forecasting stock prices using qualitative data

Effects of Directors Qualifications, Firm Efficiency Metrics and

Stock Moving Average in Stock Price Prediction

Espiritu, Kimberly

G31588

A dissertation submitted in partial fulfilment of the requirements of the University of

Chester for the degree of Business Studies

CHESTER BUSINESS SCHOOL

JUNE 2013

9,774 words

(Excluding abstract, acknowledgements, table of contents, figures, tables, reference list and

appendices)

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Abstract

There are a lot of researches done for the benefit of forecasting stock prices. However,

the previous studies vary on what factors or variables have been used. The purpose of

this research is to establish a stock price prediction tool from using factors namely,

directors qualifications, firm efficiency metrics and stock moving average. The data

is collected from the annual reports of five companies from different industries for a

period of ten years. The scores for the directors qualifications and firm efficiency

metrics are calculated in the light of the knowledge obtained from an interview with a

financial analyst, an expert in the research area. For the analysis of data, the Pearson-

R Correlation demonstrated the significant relationship between the directors

qualifications and the stock moving average. The stock moving average represents the

stock price for the regression model thus; it is selected as the dependent variable. The

two remaining factors are selected as independent variables of the study. The analysis

of variance in the regression model output demonstrated that the independent

variables significantly explain part of the variance of the stock moving average. That

being said, the three factors are successfully used and was able to produce a stock

price prediction tool using a multiple regression analysis. The regression equation

produced from all the data gathered is:

Stock Price = (15.83 x Directors' Qualifications) + (114.30 x Firm Efficiency Metrics)

- 1488.66

This type of research is the first of its kind because the factors used in the study is

rarely combined together or used at all for forecasting stock trends.

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Acknowledgements

I would like to thank my supervisor, Mr. Ryk Ramos, for helping and encouraging me

all throughout my dissertation. Without his guidance, my dissertation would not have

come up with great results. Also, he made the dissertation process fun and light.

I would also like to thank Dr. Ciel Nuyda for motivating me with regard to my

dissertation.

I would like to show my appreciation to my boyfriend, John Carlo, for his unending

support and for pushing me to work hard on my dissertation.

I would like to show my gratitude to my classmates; Denise, Jing, Deandro, Marie,

AC, Dria, Kit, Ann, Tof, Dan, Jeremy, Neil, John, Christian and Sasa, who were all

with me on this journey. Even though the process is hard, we all had fun during the

all-nighters at each others houses, cafe shop study sessions and all the chat sessions

late at night all sharing our hardships and worries together.

Finally, this dissertation is for my dad who I want to thank because he is always

concerned and has high hopes with my studies and my future career.

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Declaration

This work is original and has not been submitted previously for any academic

purpose. All secondary sources are acknowledged.

Signed: ____________________

Date: ____________________

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Table of Contents

Abstract ..................................................................................................................... 1

Acknowledgements ................................................................................................... 2

Declaration ................................................................................................................ 3

List of Tables............................................................................................................. 6

List of Figures ........................................................................................................... 7

1 Introduction ............................................................................................................ 8

1.1 Background to the research ............................................................................... 8

1.2 Research Question ............................................................................................ 9

1.3 Research Objectives ....................................................................................... 10

1.4 Justification for the research ........................................................................... 10

1.5 Outline of research methodology .................................................................... 11

1.6 Outline of Chapters ......................................................................................... 11

1.7 Definition of terms ......................................................................................... 12

1.8 Summary ........................................................................................................ 13

2 Literature Review ................................................................................................. 13

2.1 Introduction .................................................................................................... 13

2.2 Fundamental and Technical Analysis .............................................................. 15

2.3 Corporate Governance .................................................................................... 16

2.3.1 Firm Efficiency Metrics ............................................................................ 17

2.3.2 Scoring Tools ........................................................................................... 18

2.4 Chapter Summary ........................................................................................... 19

3 Methodology ........................................................................................................ 20

3.1 Introduction .................................................................................................... 20

3.2 Research Philosophy....................................................................................... 20

3.2.1 Justification of the Research Philosophy ................................................... 20

3.3 Research Approach ......................................................................................... 21

3.3.1 Qualitative Research Approach ................................................................. 21

3.4 Research Methods .......................................................................................... 21

3.4.1 Literature Review ..................................................................................... 22

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3.4.2 Semi-structured Interview ......................................................................... 22

3.4.3 Document Analysis .................................................................................. 23

3.4.4 Scoring Tool ............................................................................................. 24

3.5 Research Design ............................................................................................. 30

3.6 Research Strategy ........................................................................................... 30

3.6.1 Ethical Considerations .............................................................................. 30

3.7 Chapter Summary ........................................................................................... 30

4 Findings................................................................................................................ 31

4.1 Introduction .................................................................................................... 31

4.2 Application of Methodology ........................................................................... 31

4.2.1 Semi-Structured Interview ........................................................................ 31

4.3 Findings for Each Research Objective ............................................................ 32

4.3.1 The relationship between Directors Qualifications and Stock Moving

Average............................................................................................................. 32

4.3.2 The relationship between Firm Efficiency Metrics and Stock Moving

Average............................................................................................................. 36

4.3.3 The relationship between Directors Qualifications, Firm Efficiency Metrics

and Stock Moving Average ............................................................................... 38

4.3.4 The established Stock Price Prediction Tool using the factors Directors

Qualifications, Firm Efficiency Metrics and Stock Moving Average ................. 41

5.0 Conclusions and Implications ............................................................................ 42

5.1 Introduction .................................................................................................... 42

5.2 Critical Evaluation of Adopted Methodologies ............................................... 42

5.2.1 Literature Review ..................................................................................... 42

5.2.2 Semi-structured Interview ......................................................................... 42

5.2.3 Scoring Tool ............................................................................................. 43

5.2.4 Document Analysis .................................................................................. 43

5.3 Analysis of Findings for each Research Objective .......................................... 43


Average............................................................................................................. 43


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Average............................................................................................................. 44


and Stock Moving Average ............................................................................... 44

5.3.4 The established Stock Price Prediction Tool ............................................. 44

5.4 Analysis and Overall Conclusions about the Research Question ..................... 44

5.5 Limitations to the Study .................................................................................. 45

5.6 Opportunities for Future Research .................................................................. 46

6 References ............................................................................................................ 46

7 Appendices ........................................................................................................... 49

7.1 Appendix 1 - Supervisor Forms ...................................................................... 49

7.2 Appendix 2 Interview .................................................................................. 56

7.3 Appendix 3 Scoring of the Directors Qualifications .................................... 62

7.4 Appendix 4 Scoring for the Firm Efficiency Metrics .................................... 84

7.5 Appendix 5 SMA, DQ and FEM Matrix of the Five Companies................... 91

List of Tables

Table 1 Samontarays Corporate Governance Scoring Tool .................................. 18

Table 2 Achchuthan and Kajanathans Measurement of Variables ........................ 19

Table 3 - Explanation of the Scoring of the Directors' Qualifications ....................... 24

Table 4 - Scoring of SMC's Directors' Qualifications for the Year 2002 ................... 26



Table 7 - Explanation of the Scoring of Firm Efficiency Metrics ............................. 28

Table 8 - Firm Efficiency Metrics of Ayala Land Incorporated ................................ 29

Table 9 - Pearson R Correlation table for ALI's SMA and DQ ................................. 33

Table 10 - Pearson R Correlation table for CHIB's SMA and DQ ............................ 33

Table 11 - Pearson R Correlation table for DMC's SMA and DQ ............................. 34

Table 12 - Pearson R Correlation table for SMC's SMA and DQ ............................. 34

