the analysis of application of fair value and historical cost

133 Maruli, Farahmita, The Analysis of Application of Fair Value and Historical …..

Asia Pacific Journal of Accounting and FinanceVolume 1 (2), June 2011

THE ANALYSIS OF APPLICATION OF FAIR VALUE ANDHISTORICAL COST APPROACHES IN THE VALUATION OF

BIOLOGICAL ASSETS IN THE AGRICULTURAL COMPANIES

Saur Marulia, Aria Farahmitab

a Universitas Indonesiab Corresponding author, Universitas Indonesia

Email: [email protected]

Abstract

This study discusses the analysis of application of fair value and historical cost approachesused by agricultural companies in valuing their biological assets. The term of biologicalassets in this study refers to the biological assets possessed by agricultural companiesspecifically plantation companies. Due to IFRS convergence process done by IAI to adoptIAS 41, this study aims to provide initial evidence on the comparison of fair valuesapproach and historical cost approach in valuing the biological assets. The comparison isintended to figure out the existence of significant differences in the value of assets, returnon assets, revenue and earnings between two groups which uses two different valuationapproaches. This study also aims to prove the existence of larger income smoothingpropensity in the group of companies using fair values approach compared to those usinghistorical cost approach. Eventually, the study tries to prove the influence of the fair valueapproach to earnings volatility which is indicated to be higher than the historical costapproach. Based on the result of statistical tests, it is concluded that there is no significantdifferences in the value and volatility of the assets, return on assets, revenue and earningsbetween the two groups. This finding implies that there is no significant influence of theapplication of fair value approach to the volatility of company’s earnings.

Keywords: Biological Assets, Fair Value, Historical Cost, IAS 41, Plantation, AgriculturalCompanies

1. INTRODUCTION

Reformation of accounting standards towards the basis measurement of accountingusing a fair value approach has resulted debates especially in the recent years. It is mainlycaused by the emergence of international accounting convergence which tends to use the fairvalue approach as the basis of accounting measurement and reporting. In Indonesia, it is

Maruli, Farahmita, The Analysis of Application of Fair Value and Historical ….. 134

recognized when the Indonesian Institute of Accountants (IAI) initiated the process ofinternational accounting convergence, thus requiring the completed revisions towards theIndonesian Generally Accounting Principles (PSAK) according to the International FinancialReporting Standards (IFRS).

One of the problems or obstacles which is possible to be countered in the applicationof IFRS requires many companies or business entities to change its accounting measurementand reporting, which are mostly based on historical cost approach, into measurement andaccounting which are based on fair value approach. Despite the trends to the application ofaccounting standards based on fair value, the reformation has resulted in controversies amongvarious parties. There are some groups and parties who support the application of fair value,but there are also groups who doubt the application. The unsolved debates also still emergeamong academicians about the value relevance of the application of the fair value. Currently,the Financial Accounting Standard Board (DSAK) is in the process of adopting IAS 41,which is related to agricultural accounting, into PSAK. This study is aimed to provide initialevidence and analysis regarding to the possible impact of the application of PSAK whichadopts IAS 41.

IAS 41 has brought these debates into the scope of agricultural accounting. There aremany parties who critically behave towards the requirements of fair value application inmeasuring biological assets and their value changes that should be recognized in company’sprofit and loss statement. Penttinen et al. (2004) stated that the application of fair value willcause unrealistic fluctuations in the forestry companies. Herbohn and Herbohn (2006) andDowling & Godfrey (2001) were emphasizing on the increase of volatility, manipulation andsubjectivity from reported income. Herbohn and Herbohn (2006) calculated the coefficient ofvariance from earnings and gains & losses of timber assets in eight public companies and fivestate-owned enterprises. They stated that the measurement using fair value would increase thevolatility of earnings. However, Argiles and Soft (2001) could accept the measurement usingfair value for biological assets because it could avoid the complexity in calculating the costs.It because there are many family farms in west countries especially in the European Unionthat have no resource and capability to afford the procedures and calculations of accounting.The nature of farming makes the calculations based on historical cost for biological assetsbecome difficult, it because the assets experience birth, growth and death, also thecomplication in allocating the joint costs. Allocation of indirect costs is also one of othersources of complexity in calculating costs in the farms. Kroll (1987) argued that thecomplexity in asset valuation using historical cost is the main barrier in the valuation basedon historical cost.

The valuation using fair value should consider the balance between costs and benefits.The simplicity in the calculation is the main benefit in applying the fair value instead ofhistorical cost. However, there has not been an agreement in the previous literatures, in whatoccasion that the abnormal volatility occurs in the income and earnings, value relevance,income smoothing and the increase or decrease of profitability, as the impact of applying fairvalue.

