Docshare

Published on April 2017 | Categories: Documents | Downloads: 37 | Comments: 0 | Views: 275
of 93
Download PDF   Embed   Report

Comments

Content

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)

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 PearsonR 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.

1

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.

2

Declaration
This work is original and has not been submitted previously for any academic
purpose. All secondary sources are acknowledged.

Signed:

____________________

Date:

____________________

3

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

4

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
5.3.1 The relationship between Directors’ Qualifications and Stock Moving
Average............................................................................................................. 43
5.3.2 The relationship between Firm Efficiency Metrics and Stock Moving

5

Average............................................................................................................. 44
5.3.3 The relationship between Directors’ Qualifications, Firm Efficiency Metrics
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 – Samontaray’s Corporate Governance Scoring Tool .................................. 18
Table 2 – Achchuthan and Kajanathan’s 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 5 - Scoring of SMC's Directors' Qualifications for the Year 2003 ................... 27
Table 6 - Scoring of SMC's Directors' Qualifications for the Year 2004 ................... 27
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

6

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

7

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 startup 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 person’s 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.

8

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

9

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 company’s stock price.



The directors’ qualifications and firm efficiency metrics are significant
predictors of a company’s 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

10

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:

11

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 company’s 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 director’s
educational background; there is a numeric value equivalent per educational
attainment (bachelor’s degree, master’s 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.)
12

Learning of Employee – a firm efficiency metric where the training or learning
programs for a company’s 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
13

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 1. Concentrates on the financial drivers
price behaviours
2. Traders

will

of the economy itself
often

incorporate 2. Traders

indicators and tools

will

often

follow

news

announcements and data releases

3. Traders attempt to anticipate future 3. Traders believe sentiment (based on
price movements using past price

news and economic data releases)

behavior

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
14

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 Nuñez (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 Nuñez (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 Nuñez (2012) concluded that fundamental analysis
15

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 Nuñez'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
today’s 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)
16

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 Samontaray’s 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-à-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

17

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 paper’s 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 – Samontaray’s 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

18

Corporate Governance. Therefore, the difference of this paper’s 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
Samontaray’s.
Table 2 – Achchuthan and Kajanathan’s Measurement of Variables

Table 2 shows how Achchuthan and Kajanathan quantized their study’s variables.
Unlike in Samontaray’s 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.
19

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

20

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

21

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 semistructured 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
22

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
23

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
Scoring for Education
Level of Education
Bachelor's Degree
Two or more Bachelor's Degree
Master's Degree
Two or more Master's Degree
Doctorate

Score
2
4
6
8
10

Scoring for Number of Affiliations
Number of Affiliations
1 - 2 affiliations
3 - 4 affiliations
5 - 6 affiliations
7 - 8 affiliations
9 - 10 affiliations
11 - 12 affiliations
13 ++ affiliations

Score
3
6
9
12
15
18
21

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
24

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 SMC’s
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
25

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
A - Number of
Educational
Attainment
4

SMC BOARD OF DIRECTORS 2002
1 EDUARDO M. COJUANGCO JR.

Score A

B - Number of
Affiliations

Score B

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

2

4

2

3

N/A

2

3

6

2

6

10

15

N/A

2

3

6

10 HECTOR L. HOFILENA
11 LEO S. ALVEZ
12 JUAN B. SANTOS
13 SHIGEKI OTA
14 NAOMICHI ASANO

1

2

3

6

15 HENRY SY SR.

2

10

12

18

60%

97.2

Sum

60

Weight Distribution

40%

Total

121.20

26

24

162

Table 5 - Scoring of SMC's Directors' Qualifications for the Year 2003
SMC BOARD OF DIRECTORS 2003
1 EDUARDO M. COJUANGCO JR.

A - Number of
Educational
Attainment
4

Score A

B - Number of
Affiliations

Score B

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
3

10 HECTOR L. HOFILENA
11 LEO S. ALVEZ
12 JUAN B. SANTOS
13 SHIGEKI OTA
14 HENRY SY SR.
15 HITOSHI OSHIMA

2

4

2

N/A

2

3

6

2

6

10

15

N/A

2

3

6

2

10

12

18

N/A

2

4

6

60%

97.2

Sum

60

Weight Distribution

40%

Total

24

162

121.20

Table 6 - Scoring of SMC's Directors' Qualifications for the Year 2004
SMC BOARD OF DIRECTORS 2004
1 EDUARDO M. COJUANGCO JR.

A - Number of
Educational
Attainment
4

Score A

B - Number of
Affiliations

Score B

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

2

6

11

18
12

6 CORAZON DELA PAZ-BERNARDO
7 WINSTON F. GARCIA
8 LEO S. ALVEZ
9 MENARDO R. JIMENEZ
10 HITOSHI OSHIMA

2

4

8

N/A

2

3

6

1

2

8

12

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

15 PACIFICO M. FAJARDO

N/A

2

2

Sum

58

Weight Distribution

40%

Total

23.2

3
174

60%

104.4

127.60

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'
27

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
28

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
Balance Scorecard Scoring for Ayala Land Incorporation
ALI
2003
2004
2005
ROE
8%
8.10%
9.40%

2006

2007

2008

2009

2010

2011

2012
13%

ROA
Learning of Employee
COMMENT/S:
Customer Experience
Enhancement
COMMENT/S:
Audit / Nomination /
Renumeration Committee
Additional Info:

10%

10.2%

10.2%

8.0%

10.0%

12.0%

4%

4.20%

4.70%

5.2%

5.4%

5.2%

3.9%

4.8%

5.2%

5%

YES

NONE

YES

YES

YES

YES

YES

YES

YES

YES

Extensive

N/A

Simple

Simple

Simple

YES

YES

YES

YES

YES

Moderate

Simple

YES

YES

YES

YES

YES

Extensive Extensive Extensive Extensive Extensive Extensive Extensive Extensive

Complete Complete Complete Complete Complete Complete Complete Complete Complete Complete

ROE year 2002

7%

ROA year 2002

4%

SCORES
ROE

Moderate Extensive Extensive Extensive Extensive

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

6

6

6

6

6

3

0

6

6

6

ROA

3

6

6

6

6

0

0

6

6

0

Learning of Employee
Customer Experience
Enhancement
Audit / Nomination /
Remuneration Committee
WEIGHTED SCORES

9

0

3

3

3

6

9

9

9

9

6

3

9

9

9

9

9

9

9

9

6

6

6

6

6

6

6

6

6

6

ROE (5%)

2003
0.3

2004
0.3

2005
0.3

2006
0.3

2007
0.3

2008
0.15

2009
0

2010
0.3

2011
0.3

2012
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%)
Customer Experience
Enhancement (35%)
Audit / Nomination /
Remuneration Committee (20%)
TOTAL SCORE

3.15

0

1.05

1.05

1.05

2.1

3.15

3.15

3.15

3.15

2.1

1.05

3.15

3.15

3.15

3.15

3.15

3.15

3.15

3.15

1.2

1.2

1.2

1.2

1.2

1.2

1.2

1.2

1.2

1.2

6.9

2.85

6

6

6

6.6

7.5

8.1

8.1

7.8

29

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
30

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 mixedmodel 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

31

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

4.3.1 The relationship between Directors’ Qualifications and Stock Moving
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 company’s directors’ qualifications, firm efficiency
metrics and stock moving average can be seen in Appendix 5.
32

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
Significance: 0.41 > 0.05
∴ Accept Ho

33

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

Pearson R: Marked and substantial (Negative relationship)
Significance: 0.21 > 0.05
∴ 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

34

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

Pearson R: Present but slight
Significance: 0.34 > 0.05
∴ Accept Ho

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

Pearson R: Present but slight
Significance: 0.005 < 0.01
∴ Reject Ho

35

4.3.2 The relationship between Firm Efficiency Metrics and Stock Moving
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

Pearson R: Marked and substantial
Significance: 0.11 > 0.05
∴ Accept Ho
Table 16 - Pearson R Correlation table for CHIB's SMA and FEM

Pearson R: Negligible (Negative relationship)
Significance: 0.76 > 0.05
∴ Accept Ho
36

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

Pearson R: Marked and substantial
Significance: 0.17 > 0.05
∴ Accept Ho
Table 18 - Pearson R Correlation table for SMC's SMA and FEM

Pearson R: Negligible
Significance: 0.93 > 0.05
∴ Accept Ho

37

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

Pearson R: Marked and substantial
Significance: 0.04 < 0.05
∴ Reject Ho
Table 20 - Pearson R Correlation table for all of the companies combined SMA
and FEM

Pearson R: Negligible
Significance: 0.71 > 0.05
∴ Accept Ho
4.3.3 The relationship between Directors’ Qualifications, Firm Efficiency Metrics
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'

38

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.

39

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
company’s stock price
40



Ha: At least one independent variable (directors’ qualifications or firm
efficiency metrics) is a significant predictor of a company’s 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 company’s stock price



Ha: This independent variable (directors’ qualifications or firm efficiency
metrics) is a significant predictor of a company’s 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
41

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

42

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.
5.3.1 The relationship between Directors’ Qualifications and Stock Moving
Average
If each company’s 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

43

0.01 level. Therefore, directors’ qualifications and stock moving average are generally
correlated with each other.
5.3.2 The relationship between Firm Efficiency Metrics and Stock Moving
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.
5.3.3 The relationship between Directors’ Qualifications, Firm Efficiency Metrics
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:
Stock Price = (15.83 x Directors' Qualifications) + (114.30 x Firm Efficiency Metrics)
- 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
44

highly correlated with the stock moving average, which is used to represent a
company’s stock price. If the 'firm efficiency metrics' is used alone, it wouldn’t 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 company’s 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 company’s 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.

45

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 average has significant relationship with each other therefore, the
factors can easily predict future stock trends. Future researchers may improve this
research by analysing at least ten or more companies with a period of at least ten years
or more. Future researchers may also revise the scoring tool made for this research to
have more efficient results. Lastly, the study may provide a better stock forecasting
tool if other factors are tested and added into the current factors used in this study.

