.

Cragg and Malkiel (1982), Litzenberger and Raul (1971)

P/E ratio is an earning growth indicator

Gordon (1962)-model Described P/E Ratio is determined by return on equity.

RELATIONSHIP BETWEEN INDEPENDENT VARIABLE (MACRO +

MICRO WITH THE DEPENDENT VARIABLE (P/E RATIO

RELATIONSHIP BETWEEN INDEPENDENT VARIABLE (MACRO +

MICRO WITH THE DEPENDENT VARIABLE (P/E RATIO

Independent Variable

Macro

Relationship Previous studies (LR)

Gross Domestic Products (GDP)) Significance Pearce (1983)

Inflation Rate Mixed (Insignificance)

Fisher (1930),Nelson et al (1993),Boudoukh et al (1994),Bodie (1976),Jafee and Mandelker (1976)

(Significance)

Solnik (1974),Cagan (1974)

Interest Rate Insignificance Darby (1986),Koraczyk (1985)

Micro

Expected Growth of Dividend Mixed (Insignificance)

Beaver and Morse (1978), Alford (1992),Kane et al (1996)

(Significance)

Zarowin (1990),Allen and Cho (1999),Basu (1983),Cook and Rozeff (1984),Fama and French(1992)

Required Rate of Return Significance Bodie, Kane, Marcus (2007),Jones (2000)

Dividend Yield significance Gordon (1962)-model

CHAPTER 3:RESEARCH &

METHODOLOGY

CHAPTER 3:RESEARCH &

METHODOLOGY

DATA COLLECTION AND DATA SOURCES

DATA COLLECTION AND DATA SOURCES

SECONDARY DATA:

DataStream

Malaysian Statistic Department (website)

Bank Negara Malaysia (website)

STATISTICAL METHOD OF ANALYSIS

Analyze the panel data by using statistical

software (E-Views)

LIST OF SAMPLELIST OF SAMPLE

BANKING & FINANCIAL

SAMPLINGSAMPLING

• Measurement Period: 10 years (2000-2009)-annually basis

• Number of Industry: Five (Consumer Goods, Construction, Banking and Financial,Healthcare,Services)

• Number of company: Five company on each industries

Research FrameworkResearch Framework

INDEPENDENT VARIABLE (MACRO)

DEPENDENT VARIABLE

PRICE EARNINGS RATIO (PER)

•GROSS DOMESTIC PRODCTS (GDP)

•INFLATION RATES•INTEREST RATES

INDEPENDENT VARIABLE

(MICRO)•DIVIDEND YIELD

•EXPECTED GROWTH OF DIVIDEND

•REQUIRED RATE OF RETURN

HYPOTHESISHYPOTHESIS• Hypothesis 1

H0: Macro factors does not impact on P/E Ratio

H1: Macro factors does impact on P/E Ratio

• Hypothesis 2

H0: Micro factors does not impact on P/E Ratio

H1: Micro factors does impact on P/E Ratio

• Hypothesis 3

H0: Macro and Micro factors does not differ with across industries

H1: Macro and Micro factors does differ with across industries

DATA ANALYSIS METHOD

DATA ANALYSIS METHOD

Chapter 4:Results

Chapter 4:Results

• Descriptive Analysis

• Summary of Result

• Individual Effect

• Pool Effect

Descriptive AnalysisDescriptive AnalysisYR LPE LGDP LIR LINF LGR LRR LDY

Mean 2004.528 1.1269 0.7189 0.4834 0.3263 -0.6829 0.9502 -0.4501

Median 2004.500 0.9852 0.7160 0.4771 0.2232 0.000000 0.8222 -0.5376

Maximum 2009.000 2.1367 0.9345 0.5798 0.7324 0.9031 2.7694 0.5563

Minimum 2000.000 1.6736 0.5229 0.3032 -1 -0.1761 -1.9582 -0.0899

Std. Dev. 2.870896 18.13906 2.073168 0.558507 1.468081 0.735656 39.57955 0.403280

