1

EFFECTIVENESS OF EVA : A COMPARATIVE STUDY ON INDUSTRIAL PRODUCT COMPANIES IN MALAYSIA

AHMAD ZOOLHELMI ALIAS

2000573389

A project submitted in partial fulfillment for the requirements of the MARA University of Technology for the Master of Accountancy programme.

December 2001

CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

In order to measure corporate performance, return on assets (ROA), return on investment (ROI), profit margin, earning per share (EPS) and many other methods have been used for quite sometime by investors, accountants and other users of financial statements.

According to Talib and San (1998), for many years, senior managers, investors and analyst have been using conventional measures such as earnings, earnings per share (EPS), P/E ratios or return on equity for setting financial goals, for measuring financial performances and for valuations. Now another method is available for measuring corporate performance that is known as economic value added (EVA).

Economic Value Added (EVA) was advocated by Stern Stewart and Co. in 1982. It is a method of measuring corporate performances in term of shareholders wealth. EVA is defined as the excess of the dollar amount of net operating profit after tax (NOPAT) over the dollar charge for capital (both debt and equity) obtained by multiplying the percentage weighted average cost of capital (WACC).

With reference to Tully et al (1993), EVA is a way of measuring an operations real profitability. Basically, this technique is a version of the residual income method of performance measurement made popular by David Solomons in the late 1960s.

Although this technique appeared as early as 1982, it received little attention until September 1993. An article in Fortune Magazine written by Tully (1993) provided a detailed description of the EVA concept, Stern Stewart practice and successful EVA adoptions by major corporations in the United States of America.

According to Ray (2001), corporate giants such as Coca-Cola, AT&T, Briggs-Stratton, DuPont, Eli Lilly, Quaker Oats and others have adopted this new financial tool and in many instances, reported significantly improved financials. For example, Coca-Cola, one of the earliest users of EVA, saw its stock price increase from US$ 3 (on a split-adjusted basis) in 1981 when the company first adopted EVA, to over US$ 60.

Basically the concept of EVA is relatively new (especially in Malaysia) and not many studies have been conducted in Malaysia as conventional financial measures are still widely used by most of the corporations instead of EVA.

This study has been carried out to find out whether EVA has an added advantage as a performance measurement compared to conventional financial measurement such as percentage increase in net tangible assets, profit margin, return on assets (ROA) and return on equity (ROE).

This study is an extension of previous research conducted by Talib and San (1998) in Singapore. This study is conducted to further test the value-added information of EVA in Malaysian business environment.

In doing so, sample consisting of companies in Malaysia were used to test EVA against conventional financial measurement techniques. Primary data was gathered from financial reports and other published data. From these data, EVA and conventional financial measurements were calculated using certain formulae.

This study aims at testing the difference between conventional financial measurement techniques and EVA techniques for measuring corporate performances.

1.1.1How EVA works

Basic formula of EVA is Net Operating Profit After Tax (NOPAT) less Cost of Capital. But first, we have to calculate equitys cost. For example, overtime, shareholders have received on average a return that is seven percentage points (7%) higher on stocks than on long-term government bond.

With bond rates around 8.3%, that put the average of equity at 15.3%. Assuming the company uses debt as well as equity capital, the cost is the weighted average of the two.

Second step is to find out how much capital is tied up in companys operation. Then multiply the capital with the rate (15.3%). To figure the companys EVA, firstly, subtracts taxes from Net Operating Profit (NOPAT). Then subtract capital cost.

If the result is positive, the company is creating wealth. If it is negative, the companys operation destroying capital.

For example, operating profit of company ABC is RM 160,000 and tax is RM 60,000.

NOPAT=Operating Profit Tax

=RM 160,000 RM 60,000

=RM 100,000

The company uses 67% of equity @ 14.3% and 33% debt @ 5.2%. Capital used by the company is RM 1,600,000.

Cost of Capital=((67% X 14.3%) + (33% X 5.2%) X

RM 1.6 million

=11.3% X RM 1,600,000

=RM 180,752

EVA

=NOPAT Cost of Capital

=RM 100,000 RM 180,752

=- RM 80,752

This indicates that the companys operation is destroying the capital. It is better for the management to fix it fast. To fix it means that the management has to find ways to increase EVA until it is positive. Basically, there are three ways to raise EVA.

First, by earning more profit without using more capital, may be by cost cutting or even downsizing. Second, by using less capital, for example as practiced by Coke, in which they are using plastic containers for concentrate instead of costlier metal. Third, by investing capital in highly return project.

1.2Statement of the Problem

There are a lot of articles discussing about the effectiveness, positive values and advantages of EVA. While successful of EVA stories are quite encouraging, the evidence supporting the theory has been primarily subjective.

Evidence is mixed regarding the efficiency of EVA.

For instance, according to Cordeiro and Kent (2001), there has been a growing literature examining the effectiveness of EVA adoption. However, this has been mainly in the form of case studies and reports that document the improved financial performance resulting from evidence on the effect of EVA adoption at individual firms.

Chen and Dodd (2001) stated that insufficient empirical research exists to support the claim of EVAs supremacy as a performance measure in term of value-relevance. In contrast, limited empirical evidence has suggested otherwise.

As EVA is a new concept especially in Malaysian business environment, this study focuses more on calculation part of EVA rather than behavioural aspects and the concept as a whole.

1.3Objectives of the Study

Objectives of this study are:

1. To identify any added value or added advantage in EVA compared to conventional methods as a performance measurement.

2. To find out whether there is any significant relationship between EVA and stock prices (stock return) compared to conventional financial measurement.

1.4Scope of the Study

This study only focuses on Industrial Product companies listed on the main board of Kuala Lumpur Stock Exchange (KLSE). As this study tests the two techniques by comparison of ranked companies, in my opinion, it is better to select companies under the same industry or sector in which the comparison is more rational.

With reference to Talib and San (1998), sample of their study consisted of companies in the property sector as the properties companies are asset based and the volatility of earnings in the property sector depend heavily on specific sales-launch and project completion dates. The recognized inefficiencies of conventional performance measures on property sector prompts one to consider if another measure would provide different result.

Some limitations of the sample in Talib and San study including, earnings in the property sector can be volatile due to dependence on specific dates of sales launch and project completion dates and complications arising from the use of percentage of completion or the completed contracts method in recognizing profit.

To overcome those problems, I have decided to extent period of study from 3 years to five years and using companies classified under Industrial Product as my sample.

The study covers five financial years to get a better view of the ranked companies under both techniques. To make it easier, Wilcoxon Test is used in evaluating both techniques.

In order to support the result, the correlation between stock return is tested against changes in those financial measurements.

1.5Organisation of the Study

Chapter Two of this dissertation discusses the literature reviews relating to previous researches on Economic Value Added (EVA) and Market Value Added (MVA), definitions of EVA and MVA, other related issue and arguments on EVA and theoretical framework.

Later research design and methodology are discussed in Chapter Three and the data analysis and discussion of result in Chapter Four.