6 References

Achchuthan, S., & Kajananthan, R. (2013). Corporate Governance Practices and Firm
Performance: Evidence from Sri Lanka. European Journal of Business and
MAnagement , 24-25.
AthleticStudyCenter. (2006). Berkeley. Retrieved February 7, 2013, from Technical
Analysis: Introduction: http://www.ocf.berkeley.edu/~jml/decal/techanalysis.pdf
Bowen, G. (2009). Document Analysis as a Qualitative Research Method. Qualitative
Research Journal , 1-3.
Brain, M., & Roos, D. (2011). How Stocks and the Stock Market Work. Retrieved
April
5,
2013,
from
How
Stuff
Works:
http://money.howstuffworks.com/personal-finance/financialplanning/stocks.htm
Brikci, N. (2007). A Guide to Using Qualitative Research Methodology. Retrieved
April
7,
2013,
from
FieldResearch:
http://fieldresearch.msf.org/msf/bitstream/10144/84230/1/Qualitative%20resear
ch%20methodology.pdf
Cohen, G., Kudryavtsev, A., & Hon-Snir, S. (2011). Stock Market Analysis in
Practice: Is It Technical or Fundamental? Journal of Applied Finance &
Banking , 126.
Deloitte. (n.d.). Remuneration. Retrieved May 31, 2013, from Deloitte:
http://www.corpgov.deloitte.com/site/au/remuneration-committees/
EconomicsHelp. (n.d.). Technical Efficiency Definition. Retrieved March 29, 2013,
from EconomicsHelp:
efficiency.html

http://www.economicshelp.org/dictionary/t/technical-

46

Elijido-Ten, E. (2011). The Impact of Sustainability and Balanced Scorecard
Disclosures on Market Performance: Evidence from Austrailia's Top 100.
Swinburne University of Technology , 68-70.
FAO. (n.d.). Semi-structured Interviews. Retrieved April 7, 2013, from Fao.org:
http://www.fao.org/docrep/x5307e/x5307e08.htm
ForexTrading. (n.d.). ForexTrading. Retrieved February 7, 2013, from Introduction to
Fundamental
Analysis:
http://forextrading.about.com/od/fundamentalanalysis/a/fundamentals.htm
Hwa, O. K. (2010). SIDC. Retrieved March 22, 2013, from Malaysian Investor:
http://min.ipnhub.com/articles/investment/analyzing-stocks-throughfundamental-and-technical-analysis
Investopedia. (n.d.). ReturnOnAssets. Retrieved May 31, 2013, from Investopedia:
http://www.investopedia.com/terms/r/returnonassets.asp
Investopedia. (n.d.). ReturnOnEquity. Retrieved May 31, 2013, from Investopedia:
http://www.investopedia.com/terms/r/returnonequity.asp
InvestorWords. (n.d.). AuditCommittee. Retrieved May 31, 2013, from Investor
Words: http://www.investorwords.com/7478/audit_committee.html
InvestorWords. (n.d.). Security. Retrieved March 29, 2013, from InvestorWords.com:
http://www.investorwords.com/4446/security.html
InvestorWords. (n.d.). Stock Price. Retrieved March 29, 2013, from
InvestorWords.com: http://www.investorwords.com/8702/stock_price.html
James, S. (2012). Determine Whether Stakeholders of Courier Company, AYS
Couriers Ltd. Consider it to be More Economically Beneficial to Rent their
Transport Vehicles or Purchase them as Assets. London: University of Chester.
Jiang, Y., & Nuñez, L. (2012, February). Reducing the Risk of Investing in Stocks. IE
Business School.
Kennon, J. (n.d.). Investing Lesson 1 - Introduction to the Stock Market. Retrieved
April
4,
2013,
from
About.com:
http://beginnersinvest.about.com/cs/investinglessons/a/aaless1intro_2.htm
Maria, I., & Sanchez, G. (2009). The Effectiveness of Corporate Governance: Board
Structure and Business Technical Efficeincy in Spain. Salamanca.
McCarthy, S., & Chapman, A. (2013). Balance Scorecard. Retrieved March 29, 2013,
from Business Balls: http://www.businessballs.com/balanced_scorecard.htm
Mladjenovic, P. (2013). Stock Investing For Dummies, 4th Edition. USA: John Wiley
& Sons Inc.
Nordquist, R. (n.d.). Literature Review. Retrieved April 7, 2013, from About.com:
http://grammar.about.com/od/il/g/literaturereviewterm.htm
Q4S.
(n.d.).
Nomination.
Retrieved May 31,
2013,
47

from

Q4S:

http://www.g4s.com/en/Who%20we%20are/Corporate%20governance/Nominat
ion%20Committee/
Sage. (2011). What Is Realism, and Why Should Qualitative Researchers Care?
Retrieved April 7, 2013, from SagePub: http://www.sagepub.com/upmdata/44131_1.pdf
Samontaray, D. P. (2010). Impact of Corporate Governance on the Stock Prices of the
Nifty 50 Broad Index Listed Companies. International Research Journal of
Finance and Economics , 7-14.
Santos, R. R., & Lazaro, V. A. (2012). Corporate Governance and Valuation in the
Philippines. SGEN Research Review , 35-50.
Saunders, M., Lewis, P., & Thornhill, A. (2010). Research Methods for Business
Studies. Manila: Pearson Education [Philippines].
Scottrade. (n.d.). Knowledge Center. Retrieved March 25, 2013, from Scottrade:
http://research.scottrade.com/public/knowledgecenter/help/article.asp?docId=42
7c407af4944a1593a72b6c1eecfb5d
Sharma, G. D., Mahendru, M., & Singh, S. (2011). How Useful is Technical Analysis
in Predicting Future Stock Trends. Gurukul Business Review.
Stanley, J. (2012, July 19). How to Combine Technical and Fundamental Analysis.
Retrieved
March
24,
2013,
from
DailyFX:
http://www.dailyfx.com/forex/education/trading_tips/daily_trading_lesson/2012
/07/18/How_to_Combine_Technical_and_Fundamental.html
Todd, D. (2010, August 8). Technical Analysis - Three Branches for Stock Trading
Described.
Retrieved
March
24,
2013,
from
Suite101:
http://suite101.com/article/technical-analysis--three-branches-for-stock-tradingdescribed-a271582
TrendsetterSoftware. (2000). Trendsoft. Retrieved February 7, 2013, from
Introduction
to
Technical
Analysis:
http://www.trendsoft.com/tasc/introduction.htm
VentureLine. (n.d.). Business Valuation Definition. Retrieved March 29, 2013, from
VentureLine:
http://www.ventureline.com/accounting-glossary/B/businessvaluation-definition/
Wilson, J. (2010). Essentials of Business Research. New Delhi: SAGE Publications
Ltd.
WiseGeek. (n.d.). What Is the Difference Between Technical and Fundamental
Analysis?
Retrieved
March
25,
2013,
from
WiseGeek:
http://www.wisegeek.net/what-is-the-difference-between-technical-andfundamental-analysis.htm

48

7 Appendices
7.1 Appendix 1 - Supervisor Forms

49

50

51

52

53

54

55

7.2 Appendix 2 – Interview
Date: April 13, 2013
Time: 9:00 am
Venue: Starbucks, Bonifacio Global City
Interview Transcript:
Questioner: Hi, good morning! Thanks for meeting with me.
R: Responder: Sure no problem, ask away!
Q: My research title is: Effects of Directors’ Qualifications, Balance Scorecard
Components and Stock Moving Average in Stock Price Prediction... My first
question is regarding the board of directors. Why does a company change their
board structure?
R: It depends on whether they find a suitable person for the board, someone credible
enough as a replacement. They change it because they want to have a different or new
outlook because they feel like the current direction they’re following is not that
efficient anymore.
Q: How often do you change the board structure?
R: There are no specific criteria on when to change the board structure but changes
should be rare.
Q: Is the existence of a good leader in a company a relevant factor in the increase
and decrease of stock price?
R: Yes, it is a very high factor on the increase and decrease of stock price. The stock
price is determined by how a company is managed; not from its earnings report. Just
the fact that the company trusts that person to do the right thing and the job is gonna
jump this conference up; you can get more than the other three would’ve done. Does
that

make

sense?

Q: Yeah, at first I was unsure (of using the factor ‘changes in leadership’)
because from the previous researches, they don’t usually use ‘changes in
leadership’...
R: An example of a good leader in the context of ‘changes in leadership’ is Marissa
Mayer. Do you know the recent news on the new President of Yahoo?
56

Q: Not really...
R: Yahoo appointed her as the new President and CEO then from then on, the
company’s stock price went up to 50% more. She was a long time executive of
Google before the transfer.
Q: So why do you think she’s good? Like do you think her education and
affiliation helped?
R: What do you mean when you say affiliation?
Q: By affiliations, I mean the number of boards they are in.
R: So her pedigree is that she comes from Google, and she was one of their first
employees and was very high up, particularly in her contact services since she worked
for them. I think she ended up being... umm... she was the top executive over there so
let’s just leave it at that and that was a big thing.
Q: From google?
R: Yeah.
Q: So is google publicly listed as well?
R: Yeah, GOOG under NYSE.
Q: So why did she transfer?
R: Well, would you rather be a top executive at a company or actually the company?
They’re paying her like $36 Million per year, so...
Q: Wow.
R: Yeah and you can take a look at Yahoo’s stock price of the last 9 months and their
recent earning’s report.
Q: What are the relevant qualitative factors that affect the increase and decrease
of stock price? (Quality of a leader)
R: What do you mean when you say qualitative?
Q: The quality of a leader.
R: Is that what they perceive as strengths? Things like that?
Q: Yeah. Like what do companies look at in a person if they’re getting them as a

57

board director.
R: Yeah yeah I mean it usually boils down to experience and results, what have they
done and what have they accomplished... What do you think that they can accomplish
when they end up with your company. Other qualitative factors are things like
whether or not they are involved in any kind of legal action or litigation, what kind of
products they have in their research and development timeline, things like the history
of the board of directors, like what you’re saying...
Q: So why would be the products they handled relevant?
R: Well... Part of the reason why people invest in this prospect because they think
that the company is gonna continue to perform in that level or better over the next
time period so if it doesn’t look like the products that they have coming out are all
packed and coming then that’s gonna be an important factor. An example of that
would be Apple for the last three years; they haven’t come out with as anything,
announced anything that as interesting as the iPad, iPhone or iPod... and as a result,
their stock price has gone down a lot even though they are still making a lot of money.
Q: Does your company follow a balance scorecard? Why?
R: Yes, all companies follow some kind of scorecard and if so that they can have
those measruements. I mean, any company that doesn’t have some kind of scorecard
is doing off so... It’s pretty much a no-brainer.
Q: Why don’t they publish it?
R: Why don’t they publish it? Because... That’s too much information for the public
to know. That’s something that should be, that should remain..
Q: Internal.
R: I mean, let’s say the scorecard says that they are doing badly at something. You
think that’s something they wanna share to their shareholders? Definitely not. So... I
mean that’s for their own internal improvement; for them to make their own decisions
about what they should focus on in the next year or whatever time period they’re
measuring but they are not going to let people know what their weaknesses are.
Q: My balance scorecard components include Return on Equity, Return on
Asset, Learning and Training of Employees, Customer Experience Enhancement
tools and Having an Audit, Nomination and Remuneration Committee. Do you
58

think these are important factors to consider?
R: I would say that everything is important but not so much for the learning of
employees and customer experience enhancement.
Q: So not for the customer experience and the employee learning?
R: Yeah I don’t think so. I mean, this is qualitative anyway. Maybe you could go as
simple as how many awards they have received for their customer service.
Q: Okay..
R: Are there any rankings for companies that have the best training programs for their
employees... You could maybe use one of those. I think the audit committee thing is
okay as it’s used for the public but the other ones would be hard to get.
Q: So if for example, they don’t have any award, their score would be zero...
Right? Well because I’m only looking at their annual reports, what if they don’t
list it in their annual reports?
R: Right and they probably don’t. Well what I was saying was... awards that you
could find somewhere else.
Q: Like google the company?
R: In magazines, you might be able to look at it there. But I mean I don’t think that
you would be able to get accurate scores for these two items (employee learning and
customer experience enhancement).
Q: So for the employees, you suggest that if there’s any ranking?
R: That might help, yeah.
Q: What do you mean ranking? Of the employees? Like awards?
R: Rankings of their training programs like for example... here, Wells Fargo is
supposed to have the best training program for their employees, better than their
competitors, better than Bank of America, better than Chase, etcetera. So, that might
be something more accurate. And you can look up rankings for who has the best
training programs, that might work.
Q: Wells Fargo has the best training program, who awarded them?
R: It’s just general knowledge but if you google that, you could probably find it.
59