Table 13 - Pearson R Correlation table for TEL's SMA and DQ............................... 35

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Table 14 - Pearson R Correlation table for all of the companies combined SMA and

DQ .......................................................................................................................... 35

Table 15 - Pearson R Correlation table for ALI's SMA and FEM ............................. 36

Table 16 - Pearson R Correlation table for CHIB's SMA and FEM .......................... 36

Table 17 - Pearson R Correlation table for DMC's SMA and FEM........................... 37

Table 18 - Pearson R Correlation table for SMC's SMA and FEM ........................... 37

Table 19 - Pearson R Correlation table for TEL's SMA and FEM ............................ 38

Table 20 - Pearson R Correlation table for all of the companies combined SMA and

FEM ........................................................................................................................ 38

Table 21 - Variables Entered in the Regression Analysis ......................................... 39

Table 22 - Model Summary of the Regression Models ............................................. 40

Table 23 - Analysis of Variance in the Regression Model Output ............................ 40

Table 24 - Coefficients of the Regression Output ..................................................... 41

List of Figures

Figure 1 - Example of a Director's Qualifications found in BusinessWeek Website.. 25

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1 Introduction

1.1 Background to the research

A company shares its assets and earnings with the general public because they need

the money. They can either borrow money or sell stocks to raise money to cover start-

up costs or expand the business. The disadvantage of borrowing money for companies

is that they have to pay back the loan with interest. By selling stocks, the company

gets money with fewer strings attached. There is no interest to pay and no requirement

to even pay the money back at all. Even better, selling stocks distributes the risk of

doing business among a large pool of stockholders. If the company fails, the founders

of the company do not lose all of their money; but the company loses several

thousand smaller chunks of other people's money. (Brain & Roos, 2011)

There are two categories of a persons perception on stock investing. People in the

first category believe that stock investing is a form of gambling. They believe that

they are more likely to end up losing all of their money. These fears are often driven

by personal experiences of family and friends who suffered similar fates or those who

lived through the Great Depression. A person who has the same kind of thinking

simply does not understand what a stock market is or why it exists. People who

belong to the second category are those who invest for the long-run but do not know

where to begin. They feel like investing is some sort of black-magic that only a few

people hold the key to. This is the reason why this type of investors leaves their

financial decisions up to professionals. They cannot explain why they own a stock or

mutual fund. Their style on investing is blind faith or bounded to this stock is going

up, we should buy it! People in this category are in more danger than the first. They

invest like everyone else and then they wonder why their results are average or, in

some cases, devastating. (Kennon, n.d.)

The two most common ways on studying stock price prediction is using the

fundamental and technical analysis. In this paper, the researcher experimented on

using corporate governance factors with the help of the stock moving average to

forecast stock trends. There are a lot of factors or economic indicators in using a

fundamental analysis for stock price prediction. Because of this, the researcher chose

to focus on two factors under corporate governance namely, Directors Qualifications

and Firm Efficiency Metrics.

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Technical analysis assumes that the market has discounted the fundamental

information, implicating that the market knows the information before it becomes

public, and seeks to interpret the market reaction to this information by analyzing

price movements for a given investment (TrendsetterSoftware, 2000). Forex Trading

(n.d.) defined fundamental analysis as basing the valuation of the stock on important

economic reports which they refer to as economic indicators. Most traders only

choose either one of the analyses stated above because one analysis works very

differently from the other. Despite this common norm, Athletic Study Center (2006)

stated that

..."although technical and fundamental analysis are seen as polar opposites -

the oil and water of investing - many traders have experienced great success

by combining the two. For example, some fundamental analysts use

technical analysis to figure out the best time to enter into an undervalued

security. Oftentimes, this situation occurs when the security is severely

oversold. By timing entry into a security, the gains on the investment can be

greatly improved" (para. 23).

The statement shows that there is a relationship between fundamental and technical

analysis. The researcher focused on combining Directors Qualifications, Firm

Efficiency Metrics and Stock Moving Average of companies to establish a stock price

prediction tool. The variables tackled under corporate governance are the

qualifications of board directors and firm efficiency metrics of a company.

1.2 Research Question

Investors seldom use fundamental and technical analysis together for stock price

forecasting purposes. There are a number of researches done that tackle either

fundamental analysis factors or technical analysis for stock price prediction but not

at the same time. There is little research done on combining technical and

fundamental analysis. Since previous studies show that either one of the analyses

show a positive effect with stock price prediction, the researcher decided to combine

both analyses for an optimal take on stock price prediction.

This problem was resolved by analysing five different companies annual reports and

their stock price moving average for a period of ten years. A financial analyst was

also interviewed about the variables that are used for the prediction tool. From the

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knowledge engaged from the interview, the scoring tool was made to quantize the

qualitative data gathered from the annual reports. From there, statistical tools were

used to establish stock price prediction formula.

Research Question: Is there a relationship between directors qualifications, firm

efficiency metrics and stock moving average in stock price prediction?

1.3 Research Objectives

The purpose of this research is to create a stock price prediction tool from using

directors qualifications, firm efficiency metrics and stock moving average five

company. The objectives of the investigation are as follows:

To determine if there is a relationship between directors qualifications and

stock moving average.

To determine if there is a relationship between firm efficiency metrics and

stock moving average.

To determine if there is a relationship between directors qualifications, firm

efficiency metrics and stock moving average.

To establish a stock price prediction tool using the factors directors

qualifications, firm efficiency metrics and stock moving average.

Hypotheses used in the study:

There is a relationship between Directors' Qualifications and Stock Moving

Average.

There is a relationship between Firm Efficiency Metrics and Stock Moving

Average.

At least one independent variable (directors qualifications or firm efficiency

metrics) is a significant predictor of a companys stock price.

The directors qualifications and firm efficiency metrics are significant

predictors of a companys stock price.

1.4 Justification for the research

Fundamental and technical analysis is widely known as leading investment decisions

tools used by investors to support their buying and selling stocks decisions (Cohen,

Kudryavtsev, & Hon-Snir, 2011). Fundamental analysis examines stock by

determining its intrinsic value while technical analysis disregards all areas of focus in

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the fundamental analysis (Hwa, 2010). Hwa (2010) stated that technical analysis is

based on the assumption that at any point in time, the stock prices reflects all known

factors that will affect the future of a company. This is one of the reasons why it is a

common norm that technical analysis dismisses all fundamental analysis factors

altogether.

A recent survey research study by Cohen, Kudryavtsev & Hon-Snir (2011) indicated

that investors use financial statements and support and resistance lines together as a

primary tool for their investment behavior. The result of their research entitled Stock

Market Analysis in Practice: Is It Technical or Fundamental? breaks a common

assumption arguing that fundamental and technical tools do not mix. Combining

fundamental and technical analysis is rarely done but it has a potential even though

only a few has considered doing so. Because only a few has considered in combining

fundamental and technical analysis, there has been little research on the said topic.

This is the reason why the researcher chose to tackle factors under the two most

common used analyses in stock forecasting. This research contributes to the

knowledge in the area of finance, specifically in stock investing. Moreover, it will

establish a foundation for future research in the same area.

1.5 Outline of research methodology

The research takes a realistic approach. The realistic approach to this research is by

gathering data from the annual reports of the five chosen companies itself thus,

recognizing procedures that are associated with qualitative research. The purpose of

this research is to experiment on whether the three factors chosen can used together to

produce a formula for stock price prediction. The research is beneficial to the

financial sector and generally, it is where the researcher intended to carry out the

study.