This study is aimed to provide empirical evidences of measuring the biological assetsusing fair value in the scope of agricultural industry, by using the samples of agriculturalcompanies which use fair value and historical cost approaches in the valuation of biological


assets. This study includes analytical description consisting of comparison in severalcomponents of financial statements, such as assets, incomes and earnings with their absolutevalues and standard deviations. Besides, this study also compares the calculation of IncomeSmoothing Index (ISI) among the groups. In addition to providing descriptive explanations,this study also figures out the impact of using the fair value approach on the earningsvolatility by using regression models. This study is mainly aimed to give an initial descriptionof the application of the fair value and historical cost approaches in the valuation of thecompany’s assets, especially for the biological assets in the agricultural companies.

2. FUNDAMENTAL THEORIES AND HYPOTHESIS DEVELOPMENT

2.1. Biological Assets and the Accounting TreatmentsAccording to IAS 41, biological assets are defined as the living plants and animals

controlled by the enterprise as a result of past events. Control may be through ownership orthrough another type of legal arrangement.

In measuring the fair value of biological assets, IAS 41 gives the hierarchy of themethods that are supposed to be use to determine the fair value. The most suggested methodis by using recent market transaction prices of the biological assets (marked-to-market) whichare available in the active market. The second, we may use the prices of similar assets orsector benchmarks related to the intended biological asset where the valuation is known asmarket-determined prices. The third, when there is no market price, the available standardssuggest to use a discounted-cash flows model (marked-to-model). Eventually, if thosementioned above are not available and cannot be measured reliably, then the biological assetsmust be measured in the historical cost less the accumulated depreciation and impairment.The graph of fair value determination is presented in Figure 1.

Gains and losses from the valuation of biological assets could be shown in the initialrecognition of the biological assets, as much as the difference between the historical cost andthe fair value of the biological assets less estimated point-of-sale costs. The gains and lossesof valuing the biological assets can also be shown up in the measurement after initialrecognition, which is the difference between the recent fair value of the biological assets lessthe estimated point-of-sale costs and the previous fair value of biological assets less theestimated point-of-sale costs. The fair value changes of biological assets could be caused bygrowth, death, birth and production that cause the qualitative and quantitative changes, newgenerations or additional biological assets. Besides, the fair value changes of biological assetscan also be caused by the changes in the market or economy within the countries such as thechanges of inflation, exchange rate, economic growth, aggregate demand or even governmentpolicy.

An entity should disclose the amount of all gains and losses included in the initialrecognition of biological assets and agricultural products and from the changes of fair valueless estimated point-of-sale costs. The methods and assumptions that are used in determiningthe fair value should also be disclosed. The fair value less the estimated point-of-sale costs ofthe harvested agricultural products during the periods should be disclosed in the point ofharvest. When the fair value can not be measured reliably, additional disclosures are needed.


2.2. Previous Literatures and HypothesisThis study is a replication of previous study by Argiles et al. (2009). The study was

intended to identify whether there was any significant difference in the value of assets,incomes, earnings, ROA and Income Smoothing Index (ISI) between agricultural companiesthat use fair value and those that use historical cost approaches in the valuation of biologicalassets. The study by Argiles et al. (2009) also investigated the impact of using one ofbiological asset valuation methods to the earnings volatility of agricultural companies. Theresult of the study showed that there was not any significant difference in the value of theassets, incomes, earnings, ROA and Income Smoothing Index (ISI) between the two groupsthat use different approaches in valuing their biological assets. Besides, the study alsoexplained that there was not any significant impact of using the biological asset valuationmethods to the earnings volatility.

These are some of other studies related to the application of using fair value andhistorical cost approaches in valuing the components in financial statements.

2.2.1. Comparisons of Assets, Incomes and Earnings between Fair Value and HistoricalCost Approaches.Elad (2004) argued that IAS 41 is a controversial subject because it shows the most

radical and comprehensive differences from historical cost accounting, and it causestheoretical and practical problems. These differences could be found in the value ofcompany’s assets, incomes and earnings. He also stated that the use of subjective valuation inassessing fair value, such as similar asset prices or the use of discounted-cash flows model,but also cause the implementation problems to the scope of different countries. Based on thisargument, we can derive a hypothesis as proposed below:H1: There is a significant difference in the total value and volatility of assets, incomes

and earnings between the agricultural companies using the fair value andhistorical cost approaches.

2.2.2. The Tendency of Earnings Management with Fair Value ApproachWatts (2003) argued that the measurement using fair value is a subject to

manipulation. Thus, it is a weak measurement tool of valuations and performances instead ofhistorical cost. He stated that every effort to ban the conservatism of accounting will fail andaccounting can not compete to market in valuing a company (Watt 2006).