The other thing is that you’re saying that you’re gonna add this SMA and this
leadership value to get some kind of correlation, right?
Q: I think I will only add up leadership and BSC..
R: So you wouldn’t look at the stock moving average?
Q: I will use the three factors for the regression analysis.
R: Ok. This is the reason why I bring it up. Because return on equity and return on
asset are umm they are already highly correlated with the stock moving average
which creates the possible scenario where those guys are being improperly weighted
because they already eat up each other. Does that make sense? If they have a high
return on equity, they’re probably going to have an equal increase in their stock
moving average too. So, you wouldn’t want to count that twice. Does that make
sense?
Q: Oh, okay.
R: Those are good metrics but they overlap.
Q: What about the return on investment?
R: Yeah, that’s gonna be similar.
Q: Same...
R: Yeah. Pretty much anything that is reflecting a return; those are packaging the
stock moving average heavily.
Q: Oh okay. So would you know other factors that are used in a BSC?
R: Let me think about that for a minute... I say they probably have some kind of
measurement of their efficiency. They have some kind of metric of their customer
service, something that measures the effectiveness of their internal processes and
something that represents the skill and innovation of their employees and then yeah
they probably have some financial metrics on there as well.
Q: Financial metrics like?
R: Probably just the numbers from their balance sheet or income statement. So things
like their gross margin, their operating income, etc. Their cost of sales, their operating
expenses... How those have been shaping over time. Whether they think that their

60

running an efficient business and whether or not they’re selling things at the right
price, etc. I think those would go on there. For your list, the first three are very
important, the last two are not as important. But you’re saying, important... To who?
Important to the board? Important to the shareholders? Important to analyst?
Q: Importance to the performance of the company.
R: So important relative to the stock price?
Q: Yeah.
R: Yeah if it’s important to the stock price, the most important would be the first two.
The leadership is very important, it’s good that you have that separately. That’s
definitely very important. Things that are important are leadership, long-term business
outlook which would be related to how well their employees are trained. And also the
direction that the leadership are taking that company in. Those are all very imporatnt.
Q: So how would they know what direction they have?
R: I mean that should be coming down from the top at some point. Obviously some
new hire at a company isn’t supposed to know the ten-year plan that the CEO has but
the CEO is supposed to make his or her vision for the company very clear because
that is something that shareholders care a lot about. By the way, I would rank
affiliations higher than education.
Q: Should I give a certain percentage for each of the factors of BSC for its
scoring?
R: Yeah, definitely. Also, do some kind of scoring that takes into account what the
other guys are doing so it will be more accurate. So work on that.
Q: Okay, thanks! Thanks for your time; I think I have everything I need.
R: Alright, you’re welcome.
Q: Bye, thanks!
R: Bye.

61

7.3 Appendix 3 – Scoring of the Directors’ Qualifications

1
2
3
4
5
6
7
8
9

1
2
3
4
5
6
7
8
9

A - Number
of
Educational
Attainment
2
2

ALI BOARD OF DIRECTORS 2003
FERNANDO ZOBEL DE AYALA
FRANCISCO H. LICUNAN III
JAIME AUGUSTO ZOBEL DE
AYALA
AURELIO R. MONTINOLA III
RAMON R. DEL ROSARIO JR.
DELFIN L. LAZARO
NIEVES R. CONFESOR
LEANDRO Y. LOCSIN JR.
MERCEDITA S. NOLLEDO
Sum
Weight Distribution
Total

Score
A

B - Number
of
Affiliations

Score
B

4
6

9
5

15
9

2

6

7

12

2
3
3
5
1
2

6
6
6
8
2
4
48
19.2

9
8
12
4
9
5

15
12
18
6
15
9
111
66.6

40%
85.8
A - Number
of
Educational
Attainment
2

ALI BOARD OF DIRECTORS 2004
FERNANDO ZOBEL DE AYALA
JAIME AUGUSTO ZOBEL DE
AYALA
JAIME I. AYALA
MERCEDITA S. NOLLEDO
RAMON R. DEL ROSARIO JR.
DELFIN L. LAZARO
NIEVES R. CONFESOR
LEANDRO Y. LOCSIN JR.
FRANCISCO H. LICUNAN III
Sum
Weight Distribution
Total

Score
A

B - Number
of
Affiliations

Score
B

4

9

15

2

6

7

12

3
2
3
3
5
1
2

6
4
6
6
8
2
6
48
19.2

3
5
8
12
4
9
5

6
9
12
18
6
15
9
102
61.2

40%
80.4
A - Number
of
Educational
Attainment
2

ALI BOARD OF DIRECTORS 2005
1 FERNANDO ZOBEL DE AYALA
JAIME AUGUSTO ZOBEL DE
2 AYALA

2

62

60%

60%

Score
A

B - Number
of
Affiliations

Score
B

4

9

15

6

7

12

3
4
5
6
7
8
9

1
2
3
4
5
6
7
8
9

1
2
3
4
5
6
7
8
9

JAIME I. AYALA
MERCEDITA S. NOLLEDO
RAMON R. DEL ROSARIO JR.
DELFIN L. LAZARO
NIEVES R. CONFESOR
LEANDRO Y. LOCSIN JR.
AURELIO R. MONTINOLA III
Sum
Weight Distribution
Total

3
2
3
3
5
1
2
40%
84
A - Number
of
Educational
Attainment
2

ALI BOARD OF DIRECTORS 2006
FERNANDO ZOBEL DE AYALA
JAIME AUGUSTO ZOBEL DE
AYALA
JAIME I. AYALA
DELFIN L. LAZARO
MERCEDITA S. NOLLEDO
CORAZON S. DELA PAZ
RAMON R. DEL ROSARIO
LEANDRO Y. LOCSIN JR.
AURELIO R. MONTINOLA III
Sum
Weight Distribution
Total

60%

6
9
12
18
6
15
15
108
64.8

B - Number
of
Affiliations

Score
B

4

9

15

2

6

7

12

3
3
2
2
3
1
2

6
6
4
6
6
2
6
46
18.4

3
12
5
11
8
9
9

6
18
9
18
12
15
15
120
72

A - Number
of
Educational
Attainment
2

60%

Score
A

B - Number
of
Affiliations

Score
B

4

9

15

2

6

7

12

3
4
3
3
2
2
7

8
6
6
6
6
4
8
54
21.6

4
5
3
12
9
5
14

6
9
6
18
15
9
21
111
66.6

40%
88.2
63

3
5
8
12
4
9
9

Score
A

40%
90.4

ALI BOARD OF DIRECTORS 2012
FERNANDO ZOBEL DE AYALA
JAIME AUGUSTO ZOBEL DE
AYALA
ANTONINO T. AQUINO
FRANCIS G. ESTRADA
JAIME C. LAYA
DELFIN L. LAZARO
AURELIO R. MONTINOLA III
MERCEDITA S. NOLLEDO
OSCAR S. REYES
Sum
Weight Distribution
Total

6
4
6
6
8
2
6
48
19.2

60%

1
2
3
4
5
6
7
8
9

1
2
3
4
5
6
7
8
9

A - Number
of
Educational
Attainment
2

ALI BOARD OF DIRECTORS 2007
FERNANDO ZOBEL DE AYALA
JAIME AUGUSTO ZOBEL DE
AYALA
JAIME I. AYALA
DELFIN L. LAZARO
MERCEDITA S. NOLLEDO
CORAZON S. DE LA PAZBERNARDO
RAMON R. DEL ROSARIO
AURELIO R. MONTINOLA III
LEANDRO Y. LOCSIN
Sum
Weight Distribution
Total

Score
A

B - Number
of
Affiliations

Score
B

4

9

15

2

6

7

12

3
3
2

6
6
4

3
12
5

6
18
9

11
8
9
9

18
12
15
15
120
72

2
3
2
1
40%
90.4
A - Number
of
Educational
Attainment
2
2

ALI BOARD OF DIRECTORS 2008
FERNANDO ZOBEL DE AYALA
MERCEDITA S. NOLLEDO
CORAZON S. DE LA PAZBERNARDO
JAIME AUGUSTO ZOBEL DE
AYALA
JAIME I. AYALA
DELFIN L. LAZARO
RAMON R. DEL ROSARIO
AURELIO R. MONTINOLA III
FRANCIS G. ESTRADA
Sum
Weight Distribution
Total

60%

B - Number
of
Affiliations

Score
B

4
4

9
5

15
9

2

6

11

18

2

6

7

12

3
3
3
2
4

6
6
6
6
6
50
20

3
12
8
9
5

6
18
12
15
9
114
68.4

A - Number
of
Educational
Attainment
2
2
64

6
6
2
46
18.4

Score
A

40%
88.4

ALI BOARD OF DIRECTORS 2009
1 FERNANDO ZOBEL DE AYALA
JAIME AUGUSTO ZOBEL DE
2 AYALA

6

60%

Score
A

B - Number
of
Affiliations

Score
B

4

9

15

6

7

12

3
4
5
6

ANTONINO T. AQUINO
DELFIN L. LAZARO
MERCEDITA S. NOLLEDO
AURELIO R. MONTINOLA III
CORAZON S. DE LA PAZ7 BERNARDO
8 FRANCIS G. ESTRADA
9 OSCAR S. REYES
Sum
Weight Distribution
Total