Primary data is gathered through conducting a semi-structured, face-to-face interview

with a financial analyst. The interview gave light to the importance of each factor to

the research. Secondary data is gathered from the annual reports and the stock closing

prices, both with a length of ten years, of five companies from different industries

namely financials, industrial, holding firms, services and property.

1.6 Outline of Chapters

This dissertation has the following structure:

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Chapter 2 Reviews relevant literature of fundamental and technical analysis,

corporate governance, board structure and the balance scorecard. The chapter ponders

on how the relevant recent literature undertaken is related on this paper.

Chapter 3 Provides the philosophy, approaches, methods, strategy and design

implemented for the research.

Chapter 4 Gives detailed information about the application of the research methods

for the collected data, the course of action for the data analysis and the presentation of

the findings.

Chapter 5 Relates research findings to the findings found in previous researches. It

sums up the implications, conclusions, recommendations, limitations of the study and

the areas for further research.

1.7 Definition of terms

Affiliations one factor under the directors qualifications that tallies the total board

directorship a director currently has

Audit Committee a group of at least 3 individuals responsible for overseeing all

internal and external audit functions of a company (InvestorWords, AuditCommittee,

n.d.)

Balance Scorecard a strategic planning and management system used to align

business activities to the vision statement of an organization (McCarthy & Chapman,

2013)

Customer Experience Enhancement a tool or program that a company has in efforts

of improving their customer service

Directors Qualifications a variable in this study that considers both the education

and affiliations of every person in a companys the board of directors; the qualitative

qualifications are quantized into a numerical value by using a scoring tool

Education one factor under the directors qualifications that considers a directors

educational background; there is a numeric value equivalent per educational

attainment (bachelors degree, masters degree and doctorate degree)

Firm Valuation - is the act or process of determining the value of a business enterprise

or ownership interest therein by determining the price that a hypothetical buyer would

pay for a business under a given set of circumstances (VentureLine, n.d.)

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Learning of Employee a firm efficiency metric where the training or learning

programs for a companys employees are measured

Nomination Committee is responsible for making recommendations on board

appointments, and on maintaining a balance of skills and experience on the board and

its committees (Q4S, n.d.)

Remuneration Committee is established to ensure that remuneration arrangements

support the strategic aims of the business and enable the recruitment, motivation and

retention of senior executives (Deloitte, n.d.)

Return on Assets (ROA) - gives an idea as to how efficient management is at using its

assets to generate earnings (Investopedia, ReturnOnAssets, n.d.)

Return on Equity (ROE) measures a corporation's profitability by revealing how

much profit a company generates with the money shareholders have invested

(Investopedia, ReturnOnEquity, n.d.)

Security - any note, stock, treasury stock, bond, debenture, certificate of interest or

participation in any profit-sharing agreement (Securities Exchange Act, 1934 as cited

in InvestorWords, n.d.)

Stock Price - The cost of purchasing a security on an exchange (InvestorWords, Stock

Price, n.d.)

1.8 Summary

This chapter gave light to the background of the research. The common use of

fundamental and technical analysis in stock price prediction is explained in the

background to the research. Corporate governance is also described as a part of

fundamental analysis and how it is studied in order to be used in stock price

prediction. The research question has been stated as well as the research objectives.

The need for the research has also been justified in this chapter. The methodology

used for this research is outlined and the definition of terms is provided. The outline

of the succeeding chapters is also reviewed.

2 Literature Review

2.1 Introduction

Mladjenovic (2013) stated that cautious investing is not just about what stock

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investors invest in but also how they invest. He advised stock investors to invest in

stocks of profitable companies that sell goods and services that a growing number of

people want. By doing so, stock investors' stocks will surely zigzag upward.

Mladjenovic (2013) also mentioned that being aware of investing tools and using

them regularly give you more control against the downside and more peace of mind.

The two most common investing tools that are used for stock investing is the technical

and fundamental analysis. Investors usually have their own style regarding which

analysis they use to predict stock prices. That is why according to Stanley (2012), a

common question of new traders is: which is better: Technical or fundamental

analysis? Stanley (2012) differentiated these two kinds of analysis on the table

below:

Technical Analysis Fundamental Analysis

1. Focuses solely on charts and past

price behaviours

2. Traders will often incorporate

indicators and tools

3. Traders attempt to anticipate future

price movements using past price

behavior

1. Concentrates on the financial drivers

of the economy itself

2. Traders will often follow news

announcements and data releases

3. Traders believe sentiment (based on

news and economic data releases)

drives markets

Technical analysis is a uniform way of analysing stock trends whereas fundamental

analysis is a wide-ranging form of analysis. On that note, since a fundamental analysis

has a lot of branches, the researcher chose a specific study under fundamental analysis

to focus on.

The art of Technical Analysis revolves around analyzing a chart and strategizing an

approach for trading stocks (Stanley, 2012). Technical analysis for stock trading

attempts to find out changes in investor sentiment through analyzing the technical

details of trading - both recent and historical (Todd, 2010). According to Martin J.

Pring, technical analysis is actually divided into three branches (Pring, 2002 as cited

in Todd, 2010). The three branches of technical analysis are:

1. Sentiment Indicators

2. Flow-of-Funds Indicators

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3. Market Structure Indicators

Sentiment Indicators reflect insider actions while Flow-of-Funds Indicators show

financial positions of investor groups (Pring, 2002 as cited in Todd, 2010). The third

branch of technical analysis is the Market Structure Indicators and it is considered as

the heart of technical analysis. Market Structure Indicators use available data to

tabulate and plot index or stock prices and the amount of shares that exchange hands.

Of the three branches of technical analysis, only market structure indicators are

readily available to the individual investor. These can be studied, analyzed, and

evaluated to improve the success of trading stocks. (Todd, 2010) This paper used the

technical analysis specifically under the branch of market structure indicators wherein

the stock moving average of each company is calculated to be able to correlate it with

each fundamental factor in the study.

The factors that are tackled in this paper under fundamental analysis are Directors

Qualifications and Firm Efficiency Metrics which are both under corporate

governance. A company's executives, board of directors and shareholders determine a

company's governance. It includes the management, policies and procedures that are

used to run a corporation. The level of accountability and transparency within a

corporation are factors that illustrate the stability and strength of a corporation.

(Scottrade, n.d.)

2.2 Fundamental and Technical Analysis

There are few researchers who studied combining fundamental and technical factors

to produce results for stock investing techniques or studies. In a recent paper, Jiang

and Nuez (2012) tested if combining fundamental and technical techniques would be

plausible for stock investing returns. The evidence that the authors found showed that

fundamental analysis is superior to technical analysis; that fundamental analysis is

always beating the market in terms of risk adjusted performance even when

transaction costs are introduced. Technical analysis is only able to beat the market in

terms of risk adjusted performance before transaction costs when conservative

strategies are applied in which the systems are long more time and short positions are

more restricted. Jiang and Nuez (2012) also argued that neither technique is useful to

beat the market in terms of return. However, fundamental analysis seems to be of help

to investors focus on absolute return instead of comparative return using a

benchmark. Overall, Jiang and Nuez (2012) concluded that fundamental analysis

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shows higher forecast ability than technical analysis, and that the combined use of

both analyses does not add value over the isolate use of fundamental analysis. This

paper is relevant to Jiang and Nuez's study because it tackles combining fundamental

and technical analysis for stock investing reasons. The main difference is the factors

used to come up with the forecasting results.

Another research by Sharma, Mahendru and Singh (2011) used technical analysis in

predicting future stock trends. The study builds on the literature already available on

financial data of the companies in India. The study is conducted to test out the

usefulness of the technical analysis in predicting the future market trends. From their

research, Sharma, Mahendru and Singh (2011) concluded that technical indicators

can play a useful role in the timing of stock market entry and exits. According to the

authors, the buy recommendations from the analysts are true but the sell

recommendations are not. They also stated that they cannot generalize the results as

they took the data of only 15 companies and analyzed it for two months.