Danbolt and Rees (2008) found that the use of fair value consistently will be morerelevant compared to historical cost. They also found the consistent evidence to the earningsmanipulation in the application of fair value by using sample from British Real Estate andInvestment Fund Industries. The results were that the fair value of assets for investmentcompanies was considered irrelevant, while for real estate samples was not, indicatingpossible earnings management in the form of income smoothing in real estate industry. Inaddition, Danbolt and Ress (2008) stated that when the fair value accounting results inambiguous measurement, the value relevance becomes weak. Based on the above discussion,the next proposed hypothesis is:


H2: The group of companies using the fair value approach tends to have larger IncomeSmoothing Index (ISI) compared to Group of companies using the historical costapproach.

2.2.3. The Influence of Using Fair Values to the Volatility of EarningsPenttinen et al. (2004) argued that the application of fair value would cause unrealistic

fluctuations in the earnings of forestry companies. While Herbohn and Herbohn (2006) andDowling and Godfrey (2001) were emphasizing on the increase of volatility, manipulationand subjectivity from reported income. Herbohn and Herbohn (2006) calculated thecoefficient of variance from earnings and gains & losses of timber assets in eight publiccompanies and five state-owned enterprises.

Plantin and Sapra (2008) concluded that when there is imperfection in market, theemergence of additional volatility as the consequence of using the fair value measurementwill be jeopardizing. Thus, the next proposed hypothesis would be:H3: Fair value approach has more impact to the earnings volatility compared to

Historical approach.

3. RESEARCH METHOD

3.1. Data Collection and Sample SelectionData used in this study is secondary data from Indonesia Capital Market Directory

(ICMD) or Indonesia Stock Exchange (IDX) with minimum four consecutive years since2001 to 2009. The type of data used in this study is pooled cross section-time series.

The method of collecting the samples in this study was done by using purposivesampling method, with criterions as stated below:1. Agricultural companies publishing their year-end financial statements each year with

minimum four consecutive years.2. Those companies have biological assets which are valued using fair value or historical

cost approaches, as identified from the notes of the financial statements.3. The published financial statements consist of profit and loss, balance sheet and cash flows

statements.4. The profit and loss statement shows the reported incomes, earnings and other gains &

losses. The balance sheet shows the total assets consisting of biological assets. The cashflows statement reports cash flow from operating activities, investing activities andfinancing activities.

After sample selection process as presented in Table 1, we identify 72 companies thatmeet the above criterias as listed in Table 2. We eliminate some outliers so that the finalsample size is 64 companies.


3.2. Operationalization and Measurement of VariablesThe variables used in this study consist of the variables for descriptive analysis and

regression tests. The financial data have been converted into US Dollar to improvecomparability among the sample companies originated from different countries.

3.2.1. Variables for Descriptive AnalysisTotal Assets is the total value of assets, including the total value of the possessed

biological assets, as reported in the balance sheet.Return on Assets (ROA) is the total value of agricultural company’s gross income less

operating expenses stated in the company’s profit and loss statement each year. The totalincome includes the company’s income from the sale of biological assets or agriculturalproducts which are measured using fair value or historical cost.

Total Earnings is the total value of agricultural company’s net income after tax andother operating expenses stated in the company’s profit and loss statement. The total earningsencompass other gains & losses from the fair value changes of the company’s biologicalassets.

Income Smoothing Index (ISI) constitutes measurement to estimate the existence ofincome smoothing in the agricultural companies using fair value and historical costapproaches in valuing the biological assets. The ISI was proposed by Eckel (1981) and usedby Iniguez and Poveda (2004). The formula to calculate the ISI is presented in Figure 2.

Standard Deviation is the deviation or distortion of values for each variables thatinclude total assets, total revenue, total earnings, Return on Assets (ROA) and IncomeSmoothing Index (ISI). This variable also explains the level of volatility of each component.

3.2.2. Variables for Regression AnalysisStandard Deviation of Earnings (STDE) is the dependent variable that explains level

of volatility of the company’s earnings (E).Valuation Method (DFV) is a dummy independent variable. Companies that use fair

value approach is coded 1 (one), while companies that use historical cost approach is coded 0(zero). Standard Deviation of Cash Flows from Operating Activities (STDCFO) and StandardDeviation of Revenue (STDrevenue are measures of volatility of cash flows from operatingactivities and revenues, respectively. These two variables serve as control variables in theregression model.

Total Assets is included in the regression mode to control the different sizes amongthe sample companies and measured based on the total value of assets. The total assets valueincludes the value of the company’s biological assets.

Average Inflation (AVINF) is the average of inflation rate for each country where thesample is originated. The average is calculated during year 2000 to 2008 for each country.This variable is also a control variable to control the value differences in the financialstatements caused by the differences and changes in the inflation rate among those countries.The inflation rate data is presented in the Table 3.


