THE FORECAST ON THE EXPORT OF CRUDE PALM OIL

INTRODUCTION

Overview

Malaysia is the world’s second largest crude palm oil (CPO) producer after

Indonesia and the largest exporter of CPO in the world. CPO is one of the

recognised global commodities as it has diverse usages, namely cooking oil, food

additives, industrial products, biofuel and recently as the popular underlying assets

for financial structured products, specifically in the futures market. This paper will

discuss on the forecast of CPO export level using selected forecast methods.

The need of the forecast

From business perspective, the forecast of future export of CPO is useful as

the forecasted figures can be utilised by palm oil producers in Malaysia to

undertake systematic approach in their business planning process, especially in

ensuring that the most appropriate level of CPO can be produced and made

available to meet the increasing demand from international markets. Exports are

made mainly to the booming China, India and the United States which contributed

41% of Malaysia’s total exports of CPO (Malaysian Palm Oil Council, 2012).

Secondly, CPO plays major role in the development of Malaysia’s economy as

CPO and processed palm oils contributes 5.6% of the nation’s gross national

income in 2004 (Koh & Wilcove, 2007). The growth forecast of CPO export level is

therefore an important input for Malaysian policymakers in formulating relevant

economic policy such as taxation and subsidies in order to ensure the healthy

growth of national income.

DISCUSSION

The data

The set of data selected are the level of CPO exported from Malaysia from

2008 to 2012 (Malaysian Palm Oil Council, 2012). The data, presented on

quarterly basis, are as follows:

Figure 1: CPO export level (tonnes) for the past 5 years

3,000,000

3,500,000

4,000,000

4,500,000

5,000,000

5,500,000

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

2008 2009 2010 2011 2012

CPO export level

The graph exhibits that the CPO export from Malaysia has an increasing

trend over time and with seasonal pattern, where the level of exports in the first

half (Q1 and Q2) are lower as compared to the exports in the second half (Q3 and

Q4) of every year, since 2008.

The increasing trend are contributed by the growing palm oil plantation

industry in Malaysia and the higher acceptance of palm oil worldwide. Meanwhile,

the seasonal pattern are contributed by the low demand for CPO from major

economies in north hemisphere, especially China, India and US, which experience

winter mostly during the first half of the year. Demand of palm oil typically

weakened during the winter as consumers reduced purchases of the tropical oil

that solidifies in cold temperature and switch to substitute oils such as soybean oil,

olive oil rapeseed oil, depending on the intended utilisation (Reuters, 2013).

The forecast

There are two categories in forecasting method, namely trend projection

and decomposition method. Under the trend projection, moving average (2 and 5-

period) and exponential smoothing (ES) are applied. The smoothing factor, α for

the ES is set to 0.2. This is an attempt to appropriately assign exponentially

decreasing weightage to older data as the future level of CPO exports are

expected to be influenced more by the newer data. In addition, the decomposition

method with an increasing trend and additive-based seasonality will also be

demonstrated.

The graphs of the actual data against the forecasted data as generated by

these methods are as follows:

Figure 2: Actual vs 2-period moving average

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

4,500,000

5,000,000

5,500,000

6,000,000

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1

2008 2009 2010 2011 2012 2013

Actual vs 2-period MA

Volume(Tonnes)

Forecast(2-quarter MA)

Figure 3: Actual vs 5-period moving average

Figure 4: Actual vs ES (a = 0.2)

Figure 5: Developing trend line for decomposition method

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

4,500,000

5,000,000

5,500,000

6,000,000

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1

2008 2009 2010 2011 2012 2013

Actual vs 5-period MA

Volume(Tonnes)

Forecast(5-quarter MA)

2,000,0002,500,0003,000,0003,500,0004,000,0004,500,0005,000,0005,500,0006,000,000

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1

2008 2009 2010 2011 2012 2013

Actual vs ES (a=0.2)

Volume(Tonnes)

Forecast

y = 40808x + 4E+06R² = 0.7905

3,800,000

3,900,000

4,000,000

4,100,000

4,200,000

4,300,000

4,400,000

4,500,000

4,600,000

4,700,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Trend (centred) and its linear trendline

Figure 6: Actual vs decomposition method (additive)

The quality of the forecast

In establishing the quality of the forecasts, the errors from each methods

are tabulated in the following table:

Table 1: Type of errors for each method

Method AE MAD MSE MPE MAPE

2-period MA 119,745 498,443 348,806,334,742 0.8 11.6

5-period MA 116,561 364,685 214,391,217,355 1.8 8.2

ES (α = 0.2) 293,302 427,415 281,848,477,360 6.2 9.7

Decomposition method -241,837 293,469 128,879,403,679 -6.1 7.2

Shaded cell refers to the lowest error for each type of error.