The research done by Sharma, Mahendru and Singh only considered using technical

analysis for predicting future stock trends. Even though they only used technical

analysis as their indicator, it is considered relevant to this paper because it mainly

specializes in forecasting stock trends. They also stated that 'past prices', which is a

factor of technical analysis, should be combined with valuable information available

to be more helpful in achieving wanted results. This statement is greatly related to this

paper as valuable information, which is directors qualifications and firm efficiency

metrics, are combined with past prices (which are used to calculate the stock moving

average) to produce a stock price prediction tool.

2.3 Corporate Governance

Samontaray (2010) stated that good corporate governance practice provides a means

to recognize the dream of justifying risks and optimizing performance concurrently in

todays aggressive and regulatory setting. Corporate governance lays down

framework for creating long-term trust between company and its stakeholders. It

solves the problem of conflict of interest between the employees and principals. It is

solved by rationalizing and monitoring risks of a company, limiting liability of top

management by carefully articulating decision making process, ensuring integrity of

financial reports, and finally providing a degree of confidence necessary for proper

functioning of an organization. (Samontaray, 2010)

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In the research done by Samontaray, the annual reports and the actual share price of

50 companies as samples from NIFTY 50 Index from India were taken. The data

collected were from the financial year 2007-2008 relating to variables that include

share price, ROCE, EPS, D/E, P/E, and Corporate Governance Score. In this paper,

the data taken from companies are also from the annual reports and share prices of

companies. Data taken from the annual reports are for the corporate governance

factors which are directors qualifications and firm efficiency metrics of companies.

According to Samontaray (2010), corporate governance has significantly affected the

share price of the sample and hence has been a very important predictor for the listed

companies' share price value. Samontaray also used the regression analysis in order

to come up with a share price value. Aside from corporate governance, other

significant independent variables in Samontarays study include Earnings per Share,

Sales and Net Fixed Assets. The final equation formed is:

Share Price = 183.73 x CGS + 0.017 x Sales 0.022 x Net Fixed Assets + 10.386 x

EPS 755.

Another related research examined the relationship of corporate governance and firm

valuation using a constructed portfolio of seven stocks awarded under the good

governance scorecard vis--vis the PSEi and all shares index from January 2008 to

July 2011 (Santos & Lazaro, 2012). The authors also used corporate governance as

their variable into finding its relationship with firm valuation. Even though the paper

does not focus on forecasting stock trends, it is related in a way that they studied the

effects of corporate governance to a firm's valuation, risk and return which are also

related to a firm's share price value. Santos and Lazaro (2012) concluded that

constructing a portfolio based on good governance ranking fails to outperform the

market index in terms of rate of return and risk. The literature serves as a stepping

ground on what measures to consider in building up a forecasting stock tool.

2.3.1 Firm Efficiency Metrics

A Balanced Scorecard attempts to convert the sometimes indistinct, pious hopes of a

company's vision-mission statement into the practicalities of managing the business

better at every level. (McCarthy & Chapman, 2013) According to McCarthy and

Chapman (2013), the following departments may be looked at for improvement:

1. Finance

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2. Internal Business Processes

3. Learning and Growth of Employees

4. Customer Service

Once an organization has analysed the specific and quantifiable results of the above,

they should be ready to utilise the Balanced Scorecard approach to improve the areas

where they are deficient (McCarthy & Chapman, 2013). Since the balance scorecard

is basically an internal measure of a company for their efficiency, the researcher

considered factors under this papers variable, firm efficiency metrics, from the

mentioned departments stated by McCarthy and Chapman. The researcher considered

ROA and ROE under the department of finance, internal business processes that

involves having certain committees (audit, nomination and remuneration), the

learning of employees and customer experience enhancement which is under

customer service. Kumudini (2011, as cited in Achchuthan & Kajananthan, 2013)

also pointed that board committees composed of audit, remuneration and nomination

committees are positively associated with firm performance.

2.3.2 Scoring Tools

Table 1 Samontarays Corporate Governance Scoring Tool

A previous research that studied the impact of corporate governance on the stock

prices of 50 listed companies used a scoring tool to quantize qualitative data and also

to be able to compare companies with each other. The scoring tool adopted by

Samontaray is under the guidelines of Narayan Murthu Committee Report on

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Corporate Governance. Therefore, the difference of this papers scoring tool is that

the researcher made a scoring tool from scratch whereas Samontaray adopted his tool

from a committee report guidelines. According to Samontaray (2010), the scores in

his study were given in the manner such that if the company followed as per

recommendation of the guidelines of Narayan Murthu Committee Report on

Corporate Governance, the score of 0.5 is given, otherwise, the score would be zero.

The scoring of Samontaray found in Table 1 quite resembles the scoring tool in this

paper. The difference is that the scoring of the researcher is more categorized than

Samontarays.

Table 2 Achchuthan and Kajanathans Measurement of Variables

Table 2 shows how Achchuthan and Kajanathan quantized their studys variables.

Unlike in Samontarays scoring in Table 1, this scoring tool was made by Achchuthan

and Kajanathan themselves. In this paper, the scoring tools used was made by the

researcher itself but with light of the opinions of a financial analyst, gathered from

conducting an interview.

2.4 Chapter Summary

This chapter has provided an insight into fundamental and technical analysis,

corporate governance and the balance scorecard. Research undertaken helped the

researcher have a clear view on what factors to consider in the study and what

analysis and statistical tools to undertake. It will also guide stock investors on using

specific factors under fundamental and technical analysis which are corporate

governance and firm efficiency metrics on forecasting stock trends.

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3 Methodology

3.1 Introduction

The research intends to determine if there is a relationship between directors

qualifications, firm efficiency metrics and stock moving average and if the said

factors can produce a formula for stock price prediction. This chapter provides an

overview of the methodology adopted by the research.

3.2 Research Philosophy

The research philosophy relates to the development of knowledge and the nature of

that knowledge. It contains important assumptions about the way a researcher views

the world. The assumptions will underpin the research strategy and the methods that a

researcher chooses as a part of that strategy. (Saunders, Lewis, & Thornhill, 2010)

A realism approach has been selected for this research. It assumes a scientific

approach to the development of knowledge and the assumption underpins the

collection of data and the understanding of those data. The research analyzes the

objective information of publicly listed companies to come up with a stock price

prediction tool. From this analysis step of the research, the realism approach has an

ontological view which is objective. Objectivism is an ontological stance that implies

that social phenomena are based on external realities that are beyond our reach or

control (Wilson, 2010). Realism under an epistemological view is also considered as

observations provide credible data and/or facts if taken from different perspectives. A

qualitative research method is chosen because it fits the subject matter best.

(Saunders, Lewis, & Thornhill, 2010)

3.2.1 Justification of the Research Philosophy

The research used three factors to create a stock price prediction tool. These factors

include directors qualifications, firm efficiency metrics and stock moving average.

These factors are objective by nature and that is why a realist philosophy under an

objective ontological view is considered for the research. Realism under an

epistemological view is also considered most especially in the collection of data. The

variables above are observable; therefore, it provides facts and credible data when

observed from different perspectives. According to Saunders, Lewis & Thornhill

(2010), insufficient data means inaccuracies. Since the research needs to have equal

data for each company for optimum results, it underlies a direct realism approach

21

wherein data collected and used should be sufficient. In a realist approach, the data

collection method chosen must fit the subject matter (Saunders, Lewis, & Thornhill,

2010). The research can only be studied using a qualitative method because the

variables considered in the research can only be measured qualitatively. Sage (2011)

argued that realism can do useful work for qualitative methodology and practice if it

is taken seriously and its implication are systematically developed. Therefore, a realist

approach to the research is believed to be the best philosophy for the research.