3.3. Data Analysis3.3.1. Analytical Description

To examine the hypothesis 1 and 2, we use descriptive analysis by using the test ofvariance or analysis of variance (ANOVA) test. The test is initiated by examining theexistence of differences in the total value of assets between two sample groups to ensure thatthe sizes are relatively similar. The next step is performing the test of variance on othervariables that include incomes, earnings, ROA and Income Smoothing Index (ISI). The test isintended to examine the existence of significant differences between the sample group usingfair value and those using historical cost approaches in the valuation of their biological assets.

3.3.2. Regression ModelsTo examine the hypothesis 3, we use regression analysis with two regression models,

as follows:

Model 1STDEi = β0 + β1. DFVi. + β2. STDCFOi + β3. TASSETi + β4. AVINFi + εi (1)

Model 2STDEi = β0 + β1. DFVi + β2. STDrevenuei + β3. TASSETi + β4. AVINFi + εi (2)

Where STDEi is the standard deviation of earnings (E) for company i; STDCFOi is thestandard deviation of CFO for company i; DFV is a dummy variable which has value 1 if thecompany uses the fair value approach in valuing the biological assets, otherwise it would be0. STDrevenuei is the standard deviation of revenue for company i; TASSETi is the total valueof assets for each company i; AVINF is the average of inflation rate experienced by thecountries where company i is originated. These models use Ordinary Least Squares (OLS).

4. DISCUSSION

4.1. Descriptive Statistics and Test of Hypothesis 1 and 2The descriptive statistics as presented in Table 4 up to Table 8 show that the sample

group of fair value approach has larger average and median of total assets than the samplegroup of historical cost. Besides, the group using the fair value approach has also largeraverage and median of standard deviation of assets than the group using the historical costapproach. This result shows that the group using the fair value approach has larger total valueand volatility of assets than the group using the historical cost. The descriptive statistics ofother variables such as incomes, earnings and Income Smoothing Index (ISI) also show thatthese components have a larger total average and standard deviation for the group using thefair value approach. However, it does not prevail for the Return on Assets (ROA), wherethere is no significant difference for the average and volatility of ROA between the twosample groups.

The result of the above descriptive statistics is further examined by using the test ofvariances consisting of t-test and ANOVA test. The results of these tests, as presented in Table9 up to Table 19, are used to examine the hypothesis 1 and 2. The test result shows that there


is no significant difference in the average total assets and standard deviation of assetsbetween the two sample groups. It implies that there is no significant difference in the sizes ofcompanies in those groups. Thus, we could proceed with examination for other variables thatinclude incomes, earnings and ROA. The result of the test is consistent with the result forassets, where there is no significant difference for incomes, earnings and ROA between thetwo sample groups. Further, the result also shows the consistent result for the average andmedian of Income Smoothing Index (ISI) between the sample groups that use differentapproaches in valuing their biological assets. Based on these results, it can be concluded thatthe hypothesis 1 and 2 are rejected. In other words, there is no sufficient evidence to supportthe hypotheses that there is significant difference in the total value and volatility of assets,incomes and earnings between the the agricultural companies that use different approachesin valuing their biological assets (hypothesis 1), and there is no sufficient evidence to supportthe hypothesis that the group of companies using fair value approach tends to have a largerIncome Smoothing Index (ISI) than the group of historical cost (hypothesis 2).

4.2. Regression Analysis and Test of Hypothesis 34.2.1. Regression Model 1

The result of regression analysis of model 1 shows that the standard deviation of cashflows from operating activities (STDCFO) and the total value of assets (TASSET) have thecoefficients as much 0.1868 and 0.0428 respectively, which are significant with p-value =0.0129 and 0.0000 respectively. Meanwhile, the valuation method which is dummy variable(DFV) and the average of inflation (AVINF) have the coefficient as much -19,249.46 and -2,742.39 respectively, which are insignificant with the p-value = 0.4270 and 0.4759respectively. The result of the regression model 1 has the adjusted R2 as much 0.57. It has thevalue of F = 24.33 which is significant with the p-value = 0.0000. The result of the regressionanalysis of model 1 is presented in Table 20.

Based on the result of the regression model 1, we conclude that there is no significantimpact of using the fair value approach (DFV) on earnings volatility of the agriculturalcompanies in the valuation of the biological assets.

4.2.2. Regression Model 2The result of regression analysis of model 2 shows that the standard deviation of

revenue (STDREV) and the total value of assets (TASSET) have the coefficients as much0.0328 and 0.0395 respectively, which are significant with p-value = 0.0015 and 0.0000respectively. While the valuation method which is dummy variable (DFV) and the average ofinflation (AVINF) have the coefficient as much -17,179.91 and -2,290.31 respectively, whichare insignificant with the p-value = 0.4642 and 0.5401 respectively. The result of theregression model 2 has the adjusted R2 as much 0.59. It has the value of F = 26.83 which issignificant with the p-value = 0.0000. The result of the regression analysis of model 2 ispresented in Table 21.