AE= Average error, MAD=Mean absolute deviation, MSE = Mean squared error, MPE = Mean percentage error and MAPE

= Mean absolute percentage error.

The errors computed refers to the deviations of the forecasted figures with

the actual figures. Based on the errors generated, the additive decomposition

method appears to be most accurate method with the least overall error (exhibited

the lowest errors in three out of the five techniques to compute errors) to estimate

future CPO export level. This paper will discuss further on the absolute deviation

(AD) errors.

The AD errors, presented in the graph form, supports the superior of

decomposition method against moving average and ES methods:

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

4,500,000

5,000,000

5,500,000

6,000,000

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

2008 2009 2010 2011 2012 2013

Actual vs decomposition (additive)

Volume(Tonnes)

Forecast(Trend + seasonal component)

Figure 7: Absolute deviation errors (Decomposition method vs 2-period MA)

Figure 8: Absolute deviation errors (Decomposition method vs 5-period MA)

Figure 9: Absolute deviation errors (Decomposition method vs ES)

The decomposition method forecasts better as the method not only

recognises the trend of the CPO export level. Rather, it is also able to adjust to the

seasonal pattern of the CPO export level.

0

500000

1000000

1500000

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

2008 2009 2010 2011 2012

Absolute deviation errors(Decomposition vs 2-period MA)

2-period Decomposition

0

500000

1000000

1500000

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

2,008 2,009 2,010 2,011 2,012

Absolute deviation errors(Decomposition method vs 5-period MA)

5-period Decomposition

-

500,000

1,000,000

1,500,000

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

2,008 2,009 2,010 2,011 2,012

Absolute deviation errors(Decomposition vs exponential smoothing, α=0.2)

ES Decomposition

Distinctive observations from the forecast

There are two observations that can be made from the forecasts

demonstrated to support the superiority of decomposition method. Firstly, as

detailed in the Figure 7, 8 and 9, both moving average and ES methods fail to

adjust to the sudden and significant increase in CPO export level in Q3 and Q4

2011.

Secondly, as tabulated in the following table, the decomposition method

forecasts lower figure for Q1 2013 relative to moving average and ES methods.

This is consistent with the constantly lower CPO export level in Q1 of every year,

since 2008. In fact, the decomposition method is capable of memorising the lower

Q1 and Q2 figures and higher Q3 and Q4 figures pattern of CPO export level

every year and provide forecast with similar pattern accordingly.

Table 2: Forecasts of CPO export level in Q1 2013

Method Forecast (Tonnes)

Naïve 5,186,535

MA-2 4,716,685

MA-5 4,552,187

ES 4,463,038

Decomposition method 4,442,484

Variation of moving average and ES

Moving average and ES forecast the CPO export level worse relative to the

decomposition method, given that these methods are designed to forecast non-

seasonal data by merely smoothing the data with a certain set of rules. While the

number of periods in moving average can be changed to get smoother forecast, it

does not provide better accuracy in the forecast. Similarly, despite the value of α

can be varied to get the best weightage distribution, ES is still not able to

adequately adapt to the seasonal changes in CPO export level and forecast as

good as decomposition method, regardless of the α selected.

To have an overview on how the ES with different set of α perform, the

following table lists the errors generated based on the best α selected on trial and

error basis to get the best α, which is 0.8, and its comparison with the

decomposition method errors:

Table 3: Comparison errors for different α in ES and decomposition method

Method AE MAD MSE MPE MAPE

ES (α = 0.2) 293,302 427,415 281,848,477,360 6.2 9.7

ES (α = 0.5) 145,291 416,748 264,115,856,426 2.6 9.7

ES (α = 0.8) 108,402 422,200 281,360,749,704 1.7 10.0

Decomposition -160,221 257,029 96,064,969,654 -4.2 6.2

Possibility of multiplicative-based seasonal data

The interim centred data of CPO export level which is developed for

decomposition method purpose as in Figure 5 suggests that the magnitude of the

seasonal pattern is growing over time and hence may be multiplicative in nature.

Accordingly, multiplicative decomposition method has been applied to test the

possibility and the errors generated do not significantly reduced. See Table 4.

Table 4: Error comparison (Additive and multiplicative decomposition method)

Decomposition method AE MAD MSE MPE MAPE

Additive -160,221 257,029 96,064,969,654 -4.2 6.2

Multiplicative -175,394 253,478 99,120,222,056 -4.5 6.2

The limitations of the forecast

In practice, the accuracy of the forecast of CPO export level would be

limited to several factors. Firstly, there are random factors that are significant

enough to cause irregularities in the overall CPO export level. This is in addition to

the seasonal pattern discussed and addressed using the seasonal component in

decomposition method. The identified random factors are as follows:

Table 5: Random factors that may affect CPO export level

Random factors Impact to

CPO export

The changes in the price of substitute vegetable oils which

could affect the demand and supply of CPO.