1
2
3
4
5
6
7
8
9

1
2
3
4
5
6
7
8
9

3
3
2
2
2
4
7
40%
95.4
A - Number
of
Educational
Attainment
2

ALI BOARD OF DIRECTORS 2010
FERNANDO ZOBEL DE AYALA
JAIME AUGUSTO ZOBEL DE
AYALA
ANTONINO T. AQUINO
FRANCIS G. ESTRADA
JAIME C. LAYA
DELFIN L. LAZARO
AURELIO R. MONTINOLA III
MERCEDITA S. NOLLEDO
OSCAR S. REYES
Sum
Weight Distribution
Total

6
8
54
21.6

6
18
9
15

11
5
14

18
9
21
123
73.8

60%

B - Number
of
Affiliations

Score
B

4

9

15

2

6

7

12

3
4
3
3
2
2
7

8
6
6
6
6
4
8
54
21.6

4
5
3
12
9
5
14

6
9
6
18
15
9
21
111
66.6

A - Number
of
Educational
Attainment
2
3

60%

Score
A

B - Number
of
Affiliations

Score
B

4
8

9
4

15
6

2

6

7

12

4
3
2
3
2
7

6
6
6
6
4
8
54
21.6

5
3
9
12
5
14

9
6
15
18
9
21
111
66.6

40%
65

6

4
12
5
9

Score
A

40%
88.2

ALI BOARD OF DIRECTORS 2011
FERNANDO ZOBEL DE AYALA
ANTONINO T. AQUINO
JAIME AUGUSTO ZOBEL DE
AYALA
FRANCIS G. ESTRADA
JAIME C. LAYA
AURELIO R. MONTINOLA III
DELFIN L. LAZARO
MERCEDITA S. NOLLEDO
OSCAR S. REYES
Sum
Weight Distribution

8
6
4
6

60%

88.2

Total

CHIB BOARD OF DIRECTORS 2002
1
2
3
4
5
6
7
8
9
10
11
12

GILBERT U. DEE
HANS T. SY
PETER S. DEE
ROBERT Y. DEE, JR.
JOAQUIN DEE
DY TIONG
CHIA-JANG LIU
HARLEY T. SY
HENRY T. SY, JR.
HERBERT T. SY, JR.
HERBERT T. SY
YVONNE S. YUCHENGCO
Sum
Weight Distribution
Total

40%
58.6

CHIB BOARD OF DIRECTORS 2003
1
2
3
4
5
6
7
8
9
10
11

A - Number
of
Educational
Attainment
2
2
1
N/A
1
1
N/A
1
2
N/A
2
1

GILBERT U. DEE
HANS T. SY
PETER S. DEE
JOAQUIN DEE
DY TIONG
PILAR N. LIAO
CHIA-JANG LIU
HARLEY T. SY
HENRY T. SY, JR.
HERBERT T. SY
YVONNE S. YUCHENGCO
Sum
Weight Distribution
Total

A - Number
of
Educational
Attainment
2
2
1
1
1
1
N/A
1
2
2
1
40%
57.8

CHIB BOARD OF DIRECTORS 2004
1 GILBERT U. DEE
66

A - Number
of
Educational
Attainment
2

Score
A
6
4
2
2
2
2
2
2
4
2
4
2
34
13.6

Score
A
6
4
2
2
2
2
2
2
4
4
2
32
12.8

B - Number
of
Affiliations
1
6
4
N/A
1
1
N/A
4
10
N/A
3
9
60%

B - Number
of
Affiliations
1
6
4
1
1
4
N/A
4
10
3
9
60%

Score
B
3
9
6
3
3
3
3
6
15
3
6
15
75
45

Score
B
3
9
6
3
3
6
3
6
15
6
15
75
45

Score
A

B - Number
of
Affiliations

Score
B

6

1

3

2
3
4
5
6
7
8
9
10

HANS T. SY
PETER S. DEE
JOAQUIN DEE
HENRY T. SY, JR.
ATTY. DONATO T. FAYLONA
HERBERT T. SY
ALBERTO S. YAO
PILAR N. LIAO
HARLEY T. SY
Sum
Weight Distribution
Total

2
1
1
2
1
2
1
1
1
40%
48

CHIB BOARD OF DIRECTORS 2005
1
2
3
4
5
6
7
8
9
10
11

GILBERT U. DEE
HANS T. SY
PETER S. DEE
HENRY T. SY, JR.
JOAQUIN DEE
ROBERT F. KUAN
DY TIONG
HERBERT T. SY
ALBERTO S. YAO
PILAR N. LIAO
HARLEY T. SY
Sum
Weight Distribution
Total

40%
54.8

CHIB BOARD OF DIRECTORS 2006
1
2
3
4
5
6
7
8
9

A - Number
of
Educational
Attainment
2
2
1
2
1
3
1
2
1
1
1

GILBERT U. DEE
HANS T. SY
PETER S. DEE
HENRY SY, SR.
HENRY T. SY, JR.
JOAQUIN DEE
ROBERT F. KUAN
DY TIONG
HERBERT T. SY
67

A - Number
of
Educational
Attainment
2
2
1
2
2
1
3
1
2

4
2
2
4
2
4
2
2
2
30
12

Score
A
6
4
2
4
2
8
2
4
2
2
2
38
15.2

6
4
1
10
2
3
1
4
4
60%

B - Number
of
Affiliations
1
6
4
10
1
3
1
3
1
4
4
60%

9
6
3
15
3
6
3
6
6
60
36

Score
B
3
9
6
15
3
6
3
6
3
6
6
66
39.6

Score
A

B - Number
of
Affiliations

Score
B

6
4
2
10
4
2
8
2
4

1
6
4
12
10
1
3
1
3

3
9
6
18
15
3
6
3
6

10 ALBERTO S. YAO
11 PILAR N. LIAO
12 HARLEY T. SY
Sum
Weight Distribution
Total

1
1
1
40%
69.6

CHIB BOARD OF DIRECTORS 2007
1
2
3
4
5
6
7
8
9
10
11
12
13

GILBERT U. DEE
HANS T. SY
PETER S. DEE
HENRY SY, SR.
JOAQUIN DEE
ROBERT F. KUAN
DY TIONG
HERBERT T. SY
ALBERTO S. YAO
PILAR N. LIAO
HARLEY T. SY
JOSE T. SIO
ATTY. CORAZON I. MORANDO
Sum
Weight Distribution
Total

40%
68.6

CHIB BOARD OF DIRECTORS 2008
1
2
3
4
5
6
7
8
9
10
11
12

A - Number
of
Educational
Attainment
2
2
1
2
1
3
1
2
1
1
1
2
3

GILBERT U. DEE
HANS T. SY
PETER S. DEE
HENRY SY, SR.
JOAQUIN DEE
ROBERT F. KUAN
DY TIONG
HERBERT T. SY
ALBERTO S. YAO
HARLEY T. SY
JOSE T. SIO
RICARDO R. CHUA
Sum
68

A - Number
of
Educational
Attainment
2
2
1
2
1
3
1
2
1
1
2
2

2
2
2
48
19.2

Score
A
6
4
2
10
2
8
2
4
2
2
2
6
6
50
20

Score
A
6
4
2
10
2
8
2
4
2
2
6
6
54

1
4
4
60%

B - Number
of
Affiliations
1
6
4
12
1
3
1
3
1
4
4
5
4
60%

B - Number
of
Affiliations
1
6
4
12
1
3
1
3
1
4
5
3

3
6
6
84
50.4

Score
B
3
9
6
18
3
6
3
6
3
6
6
9
6
81
48.6

Score
B
3
9
6
18
3
6
3
6
3
6
9
6
78

Weight Distribution
Total

40%
68.4

CHIB BOARD OF DIRECTORS 2009
1
2
3
4
5
6
7
8
9
10
11

GILBERT U. DEE
HANS T. SY
PETER S. DEE
JOAQUIN DEE
ROBERT F. KUAN
DY TIONG
HERBERT T. SY
ALBERTO S. YAO
HARLEY T. SY
JOSE T. SIO
RICARDO R. CHUA
Sum
Weight Distribution
Total

40%
53.6

CHIB BOARD OF DIRECTORS 2010
1
2
3
4
5
6
7
8
9
10
11
12
13

A - Number
of
Educational
Attainment
2
2
1
1
3
1
2
1
1
2
2

GILBERT U. DEE
HANS T. SY
PETER S. DEE
HENRY SY, SR.
JOAQUIN DEE
DY TIONG
ROBERT F. KUAN
HERBERT T. SY
ALBERTO S. YAO
HARLEY T. SY
JOSE T. SIO
RICARDO R. CHUA
PILAR N. LIAO
Sum
Weight Distribution
Total

A - Number
of
Educational
Attainment
2
2
1
2
1
1
3
2
1
1
2
2
1
40%
68.6

69

21.6

60%

46.8

Score
A

B - Number
of
Affiliations

Score
B

6
4
2
2
8
2
4
2
2
6
6
44
17.6

Score
A
6
4
2
10
2
2
8
4
2
2
6
6
2
50
20

1
6
4
1
3
1
3
1
4
5
3
60%

B - Number
of
Affiliations
1
6
4
12
1
1
3
3
1
4
5
3
4
60%

3
9
6
3
6
3
6
3
6
9
6
60
36

Score
B
3
9
6
18
3
3
6
6
3
6
9
6
6
81
48.6

CHIB BOARD OF DIRECTORS 2011
1
2
3
4
5
6
7
8
9
10
11

HANS T. SY
GILBERT U. DEE
PETER S. DEE
JOAQUIN DEE
DY TIONG
HERBERT T. SY
HARLEY T. SY
ROBERT F. KUAN
ALBERTO S. YAO
JOSE T. SIO
RICARDO R. CHUA
Sum
Weight Distribution
Total

40%
53.6

DMCI BOARD OF DIRECTORS 2002
1
2
3
4
5
6
7
8
9
10
11

DAVID M. CONSUNJI
CESAR A. BUENAVENTURA
ISIDRO A. CONSUNJI
HERBERT M. CONSUNJI
MA. EDWINA C. LAPERAL
VICTOR A. CONSUNJI
JORGE A. CONSUNJI
OSCAR A. REYES
EVARISTO T. FRANCISCO
ATTY. NOEL A. LAMAN
ALFREDO R. AUSTRIA
Sum
Weight Distribution
Total