Conclusions of the study are presented in Chapter Five including limitations and the potential future research.

1.6Motivation of the Study

This study is conducted in Malaysian business environment, as results of the previous researches are mixed regarding the efficacy of EVA. With reference to the three researches done by Talib and San (1998), Chen and Dodd (2001) and Cordeiro and Kent (2001) in which the findings are contrary to the theory of EVA in term of value added in performance measurement and as the best measure for valuation purposes.

As the EVA concept is quite new to Malaysian business environment I am curious to find out whether EVA has superior advantage in term of value-added information and as performance measurement compared to conventional financial measurement.

Another reason that drives me into selecting this topic is due to the state of knowledge in this field. It is hoped that it will give at least a basic idea of EVA and also explain correlation between financial parameters and stock return related to Malaysian business environment. Personally, I would like to enhance my knowledge and share it with others who are interested to read this dissertation.

CHAPTER TWO

LITERATURE REVIEW

2.1EVA and its Benefits

Brewer and Chandra (1999) defined EVA as a financial performance measure based on operating income after taxes (NOPAT), the investment in assets required to generate that income and cost of the investment in assets (or, weighted average cost of capital) while Shand (2000) defined EVA as measures of corporations true economic.

Shand (2000) stated that, EVA can change organisations capital allocation process because EVA is like NPV with memory, which estimates how much money a project will generate over a number of years and then determines how much the potential cash flows is worth today.

With reference to case study by Klinkerman (1997), he stated that Centura Banks stock rose more than 90% between December 31, 1993 and September 30, 1996 and according to SNL Securities its outpaced more than 70% rise in the SNL bank stock index after implementation of EVA.

Chen and Dodd (1997) stated that, EVA is the one and only internal measure of corporate performance to tie directly to value. It is the fuel that fires a premium (or accounts for the discount) in the market's valuation of any business. No other measure can make the connection between performance and value as clear as EVA.

According to Ray (2001), from the firms perspective, EVA is an internal financial tool which holds every manager in the firm accountable for every dollar allotted to them. From the markets perspective, EVA is very useful tool with which the firm can maximize its value. EVA and productivity are vitally and inseparably linked one to another. Adopting the EVA measuring tool allows firms to see where value is being created and where its not.

EVA is closely related with Market Value Added (MVA) the difference between the market value of a firm and the economic value of the capital it employs, as stated by Lehn and Makhlija (1996).

Moreover, based on research by Lehn and Makhlija (1996), using the relation of a measure with the stock returns, they concluded that EVA and MVA, like traditional measures, are effective measures of performance. They also found that the correlation of EVA with stock returns is higher than the correlation of any of the other financial measures (ROA, ROS, ROE and MVA) with stock returns.

Tully and Hadjian (1993) cited that, one of EVAs most powerful properties is its strong link to stock prices. EVA and stock prices show a remarkable tendency to move up and down together. Stock prices track EVA far more closely than they track such popular measures as earnings per share or operating margins or returns on equity.

This is in line with study conducted by Yook and McCabe (2001), they concluded that there is evidence of a strong relationship between MVA per share and average returns.

Chen and Dodd (2001) stated that EVAs purported ability to deliver superior stock returns appears to be its main selling point as evidenced by the following Stern Stewart advertisement: Forget EPS, ROE and ROI. EVA is what drives stock prices. This statement supported by Brewer and Chandra (2001) who cited that EVA is better goal congruence than ROI.

Some advantages of EVA as highlighted by Chen and Dodd (2001) including, the cost of equity capital is the opportunity cost which stockholders forgo by investing in a specific company. Stern Stewart approximates it, based on capital assets pricing model (CAPM), by adding an individuals company adjusted risk premium to the return on long-term government bonds.

Another advantage is that EVAs alleged distortions introduced by General Acceptance Accounting Principals (GAAP) are eliminated. (e.g. Research and Development (R&D) expenses are capitalized as it will bring future profit to the company).

Cordeiro and Kent (2001) highlighted several advantages claimed by EVA, that are:

EVA eliminates economic distortions of GAAP to focus decisions on real economic results.

EVA provides for better assessment of decisions that affect the balance sheet and income statement or trade offs between each through the use of the capital charge against NOPAT.

EVA decouples bonus plans from budgetary targets.

EVA covers all aspects of the managerial cycle.

EVA aligns and speeds decision-making, and enhances communication and teamwork.

Dierks and Patel (1997) cited that, EVA measures the amount of value a firm creates during a defined period through operating decisions it makes to increase margins, improve working capital management, efficiently uses its production facilities, and redeploys under utilised assets. Thus, EVA can be used to hold management accountable for all economic outlays whether they appear in the income statement, on the balance sheet, or in the financial statement's footnotes.

EVA creates one financial statement that includes all the costs of being in business, including the carrying cost of capital. The EVA financial statements give managers a complete picture of the connections among capital, margin, and EVA. It makes managers conscious of every dollar they spend whether that dollar is spent on or off the income statement or on operating costs or the carrying cost of working capital and fixed assets.

According to Booth (1997), in summary, it offers a number of advantages over alternatives:

EVA provides the correct incentives for capital allocation, unlike return on capital employed, which can lead to under-capitalisation, and earnings, which can lead to over-capitalisation;

EVA does not encourage actions to flatter short-term results at the expense of long-term performance, e.g. actions such as cutting research and development;

EVA shows a good correlation with historical share prices, thus providing managers with a proxy for share price which they can influence (although more complex measures such as cash flow return on investment can provide a higher correlation);

EVA lends itself to use as an annual performance measure, linked to executive pay, unlike some cash flow measures. Positive economic profit implies that value creation for shareholders and can be used to reward managers accordingly.

EVA is not, however, a new means of corporate valuation, since it can be shown that the present value of the forecast economic profits plus the net assets is equal to the present value of the cash flows. It is something more useful: it provides a decision-making and performance measurement framework which is aligned with the strategic analysis and makes strategy come alive within an organisation as opposed to remaining an ivory tower top-level annual activity. It should displace other financial measures which are not consistent with the creation of shareholder value.

Epstein and Young (1997) stated that, EVA and how its use can aid corporate environmental managers in promoting more proactive environmental investments, and in funding capital investments on environmental improvement, waste reduction, and pollution control in their companies are discussed. The use of EVA and other shareholder value measures can also improve general capital investment decisions by integrating environmental factors that affect the long-term interests of the corporation into the managerial decision-making process.

2.2EVA is better than Conventional Financial Measurement

According to McIntyre (1999), advocator of EVA argues that one important difference between Residual Income (RI) and EVA is that, it adjusts reported accounting results to eliminate distortions encountered in measuring true economic performance.

Brewer and Chandra (1999) stated that EVA is better goal congruence than Return On Investment (ROI). EVA helps overcome the goal incongruence that exists between the manager and the firm which cannot be resolved using ROI.