Skewness 0.001100 2.235621 -0.868390 -0.498074 0.848848 5.858563 12.71878 2.666291

Kurtosis 1.772485 14.19868 4.079969 2.354750 3.247978 56.57034 188.2309 23.29723

Summary of resultsSummary of resultsINDUSTRY

constant GDP IR INF GR RR DY R2 Adj R2 DW

Consumer Goods

1.430928 ª

(0.4281)

0.093843

(0.0162)**

0.353607

(0.1731)

0.135914

(0.0333)**

1.213661

(0.0060)***

0.003707

(0.3749)

2.395079

(0.1750)

0.530 0.410 2.045

Construction sectors

6.604957

(0.8169)

0.401431

(0.6323)

1.700445

(0.0185)***

0.571458

(0.1862)

4.442028

(0.7227)

0.190834

(0.5062)

4.417605

(0.0580)

0.673 0.583 2.291

Banking & Finance

12.44450

(0.0925)

0.974148

(0.3342)

3.559677

(0.5049)

1.392993

(0.7356)

2.711456

(0.8638)

0.081011

(0.7629)

10.04026

(0.0213)**

0.266 0.078 2.130

Healthcare 4.810575

(0.0635)

0.367638

(0.5208)

1.443093

(0.8146)

0.519745

(0.5034)

0.836936

(0.7961)

0.077995

(0.7094)

4.568251

(0.9467)

0.380 0.217 1.640

Services 15.26746

(0.3642)

0.998320

(0.3161)

4.155706

(0.7850)

1.385399

(0.1426)

2.668614

(0.2069)

0.427340

(0.1371)

3.875112

(0.0000)***

0.748 0.684 1.633

Pooled 3.354477

(0.0138)***

0.249477

(0.2604)

0.902793

(0.9882)

0.353082

(0.3453)

1.399206

(0.0079)***

0.006845

(0.6758)

2.503290

(0.0001)***

0.353 0.326 1.413

ª = standard error , p-value in parenthesis ( )

***The coefficient is statistically significance at 1% level

** The coefficient is statistically significance at 5% level

Individual Effect (Industry)Individual Effect (Industry)

• Consumer Goods

• Construction

• Banking and Financial

• Healthcare

• Services

• Significance variable (p-value): Expected Growth of Dividend, Inflation Rate and GDP

• R² determination: moderate confidence relationship between IV and DV

• Durbin Watson stat: closer to 2- less autocorrelation occur

Consumer goodsConsumer goods

cont..cont..

• Correlation Coefficient :Interest Rate, Expected Growth of Dividend (strong positive linear relationship) GDP(weak positive linear correlation), Inflation Rate (weak negative linear correlation)Required Rate of Return, Dividend Yield (strong negative linear correlation)

• Fixed Effect Testing - significance

ConstructionConstruction

• Significance variable (p-value): Interest Rate

• R² determination: moderate confidence relationship between IV and DV


MEASUREMENT INDICATORMEASUREMENT INDICATORMethod

Panel Data-Redundant Fixed Effect Test P-value <0.05 (significance)

Correlation of determination (R²) Closer to 1: good Less than 0.5:poor/low confidence

Correlation Coefficient (r) r=1:perfect positive linear correlation 0.75<r<1: strong positive linear correlation 0<r<0.25: weak positive linear correlation r=0: no linear correlation -0.25<r<0:weak negative linear correlation-1<r<-0.75:strong negative linear correlation r=1: perfect negative linear correlation

P-value p-value <0.01 (1%) or 0.05 (5%) - significance

DW stat :DW=2 (no autocorrelation occurs):closer to 2 (less autocorrelation occurs):1>DW<2 (less autocorrelation occurs-evidence of positive linear correlation)

T-stat * Analyze by using P-value

cont..cont..

• Correlation Coefficient :Interest Rate, Dividend Yield (strong positive linear relationship) GDP(weak positive linear correlation),Required Rate of Return(weak negative linear correlation Inflation Rate, Expected Growth of Dividend(strong negative linear correlation)


Banking and financialBanking and financial

• Significance variable (p-value): Dividend Yield

• R² determination: low confidence relationship between IV and DV


cont..cont..