Based on the result of the regression analysis of model 2, we arrive in a conclusionthat the use of fair value approach (DFV) does not have impact on the earnings volatility ofthe agricultural companies in the valuation of the biological assets. Based on the result of theregression model 1 and the regression model 2, it is concluded that there is no sufficient


evidence to support the hypothesis that the valuation using fair value approach has a largerimpact on the volatility of the company’s earnings than the valuation using the historical costapproach (hypothesis 3).

5. CONCLUSION, LIMITATIONS AND SUGGESTIONS FOR FUTURERESEARCH

5.1. ConclusionThe results of this study reject all the proposed hypothesizes. In other words, the

results of this study are not consistent with those of the previous studies stating that theapplication of fair value would cause the increase of earnings volatility and the incomesmoothing practice. However, the results of this study are consistent with the results of theprevious study by Argiles et al. (2009) which is replicated in this study.

This study does not find any significant difference between the sample group ofcompanies using fair value accounting and those using historical cost in the components offinancial statements. This result is not consistent with the findings of previous studies thatargued that the application of IAS 41 with the fair value approach would cause significantdifferences in the components of financial statements, particularly the value of assets,incomes and earnings.

This study also does not find any indication of income smoothing practices which arepresumed to be higher in the companies using the fair value approach. This conclusion is notconsistent with the result of previous studies that found that the measurement by fair valueis subject to earnings management. The Test of Regression Models also shows inconsistentresults with the previous study stating that the application of fair value will cause unrealisticfluctuations in earnings. It is not consistent either with the previous study stating that therewould be earnings volatility caused by the measurement using the fair value approach.

In conclusion, the results of this study are consistent with the previous study thatshowed no significant difference in the value and volatility of assets, incomes, earnings, ROAand Income Smoothing Index (ISI) between two sample groups of companies using the fairvalue and the historical cost approaches. However, this study did not find any significantimpact of using the fair value approach on earnings volatility of agricultural companies.

5.2. Limitations and Suggestions for Further ResearchThis study has several limitations and weaknesses as follows. First, this study uses

samples from several countries which may impede the comparability of the samplecompanies. Incomparability among samples is due to different economic condition among thesample countries in the terms of inflation, exchange rate, economic growth and welfare.These differences will cause the value of money in the financial statements to have thedifferent materiality for each sample originated from different country. For example, $1 forAustralian sample will definitely have a different purchasing power for Indonesian sample.Consequently, it will cause difficulties in deciding the materiality in the financial statementsamong samples originated from different countries. It suggested that future studies takesamples from one country when it is possible, or take samples from the countries with similareconomic characteristics. Otherwise, we need to include control variables in the analysis to


control the differences among the country’s characteristics to make it comparable enoughwithout causing bias. The comparability could also be improved by using specific samples,for example using only palm oil companies.

Secondly, this study uses limited sample size and simple statistical analysis. Thus, theresults of this study may subject to biases or errors caused by the use of inappropriatestatistical tests. For the future study, it is suggested to use more samples and moresophisticated statistical test to arrive in more accurate analysis. .

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APPENDIX

Figure 1. Hierarchy Standards in Determining Fair Value Approach

Source: Anders Svensson et al. (2008)











APPENDIX













APPENDIX




Figure 2. Income Smoothing Index (ISI)

Table 1. Sample Selection Process

Criteria Fair Value HistoricalCost Total

Agricultural companies in OSIRIS Database 100 300 400Agricultural companies without biological assets (30) (70) (100)Companies with incomplete data (34) 230 300Companies that meet sample criterias 36 36 72Outliers (4) (4) (8)The Final Samples 32 32 64

Table 2. Sample Companies

No. Name of Companies (FV) Country1 FRANKLAND RIVER OLIVE COMPANY LTD Australia2 RURALAUS INVESTMENTS LIMITED Australia3 TFS CORPORATION LIMITED Australia4 VIENTO GROUP LIMITED Australia5 WILLMOTT FORESTS LIMITED Australia6 TANDOU LIMITED Australia7 TIMBERCORP LIMITED Australia8 FOREST ENTERPRISES AUSTRALIA LIMITED Australia9 GREAT SOUTHERN LIMITED Australia