There are critics on historical accounting performance measures such as return on assets (ROA), return on equity (ROE), return on sales (ROS), or earning-per-share are deficient because they are unidimensional and thus unsuited to fully assessing firms strategic accounting, firms strategic outcomes and performance (Dalton et al, 1982; Venktraman and Ramanujam, 1986). They also display that they reflect only past performance and not future performance.

In research conducted by Cordeiro and Kent (2001), they stated that accounting performance measures ignore differences in risk-taking between firms in their quest for profits. Managers may manipulate reported accounting profits to their advantage and by choosing alternative accounting procedures within the GAAP framework. Some popular techniques involve switching between inventory policies, switching depreciation methods, and expense pension fund allocation.

Another critic Morse et al (1996) states, the primary limitation of Return on Investment (ROI) is that it encourages managers, who are evaluated and rewarded based solely on this measures, to make investment decisions that are in their own best interests, while not being in the best interests of the company as a whole.

Chen and Dodd (1997) cited that, while accounting profits such as earnings per share and return on equity are among the most commonly used performance measures; they are criticized for not taking into consideration the total cost of capital and for being unduly influenced by accrual-based accounting conventions. In contrast, EVA, the difference between after-tax operating profits and the total cost of capital, is promoted as a measure of a company's real profitability.

EVA allows investors to evaluate whether the return being earned on invested capital exceeds its cost as measured by the returns from alternative capital uses. Management may do different things to create value for the business. Whatever it does, the value created will ultimately be reflected in the EVA measure.

According to Shand (2000), the use of EVA has grown steadily as business managers have become disgruntled with standard accounting practices that often fail to generate information helpful to decision making. More companies have turned to performance measurement tools, such as EVA, to bolster their understanding of and ability to achieve profitability.

Yook and McCabe (2001) stated that the conventional measures have some deficiencies as guides to shareholders wealth maximization, because they ignore the cost of capital, such measures lack a formal mechanism for determining whether achieving such goals create values for shareholders. Although a firm earns a positive net income and a high accounting rate of return, it may not contribute to shareholder wealth if earnings fall short of the required returns that shareholders could earn by investing in other securities of comparable risk.

2.3Wrong Application of EVA

The three elements used in calculating EVA are operating income after taxes, investment in assets and the cost of capital. Highlighted by Stewart III (1995) EVA may be wrongly applied by business people. There are five main area highlighted by him.

Firstly, they dont make EVA way of life. The company by not adopting EVA as the centerpiece of a comprehensive financial management system, in which, all the policies, procedures, measures and methods companies use to guide and control their operations and strategy.

Secondly, most managers try to implement EVA too fast. EVA cannot be implemented overnight. It should start with top management using it day to day, and then gradually push it down through the rank. While EVA should be fully integrated into the company, it should not be overdone by trying to apply it to every corner and gap. According to Kudla and Arendt (2000), depending on a firms sales, integration of EVA can take anywhere from months to a few years, and the full impact of the system on shareholder wealth creation may take three to four years.

Thirdly, the boss lacks conviction. If the boss is not totally committed, staffs can form war among themselves.

Fourthly, managers fuss too much about it. When implementing EVA, companies tend to make it a big philosophical issue.

Lastly, training gets short shrift. This is happens because companies sometimes dont disseminate EVA knowledge widely enough through the organisation.

According to Kudla and Arendt (2000), a solid commitment from senior management is vital for successful integration and implementation of EVA programs. Without management buy-in, employees may view the program as just another temporary corporate trend.

The EVA implementation plan should provide for continuous training at the appropriate organisational levels and link EVA performance to manager and employee compensation. Before proceeding with the development of an implementation plan, management should conduct a feasibility study that calculates EVA for both the company and its competitors. This allows the company to benchmark its performance.

2.4Critics of EVA

In research conducted by Ray (2001), he cited that EVA is nothing more than Net Present Value (NPV) re-packaged at departmental, divisional and firm-wide levels. Further, the critics argue that the Stern Stewart and Co. only real contribution to the capital budgeting and incentive compensation process is the development of (arguably, arbitrary) measures with which to implement NPV on scales larger than a project basis.

Another author, Barfield (1998), stated that a key criticism of EVA is that it is simply a retreated model of Residual Income (RI) and that the large number of equity adjustments incorporated in the Stern Stewart system may not be necessary. The similarity between EVA and RI is supported by Chen and Dodd (1997) who noted that most of the EVA and RI variables are highly correlated and are almost identical in terms of association to stock returns.

With reference to Cordeiro and Kent (2001), in their conclusion, they agree that it is reasonable to assume that EVA adoption may not be suitable for all firms all the times as other industry factors need to be considered such as the firms strategic goals and drivers, management power and style, and buy-in, understanding and accountability of its employees.

According to Chen and Dodd (2001), there were insufficient empirical researches existing to support the claim of EVAs supremacy as a performance measure in terms of value-relevance. Further in their research, they found that their data do not support the assertion that EVA is the best measure for valuation purposes.

In study conducted by McIntyre (1999) also highlighted that there is no consensus of whether EVA or ROA provides the more useful performance measure. Academic accountants, in general, appear to favour EVA because its goal-congruence property. Many managers and practitioners, on the other hand, appear to favour ROA, perhaps because it is similar to the way returns on many types of investment are reported.

In research done by Talib and San (1998), the results showed that both methods, conventional financial measures and EVA produced relatively similar results. According to them, the findings implied that there would be no added value in using EVA and Market Value Added (MVA) measures over the conventional financial measures.

In research conducted by Chen and Dodd (1997) there are three main findings highlighted. Firstly, as rightfully claimed by EVA advocates, improving EVA performance is associated with a higher stock return. However, the association of EVA with stock return is not as strong as suggested in anecdotal EVA stories.

Secondly, the EVA measures provide relatively more information than the traditional measures of accounting profit in terms of the strength of their associations with stock return. This evidence is consistent with what the EVA companies have experienced. However, different from the prescription that EVA should completely replace accounting earnings as a performance measure, their study finds that the accounting profit measures are still of significant information value even if EVA is already in use. Consequently, their empirical evidence suggests that along with EVA, companies should continue monitoring the traditional measures of accounting profit such as earnings per share, return on assets, and return on equity.

Finally, not only is EVA akin to residual income in concept, it is also empirically comparable. Most of the EVA and residual income variables are highly correlated and they are almost identical in terms of association to stock return.

Apart from the critics and contrary findings from the previous researches Brewer and Chandra (1999) highlighted the following four limitations of EVA. Firstly, size differences.

Supported by Hansen and Mowen (1997), EVA does not control for size differences. A larger plant or division will tend to have a higher EVA relative to its smaller counterparts.

Secondly, lacks financial orientation in which, EVA is a computed number that relies on financial accounting methods of revenue realisation and expense recognition. According to Homgren et al (1997) managers can manipulate these numbers by altering their decision-making processes.

Thirdly, related to short-term orientation. The intent of a performance measurement system should be to match employees effort, ingenuity and accomplishments with their compensation. If a manager conceives of an innovative idea and put efforts to implement it, some measure of compensation should be afforded to the manager in the current period for the effort and ingenuity expended. However, that is not how financial measures, such as EVA, work when they are used to evaluate employee performance.