A - Number
of
Educational
Attainment
2
2
3
2
3
1
1
6
N/A
4
N/A
40%
84.4

DMCI BOARD OF DIRECTORS 2003
1
2
3
4

A - Number
of
Educational
Attainment
2
2
1
1
1
2
1
3
1
2
2

DAVID M. CONSUNJI
CESAR A. BUENAVENTURA
ISIDRO A. CONSUNJI
HERBERT M. CONSUNJI
70

A - Number
of
Educational
Attainment
2
2
3
2

Score
A
4
6
2
2
2
4
2
8
2
6
6
44
17.6

Score
A
10
6
8
4
6
2
2
8
2
8
2
58
23.2

B - Number
of
Affiliations
6
1
4
1
1
3
4
3
1
5
3
60%

B - Number
of
Affiliations
2
10
10
7
5
4
5
14
4
2
1
60%

Score
B
9
3
6
3
3
6
6
6
3
9
6
60
36

Score
B
3
15
15
12
9
6
9
21
6
3
3
102
61.2

Score
A

B - Number
of
Affiliations

Score
B

10
6
8
4

2
10
10
7

3
15
15
12

5
6
7
8
9
10

MA. EDWINA C. LAPERAL
VICTOR A. CONSUNJI
JORGE A. CONSUNJI
OSCAR S. REYES
EVARISTO T. FRANSISCO
ATTY. NOEL A. LAMAN
Sum
Weight Distribution
Total

3
1
1
6
N/A
4
40%
81.8

DMCI BOARD OF DIRECTORS 2004
1
2
3
4
5
6
7
8
9

DAVID M. CONSUNJI
CESAR A. BUENAVENTURA
ISIDRO A. CONSUNJI
HERBERT M. CONSUNJI
MA. EDWINA C. LAPERAL
VICTOR A. CONSUNJI
JORGE A. CONSUNJI
OSCAR S. REYES
EVARISTO T. FRANSISCO
Sum
Weight Distribution
Total

40%
76.8

DMCI BOARD OF DIRECTORS 2005
1
2
3
4
5
6
7
8
9

A - Number
of
Educational
Attainment
2
2
3
2
3
1
1
6
N/A

DAVID M. CONSUNJI
CESAR A. BUENAVENTURA
ISIDRO A. CONSUNJI
HERBERT M. CONSUNJI
MA. EDWINA C. LAPERAL
VICTOR A. CONSUNJI
JORGE A. CONSUNJI
OSCAR S. REYES
EVARISTO T. FRANSISCO
Sum
Weight Distribution
Total

A - Number
of
Educational
Attainment
2
2
3
2
3
1
1
6
N/A
40%
76.8

71

6
2
2
8
2
8
56
22.4

Score
A
10
6
8
4
6
2
2
8
2
48
19.2

Score
A
10
6
8
4
6
2
2
8
2
48
19.2

5
4
5
14
4
2
60%

B - Number
of
Affiliations
2
10
10
7
5
4
5
14
4
60%

B - Number
of
Affiliations
2
10
10
7
5
4
5
14
4
60%

9
6
9
21
6
3
99
59.4

Score
B
3
15
15
12
9
6
9
21
6
96
57.6

Score
B
3
15
15
12
9
6
9
21
6
96
57.6

DMCI BOARD OF DIRECTORS 2006
1
2
3
4
5
6
7
8
9

DAVID M. CONSUNJI
CESAR A. BUENAVENTURA
ISIDRO A. CONSUNJI
HERBERT M. CONSUNJI
VICTOR A. CONSUNJI
JORGE A. CONSUNJI
OSCAR S. REYES
EVARISTO T. FRANSISCO
VICTOR S. LIMLINGAN
Sum
Weight Distribution
Total

40%
74.8

DMCI BOARD OF DIRECTORS 2007
1
2
3
4
5
6
7
8

DAVID M. CONSUNJI
CESAR A. BUENAVENTURA
ISIDRO A. CONSUNJI
HERBERT M. CONSUNJI
VICTOR A. CONSUNJI
JORGE A. CONSUNJI
EVARISTO T. FRANSISCO
VICTOR S. LIMLINGAN
Sum
Weight Distribution
Total

A - Number
of
Educational
Attainment
2
2
3
2
1
1
N/A
4
40%
59

DMCI BOARD OF DIRECTORS 2008
1
2
3
4
5
6
7
8
9

A - Number
of
Educational
Attainment
2
2
3
2
1
1
6
N/A
4

DAVID M. CONSUNJI
CESAR A. BUENAVENTURA
ISIDRO A. CONSUNJI
HERBERT M. CONSUNJI
MA. EDWINA C. LAPERAL
VICTOR A. CONSUNJI
JORGE A. CONSUNJI
EVARISTO T. FRANSISCO
ATTY. NOEL A. LAMAN
72

A - Number
of
Educational
Attainment
2
2
3
2
3
1
1
N/A
4

Score
A
10
6
8
4
2
2
8
2
10
52
20.8

Score
A
10
6
8
4
2
2
2
10
44
17.6

B - Number
of
Affiliations
2
10
10
7
4
5
14
4
2
60%

B - Number
of
Affiliations
2
10
10
7
4
5
4
2
60%

Score
B
3
15
15
12
6
9
21
6
3
90
54

Score
B
3
15
15
12
6
9
6
3
69
41.4

Score
A

B - Number
of
Affiliations

Score
B

10
6
8
4
6
2
2
2
8

2
10
10
7
5
4
5
4
2

3
15
15
12
9
6
9
6
3

10 VICTOR S. LIMLINGAN
Sum
Weight Distribution
Total

4
40%
71.8

DMCI BOARD OF DIRECTORS 2009
1
2
3
4
5
6
7
8
9
10

DAVID M. CONSUNJI
CESAR A. BUENAVENTURA
ISIDRO A. CONSUNJI
HERBERT M. CONSUNJI
MA. EDWINA C. LAPERAL
VICTOR A. CONSUNJI
JORGE A. CONSUNJI
EVARISTO T. FRANSISCO
ATTY. NOEL A. LAMAN
HONORIO O. REYES-LAO
Sum
Weight Distribution
Total

40%
70.2

DMCI BOARD OF DIRECTORS 2010
1
2
3
4
5
6
7
8
9
10

A - Number
of
Educational
Attainment
2
2
3
2
3
1
1
N/A
4
3

DAVID M. CONSUNJI
CESAR A. BUENAVENTURA
ISIDRO A. CONSUNJI
HERBERT M. CONSUNJI
MA. EDWINA C. LAPERAL
VICTOR A. CONSUNJI
JORGE A. CONSUNJI
ATTY. NOEL A. LAMAN
HONORIO O. REYES-LAO
ANTONIO JOSE U. PERIQUET
Sum
Weight Distribution
Total

A - Number
of
Educational
Attainment
2
2
3
2
3
1
1
4
3
3
40%
73.6

DMCI BOARD OF DIRECTORS 2011
1 DAVID M. CONSUNJI
73

A - Number
of
Educational
Attainment
2

10
58
23.2

Score
A
10
6
8
4
6
2
2
2
8
6
54
21.6

Score
A
10
6
8
4
6
2
2
8
6
6
58
23.2

2
60%

B - Number
of
Affiliations
2
10
10
7
5
4
5
4
2
2
60%

B - Number
of
Affiliations
2
10
10
7
5
4
5
2
2
6
60%

3
81
48.6

Score
B
3
15
15
12
9
6
9
6
3
3
81
48.6

Score
B
3
15
15
12
9
6
9
3
3
9
84
50.4

Score
A

B - Number
of
Affiliations

Score
B

10

2

3

2
3
4
5
6
7
8
9

CESAR A. BUENAVENTURA
ISIDRO A. CONSUNJI
HERBERT M. CONSUNJI
MA. EDWINA C. LAPERAL
VICTOR A. CONSUNJI
JORGE A. CONSUNJI
HONORIO O. REYES-LAO
ANTONIO JOSE U. PERIQUET
Sum
Weight Distribution
Total

2
3
2
3
1
1
3
3
40%
68.6

SMC BOARD OF DIRECTORS 2002
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

EDUARDO M. COJUANGCO JR.
RAMON S. ANG
ESTELITO P. MENDOZA
MANUEL M. COJUANGCO
INIGO ZOBEL
WINSTON F. GARCIA
CORAZON DELA PAZ-BERNARDO
MENARDO R. JIMENEZ
PACIFICO M. FAJARDO
HECTOR L. HOFILENA
LEO S. ALVEZ
JUAN B. SANTOS
SHIGEKI OTA
NAOMICHI ASANO
HENRY SY SR.
Sum
Weight Distribution
Total

40%
121.20

SMC BOARD OF DIRECTORS 2003
1
2
3
4
5
6

A - Number
of
Educational
Attainment
4
1
2
N/A
N/A
2
2
1
N/A
2
N/A
2
N/A
1
2

EDUARDO M. COJUANGCO JR.
RAMON S. ANG
ESTELITO P. MENDOZA
MANUEL M. COJUANGCO
INIGO ZOBEL
WINSTON F. GARCIA
74

A - Number
of
Educational
Attainment
4
1
2
N/A
N/A
2

6
8
4
6
2
2
6
6
50
20

Score
A
8
2
6
2
2
4
6
2
2
4
2
6
2
2
10
60
24

10
10
7
5
4
5
2
6
60%

B - Number
of
Affiliations
5
23
9
N/A
9
8
11
8
2
2
3
10
3
3
12
60%

15
15
12
9
6
9
3
9
81
48.6

Score
B
9
21
15
3
15
12
18
12
3
3
6
15
6
6
18
162
97.2

Score
A

B - Number
of
Affiliations

Score
B

8
2
6
2
2
4

5
23
9
N/A
9
8

9
21
15
3
15
12

7
8
9
10
11
12
13
14
15

CORAZON DELA PAZ-BERNARDO
MENARDO R. JIMENEZ
PACIFICO M. FAJARDO
HECTOR L. HOFILENA
LEO S. ALVEZ
JUAN B. SANTOS
SHIGEKI OTA
HENRY SY SR.
HITOSHI OSHIMA
Sum
Weight Distribution
Total

2
1
N/A
2
N/A
2
N/A
2
N/A
40%
121.20

SMC BOARD OF DIRECTORS 2004
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

EDUARDO M. COJUANGCO JR.
RAMON S. ANG
ESTELITO P. MENDOZA
MANUEL M. COJUANGCO
INIGO ZOBEL
CORAZON DELA PAZ-BERNARDO
WINSTON F. GARCIA
LEO S. ALVEZ
MENARDO R. JIMENEZ
HITOSHI OSHIMA
YOSHINORI ISOZAKI
HENRY SY JR.
OCTAVIO VICTOR R. ESPIRITU
EGMIDIO DE SILVA JOSE
PACIFICO M. FAJARDO
Sum
Weight Distribution
Total