For example, arising from the Haiyan typhoon in Philippines,

the world’s largest coconut oil exporter, the coconut

plantation was reported to be totally disrupted (BBC News,

2013). This could potentially increase the demand of palm

oil as substitute to coconut oil and hence increase the CPO

export level.

Reduce or increase

Economic development of major importers, where during

the economic downturn, these countries would typically

import less CPO from Malaysia

Reduce or increase

Taxation policy in Malaysia and importing countries:

Sudden imposition of 20% tax on CPO import by Indian

authority as palm oil allegedly give bad impact on health

(Shankar & Hawkes, 2013)

Reduction of palm oil tax rate from by Malaysian

authority to reduce inventories and strengthen export

(Bloomberg, 2012)

Reduce or

Increase

Secondly, there is a possibility of multiple seasonal patterns in the overall

CPO export level. In the decomposition method, the seasonality arising from the

lower demand during winter season has been addressed adequately.

Nevertheless, CPO export level is also affected by the different seasonal factor, i.e

lower CPO production at every year end due to the raining season in Malaysia

(Reuters, 2013). Heavy rain could flood palm oil estate and adversely affect CPO

production, albeit with minimal impact.

Possible improvements

Based on the forecasted figures generated and the limitations of the

forecast methods, this paper would provide possible recommendations to improve

the accuracy of the forecast from qualitative and quantitative perspective.

From quantitative perspective, the decomposition method can be improved

by using a longer period (10 years or more) of the historical CPO export level as

forecast inputs. The forecast methods demonstrated in this paper utilise the limited

5-year data on quarterly basis due to unavailability of data prior to 2008.

Furthermore, the usage of more complicated methods is expected to

improve the accuracy of the forecast of seasonal data. There are many methods

available such as Holt’s linear exponential smoothing, which is a double

exponential smoothing, for non-seasonal data as well as Holt-Winter’s method

which can cater for seasonal data.

Additionally, the proposed decomposition method can be improvised

through developing better centred trend line than the one as generated in Figure 5

above. Better trend line can be indicated with trend line that has R2 value closer to

1 than R2 of centred line developed in Figure 5 (0.7905). Better centred trend line

can be generated using 5-period moving average, exponential smoothing or Holt’s

linear exponential smoothing, instead of the standard 2-period moving average.

From qualitative perspective, the random factors can be addressed by

incorporating judgmental method in the forecast techniques to identify factors that

may not previously be considered. In the case of CPO export level, experts from

various sectors can be engaged such as policymakers on the possible regulatory

changes in Malaysia and importing countries, researchers on the new science and

technology discovery that can impact palm oil production tremendously. Finance

expert can also be consulted to gauge the impact of the role of CPO in financial

market which could further boost the demand of the CPO worldwide.

References

BBC News. (2013). Typhoon wreaks havoc on agriculture with over a million farmers affected. Retrieved from http://www.bbc.co.uk/news/science-environment-24913139?print=true

Bloomberg. (2012). Malaysia to Reduce Tax on Palm-Oil Exports ; Futures Decline. Retrieved from http://www.businessweek.com/news/2012-10-12/malaysia-to-reduce-palm-oil-export-tax-abolish-duty-free-quota

Koh, L. P., & Wilcove, D. S. (2007, August 30). Cashing in palm oil for conservation. Nature Magazine

Malaysian Palm Oil Council. (2012). Monthly Palm Oil Trade Statistics. Retrieved from http://www.mpoc.org.my/Monthly_Palm_Oil_Trade_Statistics.aspx

Reuters. (2013). VEGOILS-Palm oil falls on higher output concerns , exports support. Retrieved from http://in.reuters.com/article/2013/11/25/markets-vegoils-idINL4N0JA17G20131125

Reuters India. (2013). VEGOILS-Palm ends near 1-wk high after weather warning triggers supply fears. Retrieved from http://in.reuters.com/article/2013/10/28/markets-vegoils-idINL3N0II19J20131028

Shankar, B., & Hawkes, C. (2013). India has a problem with palm oil. British Medical Journal, (October)

APPENDIX

COMPUTATION OF 2-PERIOD MOVING AVERAGE FORECAST

COMPUTATION OF 5-PERIOD MOVING AVERAGE

COMPUTATION OF EXPONENTIAL SMOOTHING

COMPUTATION OF DECOMPOSITION METHOD (ADDITIVE) - Part 1

COMPUTATION OF DECOMPOSITION METHOD (ADDITIVE) - Part 2

DECOMPOSITION METHOD (MULTIPLICATIVE) – part 1

DECOMPOSITION METHOD (MULTIPLICATIVE) – part 2