Lastly, related to results orientation. The accountants reports state the obvious -that performance was less than expected- but they do not help offer solutions to the non-accounting business managers who are responsible for continuously improving the value delivered to customers. Like conventional financial metrics, EVA is guilty of this charge.

2.5Conceptual Framework

The conceptual framework in this study is basically based on previous study conducted by Talib and San (1998). Companies are ranked using average of conventional financial measures and compared with the same companies ranked with average of Economic Value added method using Wilcoxon Test statistic.

Theoretically, if it is true that EVA is better than conventional financial measurements, there should be significant difference between the two rankings of companies. Indirectly, it gives an indication of existence of value added information in EVA.

To further test the result of the comparison, correlation between stock return and EVA and conventional financial measurement are determined and observed.

According to Lehn and Makhlija (1996), the correlation of EVA with stock returns is higher than the correlation of any of the other financial measures (ROA, ROS, ROE and MVA) with stock returns. This is in line with Stern Stewart advertisement: Forget EPS, ROE and ROI. EVA is what drives stock prices.

The companies are ranked using the average of conventional method and compared with average of EVA. The companies are from the same industry. In this study, the companies that are listed under Industrial Product of main board of KLSE are chosen. In my opinion, the comparison will be more effective and accurate when it involves the companies with the same industry.

Correlation between stock return and changes in financial measurement (Increase in NTA, ROA, ROE, Operating Margin, EVA and MVA) are tested to support the findings.

For the purpose of this study, in my opinion, the framework can give a clear guideline on identifying the existence of value added information in EVA.

CHAPTER THREE

RESEARCH DESIGN AND METHODOLOGY

3.1 Sampling Design

The sample of this study consists of companies listed under Industrial Product of main board of Kuala Lumpur Stock Exchange (KLSE). As in October 2001, there were 109 companies on main board of KLSE listed under Industrial Product.

The reason for selecting Industrial Products sector is because it consists of more companies compared to other sector. Another reason is to be free from bias on selection of companies.

With reference to Talib and San (1998), they choose property sector as their sample because EVA heavily depends on asset. As property sector consist of asset based companies, they believe that a significant difference can be observed.

One major problem is that the complication of determination of income, which property sector companies heavily based on completion of contract distort their result. To avoid such problem in this study, the researcher tends to choose Industrial Product companies.

In research conducted by Talib and San (1998), they carried out their study based on three (3) years of financial data. They also stated that one of their limitation of the study is the short period of study. This study covers five (5) years of companies financial year-end from 1995 to 1999 to overcome such limitation faced by previous researchers.

Since some of the companies financial data for the year ended 2000 was still not available at the period of this research, the researcher had to limit his study up to year ended 1999.

As this study covers 5 years of the financial years, only 78 companies fit to be selected as sample. This is due to some limitation. Out of 109 companies listed under Industrial Product of main board of KLSE, 29 companies financial data are not available because those companies were listed on the KLSE (either main board or second board) after 1995.

One of the companies, Westmont Industries Berhad, is under restraining order; therefore the share price is not available. This company had to be excluded from the sample due to incomplete financial data.

Another company, Kramat Tin Dredging Berhad is inactive for several years in which the turnover of the company is nil (0) up to 1999. This company will distort the result and it had been excluded from the sample as well.

3.2Research design

The main purpose of this study is to identify any added value or added advantage in EVA compared to conventional method as a performance measurement. First, we have to find out whether there is significant difference between conventional financial measurement and EVA. In doing so, ranking of the companies based on both methods are tested and statistically analysed.

If there is significant difference between the two financial measurement techniques, correlation between stock return and the financial measurement techniques used in this study will be tested. If EVA is the highest correlated with stock return, it can be concluded that EVA has an added value or added advantage compare to conventional method as a performance measurement.

EVA and MVA are not very familiar in Malaysia; hopefully this study will generate the basic idea of EVA in relation Malaysian business environment and will lead to other research in related area in the near future.

As per described in the theoretical framework, nine financial parameters were used. Five conventional financial measures that have been used including:

1) Percentage increase in net tangible assets (NTA) per share

=(NTA per share)t (NTA per share)t-1

(NTA per share)t-1

where t is the reference year.

Figures of NTA per share are extracted from KLSE Handbooks and Corporate Handbooks.

2) Operating Margin

=Profit (After Tax and Extraordinary Items)

Total Turnover

Figures of Total Turnover are extracted from annual Reports, KLSE Handbooks and Corporate Handbooks.

3) Percentage Increase in Operating Margin

=(Operating Margin)t (Operating Margin)t-1

(NTA per share)t-1

where t is the reference year.

4) Return on Equity (ROE)

=Profit After Tax and Extraordinary Items

Total Equity

5) Return on Total Assets (ROA)

=Profit After Taxation and Extraordinary Items

Total Assets

Where Total Assets refer to sum of fixed and current assets, extracted from KLSE Handbooks and Coporate Handbooks.

Noted that both Return on Equity (ROE) and Return on Assets (ROA) have the same denominator. According to Brigham E.F., Gapenski L.C. and Ehrhardt M.C. (1999), the dominator for both ROE and ROA is Net Income Availabe to Common Stockholders. As to simplify the calculation, the researcher used the stated formula above.

Actually, some of these conventional financial measures like ROE and ROA, are available in the KLSE Handbooks and Corporate Handbooks but it is stated in two decimal point. To derive at more accurate result, the researcher has decided to recalculate those financial parameters up to four decimal point.

Another four financial measurements are value added financial measures that been used in this study including:

1) Market Value Added (MVA)

The MVA were calculated with respect to the Net Tangible Asset of the company;

=(Share Price NTA/Share) X Number of Shares

where share price is the closing price at the last trading day of the financial year extracted from Investor Digests.

2) Economic Value Added (EVA)

The EVA were calculated using the residual income method;

=Net Operating Profit After Tax (Capital X Cost of Capital Rate (CCR))

where CCR = WACC = (CE X E ) + (CD X E )

D+E D+E

CE = E (Ri) = Rf + (i (E(Rm) Rf)

Rf = Interest Rate of 20 years Government Securities for the year.

(I = Beta, computed using natural log of weekly returns of the stock price against the weekly returns of the KLSE EMAS index as the market index for the period. For 1998 and 1999, beta was calculated using data from mid December 1997 to mid December 1999. For 1996 and 1997, beta was calculated using data from 27th November 1995 to 21st November 1997. For 1994 and 1995, beta was calculated using data from 18th February 1994 to 1st March 1996.

E(Rm) = Growth of EMAS index or market return based on EMAS index.

CD = Average of Base Lending Rate (BLR) X (1 T).

T = Corporate Tax Rate; 32% for 1994 1995 and 1996, 30% for 1997 and 28% for 1998 and 1999.