40%
127.60

SMC BOARD OF DIRECTORS 2005
1
2
3
4
5

A - Number
of
Educational
Attainment
4
1
2
N/A
N/A
2
2
N/A
1
N/A
2
2
4
N/A
N/A

EDUARDO M. COJUANGCO JR.
RAMON S. ANG
ESTELITO P. MENDOZA
INIGO ZOBEL
CORAZON DELA PAZ-BERNARDO
75

A - Number
of
Educational
Attainment
4
1
2
N/A
2

6
2
2
4
2
6
2
10
2
60
24

Score
A
8
2
6
2
2
6
4
2
2
2
6
4
8
2
2
58
23.2

11
8
2
2
3
10
3
12
4
60%

B - Number
of
Affiliations
5
23
9
N/A
9
11
8
3
8
4
4
10
9
11
2
60%

18
12
3
3
6
15
6
18
6
162
97.2

Score
B
9
21
15
3
15
18
12
6
12
6
6
15
15
18
3
174
104.4

Score
A

B - Number
of
Affiliations

Score
B

8
2
6
2
6

5
23
9
9
11

9
21
15
15
18

6
7
8
9
10
11
12
13
14
15

WINSTON F. GARCIA
LEO S. ALVEZ
MENARDO R. JIMENEZ
HITOSHI OSHIMA
YOSHINORI ISOZAKI
HENRY SY JR.
EGMIDIO DE SILVA JOSE
PACIFICO M. FAJARDO
KAZUHIRO SATO
JOSE MA. A. RUFINO
Sum
Weight Distribution
Total

2
N/A
1
N/A
2
2
N/A
N/A
N/A
N/A
40%
118.00

SMC BOARD OF DIRECTORS 2006
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

EDUARDO M. COJUANGCO JR.
RAMON S. ANG
ESTELITO P. MENDOZA
HITOSHI OSHIMA
YOSHINORI ISOZAKI
INIGO ZOBEL
CORAZON DELA PAZ-BERNARDO
WINSTON F. GARCIA
LEO S. ALVEZ
MENARDO R. JIMENEZ
HENRY SY JR.
EGMIDIO DE SILVA JOSE
PACIFICO M. FAJARDO
KAZUHIRO SATO
SILVESTRE M. BELLO III
Sum
Weight Distribution
Total

40%
122.40

SMC BOARD OF DIRECTORS 2007
1
2
3
4

A - Number
of
Educational
Attainment
4
1
2
N/A
2
N/A
2
2
N/A
1
2
N/A
N/A
N/A
2

EDUARDO M. COJUANGCO JR.
RAMON S. ANG
LEO S. ALVEZ
MENARDO R. JIMENEZ
76

A - Number
of
Educational
Attainment
4
1
N/A
1

4
2
2
2
6
4
2
2
2
2
52
20.8

Score
A
8
2
6
2
6
2
6
4
2
2
4
2
2
2
4
54
21.6

8
3
8
4
4
10
11
2
2
N/A
60%

B - Number
of
Affiliations
5
23
9
4
4
9
11
8
3
8
10
11
2
2
5
60%

12
6
12
6
6
15
18
3
3
3
162
97.2

Score
B
9
21
15
6
6
15
18
12
6
12
15
18
3
3
9
168
100.8

Score
A

B - Number
of
Affiliations

Score
B

8
2
2
2

5
23
3
8

9
21
6
12

5
6
7
8
9
10
11
12
13
14
15

HENRY SY JR.
YOSHINORI ISOZAKI
ESTELITO P. MEDOZA
INIGO ZOBEL
CORAZON DELA PAZ-BERNARDO
WINSTON F. GARCIA
EGMIDIO DE SILVA JOSE
PACIFICO M. FAJARDO
KAZUHIRO TSUKAHARA
SILVESTRE H. BELO III
KOICHI MATSUZAWA
Sum
Weight Distribution
Total

2
2
2
N/A
2
2
N/A
N/A
N/A
2
3
40%
129.20

SMC BOARD OF DIRECTORS 2008
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

EDUARDO M. COJUANGCO JR.
RAMON S. ANG
ESTELITO P. MEDOZA
INIGO ZOBEL
PACIFICO M. FAJARDO
KAZUHIRO TSUKAHARA
SILVESTRE H. BELO III
KOICHI MATSUZAWA
WINSTON F. GARCIA
MENARDO R. JIMENEZ
LEO S. ALVEZ
EGMIDIO DE SILVA JOSE
HIROTAKE KOBAYASHI
HECTOR L. HOFILENA
CARMELO L. SANTIAGO
KEISIKE NISHIMURA
Sum
Weight Distribution
Total

A - Number
of
Educational
Attainment
4
1
2
N/A
N/A
N/A
2
3
2
1
N/A
N/A
2
2
1
2
40%
114.20

SMC BOARD OF DIRECTORS 2009
1 EDUARDO M. COJUANGCO JR.
2 RAMON S. ANG
77

A - Number
of
Educational
Attainment
4
1

4
6
6
2
6
4
2
2
2
4
10
62
24.8

Score
A
8
2
6
2
2
2
4
10
4
2
2
2
6
4
2
6
56
22.4

10
4
9
9
11
8
11
2
5
5
4
60%

B - Number
of
Affiliations
5
23
9
9
2
5
5
4
8
8
3
11
4
2
7
4
60%

15
6
15
15
18
12
18
3
9
9
6
174
104.4

Score
B
9
21
15
15
3
9
9
6
12
12
6
18
6
3
12
6
153
91.8

Score
A

B - Number
of
Affiliations

Score
B

8
2

5
23

9
21

3 ESTELITO P. MEDOZA
4 INIGO ZOBEL
5 PACIFICO M. FAJARDO
JESUSA VICTORIA HERNANDEZ6 BAUTISTA
7 HECTOR L. HOFILENA
8 CARMELO L. SANTIAGO
9 WINSTON F. GARCIA
10 MENARDO R. JIMENEZ
11 LEO S. ALVEZ
12 EGMIDIO DE SILVA JOSE
13 ROBERTO V. ONGPIN
14 MIRZAN MAHATIR
15 ALEXANDER J. POBLADOR
Sum
Weight Distribution
Total

6
2
2

9
9
2

15
15
3

N/A
2
1
2
1
N/A
N/A
2
2
N/A

2
4
2
4
2
2
2
6
6
2
52
20.8

3
2
7
8
8
3
11
19
11
1

6
3
12
12
12
6
18
21
18
3
174
104.4

40%
125.20

SMC BOARD OF DIRECTORS 2010
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

2
N/A
N/A

EDUARDO M. COJUANGCO JR.
RAMON S. ANG
INIGO ZOBEL
WINSTON F. GARCIA
HECTOR L. HOFILENA
ROBERTO V. ONGPIN
JOSELITO D. CAMPOS JR.
ESTELITO P. MEDOZA
LEO S. ALVEZ
MENARDO R. JIMENEZ
CARMELO L. SANTIAGO
ALEXANDER J. POBLADOR
FERDINAND K. CONSTANTINO
ERIC O. RECTO
REYNATO S. PUNO
Sum
Weight Distribution
Total

A - Number
of
Educational
Attainment
4
1
N/A
2
2
2
2
2
N/A
1
1
N/A
2
2
3
40%
139.00

SMC BOARD OF DIRECTORS 2011
1 EDUARDO M. COJUANGCO JR.
78

A - Number
of
Educational
Attainment
4

Score
A
8
2
2
4
4
6
6
6
2
2
2
2
4
6
8
64
25.6

60%

B - Number
of
Affiliations
5
23
9
8
2
19
6
9
3
8
7
1
16
17
5
60%

Score
B
9
21
15
12
3
21
9
15
6
12
12
3
21
21
9
189
113.4

Score
A

B - Number
of
Affiliations

Score
B

8

5

9

2
3
4
5
6
7
8
9
10
11
12
13
14
15

RAMON S. ANG
ESTELITO P. MEDOZA
INIGO ZOBEL
WINSTON F. GARCIA
MENARDO R. JIMENEZ
LEO S. ALVEZ
HECTOR L. HOFILENA
CARMELO L. SANTIAGO
ROBERTO V. ONGPIN
FERDINAND K. CONSTANTINO
ALEXANDER J. POBLADOR
JOSELITO D. CAMPOS JR.
ERIC O. RECTO
REYNATO S. PUNO
Sum
Weight Distribution
Total

1
2
N/A
2
1
N/A
2
1
2
2
N/A
2
2
3
40%
139.00

TEL BOARD OF DIRECTORS 2002
1
2
3
4
5
6
7
8
9
10
11
12
13

ANTONIO O. COJUANGCO
MANUEL V. PANGILINAN
NAPOLEON L. NAZARENO
HELEN Y. DEE
RAY C. ESPINOSA
BIENVENIDO F. NEBRES, S.J.
CORAZON S. DELA PAZ
ALBERT F. DEL ROSARIO
PEDRO E. ROXAS
JUAN B. SANTOS
TAKETO SUZUKI
MITSUHIRO TAKASE
RICARDO R. ZARATE
Sum
Weight Distribution
Total

A - Number
of
Educational
Attainment
2
4
2
2
3
2
2
3
2
2
1
1
1
40%
118.4

TEL BOARD OF DIRECTORS 2003
1 ANTONIO O. COJUANGCO
2 MANUEL V. PANGILINAN
79

A - Number
of
Educational
Attainment
2
4

2
6
2
4
2
2
4
2
6
4
2
6
6
8
64
25.6

Score
A
4
10
6
6
6
10
6
4
4
6
2
2
2
68
27.2

23
9
9
8
8
3
2
7
19
16
1
6
17
5
60%

B - Number
of
Affiliations
5
19
12
11
11
3
11
5
6
10
1
3
1
60%

21
15
15
12
12
6
3
12
21
21
3
9
21
9
189
113.4

Score
B
9
21
18
18
18
6
18
9
9
15
2
6
3
152
91.2

Score
A

B - Number
of
Affiliations

Score
B

4
10

5
19

9
21

3
4
5
6
7
8
9
10
11
12
13

NAPOLEON L. NAZARENO
HELEN Y. DEE
RAY C. ESPINOSA
BIENVENIDO F. NEBRES, S.J.
CORAZON S. DELA PAZ
ALBERT F. DEL ROSARIO
PEDRO E. ROXAS
JUAN B. SANTOS
TAKETO SUZUKI
MITSUHIRO TAKASE
RICARDO R. ZARATE
Sum
Weight Distribution
Total