3.3 Research Approach

The research has taken an inductive approach for the formulation of a stock price

prediction tool. A research using an inductive approach has a theory-building process

and is likely to be particularly concerned with the context in which such events are

taking place (Saunders, Lewis, & Thornhill, 2010). Researchers in this tradition are

more likely to work with qualitative data and to use a variety of methods to collect

these data in order to establish different views of phenomena (Easterby-Smith et al.,

2008 as cited in Saunders, Lewis & Thornhill, 2010).

3.3.1 Qualitative Research Approach

Qualitative research is characterised by its aims, which relate to understanding some

aspect of social life, and its methods which (in general) generate words, rather than

numbers, as data for analysis (Brikci, 2007). Getting documents from ten publicly

listed companies is the main qualitative data collection method in this research. The

factors used to establish a stock price prediction tool can only be accessed from the

annual and financial reports of each company. The researcher gained insight on how

to formulate the scoring tool for the directors qualifications and firm efficiency

metrics from conducting a semi-structured interview with a financial analyst who has

significant knowledge in the field of finance and stock investing.

3.4 Research Methods

Research methods refer to the techniques and procedures used to obtain and analyse

research data (Saunders, Lewis, & Thornhill, 2010). A mixed-model research method

is used for the research as to provide numerical analysis for the results needed. A

mixed-model research combines quantitative and qualitative data collection

techniques and analysis procedures as well as combining quantitative and qualitative

approaches at other phases of the research. The research took qualitative data and then

22

quantized the data which is converted it into narrative that can be analysed

quantitatively.

3.4.1 Literature Review

A literature review is the process of reading, analyzing, evaluating, and summarizing

scholarly materials about a specific topic (Nordquist, n.d.). It is a detailed and

justified analysis and commentary of the merits and faults of the literature within a

chosen area, which demonstrates familiarity with what is already known about the

research topic (Saunders, Lewis, & Thornhill, 2010). Reviewing the related literature

gave the researcher an insight into different methodological approaches previously

used by researchers. Previous work on the topic is used as a guide but the findings of

previous works were not enhanced or developed because the variables used, although

in the same field, are very different from the variables used in this research. The

keywords used in searching for the related literature are: 'combined fundamental and

technical analysis in stock prediction', 'corporate governance factors on stock prices',

'effects of the balance scorecard on stock performance', and 'board structure on stock

performance'. Overall, the related literature served as a support for the new insight

that is contributed by this research.

3.4.2 Semi-structured Interview

A semi-structured interview is a kind of primary data collection method and is

conducted with a moderately open structure which allow for focused, conversational,

two-way communication. This kind of interview can be used both to give and receive

information. Unlike the questionnaire framework, where detailed questions are

formulating ahead of time, semi structured interviews starts with general questions or

topics. Relevant topics are initially identified and the possible relationship between

these topics and the issues become the basis for more specific questions which do not

need to be prepared in advance. (FAO, n.d.)

Since the research is a case study of the financial sector, purposive sampling was

selected as the chosen method for conducting semi-structured interviews. This method

is to be used when you wish to select participants that are particularly relevant in

meeting the research objectives (Neuman, 2005 as cited in James 2012). One semi-

structured interview is conducted for this research and it is deemed sufficient to be

able to reach the aims of the research. The interview was audio-recorded to ensure

everything is covered and then transcribed at a later time. The interview facilitated an

23

examination of the relationship of the directors qualifications, balance scorecard

components/ firm efficiency metrics and stock moving average. It also assessed how

important the chosen factors are from the point of view of a financial analyst. The

interview was conducted before the scoring was done for the directors qualifications

and firm efficiency metrics. The scoring is done to generate a quantitative variable

from the qualitative data gathered so that the two factors can be correlated with the

technical analysis factor. Interviewing a financial analyst helped the researcher in

constructing the scoring tool used for quantizing the qualitative data gathered.

3.4.3 Document Analysis

Document analysis is a systematic procedure for reviewing or evaluating documents -

both printed and electronic (computer-based and internet-transmitted) material. Like

other analytical methods in qualitative research, document analysis requires that data

be examined and interpreted in order to elicit meaning, gain understanding and

develop empirical knowledge. (Corbin & Strauss, 2008; Rapley, 2007 as cited in

Bowen, 2009)

Documents such as published annual and financial reports are taken from five

publicly listed companies. The documents are analyzed and quantized statistically to

distinguish important patterns existing within and between the documents. The annual

and financial reports are either taken from the websites of each company as electronic

files or requested from the company itself as hard copies of the files. The technical

analysis factors that involve closing stock prices of the companies for ten years are all

taken from the Philippine Stock Exchange for a uniform and credible take on data

collection.

The documents taken from each company are all considered as secondary data.

Wilson (2010) defined secondary data as data that have been collected by other

researchers. Secondary data include everything from annual reports, promotional

material, parent company documentation, published case descriptions, magazines,

journal articles and newspaper reports as well as government printed sources (Wilson,

2010). According to Wilson (2010), the availability of secondary data is a real

concern to student researchers. That being said, the researcher's choice on which

companies to partake the research on is affected. It is mentioned earlier that the five

companies in this research are from different industries. Not all companies have

complete annual reports available on their website for the past ten years or so. That is

24

why the companies are not randomly selected. They are selected whether they have at

least ten years of data available on their website or available for pick up in their

office. The five companies that fit this certain criterion are Ayala Land Incorporated

(ALI), China Bank (CHIB), DMCI Holdings Incorporated (DMC), San Miguel

Corporation (SMC) and Philippine Long Distance Telephone Company (TEL).

3.4.4 Scoring Tool

Two of the factors used in the study are relatively new to the research area. These

factors include directors' qualifications and firm efficiency metrics. The said factors

need to be analysed in a way that numerical information should be garnered from

them. Therefore, the researcher created a scoring tool on how to quantize the

qualitative data that determines a company's directors' qualifications and balance

scorecard. This step explains how the researcher pursued a mixed-model research.

3.4.4.1 Directors' Qualifications - Scoring Tool

Table 3 - Explanation of the Scoring of the Directors' Qualifications

Table 3 shows how the researcher scored each director of every company. Before

scoring the directors, the researcher ensured that the data gathered regarding the

directors' education and affiliations are from a credible source. The names of the

directors of each company per year are all taken from the companies' published

annual reports. These annual reports are taken from the websites of the companies or

Scoring for Education

Level of Education Score

Bachelor's Degree 2

Two or more Bachelor's Degree 4

Master's Degree 6

Two or more Master's Degree 8

Doctorate 10

Scoring for Number of Affiliations

Number of Affiliations Score

1 - 2 affiliations 3






13 ++ affiliations 21

25

from the corporate headquarters itself. The education and affiliations of each director

are researched online, specifically from a website named BusinessWeek

(http://investing.businessweek.com). This step is done because not all companies

include the qualifications of their directors in their annual reports. The use of a

website is greatly needed for uniformity of data.