• Correlation Coefficient :Inflation Rate, Dividend Yield, Expected Growth of Dividend (strong positive linear relationship) Required Rate of Return(weak positive linear correlation)GDP,Interest Rate(strong negative linear correlation)


HealthcareHealthcare

• Significance variable (p-value): None

• R² determination: low confidence relationship between IV and DV

• Durbin Watson stat: less autocorrelation occur (evidence of positive serial correlation)-less than 2,more than 1

cont..cont..

• Correlation Coefficient :Interest Rate, Dividend Yield, (strong positive linear relationship) Expected Growth of Dividend, GDP (weak positive linear correlation) Required Rate of Return (weak negative linear correlation) Inflation Rate (strong negative linear correlation)


ServicesServices

• Significance variable (p-value): Dividend Yield

• R² determination: high confidence relationship between IV and DV


cont..cont..

• Correlation Coefficient :GDP (perfect positive linear relationship) Expected Growth of Dividend, Dividend Yield (strong positive linear relationship) Inflation Rate, Required Rate of Return (strong negative linear correlation) Interest Rate (perfect negative linear correlation)


Pool EffectPool Effect• Significance variable (p-value): Expected

Growth of Dividend and Dividend Yield

• R² determination: Low confidence relationship between IV and DV


cont..cont..

• Correlation Coefficient :Dividend Yield, Expected Growth of Dividend (strong positive linear relationship) GDP, Interest Rate, Required Rate of Return (weak positive linear correlation)and Inflation Rate (strong negative linear correlation)

Summary of FindingsSummary of FindingsIndependent Variable

Macro

Relationship

(Assumption)

Previous studies (LR) Result (Findings)

Gross Domestic

Products (GDP)

positive Pearce (1983) negative

Inflation Rate Mixed (Negative)

Fisher (1930),Nelson et

al(1993),Boudoukh et al

(1994),Bodie (1976),Jafee and

Mandelker (1976)

(Positive)

Solnik (1974),Cagan (1974)

negative

Interest Rate negative Darby (1986),Koraczyk (1985) negative

Cont….Cont….Micro

Expected Growth

of Dividend

Mixed (Negative)

Beaver and Morse (1978),

Alford (1992),Kane et al

(1996)

(Positive)

Zarowin (1990),Allen and Cho

(1999),Basu(1983),Cook and

Rozeff (1984),Fama and

French(1992)

Positive

Required Rate of

Return

positive Bodie, Kane, Marcus

(2007),Jones (2000)

negative

Dividend Yield positive Gordon (1962)-model positive

Chapter 5:Conclusion and

Recommendations

Chapter 5:Conclusion and

Recommendations

1st objectives: Do macro factors gave an impact on P/E Ratio?

2nd objectives: Do micro factors gave an impact on P/E Ratio?

Our Findings: The result shows that only two of the independent

variables (Expected Growth of Dividend and Dividend Yield ) have a

significance relationship with Dependent Variable. Both of that variables

are micro factors, so, in Malaysian market only micro factors gave an

impacts on P/E Ratio while macro factors does not give impacts at all.

However, in micro factors only required rate of return insignificance.

3rd objectives: Is there have any determinants differ across industries in

Malaysian?

Our Findings: The result shows that each industry in Malaysian market

have different variables that effects the P/E Ratio. However, the

healthcare industry showed that none of the variables was affected P/E

ratio. This is unique case because others industry at least have one the

significance variables.

RecommendationsRecommendationsFuture studies should:• Continue studies to explore the determination factors of

P/E ratio in another variables (ex: risk, market value,etc…)

• Apply this area of study (P/E ratio) in another market (country/region) and choose more numbers of industries

• Deeply examine on issue of why certain industry not have significance variable in determine P/E Ratio. Example in our study: Healthcare Industry

• Extend the study in this area and get most significance variables to determine P/E Ratio.

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