2
2
3
2
2
3
2
2
1
1
1
40%
118.4

TEL BOARD OF DIRECTORS 2004
1
2
3
4
5
6
7
8
9
10
11
12
13

ANTONIO O. COJUANGCO
MANUEL V. PANGILINAN
NAPOLEON L. NAZARENO
HELEN Y. DEE
RAY C. ESPINOSA
SADAO MAKI
BIENVENIDO F. NEBRES, S.J.
CORAZON S. DELA PAZ
OSCAR S. REYES
ALBERT F. DEL ROSARIO
PEDRO E. ROXAS
TERESITA T. SY-COSON
SHIGERU YOSHIDA
Sum
Weight Distribution
Total

40%
133.8

TEL BOARD OF DIRECTORS 2005
1
2
3
4
5

A - Number
of
Educational
Attainment
2
4
2
2
3
2
2
2
7
3
2
4
1

ANTONIO O. COJUANGCO
MANUEL V. PANGILINAN
NAPOLEON L. NAZARENO
HELEN Y. DEE
RAY C. ESPINOSA
80

A - Number
of
Educational
Attainment
2
4
2
2
3

6
6
6
10
6
4
4
6
2
2
2
68
27.2

Score
A
4
10
6
6
6
6
10
6
8
4
4
6
2
78
31.2

12
11
11
3
11
5
6
10
1
3
1
60%

B - Number
of
Affiliations
5
19
12
11
11
5
3
11
13
5
6
7
2
60%

18
18
18
6
18
9
9
15
2
6
3
152
91.2

Score
B
9
21
18
18
18
9
6
18
21
9
9
12
3
171
102.6

Score
A

B - Number
of
Affiliations

Score
B

4
10
6
6
6

5
19
12
11
11

9
21
18
18
18

6
7
8
9
10
11
12

BIENVENIDO F. NEBRES, S.J.
CORAZON S. DELA PAZ
OSCAR S. REYES
ALBERT F. DEL ROSARIO
PEDRO E. ROXAS
TERESITA T. SY-COSON
SHIGERU YOSHIDA
Sum
Weight Distribution
Total

2
2
7
3
2
4
1
40%
126

TEL BOARD OF DIRECTORS 2006
1
2
3
4
5
6
7
8
9
10
11
12
13

MANUEL V. PANGILINAN
NAPOLEON L. NAZARENO
HELEN Y. DEE
RAY C. ESPINOSA
TSUYOSHI KAWASHIMA
TATSU KONO
BIENVENIDO F. NEBRES, S.J.
CORAZON S. DELA PAZ
MA. LOURDES C. RAUSA-CHAN
OSCAR S. REYES
ALBERT F. DEL ROSARIO
PEDRO E. ROXAS
ALFRED V. TY
Sum
Weight Distribution
Total

40%
119.8

TEL BOARD OF DIRECTORS 2007
1
2
3
4
5
6
7
8
9

A - Number
of
Educational
Attainment
4
2
2
3
1
1
2
2
2
7
3
2
1

MANUEL V. PANGILINAN
NAPOLEON L. NAZARENO
HELEN Y. DEE
RAY C. ESPINOSA
TATSU KONO
OSCAR S. REYES
ALBERT F. DEL ROSARIO
PEDRO E. ROXAS
ALFRED V. TY
81

A - Number
of
Educational
Attainment
4
2
2
3
1
7
3
2
1

10
6
8
4
4
6
2
72
28.8

Score
A
10
6
6
6
2
2
10
6
4
8
4
4
2
70
28

3
11
13
5
6
7
2
60%

B - Number
of
Affiliations
19
12
11
11
2
1
3
11
1
13
5
6
3
60%

6
18
21
9
9
12
3
162
97.2

Score
B
21
18
18
18
3
3
6
18
3
21
9
9
6
153
91.8

Score
A

B - Number
of
Affiliations

Score
B

10
6
6
6
2
8
4
4
2

19
12
11
11
5
13
5
6
3

21
18
18
18
9
21
9
9
6

Sum
Weight Distribution
Total

40%
162.88

TEL BOARD OF DIRECTORS 2008
1
2
3
4
5
6
7
8
9
10
11
12
13

MANUEL V. PANGILINAN
NAPOLEON L. NAZARENO
DONALD G. DEE
HELEN Y. DEE
RAY C. ESPINOSA
TATSU KONO
REV. FR. BIENVENIDO F. NEBRES,
S.J.
TAKASHI OOI
OSCAR S. REYES
ALBERT F. DEL ROSARIO
PEDRO E. ROXAS
TONY TAN CAKTIONG
ALFRED V. TY
Sum
Weight Distribution
Total

2
2
7
3
2
4
1
40%
119.4

TEL BOARD OF DIRECTORS 2009
1
2
3
4
5
6
7
8
9
10
11
12
13

A - Number
of
Educational
Attainment
4
2
2
2
3
1

MANUEL V. PANGILINAN
NAPOLEON L. NAZARENO
DONALD G. DEE
HELEN Y. DEE
RAY C. ESPINOSA
TATSU KONO
REV. FR. BIENVENIDO F. NEBRES,
S.J.
TAKASHI OOI
OSCAR S. REYES
PEDRO E. ROXAS
ALBERT F. DEL ROSARIO
TONY TAN CAKTIONG
ALFRED V. TY
Sum
Weight Distribution

A - Number
of
Educational
Attainment
4
2
2
2
3
1
2
2
7
2
3
4
1
40%

82

76
30.4

60%

220.8
132.48

Score
A

B - Number
of
Affiliations

Score
B

10
6
4
6
6
2

19
12
2
11
11
5

21
18
3
18
18
9

10
6
8
4
4
10
2
78
31.2

3
1
13
5
6
3
3

6
3
21
9
9
6
6
147
88.2

60%

Score
A

B - Number
of
Affiliations

Score
B

10
6
4
6
6
2

19
12
2
11
11
5

21
18
3
18
18
9

10
6
8
4
4
10
2
78
31.2

3
1
13
6
5
3
3

6
3
21
9
9
6
6
147
88.2

60%

Total

119.4

TEL BOARD OF DIRECTORS 2010
1
2
3
4
5
6
7
8
9
10
11
12
13

MANUEL V. PANGILINAN
NAPOLEON L. NAZARENO
HELEN Y. DEE
RAY C. ESPINOSA
TATSU KONO
REV. FR. BIENVENIDO F. NEBRES,
S.J.
TAKASHI OOI
OSCAR S. REYES
ALBERT F. DEL ROSARIO
PEDRO E. ROXAS
JUAN B. SANTOS
TONY TAN CAKTIONG
ALFRED V. TY
Sum
Weight Distribution
Total

2
2
7
3
2
2
4
1
40%
127.4

TEL BOARD OF DIRECTORS 2011
1
2
3
4
5
6
7
8
9
10
11
12
13

A - Number
of
Educational
Attainment
4
2
2
3
1

MANUEL V. PANGILINAN
NAPOLEON L. NAZARENO
HELEN Y. DEE
RAY C. ESPINOSA
JAMES L. GO
SETSUYA KIMURA
REV. FR. BIENVENIDO F. NEBRES,
S.J.
HIDEAKI OZAKI
ATTY. MA. LOURDES RAUSA-CHAN
PEDRO E. ROXAS
JUAN B. SANTOS
TONY TAN CAKTIONG
ALFRED V. TY
Sum
Weight Distribution
Total

83

A - Number
of
Educational
Attainment
4
2
2
3
2
1
2
2
2
2
2
4
1
40%
117.6

Score
A

B - Number
of
Affiliations

Score
B

10
6
6
6
2

19
12
11
11
5

21
18
18
18
9

10
6
8
4
4
6
10
2
80
32

3
1
13
5
6
10
3
3

6
3
21
9
9
15
6
6
159
95.4

60%

Score
A

B - Number
of
Affiliations

Score
B

10
6
6
6
6
2

19
12
11
11
11
2

21
18
18
18
18
3

10
6
4
4
6
10
2
78
31.2

3
1
1
6
10
3
3

6
3
3
9
15
6
6
144
86.4

60%

7.4 Appendix 4 – Scoring for the Firm Efficiency Metrics
Firm Efficiency Metrics - Scoring for Ayala Land Incorporation
BSC Factors
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
COMMEN
Extensi
Moderat Extensi Extensi Extensi Extensi
N/A
Simple
Simple
Simple
T/S:
ve
e
ve
ve
ve
ve
Customer Experience
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
Enhancement
COMMEN Moderat
Extensi Extensi Extensi Extensi Extensi Extensi Extensi Extensi
Simple
T/S:
e
ve
ve
ve
ve
ve
ve
ve
ve
Audit / Nomination /
Complet Complet Complet Complet Complet Complet Complet Complet Complet Complet
Renumeration
e
e
e
e
e
e
e
e
e
e
Committee
Additional Info:
ROE year 2002
7%
ROA year 2002
4%
SCORES
ROE
ROA
Learning of Employee
Customer Experience
Enhancement
Audit / Nomination /
Remuneration

2003
6
3
9

2004
6
6
0

2005
6
6
3

2006
6
6
3

2007
6
6
3

2008
3
0
6

2009
0
0
9

2010
6
6
9

2011
6
6
9

2012
6
0
9

6

3

9

9

9

9

9

9

9

9

6

6

6

6

6

6

6

6

6

6

84

Committee
WEIGHTED SCORES
ROE (5%)
ROA (5%)
Learning of Employee
(35%)
Customer Experience
Enhancement (35%)
Audit / Nomination /
Remuneration
Committee (20%)
TOTAL SCORE

2003
0.3
0.15

2004
0.3
0.3

2005
0.3
0.3

2006
0.3
0.3

2007
0.3
0.3

2008
0.15
0

2009
0
0

2010
0.3
0.3

2011
0.3
0.3

2012
0.3
0

3.15

0

1.05

1.05

1.05

2.1

3.15

3.15

3.15

3.15

2.1

1.05

3.15

3.15

3.15

3.15

3.15

3.15

3.15

3.15

1.2

1.2

1.2

1.2

1.2

1.2

1.2

1.2

1.2

1.2

6.9

2.85

6

6

6

6.6

7.5

8.1

8.1

7.8

Firm Efficiency Metrics - Scoring for China Bank
BSC Factors
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
ROE
18.54% 15.89% 14.37% 15.28% 15.93% 15.57% 11.98% 15.36% 15.37% 13.72%
ROA
2.72%
2.47%
2.46%
2.58%
2.47%
2.25%
1.53%
1.90%
2.10%
2.04%
Learning of Employee
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
COMMEN
Moderat Moderat Moderat Extensi Extensi Moderat Extensi Extensi Moderat
Simple
T/S:
e
e
e
ve
ve
e
ve
ve
e
Customer Experience
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
Enhancement
COMMEN
Extensi Extensi Moderat Moderat Extensi Extensi Moderat Extensi Moderat
Simple
T/S:
ve
ve
e
e
ve
ve
e
ve
e
Audit / Nomination /
Audit
Audit
Complet Complet Complet Complet Complet Complet Complet Complet
Renumeration
only
only
e
e
e
e
e
e
e
e
Committee
Additional Info:
85