Figure 1 - Example of a Director's Qualifications found in BusinessWeek

Website

Figure 1 shows how the qualifications of Eduardo M. Cojuangco Jr., one of SMCs

directors, are enlisted in the website BusinessWeek. In relation to Table 1, wherein

the scoring for a director's qualifications are stated, Mr. Cojuangco's score for his

education should be 8 because he has two Master's degree enlisted, regardless of how

many education titles he had. His affiliations should have a score of 9 because he has

five affiliations enlisted. All of the directors are scored this way but there are a few

instances wherein either one out of education and affiliation has no data available in

the website. In these instances, a score of 2 for the education and 3 for the affiliations

26

is given to the director; it is assumed that the particular director has at least one

education title which is a bachelor's degree and one affiliation which is the company

that they are serving as a board director to.

Table 4 - Scoring of SMC's Directors' Qualifications for the Year 2002

SMC BOARD OF DIRECTORS 2002

A - Number of

Educational

Attainment

Score AB - Number of

AffiliationsScore B

1 EDUARDO M. COJUANGCO JR. 4 8 5 9

2 RAMON S. ANG 1 2 23 21

3 ESTELITO P. MENDOZA 2 6 9 15

4 MANUEL M. COJUANGCO N/A 2 N/A 3

5 INIGO ZOBEL N/A 2 9 15

6 WINSTON F. GARCIA 2 4 8 12

7 CORAZON DELA PAZ-BERNARDO 2 6 11 18

8 MENARDO R. JIMENEZ 1 2 8 12

9 PACIFICO M. FAJARDO N/A 2 2 3

10 HECTOR L. HOFILENA 2 4 2 3

11 LEO S. ALVEZ N/A 2 3 6

12 JUAN B. SANTOS 2 6 10 15

13 SHIGEKI OTA N/A 2 3 6

14 NAOMICHI ASANO 1 2 3 6

15 HENRY SY SR. 2 10 12 18

Sum 60 162

Weight Distribution 40% 24 60% 97.2

Total 121.20

27



Tables 4, 5 and 6 show how the total values for SMC's board of directors'

qualifications are calculated for the years 2002, 2003 and 2004. The tables show how

the researcher quantized the qualitative data gathered for SMC's directors'


A - Number of

Educational

Attainment


AffiliationsScore B


2 RAMON S. ANG 1 2 23 21








10 HECTOR L. HOFILENA 2 4 2 3


12 JUAN B. SANTOS 2 6 10 15

13 SHIGEKI OTA N/A 2 3 6

14 HENRY SY SR. 2 10 12 18

15 HITOSHI OSHIMA N/A 2 4 6

Sum 60 162

Weight Distribution 40% 24 60% 97.2

Total 121.20


A - Number of

Educational

Attainment


AffiliationsScore B


2 RAMON S. ANG 1 2 23 21








10 HITOSHI OSHIMA N/A 2 4 6

11 YOSHINORI ISOZAKI 2 6 4 6

12 HENRY SY JR. 2 4 10 15

13 OCTAVIO VICTOR R. ESPIRITU 4 8 9 15

14 EGMIDIO DE SILVA JOSE N/A 2 11 18


Sum 58 174

Weight Distribution 40% 23.2 60% 104.4

Total 127.60

28

qualifications. Two values are considered under a director's qualifications namely

education and affiliations. After the scores are given, each of the values are summed

up and weighted 40% for the education and 60% for the affiliations because according

to the interview conducted by the researcher, the affiliations of a director is more

important than the education obtained. A director gets his or her experience from all

the affiliations he or she had. Also, having more affiliations means you know a lot or

people thus a good source of network which is good for business. While having a

good education is important, the number of affiliations says more about a director.

Tables 4, 5 and 6 tells us that SMC's numerical data for its directors' qualifications for

the years 2002, 2003 and 2004 are 121.20, 121.20 and 127.60 respectively.

3.4.4.2 Firm Efficiency Metrics - Scoring Tool

Table 7 - Explanation of the Scoring of Firm Efficiency Metrics

Table 7 shows how the researcher scored each firm-efficiency metric of a company.

According to the related literature reviewed about a balance scorecard, McCarthy and

Chapman (2013) stated that departments including finance, learning and growth,

internal business processes, and customer service should be looked at for

improvement and it can be done by utilising a balance scorecard approach. The firm

29

ALI 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

ROE 8% 8.10% 9.40% 10% 10.2% 10.2% 8.0% 10.0% 12.0% 13%

ROA 4% 4.20% 4.70% 5.2% 5.4% 5.2% 3.9% 4.8% 5.2% 5%

Learning of Employee YES NONE YES YES YES YES YES YES YES YES

COMMENT/S: Extensive N/A Simple Simple Simple Moderate Extensive Extensive Extensive Extensive

Customer Experience

Enhancement YES YES YES YES YES YES YES YES YES YES

COMMENT/S: Moderate Simple Extensive Extensive Extensive Extensive Extensive Extensive Extensive Extensive

Audit / Nomination /

Renumeration Committee Complete Complete Complete Complete Complete Complete Complete Complete Complete Complete

Additional Info:

ROE year 2002 7%

ROA year 2002 4%

SCORES 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

ROE 6 6 6 6 6 3 0 6 6 6

ROA 3 6 6 6 6 0 0 6 6 0

Learning of Employee 9 0 3 3 3 6 9 9 9 9

Customer Experience

Enhancement 6 3 9 9 9 9 9 9 9 9


Remuneration Committee 6 6 6 6 6 6 6 6 6 6

WEIGHTED SCORES 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

ROE (5%) 0.3 0.3 0.3 0.3 0.3 0.15 0 0.3 0.3 0.3

ROA (5%) 0.15 0.3 0.3 0.3 0.3 0 0 0.3 0.3 0

Learning of Employee (35%) 3.15 0 1.05 1.05 1.05 2.1 3.15 3.15 3.15 3.15

Customer Experience

Enhancement (35%)2.1 1.05 3.15 3.15 3.15 3.15 3.15 3.15 3.15 3.15


Remuneration Committee (20%)1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

TOTAL SCORE 6.9 2.85 6 6 6 6.6 7.5 8.1 8.1 7.8

Balance Scorecard Scoring for Ayala Land Incorporation

efficiency metrics selected by the researcher refer to the statement by McCarthy and

Chapman. The factors ROE and ROA is from the finance department, the learning of

employee is from the learning and growth department, and the customer experience

enhancement is related to the customer service department.

Kumudini (2013) pointed that board committees composed of audit, remuneration

and/or nomination committees are positively associated with firm performance thus

the 'audit, nomination and remuneration' factor chosen by the researcher. With

regard to the financial metrics, it is obvious that the ROE and ROA are already

quantitative. The reason why the researcher scored them still is to make the research

simpler and the firm efficiency metrics to be parallel with each other. Also, from the

interview conducted, the financial analyst indicated that if the ROE and ROA is used

as is, those specific firm-efficiency metrics will add up to the stock moving average

thus, doubling up the value of the factors studied. The interviewee/ financial analyst

noted that calculated returns of the company are already considered in the assessment

of why a company has this certain stock price.

Table 8 - Firm Efficiency Metrics of Ayala Land Incorporated

30

Table 8 shows how the researcher scored the chosen firm efficiency metrics for Ayala

Land Incorporation with a length of ten years. After analysing the firm efficiency

metrics, they are given scores in relation to the explanation of scoring in table 7. The

scores are then weighted with a certain percentage that can be seen in table 8. The

percentages given for each firm-efficiency metric are established from the knowledge

and insights given by a financial analyst whom the researcher interviewed.

3.5 Research Design

This paper is an explanatory research wherein it focuses on studying a situation or a

problem in order to explain the relationships between variables (Saunders, Lewis, &

Thornhill, 2010). This kind of research design is followed because there is a need to

correlate and explain the factors of the research in order to come up with a stock price

prediction tool. Aside from the Pearson-R Correlation, other statistical tools like

ANOVA and Multiple Regression are used for a clearer view of the relationship

between the factors.