10 ELDERS LIMITED Australia11 GUNNS LIMITED Australia12 NARBOROUGH PLANTATIONS PLC England13 R.E.A. HOLDINGS PLC England14 ANGLO-EASTERN PLANTATIONS PLC England15 CAMELLIA PLC England16 M.P. EVANS GROUP PLC England17 KENCANA AGRI LIMITED Singapore18 SAMKO TIMBER LIMITED Singapore19 FIRST RESOURCES LIMITED Singapore20 HLH GROUP LIMITED Singapore21 WILMAR INTERNATIONAL LIMITED Singapore22 UNITED FIBER SYSTEM LIMITED Singapore23 GOLDEN AGRI-RESOURCES LTD Singapore24 INDOFOOD-AGRI RESOURCES LTD Singapore25 ASIAN CITRUS HOLDINGS LIMITED Bermuda26 SAMLING GLOBAL LIMITED Bermuda27 JARDINE MATHESON HOLDINGS LIMITED Bermuda28 NEW BRITAIN PALM OIL LIMITED P. New Guinea29 YORK TIMBER HOLDINGS LTD South Africa30 MASONITE (AFRICA) LIMITED South Africa31 MUMIAS SUGAR COMPANY LTD Kenya32 R.E.A VIPINGO PLANTATION Kenya33 TECK GUAN PERDANA BERHAD Malaysia34 KWANTAS CORPORATION BERHAD Malaysia35 TDM BERHAD Malaysia36 RIVERVIEW RUBBER ESTATES Malaysia







No. Name of Companies (FV) Country1 FRANKLAND RIVER OLIVE COMPANY LTD Australia2 RURALAUS INVESTMENTS LIMITED Australia3 TFS CORPORATION LIMITED Australia4 VIENTO GROUP LIMITED Australia5 WILLMOTT FORESTS LIMITED Australia6 TANDOU LIMITED Australia7 TIMBERCORP LIMITED Australia8 FOREST ENTERPRISES AUSTRALIA LIMITED Australia9 GREAT SOUTHERN LIMITED Australia

10 ELDERS LIMITED Australia11 GUNNS LIMITED Australia12 NARBOROUGH PLANTATIONS PLC England13 R.E.A. HOLDINGS PLC England14 ANGLO-EASTERN PLANTATIONS PLC England15 CAMELLIA PLC England16 M.P. EVANS GROUP PLC England17 KENCANA AGRI LIMITED Singapore18 SAMKO TIMBER LIMITED Singapore19 FIRST RESOURCES LIMITED Singapore20 HLH GROUP LIMITED Singapore21 WILMAR INTERNATIONAL LIMITED Singapore22 UNITED FIBER SYSTEM LIMITED Singapore23 GOLDEN AGRI-RESOURCES LTD Singapore24 INDOFOOD-AGRI RESOURCES LTD Singapore25 ASIAN CITRUS HOLDINGS LIMITED Bermuda26 SAMLING GLOBAL LIMITED Bermuda27 JARDINE MATHESON HOLDINGS LIMITED Bermuda28 NEW BRITAIN PALM OIL LIMITED P. New Guinea29 YORK TIMBER HOLDINGS LTD South Africa30 MASONITE (AFRICA) LIMITED South Africa31 MUMIAS SUGAR COMPANY LTD Kenya32 R.E.A VIPINGO PLANTATION Kenya33 TECK GUAN PERDANA BERHAD Malaysia34 KWANTAS CORPORATION BERHAD Malaysia35 TDM BERHAD Malaysia36 RIVERVIEW RUBBER ESTATES Malaysia

No. Name of Companies (Non-FV/Historical Cost) Country1 PP LONDON SUMATRA INDONESIA TBK Indonesia2 ASTRA AGRO LESTARI TBK Indonesia3 SURYA DUMAI INDUSTRI TBK Indonesia4 TUNAS BARU LAMPUNG TBK Indonesia5 PT BAKRIE SUMATERA PLANTATIONS TBK Indonesia6 PT SINAR MAS AGRO RESOURCE AND TECH TBK Indonesia7 PT LIONMESH PRIMA TBK Indonesia8 PT SUMALINDO LESTARI JAYA TBK Indonesia9 PT ARGO PANTES TBK Indonesia

10 KUMPULAN FIMA BERHAD Malaysia11 CEPAT WAWASAN GROUP BHD Malaysia12 KURNIA SETIA BERHAD Malaysia13 RIMBUNAN SAWIT BERHAD Malaysia14 BLD PLANTATIONS BERHAD Malaysia15 IJM PLANTATIONS BERHAD Malaysia16 MHC PLANTATIONS BHD Malaysia17 UNICO-DESA PLANTATIONS BERHAD Malaysia18 PLS PLANTATIONS BERHAD Malaysia19 GOLDEN LAND BERHAD Malaysia20 SARAWAK OIL PALMS BERHAD Malaysia21 GLENEALY PLANTATIONS (MALAYA) BERHAD Malaysia22 GENTING PLANTATIONS BERHAD Malaysia23 NEGRI SEMBILAN OIL PALMS BERHAD Malaysia24 UNITED PLANTATIONS BERHAD Malaysia25 CHIN TECK PLANTATIONS BHD Malaysia26 INNOPRISE PLANTATIONS BERHAD Malaysia27 KUALA LUMPUR KEPONG BERHAD Malaysia28 PPB GROUP BERHAD Malaysia29 AIKBEE RESOURCES BERHAD Malaysia30 KIM LOONG RESOURCES BERHAD Malaysia31 UNITED MALACCA BERHAD Malaysia32 TSH RESOURCES BERHAD Malaysia33 MULTIVEST RESOURCES BERHAD Malaysia34 KRETAM HOLDINGS BERHAD Malaysia35 DUTALAND BERHAD Malaysia36 GMG GLOBAL LTD Singapore