ROE year 2002
ROA year 2002
SCORES
ROE
ROA
Learning of Employee
Customer Experience
Enhancement
Audit / Nomination /
Remuneration
Committee
WEIGHTED SCORES
ROE (5%)
ROA (5%)
Learning of Employee
(35%)
Customer Experience
Enhancement (35%)
Audit / Nomination /
Remuneration
Committee (20%)
TOTAL SCORE

15.20%
2.25%
2002
6
6
3

2003
0
0
6

2004
0
0
6

2005
6
0
6

2006
6
0
9

2007
0
0
9

2008
0
0
6

2009
6
6
9

2010
6
6
9

2011
0
0
6

3

9

9

6

6

9

9

6

9

6

0

0

6

6

6

6

6

6

6

6

0.3
0.3

0
0

0
0

0.3
0

0.3
0

0
0

0
0

0.3
0.3

0.3
0.3

0
0

1.05

2.1

2.1

2.1

3.15

3.15

2.1

3.15

3.15

2.1

1.05

3.15

3.15

2.1

2.1

3.15

3.15

2.1

3.15

2.1

0

0

1.2

1.2

1.2

1.2

1.2

1.2

1.2

1.2

2.7

5.25

6.45

5.7

6.75

7.5

6.45

7.05

8.1

5.4

2005
15.28%
2.58%

2006
15.93%
2.47%

2007
15.57%
2.25%

2008
11.98%
1.53%

2009
15.36%
1.90%

2010
15.37%
2.10%

2011
13.72%
2.04%

Firm Efficiency Metrics - Scoring for DMCI Holdings
Inc.
BSC Factors
2002
2003
2004
ROE
18.54% 15.89% 14.37%
ROA
2.72%
2.47%
2.46%

86

Learning of Employee
COMMEN
T/S:
Customer Experience
Enhancement
COMMEN
T/S:
Audit / Nomination /
Renumeration
Committee
Additional Info:
ROE year 2002
ROA year 2002
SCORES
ROE
ROA
Learning of Employee
Customer Experience
Enhancement
Audit / Nomination /
Remuneration
Committee
WEIGHTED SCORES
ROE (5%)
ROA (5%)
Learning of Employee
(35%)

NONE

NONE

NONE

YES

YES

NONE

NONE

YES
YES
Moderat Moderat
e
e

YES
Moderat
e

N/A

N/A

N/A

Simple

Simple

N/A

N/A

NONE

NONE

NONE

YES

YES

YES

YES

YES

YES

YES

N/A

N/A

N/A

Simple

Simple

Extensi
ve

Extensi
ve

Moderat
e

Simple

Simple

Complet Complet Complet Complet Complet Complet Complet Complet Complet Complet
e
e
e
e
e
e
e
e
e
e
15.00%
2.20%
2002
6
6
0

2003
0
0
0

2004
0
0
0

2005
6
6
3

2006
6
0
3

2007
0
0
0

2008
0
0
0

2009
6
6
6

2010
6
6
6

2011
0
0
6

0

0

0

3

3

9

9

6

3

3

6

6

6

6

6

6

6

6

6

6

18
0.3
0.3

6
0
0

6
0
0

24
0.3
0.3

18
0.3
0

15
0
0

15
0
0

30
0.3
0.3

27
0.3
0.3

15
0
0

0

0

0

1.05

1.05

0

0

2.1

2.1

2.1

87

Customer Experience
Enhancement (35%)
Audit / Nomination /
Remuneration
Committee (20%)
TOTAL SCORE

0

0

0

1.05

1.05

3.15

3.15

2.1

1.05

1.05

1.2

1.2

1.2

1.2

1.2

1.2

1.2

1.2

1.2

1.2

1.8

1.2

1.2

3.9

3.6

4.35

4.35

6

4.95

4.35

2005
7%
3%
NONE

2006
7%
3%
NONE

2007
6%
3%
YES

2008
12%
6%
NONE

2009
25%
14%
NONE

2010
9%
3%
NONE

2011
10%
3%
NONE

N/A

N/A

Simple

N/A

N/A

N/A

N/A

NONE

YES

NONE

NONE

NONE

NONE

NONE

N/A

Moderat
e

N/A

N/A

N/A

N/A

N/A

Has two

Has two

2005
3
0

2006
3
0

Firm Efficiency Metrics - Scoring for San Miguel
Corporation
BSC Factors
2002
2003
2004
ROE
7%
7%
7%
ROA
4%
4%
4%
Learning of Employee
NONE
YES
NONE
COMMEN
N/A
Simple
N/A
T/S:
Customer Experience
NONE
NONE
NONE
Enhancement
COMMEN
N/A
N/A
N/A
T/S:
Audit / Nomination /
Renumeration
Has two Has two Has two
Committee
Additional Info:
ROE year 2002
10%
ROA year 2002
4%
SCORES
ROE
ROA

2002
0
3

2003
3
3

2004
3
3

88

Complet Complet Complet Complet Complet
e
e
e
e
e

2007
0
3

2008
6
6

2009
6
6

2010
0
0

2011
6
3

Learning of Employee
Customer Experience
Enhancement
Audit / Nomination /
Remuneration
Committee
WEIGHTED SCORES
ROE (5%)
ROA (5%)
Learning of Employee
(35%)
Customer Experience
Enhancement (35%)
Audit / Nomination /
Remuneration
Committee (20%)
TOTAL SCORE

0

3

0

0

0

3

0

0

0

0

0

0

0

0

6

0

0

0

0

0

3

3

3

3

3

6

6

6

6

6

6
0
0.15

12
0.15
0.15

9
0.15
0.15

6
0.15
0

12
0.15
0

12
0
0.15

18
0.3
0.3

18
0.3
0.3

6
0
0

15
0.3
0.15

0

1.05

0

0

0

1.05

0

0

0

0

0

0

0

0

2.1

0

0

0

0

0

0.6

0.6

0.6

0.6

0.6

1.2

1.2

1.2

1.2

1.2

0.75

1.95

0.9

0.75

2.85

2.4

1.8

1.8

1.2

1.65

2006
33.96%
14.60%
NONE

2007
31.98%
14.98%
YES

N/A

Simple

2008
33.00%
13.98%
YES
Moderat
e

2009
40.45%
14.31%
YES
Extensi
ve

2010
41.34%
14.49%
YES
Extensi
ve

2011
20.78%
8.00%
YES
Extensi
ve

NONE

YES

NONE

YES

YES

YES

N/A

Simple

n/a

Simple

Simple

Moderat

Firm Efficiency Metrics - Scoring for Philippine Long Distance
Company
BSC Factors
2002
2003
2004
2005
ROE
23.47% 31.94% 57.63% 46.36%
ROA
6.81%
11.76% 10.53% 13.78%
Learning of Employee
NONE
NONE
NONE
NONE
COMMEN
N/A
N/A
N/A
N/A
T/S:
Customer Experience
NONE
NONE
NONE
NONE
Enhancement
COMMEN
N/A
N/A
N/A
N/A
89

T/S:
Audit / Nomination /
Renumeration
Committee
Additional Info:
ROE year 2002
ROA year 2002
SCORES
ROE
ROA
Learning of Employee
Customer Experience
Enhancement
Audit / Nomination /
Remuneration
Committee
WEIGHTED SCORES
ROE (5%)
ROA (5%)
Learning of Employee
(35%)
Customer Experience
Enhancement (35%)
Audit / Nomination /
Remuneration
Committee (20%)
TOTAL SCORE

e
Complet Complet Complet Complet Complet Complet Complet Complet Complet Complet
e
e
e
e
e
e
e
e
e
e
18.62%
5.36%
2002
6
6
0

2003
6
6
0

2004
6
0
0

2005
0
6
0

2006
0
6
0

2007
0
3
3

2008
6
0
6

2009
6
3
9

2010
6
3
9

2011
0
0
9

0

0

0

0

0

3

0

3

6

6

6

6

6

6

6

6

6

6

6

6

18
0.3
0.3

18
0.3
0.3

12
0.3
0

12
0
0.3

12
0
0.3

15
0
0.15

18
0.3
0

27
0.3
0.15

30
0.3
0.15

21
0
0

0

0

0

0

0

1.05

2.1

3.15

3.15

3.15

0

0

0

0

0

1.05

0

1.05

2.1

2.1

1.2

1.2

1.2

1.2

1.2

1.2

1.2

1.2

1.2

1.2

1.8

1.8

1.5

1.5

1.5

3.45

3.6

5.85

6.9

6.45

90

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

ALI

CHIB

DMC

SMC

SMA
5.84
5.97
8.35
13.21
16.21
9.00
9.22
15.07
15.80
22.17
613.20
669.39
601.54
682.65
738.13
815.25
523.74
352.96
419.38
408.01
0.21
0.23
1.54
2.99
4.19
8.74
5.06
7.25
23.19
40.44
51.06
55.04
57.59
62.35
64.34
63.58
44.22
59.97

DQ
85.80
80.40
84.00
90.40
88.20
90.40
88.40
95.40
88.20
88.20
58.60
57.80
48.00
54.80
69.60
68.60
68.40
53.60
68.60
53.60
84.40
81.80
76.80
76.80
74.80
59.00
71.80
70.20
73.60
68.60
121.20
121.20
127.60
118.00
122.40
129.20
114.20
125.20
91

BSC
6.90
2.85
6.00
6.00
6.00
6.60
7.50
8.10
8.10
7.80
2.70
5.25
6.45
5.70
6.75
7.50
6.45
7.05
8.10
5.40
1.80
1.20
1.20
3.90
3.60
4.35
4.35
6.00
4.95
4.35
0.75
1.95
0.90
0.75
2.85
2.40
1.80
1.80

TEL

81.55
127.58
353.88
573.69
1211.90
1586.78
2091.35
2717.50
2484.73
2387.98
2488.93
2343.52

139.00
139.00
118.40
118.40
133.80
126.00
119.80
162.88
119.40
119.40
127.40
117.60

92

1.20
1.65
1.80
1.80
1.50
1.50
1.50
3.45
3.60
5.85
6.90
6.45

Sponsor Documents

Or use your account on DocShare.tips

Hide

Forgot your password?

Or register your new account on DocShare.tips

Hide

Lost your password? Please enter your email address. You will receive a link to create a new password.

Back to log-in

Close