3.6 Research Strategy

A case study design was adopted for the research. It has considerable ability to

generate answers to the questions 'why', 'what' and 'how' and uses multiple sources for

triangulation. This is the reason why the case study strategy is often used in

explanatory research. (Saunders, Lewis, & Thornhill, 2010). Wilson (2010) said that

in business research, a case study design often involves an in-depth analysis of an

individual, a group of individuals, an organization, or a particular sector.

3.6.1 Ethical Considerations

A letter was given to the participant who was deemed appropriate for the interview.

The letter contained the topic of the research and a notation of their rights not to

participate and that all personal information will not be used or divulged. The

participant replied to the letter stating the time and date of his availability and also

said that he preferred his personal information not be divulged as well as the name of

his company. The interview is recorded and transcribed completely but no personal

information is divulged.

3.7 Chapter Summary

This chapter has outlined the research methodology adopted by the research. The

research philosophy, approach, methods, strategy and design are examined to give the

31

readers an insight on how the research is conducted. The results of the research is

analysed and discussed in the following chapter.

4 Findings

4.1 Introduction

This chapter discusses the presentation and analysis of the research findings based on

the methodologies used. The conclusions and recommendations brought together by

the research findings are further discussed in the following chapter.

4.2 Application of Methodology

A mixed-method research approach was adopted to study the research question, using

a qualitative semi-structured interview and a qualitative document analysis to ensure

triangulation of findings. Wilson (2010) defined data triangulation as data collected at

different times or from different sources in the study of a phenomenon. In order to get

reliable information to answer the research question, the researcher followed a mixed-

model research, which is a branch of mixed-method research, to be able to extract

quantitative data from a qualitative source of data. The complete interview transcript

can be seen in Appendix 2.

4.2.1 Semi-Structured Interview

The semi-structured interview gave light on how the researcher made the scoring tool

for quantizing the qualitative data from the annual report analysis. As explained in the

research methodology, a director's qualifications include their education and

affiliations. 'Affiliations' has a higher percentage than 'education' because the financial

analyst (interviewee) said that a director's connections and experience is more

important than his or her education and that affiliations is a more important factor in

general. That is why the researcher got 60% from the total score of 'affiliations' and

40% from the total score of 'education' to get the final score of a company's board of

directors per year. For the firm efficiency metrics, the financial analyst focused more

on the metrics that concerns the learning of employees and customer service. The

financial analyst said that every company for sure has a tool for their customer service

but it varies and the learning and training of employees is important for the overall

performance of the company. He mentioned briefly that measuring companies

through having certain committees like audit, nomination and remuneration is a good

32

thing. Including financial metrics in the scoring tool is important as well but the

interviewee said that since the research is considering the stock moving average as

one of its variables, the ROE and ROA should be given the least weight since these

financial metrics are already measured in the stock prices therefore, it will be counted

twice. The weighted scores used in the firm efficiency scoring for ROE, ROA,

learning of employee, customer experience enhancement and committees are 5%, 5%

35%, 35% and 20% respectively.

4.3 Findings for Each Research Objective

The following list includes factors and companies that are used in this research. The

list also includes the factors' acronyms and the companies' stock symbols.

SMA Stock Moving Average

DQ Directors' Qualifications

FEM Firm Efficiency Metrics

ALI Ayala Land Incorporated

CHIB China Bank

DMC DMCI Holdings Incorporated

SMC San Miguel Corporation

TEL Philippine Long Distance Telephone Company


Average

The tables below represent the correlation of the two factors, directors' qualifications

and stock moving average. The correlation is also tested for significance with the null

and alternate hypothesis:

Ho: There is no relationship between the Directors' Qualifications and the

Stock Moving Average

Ha: There is a relationship between the Directors' Qualifications and the Stock

Moving Average

The Pearson R Correlation and its significance are computed by using the IBM SPSS

Statistics software. The Scoring for the directors qualifications and firm efficiency

metrics can be seen in Appendix 3 and Appendix 4 respectively. The matrix for the

final numerical value of each companys directors qualifications, firm efficiency

metrics and stock moving average can be seen in Appendix 5.

33

Table 9 - Pearson R Correlation table for ALI's SMA and DQ

Pearson R: Marked and substantial

Significance: 0.14 > 0.05

Accept Ho

Table 10 - Pearson R Correlation table for CHIB's SMA and DQ

Pearson R: Present but slight


Accept Ho

34

Table 11 - Pearson R Correlation table for DMC's SMA and DQ

Pearson R: Marked and substantial (Negative relationship)


Accept Ho

Table 12 - Pearson R Correlation table for SMC's SMA and DQ

Pearson R: High relationship

Significance: 0.005 < 0.01

Reject Ho

35

Table 13 - Pearson R Correlation table for TEL's SMA and DQ



Accept Ho

Table 14 - Pearson R Correlation table for all of the companies combined SMA

and DQ



Reject Ho

36


Average

The tables below represent the correlation of the two factors, firm efficiency metrics

and stock moving average. The correlation is also tested for significance with the null

and alternate hypothesis:

Ho: There is no relationship between the Firm Efficiency Metrics and the

Stock Moving Average

Ha: There is a relationship between the Firm Efficiency Metrics and the Stock

Moving Average

Table 15 - Pearson R Correlation table for ALI's SMA and FEM



Accept Ho

Table 16 - Pearson R Correlation table for CHIB's SMA and FEM

Pearson R: Negligible (Negative relationship)


Accept Ho

37

Table 17 - Pearson R Correlation table for DMC's SMA and FEM



Accept Ho

Table 18 - Pearson R Correlation table for SMC's SMA and FEM

Pearson R: Negligible


Accept Ho

38

Table 19 - Pearson R Correlation table for TEL's SMA and FEM



Reject Ho

Table 20 - Pearson R Correlation table for all of the companies combined SMA

and FEM

Pearson R: Negligible


Accept Ho


and Stock Moving Average

The relationship between directors' qualifications, firm efficiency metrics and stock

moving average are determined by using a multiple regression method using the IBM

SPSS Statistics software. The data entered is from the combined values of directors'

39

qualifications, firm efficiency metrics and stock moving average of the five

companies.

Table 21 - Variables Entered in the Regression Analysis

Table 21 shows the variables entered in the regression model using the stepwise

method. The stepwise method eliminates insignificant variables from the model. The

dependent variable is the stock moving average because it represents the stock price

of a company. The independent variables include the directors' qualifications and firm

efficiency metrics. They are also called explanatory variables because they

determinate the stock price of a company. The first model includes directors'

qualifications only while the second model shows both directors' qualifications and

firm efficiency metrics as independent variables. The stepwise method is considered

because the firm efficiency metrics' correlation for all the combined data from all the

companies has a negligible positive relationship and is not significant.

40

Table 22 - Model Summary of the Regression Models

Table 22 shows the summary of the two models considered. The R-Square provides

an indication of the explanatory power of the regression model. It is simply the

percentage of variance explained by the collection of independent variables. In the

first model, directors' qualifications alone explain 15.1% of the variance in the stock

moving average. The second model tells us that 23.9% of the variance in the stock

moving average is explained by the combined directors' qualifications and firm

efficiency metrics. Therefore, the combined power of directors qualifications and

firm efficiency metrics are greater indicators of the explanatory power of the

regression than just the directors' qualifications alone.

Table 23 - Analysis of Variance in the Regression Model Output

Table 23 pertains to a hypothesis in this study to determine if the regression model

includes independent variables that are significant.