No. Name of Companies (Non-FV/Historical Cost) Country1 PP LONDON SUMATRA INDONESIA TBK Indonesia2 ASTRA AGRO LESTARI TBK Indonesia3 SURYA DUMAI INDUSTRI TBK Indonesia4 TUNAS BARU LAMPUNG TBK Indonesia5 PT BAKRIE SUMATERA PLANTATIONS TBK Indonesia6 PT SINAR MAS AGRO RESOURCE AND TECH TBK Indonesia7 PT LIONMESH PRIMA TBK Indonesia8 PT SUMALINDO LESTARI JAYA TBK Indonesia9 PT ARGO PANTES TBK Indonesia

10 KUMPULAN FIMA BERHAD Malaysia11 CEPAT WAWASAN GROUP BHD Malaysia12 KURNIA SETIA BERHAD Malaysia13 RIMBUNAN SAWIT BERHAD Malaysia14 BLD PLANTATIONS BERHAD Malaysia15 IJM PLANTATIONS BERHAD Malaysia16 MHC PLANTATIONS BHD Malaysia17 UNICO-DESA PLANTATIONS BERHAD Malaysia18 PLS PLANTATIONS BERHAD Malaysia19 GOLDEN LAND BERHAD Malaysia20 SARAWAK OIL PALMS BERHAD Malaysia21 GLENEALY PLANTATIONS (MALAYA) BERHAD Malaysia22 GENTING PLANTATIONS BERHAD Malaysia23 NEGRI SEMBILAN OIL PALMS BERHAD Malaysia24 UNITED PLANTATIONS BERHAD Malaysia25 CHIN TECK PLANTATIONS BHD Malaysia26 INNOPRISE PLANTATIONS BERHAD Malaysia27 KUALA LUMPUR KEPONG BERHAD Malaysia28 PPB GROUP BERHAD Malaysia29 AIKBEE RESOURCES BERHAD Malaysia30 KIM LOONG RESOURCES BERHAD Malaysia31 UNITED MALACCA BERHAD Malaysia32 TSH RESOURCES BERHAD Malaysia33 MULTIVEST RESOURCES BERHAD Malaysia34 KRETAM HOLDINGS BERHAD Malaysia35 DUTALAND BERHAD Malaysia36 GMG GLOBAL LTD Singapore


Table 3. Average Inflation Rate of Sample Countries

No. CountriesAverage Inflation

(2000-2008)1 Australia 3.742 Singapore 1.533 United Kingdom 2.444 Bermuda 3.25

5 Papua New Guinea 8.426 South Africa 7.757 Indonesia 12.528 Malaysia 4.859 Kenya 5.86Source: World Bank

Table 4. Descriptive Statistics of Total AssetsTotal Assets Min Max Mean Median

Fair Value (FV) 723 2,484,287 281,828 121,000Historical Cost (HC) 1,990 3,815,313 258,271 122,006

STD. Assets Min Max Mean MedianFair Value (FV) 3,023 851,351 159,252 95,955Historical Cost (HC) 930 974,816 95,292 39,069

Table 5. Descriptive Statistics of of EarningsTotal Earnings Min Max Mean Median

Fair Value( FV) -67564 98780 12227 4771Historical Cost (HC) -57756 210107 12148 4167

STD. Earnings Min Max Mean MedianFair Value (FV) 284 52700 15887 13978Historical Cost (HC) 258 70417 14663 9582

Table 6. Descriptive Statistics of Return on Assets (ROA)ROA Min (%) Max (%) Mean (%) Median (%)

Fair Value (FV) -78 66 6 7Historical Cost (HC) -62 75 7 7

STD. ROA Min (%) Max (%) Mean (%) Median (%)Fair Value (FV) 1 37 10 7Historical Cost (HC) 2 34 8 6


Table 7. Descriptive Statistics of RevenueTotal Revenue Min Max Mean Median

Fair Value (FV) -1,989 1,059,316 106,413 47,567Historical Cost (HC) 1,104 2464862 102,358 34,036

STD. Revenue Min Max Mean MedianFair Value (FV) 392 380,656 54,272 27,222Historical Cost (HC) 2,790 498,084 50,128 22,910