3) Increase in Economic Value Added

=EVAt EVAt-1

where t is the reference year.

4) Economic Value Added per Capital

=EVA / Capital

where Capital refers to the Total Assets less the Current Liabilities.

Note that in calculating EVA EMAS Index was used instead of Composite Index. This is because EMAS Index is calculated using 511 companies data with 1984 as a base year. It involves all companies listed in the main board of KLSE (EMAS actually stands for Exchange Main Board All-Share Index). Composite Index is calculated using only 100 selected companies data with 1977 as a base year.

Another issue to be clarified is Research and Development Expenditure (R&D). According to Stewart III (1995), investments in Research and Development (R&D) or training should be treated as capital,and should have useful life.

Actually, this statement is in line with SSAP 13, IAS 38 and MASB 4. All the three standards agree that development expenditure can be capitalised if certain criteria are met. In situations where all the relevant criteria are met, it is permissible to defer development expenditure to the extent that its recovery can reasonably be regarded as assured. Such deferred development costs must be amortised in future years. However, MASB 4 is only effective after 1st July 1999 in Malaysia.

In this study no ammendmend was made to Research and Development expenditure due to some reasons. Most of the companies in the sample capitalised the development expenditure, however, Hume Industries (Malaysia) Berhad, Esso Malaysia Berhad and DMIB Berhad did not capitalise the development expenditure. Adjustment was not made because those companies do not split between research and development expenditure. Maybe the R&D expenses are totally research expenditure which shouldnt be capitalised.

To determine the research and development expenditure, the researcher had to flip through the annual reports of the companies. Unfortunately, not all annual reports are available in the KLSE library. The researcher managed to go through approximately 80% of the annual reports and find out that those three companies did not split the research expenditure and development expenditure.

Because of the two reasons I had decided to use the Net Operating Profit after Tax (NOPAT) as stated in the KLSE handbooks and Corporate Handbooks as most of the companies adhere to the standards.

3.3Data collection

For the purpose of data gathering, published data were collected from Investor Digests, KLSE Handbooks, Corporate Handbooks and Bank Negara Reports.

Various sources were used to trace back the financial data. In period of the study, some of the companies changed their names, some of them listed under different sector and some of them were listed under second board of KLSE. Another reason for using various sources of published data is to double check and ensure that the collected data are accurate.

All data were gathered from KLSE library. The researcher was permitted to use the facilities in the KLSE library for two months starting from 16th September 2001 to 17th November 2001. All the data was collected during this period.

The sample was reduced from 109 companies to 78 companies during the data collection stages in which the company is not selected as sample if the financial data is not complete or not available to avoid distortion in the analysis.

3.4Data analysis

Collected data was analysed using the Statistical Package for Social Science (SPSS) version 10.0. However all the formulae and ranking process were done using Microsoft Excel.

Although the process can actually be done using SPSS, the data had been keyed in the Microsoft Excel and the formulae actually had been performed using Microsoft Excel. The results then posted to SPSS to be statistically analysed. All the nine financial parameters were calculated and ranked based on period of study. After all the financial parameters had been calculated and ranked, point were given based on the ranking.

Company in ranking number one (1) is given 78 points and the last ranking company is given one (1) point. Then each companys points is summed up using those two financial parameters (conventional and EVA). Weighted average point of the two parameters are calculated.

Again, the companies are ranked based on the weighted average points. As this study using non parametric data, Wilcoxon signed-rank test is used to compare the two ranking. If it is proven that there is a significant difference between the two ranking, it can be concluded that at least there is a difference in EVA compared to Conventional Financial Measurement.

A test of hypotheses had been conducted in order to determine the significant difference between the two rangkings. The hypotheses that were tested :

H0=There is no significant difference between Conventional Financial Measurement ranking and EVA ranking.

H1=There is a significant difference between Conventional Financial Measurement ranking and EVA ranking.

To interpret the Wilcoxon signed-rank test, Z-score value and the two-tailed P-value (from the SPSS output result) need to be examined. Under 95% confidence level, the output indicates that there is a significant difference if P-value is less than 0.05 (p < 0.05).

To further test the findings, Spearmans rank order correlation to test the correlation between Stock Return and changes in the financial measurements (% increase in NTA, Operating Margin, ROA, ROE, EVAand MVA).

CHAPTER FOUR

DATA ANALYSIS AND DISCUSSION OF RESULTS

4.1 Descriptive Analysis

For the following analysis, please refer to Table 1 to Table 10 (For details kindly refer to Appendix 1).

Table 1 : Summary on Average or Mean of the Descriptive Statistics

19951996199719981999

NTA0.4867 0.1568 0.1752 0.0783 -0.0482

Operating Margin 0.0794 0.0545-0.0331-0.3097 -22.1931

Inc. in Op. Margin -0.03210.0035-0.0313-0.0692-2.9094

ROE0.1199 0.2113 0.1807 0.5002 -1.0083

ROA0.06140.0566-2.4859-0.1371-0.064

MVA-16297.1344 -32394.0829 -429166.3810 -878776.7586 -764285.4206

EVA-1018872.5580 -1762989.0986 -2904053.5864 -39148941.0784 -2506821.0906

Inc. in EVA-674534.0743 -744116.5408 -1141064.4878 -36244887.4798 36642119.9862

EVA per Capital-1.9888 -2.6003 -3.1964 56.6204 -2.7367

4.1.1Percentage Increase in Net Tangible Assets (NTA) per share Using this financial measurement, changes in net tangible assets per share can be observed. Its percentage increase represents the increase in asset valuations.

In year 1995, 63 companies (80.77%) showed an increase in NTA per share. But in 1996, it declined to 53 companies (67.95%). Perhaps this was due to economic downturn. Many companies not performing well and started to cut their costs during this year.

In 1997, it showed an improvement in which 61 companies (78.21%) showed an increased in NTA per share. In 1998, only 42 companies (53.85%) showed a positive figure and further declining in 1999 in which only 24 companies (30.77%) showed an increased in NTA per share.

Table 2 : Descriptive Statistics for Percentage Increase in Net Tangible Assets

MINIMUMMAXIMUMMEAN

1995-0.78275.21600.4867

1996-0.63262.11600.1568

1997-0.77351.51230.1752

1998-0.73071.97020.0782

1999-0.74731.4623-0.0482

From table 2, minimum NTA per share in 1995 was 0.7827, maximum was 5.2160 and had an average of 0.4867. In 1996, the minimum NTA per share was -0.6326 but the maximum NTA per share reduced to 2.1160 and the average of NTA per share to 0.1568.

Further declining occurred in 1997 with the minimum of NTA per share dropped to 0.7735, the maximum to 1.5123 and the average to 0.1752. Not much improvement can be observed in 1998. The minimum NTA per share was 0.7307, the maximum NTA per share was 1.9702 but further declining in the average of NTA per share to 0.07828. In 1999, the minimum NTA per share was 0.7473 but the maximum NTA per share dropped to 1.4623 and the average NTA per share to 0.04819.