Ho: None of the independent variables are significant predictors of a

companys stock price

41

Ha: At least one independent variable (directors qualifications or firm

efficiency metrics) is a significant predictor of a companys stock price

Both of the models are significant in level 0.01, two-tailed. Therefore, the null

hypothesis is rejected. It is also noted that the model including both independent

variables is more significant than the other. This means that the combined

independent variables are more efficient in determining a stock price rather than

directors' qualifications alone.

4.3.4 The established Stock Price Prediction Tool using the factors Directors

Qualifications, Firm Efficiency Metrics and Stock Moving Average

Table 24 - Coefficients of the Regression Output

Based on the analysis of variance in the regression model in table 23, the second

model is considered in making a stock price prediction tool; the one that includes both

of the independent variables. Table 24 shows us the significance of each independent

variable in the regression output. The null and alternate hypotheses are:

Ho: This independent variable (directors qualifications or firm efficiency

metrics) is not a significant predictor of a companys stock price

Ha: This independent variable (directors qualifications or firm efficiency

metrics) is a significant predictor of a companys stock price

It is seen in the second model of table 24 that both of the independent variables are

significant. Directors' qualifications is significant at level 0.01 (two-tailed test) and

the firm efficiency metrics is significant at level 0.05 (two-tailed test). Therefore,

the null hypothesis for both independent variables is rejected. It is also noted that the

42

constant value of the second model is also significant at level 0.01 (two-tailed test).

This means that the independent variables in this research and the constant value from

the regression model output are all significant therefore, the factors are good to be

used in the stock price prediction formula.

The equation adopted from the regression output is:


- 1488.66

5.0 Conclusions and Implications

5.1 Introduction

This chapter evaluates the adopted methodology and its successful application. It also

provides conclusions based on the research objectives and the reviewed literature. The

limitations of the study are also discussed in this chapter. Recommendations for

further research complete the chapter.

5.2 Critical Evaluation of Adopted Methodologies

This section reviews the methodology used which is explained in chapter 3 and also

its limitations.

5.2.1 Literature Review

Reviewing related literature gave insights to the researcher on how to go about the

research. Statistical tools used in the research are also quite relevant to previous

studies. However, the literature reviewed in chapter 2 is not specifically related

enough to provide optimal support on the conclusions garnered from the results of the

research. Nevertheless, previous studies reviewed are general enough to be compared

with the results in this research.

5.2.2 Semi-structured Interview

The researcher interviewed one financial analyst of a reputable company wherein the

knowledge gained is sufficient enough to support the scoring tool made for the

research. The semi-structured interview is the best interview approach because the

researcher gained additional knowledge aside from the answers given from the listed

interview questions.

43

5.2.3 Scoring Tool

The scoring tool made for the directors' qualifications gave high, positive and

significant correlations between the directors' qualifications and the stock moving

average. Therefore, the scoring tool for the directors' qualifications can be used as is

by future researchers if they wish to do a research that includes directors'

qualifications as a factor. This scoring tool is deemed efficient and effective by the

researcher. The scoring tool for the firm efficiency metrics however did not give

significant results. Since the scoring tool for the firm efficiency metrics somehow

gave a low positive correlation, it means that this scoring tool has the right foundation

but needs to be enhanced further for significant results. Nevertheless, it is still deemed

efficient by the researcher because when the numerical values from this scoring tool is

used in the regression analysis, it managed to give out high significant results that are

used in the stock price prediction tool.

5.2.4 Document Analysis

In the assignment of scores in a previous study reviewed, only annual reports have

been recommended by the Corporate Governance committee for the gathering of data

for credibility (Samontaray, 2010). In this note, the researcher also considered factors

that are exclusive for annual reports of publicly listed companies. Therefore,

analysing annual reports is the best choice of document analysis because the factors

considered can only be seen in an annual report and also, data can be compared as all

public companies publish their own annual report.

5.3 Analysis of Findings for each Research Objective

This section deals with each research objective; whether they were met in relation to

the results of the research.


Average

If each companys results are to be considered, it is generalized that the directors

qualifications and the stock moving average does not have a relationship with each

other. Although most of the correlations of each company have a positive correlation,

the researcher still accepted the null hypothesis because most of the correlations are

not significant. However, when all the data for the directors qualifications from each

of the five companies are combined, the positive correlation result is significant at the

44

0.01 level. Therefore, directors qualifications and stock moving average are generally

correlated with each other.


Average

The results of the correlation for the firm efficiency metrics and stock moving average

have mostly a positive correlation but only one out of five is significant. When all

data are combined, the correlation result is positive but negligible and is not

significant. Therefore, firm efficiency metrics and the stock moving average does not

have a relationship with each other.


and Stock Moving Average

To assess the relationship between the three factors stated, the researcher used a

regression model with a stepwise method to eliminate insignificant variables from the

model. The results show that even though the firm efficiency metrics is not correlated

with the stock moving average, it is still an accepted factor in this research when

combined with the directors qualifications because the explanatory power of the

two factors combined is greater than the explanatory power of the directors

qualifications alone (23.9% > 15.1%). Both of the independent variable is significant

therefore, all of the factors in this research are qualified for the stock price prediction

tool.

5.3.4 The established Stock Price Prediction Tool

Based from the results, the directors qualifications and the firm efficiency metrics

together with the constant value from the regression output are all significant

therefore; all factors mentioned are used to establish a stock price prediction formula.

Stock Price Prediction Tool:


- 1488.66

5.4 Analysis and Overall Conclusions about the Research Question

Overall, the research indicates that directors qualifications and firm efficiency

metrics have the ability to forecast stock prices. When the 'directors qualifications' is

used alone, it is able to forecast stock prices but not as powerful as when it is used

with firm efficiency metrics. This is possible because the 'directors qualifications' is

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highly correlated with the stock moving average, which is used to represent a

companys stock price. If the 'firm efficiency metrics' is used alone, it wouldnt be

able to predict stock prices as it does not have a significant relationship with the stock

moving average. The directors qualifications and the firm efficiency metrics are both

considered under a companys corporate governance. Although the research does not

have a relevant literature that specifically tackles directors qualifications and firm

efficiency metrics as factors that predicts a stock price, a recent study by Samontaray

(2010) concludes that corporate governance significantly affects the share price of a

company and therefore has been a very important predictor a companys share price

value. The said research by Samontaray supports the findings in this research

generally, when the point of view is from the bigger picture which is corporate

governance. A previous study by Sharma, Mahendru and Singh (2011) also concluded

that 'past prices' should be combined with valuable information available to be more

helpful in achieving wanted results. Past prices in this study is used to get the stock

moving average and it is studied with two other factors to get a predictive formula,

positively relating to the study of Sharma, Mahendru and Singh.

5.5 Limitations to the Study

The research question is successfully answered with the help of the chosen

methodologies for the research. However, the researcher cannot generalize the results

as the research has its limitations. With regard to the interview conducted, it might be

better if the researcher interviewed more than one financial analyst so that the

opinions and knowledge gathered can be weighed for a more generalized approach on

the scoring tools.

The analysis of each factor for ten years from each company is sufficient enough to

get good quality results. Due to time and resource constraints, the researcher only

used five companies for the research. This may have affected the results of the

statistical tools used. The researcher deemed that if time and resource is highly

available, getting data from at least 10 companies may have produced results that can

be generalized in the financial sector. Lastly, the process of quantizing qualitative data

from the annual reports may not have been standardized enough as the researcher is

solely the one who decides on what score to give based on the annual reports.

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5.6 Opportunities for Future Research

Future research is always appreciated as it enhances knowledge in the research area.

The research concluded that the directors qualifications, firm efficiency metrics and

stock moving aver