Table 8. Descriptive Statistics of Income Smooting IndexISI Min Max Mean Median

Fair Value (FV) 0.04 7.39 1.38 1.18Historical Cost (HC) 0.46 2.56 1.26 1.15

Table 9. Result of Variance Test of Average Total AssetsMethod Value Probability

t-test 0.223545 0.8232Anova F-test 0.049972 0.8232

Variable Average Std. Dev Std. ErrorAFC 281827.8 406278.9 25644.10AHC 272731.2 500567.0 31595.51Total 277279.5 455434.8 20327.05

Table 10. Result of Variance Test of Average Standard Deviation Total AssetsMethod Value Probability


Variable Average Std. Dev Std. ErrorSTDAFV 159252.3 215851.1 38157.44STDAHC 95291.59 191209.4 33801.37Total 127271.9 204830.2 25603.77

Table 11. Result of Variance Test of Median Standard Deviation Total Assets

Method Value Probability

Wilcoxon/Mann-Whitney 1.483701 0.1379

Kruskal-Wallis 2.221334 0.1361Variable Value Probability

MSTDAFV 32 95955.50MSTDAHC 32 39069.00Total 64 49505.00


Table 12. Result of Variance Test of Average Total EarningsMethod Value Probability

t-test -0.373591 0.7089Anova F-test 0.139570 0.7089

Variable Average Std. Dev Std. ErrorEFV 12214.11 24516.75 1566.317EHC 13180.61 32228.68 2059.015Total 12697.36 28608.33 1292.393

Table 13. Result of Variance Test of Average Standard Deviation Total EarningsMethod Value Probability


Variable Average Std. Dev Std. ErrorSTDEFV 15886.88 13990.10 2473.124STDEHC 14779.44 16509.18 2918.438Total 15333.16 15189.89 1898.737

Table 14. Result of Variance Test of Median Standard Deviation Total EarningsMethod Value Probability

Wilcoxon/Mann-Whitney 0.597508 0.5502Kruskal-Wallis 0.365084 0.5457

Variable Value ProbabilityMSTDEFV 32 13978.00MSTDEHC 32 9582.000Total 64 11012.00

Table 15. Result of Variance Test of Average Return on Assets (ROA)Method Value Probability


Variable Average Std. Dev Std. ErrorROAFV 5.649919 14.00772 0.889491ROAHC 7.433790 12.79512 0.812491Total 6.541855 13.43127 0.603082


Table 16. Result of Variance Test of Average Standard Deviation Return on Assets(ROA)

Method Value Probabilityt-test 0.819842 0.4154Anova F-test 0.672142 0.4154

Variable Average Std. Dev Std. ErrorSDROAFV 9.541563 8.418726 1.488235SDROAHC 7.972812 6.803654 1.202727Total 8.757188 7.633967 0.954246

Table 17. Result of Variance Test of Average RevenueMethod Value Probability


Variable Average Std. Dev Std. ErrorREVFV 106369.3 154891.4 10125.57REVHC 108813.1 266098.0 17395.37Total 107591.2 217485.0 10053.25

Table 18. Result of Variance Test of Average Standard Deviation RevenueMethod Value Probability


Variable Average Std. Dev Std. ErrorSDREVFV 54155.69 80849.01 14292.22SDREVHC 50128.13 89305.61 15787.15Total 52141.91 84527.94 10565.99

Table 19. Result of Variance Test of Average Income Smoothing Index (ISI)Method Value Probability


Variable Average Std. Dev Std. ErrorISIFV 1.383125 1.263353 0.223331ISIHC 1.258125 0.531956 0.094037Total 1.320625 0.963626 0.120453


Table 20. The Result of the Regression Test of Model 1Variable Coefficient t value Prob. Note

Constanta [c] 39551.67 1.336654 0.1859Fair Value Method [DFV] -19249.46 -0.799208 0.4270STD of CFO [STDCFO] 0.186796 2.554923 0.0129 **Total Assets [TASSET] 0.042793 5.371905 0.0000 ***Average Inflation [AVINF] -2742.389 -0.717015 0.4759R-squared 0.592234 F-statistic 24.32743Adjusted R-squared 0.567889 Prob (F-statistic) 0.000000

The Level of Significant: *p<0.1, **p<0.05, ***p<0.01

Table 21. The Result of the Regression Test Model 2Variable Coefficient t value Prob. Note

Constanta [c] 37466.58 1.303492 0.1969Fair Value Method [DFV] -17179.91 -0.736175 0.4642STD of REV [STDREV] 0.032837 3.319178 0.0015 ***Total Assets [TASSET] 0.039508 5.133317 0.0000 ***Average Inflation [AVINF] -2290.306 -0.615833 0.5401R-squared 0.615698 F-statistic 26.83546Adjusted R-squared 0.592754 Prob(F-statistic) 0.000000

The Level of Significant: * p<0.1, **p<0.05, ***p<0.01