Based on the average of NTA per share from 1995 to 1999, it can be concluded that the average performance of the companies is declining.

4.1.2Operating Margin and Increase in Operating Margin.Using these two financial measurements, the effectiveness of a companys operation can be examined, more specifically in term of operating profit over it sales.

In year 1995, 71 companies (91.03%) showed positive operating margin but only 33 (42.31%) companies showed an increase in operating margin. In 1996, it decline to 68 companies (87.18%) that showed positive operating margin and only 29 companies (37.18%) showed an increased in operating margin.

In 1997, it showed a further declined in operating margin, in which only 64 companies (82.05%) showed a positive result but 31 companies (39.74%) showed an increase in operating margin. In 1998, again it showed a further declined in operating margin with only 45 companies (57.69%) showed a positive result and only 15 companies (19.23%) showed an increased in operating margin. Although only 37 companies (47.44%) showed a positive result in operating margin in 1999, 40 companies (51.28%) showed an increased in operating margin.

Table 3 : Descriptive Statistics for Operating Margin

MINIMUMMAXIMUMMEAN

1995-0.85360.49770.0794

1996-1.31310.37460.0545

1997-4.14220.3291-0.0331

1998-6.85760.8372-0.3097

1999-1695.87100.4381-22.1931

From table 3, minimum operating margin in 1995 was 0.8536, maximum is 0.4977 and had an average of 0.0794. In 1996, the minimum operating margin is dropped to -1.3131, the maximum operating margin decreased to 0.3746 and the average of operating margin decreased to 0.05445.

Further declining occurred in 1997 with the minimum of operating margin dropped to 4.1422, maximum decreased to 0.3291 and an average of operating margin dropped to 0.03301. Again a further decreased occurred in 1998. The minimum operating margin is 6.8576, the maximum of operating margin is 0.8372 and the average of operating margin is 0.3097.

In 1999, the minimum operating margin decreased tremendously to 1,695.8710 thus affecting the average of operating margin to reduce to 22.1931. The maximum operating margin was 1.4623. This is because of tremendous dropped in sales of one company, Wembley Industries Holding Berhad. The sales were dropped from RM 65 million to RM 31,000 which has affected the operating margin.

Table 4 : Descriptive Statistics for Increase in Operating Margin

MINIMUMMAXIMUMMEAN

1995-1.09110.4687-0.0321

1996-1.21762.09250.0035

1997-1.00550.1836-0.0313

1998-1.54580.3475-0.0692

1999-235.44287.2422-2.9094

From table 4, minimum increased in operating margin in 1995 was 1.0911, maximum was 0.4687 and has an average of 0.03211. In 1996, the minimum increase in operating margin was dropped to -1.2176, the maximum increase in operating margin increased to 2.0925 and the average of increase in operating margin slightly increased to -0.0035.

Not much improvement in 1997 with the minimum of increase in operating margin increased to 1.0055, maximum decreased to 0.1836 and an average of increase in operating margin dropped to 0.03126. Again a further decreased occurred in 1998. The minimum increased in operating margin was 1.5458, the maximum increased in operating margin was 0.3475 and the average increased in operating margin was 0.0692. In 1999, same as operating margin, the minimum increased in operating margin decreased tremendously to 235.4428 thus affecting the average increased in operating margin to reduce to 2.9093. The maximum operating margin was 7.2422.

Based on the average of operating margin and increased in operating margin, it can be concluded that the average performance of the companies was declining.

4.1.3Return on Equity (ROE)In year 1995, 73 companies (93.59%) showed positive ROE. But in 1996, it declined to 69 companies (88.46%). In 1997, it showed a decreased in ROE in which only 64 companies (82.05%) showed a positive ROE. Further decreased in 1998 in which, only 52 companies (66.67%) showed a positive figure and further declined in 1999 in which only 45 companies (57.69%) showed an increased in ROE.

Table 5 : Descriptive Statistics for Return on Equity (ROE)

MINIMUMMAXIMUMMEAN

1995-1.04160.71110.1199

1996-0.94158.05720.2113

1997-15.646412.13800.1807

1998-5.039224.97900.5002

1999-60.59841.2146-1.0083

From table 5, minimum ROE in 1995 was 1.0416, maximum was 0.7111 and had an average of 0.1199. In 1996 the minimum ROE was -0.9415, the maximum ROE increased to 8.0572 and the average of ROE increased to 0.2113.

In 1997 the minimum ROE dropped to 15.6464 but the maximum increase to 12.1380. Not much different in the average ROE which was 0.1807. The minimum ROE in 1998 was 5.0392, the maximum ROE was 24.979 and the average ROE was 0.5002. In 1999, the minimum ROE was 60.5984 but the maximum ROE dropped to 1.2146 and the average ROE dropped to 1.0083.

Based on the average ROE from 1995 to 1999, it can be concluded that the average performance of the companies was quite consistent from 1995 to 1998 but slightly dropped in 1999.

4.1.4Return on Assets (ROA)In year 1995, 72 companies (92.31%) showed positive ROA. But in 1996, it declined to 68 companies (87.18%). In 1997, it showed a further decreased in ROA in which only 64 companies (82.05%) showed a positive ROA. Further decreased in 1998 in which, only 45 companies (57.69%) showed a positive figure and again, further declined in 1999 in which only 37 companies (47.44%) showed an increased in ROA.

Table 6 : Descriptive Statistics for Return on Assets (ROA)

MINIMUMMAXIMUMMEAN

1995-0.29360.23540.0614

1996-0.28270.26640.0566

1997-4.08750.2000-0.0249

1998-3.44010.2074-0.1371

1999-1.72170.1427-0.0636

From table 6, minimum ROA in 1995 was 0.2936, maximum ROA was 0.2354 and with an average of 0.0614. In 1996 the minimum ROA was -0.2827, the maximum ROA was 0.2664 and the average of ROA to 0.0566.

In 1997 the minimum ROA dropped to 4.0875, the maximum ROA decreased to 0.2000 and the average ROA dropped to 0.0249. The minimum ROA in 1998 was 3.4401, the maximum ROA was 0.2074 and the average ROA dropped to 0.1371. In 1999, the minimum ROA was slightly increased to 1.7217 but the maximum ROA dropped to 0.1427 and the average ROA was 0.0636.

Based on the average ROA from 1995 to 1999, it can be concluded that the average performance of the companies was quite consistent.

4.1.5Market Value Added (MVA)In year 1995, 50 companies (64.10%) showed positive MVA. But in 1996, it increased to 54 companies (69.23%). In 1997, it showed a decreased in MVA in which only 27 companies (34.62%) showed a positive MVA. A tremendous decreased in 1998 occurred in which, only 6 companies (7.29%) showed a positive figure and further in 1999, 9 companies (11.54%) showed an increased in MVA.

Table 7 : Descriptive Statistics for Market Value Added (MVA)

MINIMUMMAXIMUMMEAN

1995-5981604.19001958752.2000-16297.1343

1996-7947126.92003443192.0000-32394.0829

1997-10147936.56002718134.6000-429166.3810

1998-14471967.2000805902.6500-878776.7586

1999-12950414.5600816881.7500-764285.4206

Minimum MVA in 1995 was 5,981,604.2, maximum was 1,958,752.2 and with an average of 16,297.134. In 1996 the minimum MVA was dropped to -7,947,126.9, the maximum MVA increased to 3,443,192.0 and the average of MVA was decreased to 32,394.083.

In 1997 the minimum MVA further decreased to 10,147,937 and the maximum MVA also decreased to 2,718,134.6. The average MVA decreased to 429,166.38. Again, the minimum MVA in 1998 decreased to 14,471,967, the maximum MVA decreased to 805,902.65 and the average MVA decreased to 878,776.76. Not much improvement in 1999, the minimum MVA was 12,950,415, the maximum MVA was 816,881.75 and the average MVA was 764,285.42.

Based on the average MVA from 1995 to 1999, it can be concluded that the average performance of the companies was declining.

4.1.6Economic Value added (EVA)In year 1995, 13 companies (16.67%) showed positive EVA. But in 1996, it declined to 3 companies (3.85%). In 1997, it shows an increased in EVA in which 16 companies (20.51%) showed a positive EVA. But in 1998 only 5 companies (6.41%) showed a positive figure and in 1999 in which only 7 companies (8.97%) showed a positive result.

Table 8 : Descriptive Statistics for Economic Value Added (EVA)

MINIMUMMAXIMUMMEAN

1995-25679716.530088458.6949-1018872.5579

1996-43835472.843037937.0981-1762989.0986

1997-62892137.2800924592.1098-2904053.5864

1998-2835703482.0000462057.3656-39148941.0784

1999-74399572.61002464190.0910-2506821.0906

From table 8, minimum EVA in 1995 was 25,679,717, maximum was 88,458.6949 and had an average 1,018,872.6. In 1996 the minimum EVA was 43,835,473, the maximum EVA increased to 37,937.0981 and the average of EVA increased to 1,762,989.1.

In 1997 the minimum EVA dropped to 62,892,137 but the maximum increased to 924,592.11. Not much different in the average EVA which was 2,904,053.6. The minimum EVA in 1998 was 2,835,703,482, the maximum EVA was 462057.37 and the average EVA was 39,148,941. In 1999, the minimum EVA was 74,399,573 but the maximum EVA dropped to 2,464,190.1 and the average EVA dropped to 2,506,821.1.

Based on the average EVA from 1995 to 1999, it can be concluded that operation for most of the companies in this study are destroying the capital not creating wealth since the average of EVA from 1995 to 1999 show a negative result.

4.1.7Increase in Economic Value added (EVA)In year 1995, 8 companies (10.26%) showed positive increase in EVA. In 1996, 9 companies (11.54%) showed positive increase in EVA. In 1997, it showed an increase in EVA in which 33 companies (42.21%) show a positive increase in EVA. Again in 1998 35 companies (44.87%) showed a positive figure and in 1999 59 companies (75.64%) showed a positive result.

Table 9 : Descriptive Statistics for Increase in EVA

MINIMUMMAXIMUMMEAN

1995-19221284.45001301851.5340-674534.0743

1996-18155756.3178146259.6977-744116.5408

1997-19056664.44004262283.4050-1141064.4878

1998-2836539605.000016963475.4300-36244887.4797

1999-10006942.19002835294095.000036642119.9862

From table 9, minimum increase in EVA in 1995 was 19,221,284, maximum was 1,301,851.5 and had an average 674,534.07. In 1996 the minimum increase in EVA was 18,155,756, the maximum increase in EVA increased to 146,259.70 and the average of increase in EVA increased to 744,116.54.

In 1997 the minimum increase in EVA dropped to 19,056,664 but the maximum increase to 4,262,283.4. Not much different in the average increase in EVA which was 1,141,064.5. The minimum increase in EVA in 1998 was 2,836,539,605, the maximum increase in EVA was 16963475 and the average increase in EVA was 36,244,887. In 1999, the minimum increase in EVA was 10,006,942 but the maximum increase in EVA dropped to 2,835,294,095 and the average increase in EVA dropped to 36,642,120.

Based on the average increase in EVA from 1995 to 1999, it can be concluded that although operation for most of the companies in this study are destroying the capital, it seems that the increase in EVA show an improvement in which in 1999 it shows a positive result.

4.1.8EVA per Capital In year 1995, 12 companies (15.38%) show positive EVA per capital. But in 1996, it declined to 3 companies (20.51%). In 1997, it showed an increase in EVA per capital in which 16 companies (20.51%) showed a positive EVA per capital. But in 1998 only 7 companies (8.97%) showed a positive figure and in 1999 in which only 3 companies (3.85%) showed a positive result.

Table 10 : Descriptive Statistics for EVA per Capital

MINIMUMMAXIMUMMEAN

1995-11.97650.2088-1.9888

1996-8.23460.6044-2.600

1997-40.21954.5949-3.1964

1998-27.85454572.321556.6204

1999-66.301574.9057-2.7368

From table 10, minimum EVA per capital in 1995 was 11.9765, maximum was 0.2088 and had an average 1.9888. In 1996 the minimum EVA per capital was 8.2346, the maximum EVA per capital increased to 0.6044 and the average of EVA per capital decreased to 2.6003.

In 1997 the minimum EVA per capital dropped to 40.2195 but the maximum increased to 4.5949. An average EVA per capital further decreased to 3.1964. Tremendous increased in EVA per capital occurred in 1998 with the minimum EVA per capital increased to 27.8545, the maximum EVA per capital increased to 4572.3215 and the average EVA per capital increased to 56.6204. In 1999, the minimum EVA per capital reduced to 66.3015, the maximum EVA per capital dropped to 74.9057 and the average EVA per capital dropped to 2.7367.

Based on the average EVA per capital from 1995 to 1999, it can be concluded that average performance declining. An increase in average of EVA per capital in 1998 due to distortion from result of EVA per capital of one company, Aokam Perdana Berhad. Actually this company had an EVA of 2,836,539,605.4998 but because of it has negative capital of 620,189 (Total Assets less Current Liabilities) it result in positive EVA per capital of 4572.3215.

4.2 Performance Ranking

Ranking of companies under Industrial Product sector from 1995 to 1999 based on the nine financial parameters that been used in this study can be observed in Appendix 2. While the ranking of the companies based on weighted average of conventional financial measurement techniques and Economic Value Added techniques can be referred in Appendix 3.

Wilcoxon Signed Ranks Test is used further test if there is significant difference between the two techniques (conventional financial measurement and EVA). (Details of the Wilcoxon Signed Ranks Test results can be observed in Appendix 4)

Table 11 : Summary of Wilcoxon Signed Ranks Test Result

19951996199719981999

P-Value0.9780.974 0.9210.956 0.978

To interpret the Wilcoxon Signed-Rank Test, we need to examine the Z-Score value and the two-tailed P-Value. Under 95% confidence level, if P-Value < 0.05, it indicates that there is a significant different in the output and vice versa.

As per table 11, in 1995 result, P-value is 0.978 which is more than 0.05. Under 95% confidence level, the result implied that there is not enough statistical evidence to reject null hypothesis (H0). Therefore, the null hypothesis that there is no difference between the two rankings is accepted.

In 1996 result, P-value is 0.974 which is more than 0.05. Still, under 95% confidence level, the result implied that there is not enough statistical evidence to reject null hypothesis (H0). Therefore, the null hypothesis that there is no difference between the two rankings is accepted.

Again, in 1997 result, P-value is 0.921 which is more than 0.05. Under 95% confidence level, the result implied that there is not enough statistical evidence to reject null hypothesis (H0). Therefore, the null hypothesis that there is no difference between the two rankings is accepted.

In 1998 result, P-value is 0.956 which is more than 0.05. Under 95% confidence level, the result implied that there is not enough statistical evidence to reject null hypothesis (H0). Again, the null hypothesis that there is no difference between the two rankings is accepted.

In 1999 result, P-value is 0.978 which is more than 0.05. Under 95% confidence level, the result implied that there is not enough statistical evidence to reject null hypothesis (H0). Therefore, the null hypothesis that there is no difference between the two rankings is accepted.

4.3Correlation between Stock Return and Financial Parameters.

The correlation between Stock Return and changes in the financial measurements (% increase in NTA, Operting Margin, ROA, ROE, EVAand MVA) is tested using Spearmans rank order correlation. The following exlpanation is based on statistic result as presented in Table 11 (Details of the result can be observed in Appendix 5).

Table 12 : Summary of Spearmans rank order correlation result19951996199719981999

NTA0.1000.0160.011-0.2880.300

Op. Margin 0.1560.1760.1560.2020.095

ROE0.0970.1770.2020.1120.349

ROA0.0730.0890.2170.1990.156

MVA0.3870.6570.5360.7230.606

EVA0.0850.212-0.0880.029-0.026

4.3.1Year 1995Market Value Added (MVA) has the highest correlation with stock return with Spearman Correlation of 0.387. Economic Value Added (EVA) showed a result of 0.085, Return on Equity (ROE) was 0.097, Percentage Increase in Net Tangible Assets (NTA) was 0.100, Operating Margin was 0.156 and the lowest correlation with stock return was Return on Assets (ROA) which only shows a result of 0.073.

4.3.2Year 1996MVA still has the highest correlation with stock return with Spearman Correlation of 0.657. EVA shows a result of 0.212, ROE was 0.177, ROA was 0.089, Operating Margin was 0.176 and the lowest correlation with stock return was NTA which only 0.016 correlated with stock return.

4.3.3Year 1997Again, MVA has the highest correlation with stock return with Spearman Correlation of 0.536. EVA shows a negative result of -0.088, ROE was 0.202, ROA was 0.217, Operating Margin was 0.156 and the NTA was only 0.011.

4.3.4Year 1998MVA has the highest correlation with stock return with Spearman Correlation of 0.723. EVA shows a result of 0.029, ROE was 0.112, ROA was 0.199, Operating Margin was 0.202 and NTA shows a negative correlation of 0.288.

4.3.5Year 1999Once again, MVA has the highest correlation with stock return with Spearman Correlation of 0.606. EVA shows a negative result of -0.026, ROE was 0.349, ROA was 0.156, Operating Margin was 0.095 and NTA was 0.300.

CHAPTER FIVE

CONCLUSIONS

5.1 Conclusions

From the findings we can conclude as below:

Based on descriptive analysis and ranking of companies using different financial measurement techniques, it can be observed that different financial measurement techniques will give different results. This is because different financial measurement techniques serve different purposes.

For example, if shareholders want to know if the equity is correctly utilized it is better for these shareholders to use Return on Equity (ROE) to measure it. Similarly, if investors want to know whether the assets have been fully utilized or otherwise, they can use Return on Assets (ROA) to measure the companies performance.

The doubts whether there is any added value or added advantage in EVA compared to conventional methods as a performance measurement have now become clearer. In analysing performance ranking from 1995 to 1999, in all statistical results, there is not enough statistical evidence to reject null hypothesis. Therefore, it can be concluded that there is no difference between the two methods of weighted rankings.

With reference to the correlation between stock return and financial parameters, based on Spearmans rank order correlation results from 1995 to 1999, MVA has the highest correlation with the stock return compared to other financial parameters. However, based on this study, correlation between EVA and stock return is not as strong as claimed by its advocator.

The correlation of EVA with stock return indicates a weak relationship in which it shows that the corelation is near to zero (0). In year 1995, 1997 and 1999, EVA has negative relationship with stock return (-0.081, -0.088 and 0.026), where as in 1996 and 1998 it shows a positive relationship with stock return (0.212 and 0.029).

From the above findings, it can be concluded that there is no added value or added advantage in EVA compared to conventional methods as a performance measurement.

5.2 Problems and Limitations

In this study, while calculating EVA, Research and Development Expenditure (R&D) for certain companies (which did not comply with International Accounting Standards) is totally ignored as there was not enough information disclosed in the Annual Reports (regarding research expenses and development expenses).

Also, income smoothing effect could not be accounted for and all data are actually based on published data in KLSE handbooks and annual reports.

This study did not consider inflation, different accounting practices and window dressing techniques used to make the financial statements look stronger which possibly distort the results.

The correlation between stock return and financial measurements is merely based on five (5) stock returns at the end of every financial year of 78 companies (78 companies X 5 stock returns). The fluctuation of share prices during the year may not be represented by the price of the last date of stock traded during the financial year. Moreover, there are other factors which also contributes to the share prices such as announcement of news (Tuttle B, Coller M and Button F G (1997)).5.3 Future Research

The results from this study suggest several areas for further research. The same research can be conducted with different sample. The sample may be from different sector or randomly chosen companies from various sectors. This is because EVA has been used in published rankings of firms in United State of America and in United Kingdom ignoring the industry to which the companies belong to.

In order to observe a better effect of correlation between financial measurements and stock return, a study based on monthly or weekly data (if possible) should be conducted.

As this research does not cover all conventional financial parameters, future research with inclusion of other conventional financial measurement such as Price Earning Ratio can be conducted.

To ensure that the Research and Development Expenditure (R&D) is well taken care of, the sample should be taken from financial year end 2000 onwards. This is because MASB 4 is only effective after 1st July 1999 in Malaysia.

Further, research can also deal with overcoming the above mentioned limitations that could possibly have influenced the findings.

FINANCIAL MEASUREMENT

Return on Total Assets

Return on Equity

% increase in Operating Margin

Operating Margin

% increase in Net Tangible Assets per Share

Conventional Method

Economic Value Added per Capital

Increase in Economic Value Added

Economic Value Added (EVA)

Market Value Added (MVA)

Economic Value Added (EVA)

61