Crude Palm Oil Price Research Articles

MODELING COOKING OIL PRODUCTION WITH CRUDE PALM OIL (CPO) PRICE USING TRANSFER FUNCTION

Indonesia is the largest palm oil producer in the world. However, Indonesia is currently experiencing a significant scarcity of cooking oil for the basic needs of the community. One of the causes of this problem is the high price of crude palm oil (CPO). The price of CPO affects the production of cooking oil produced by the cooking oil industry. CPO prices are released in the form of daily prices in the market which form a time series pattern that affects the production of cooking oil which is also produced daily in the industry. To forecast cooking oil production from CPO prices can be done with a transfer function model. The stages of analysis are forming an ARIMA model, processing input time series whitening, and output time series whitening. Checking white noise by plotting ACF αt, ACF βt. Make a CCF graph and determine the order (r,s,b). Estimate the first parameter of the disturbance series (nt) and identify the disturbance series (nt) in the ARIMA model. Next, perform a diagnostic check of the transfer function model by checking the cross-correlation between Xt & Yt and checking the autocorrelation of Xt and Yt for model feasibility testing. Then make an ACF graph of the transfer function model's residuals to test the model's suitability. The final stage is to select the best model based on the smallest AIC value, ensuring the accuracy of the selected model using MAPE value, and making predictions. As a result, the model for CPO price is an input series variable, and cooking oil production is an output series variable. However, the MAPE result was 100%, indicating that the model is not very accurate for this data. Nevertheless, by ignoring the result and continuing with the forecasting, this model shows that the forecasted values for cooking oil based on CPO price have increased from the form of the model obtained for the time data from 2013 to 2022.

Deep Learning Based Prediction Model for Crude Palm Oil Prices Using News Sentiment Analysis with Sliding Window

Crude palm oil (CPO) price prediction plays an important role in agricultural economic development. Various economics and agricultural related factors have been used to predict CPO prices. Nevertheless, understanding news sentiment features will also be important in CPO price prediction. This paper proposes a CPO price prediction model to help the plantation organizations in the palm oil sector to successfully anticipate CPO price fluctuations and manage the resources more effectively. The CPO price behavior is non-linear in nature, and thus prediction is very difficult. In this paper, an improved version of recurrent network, Long Short-Term Memory (LSTM) based CPO price prediction model with news sentiment is used to produce an enhanced prediction model. The findings of this study show that the LSTM based forecasting model with news headline sentiment using a six-month sliding window produced the best result in forecasting the CPO price movement compared to other sliding window sizes.

Volatility Spillover of Crude Palm Oil (CPO) Commodity between Spot and Futures Market in Indonesia

The development of trade globalization has led to the expansion of market interactions between countries, specifically for Crude Palm Oil (CPO) commodities. This study aims to determine the volatility of CPO prices between the Medan spot market and the futures market consisting of the Jakarta futures market, Malaysia futures market, and the Rotterdam forward market. The daily data on CPO prices in each market from January 2016 to December 2020. The data analysis method uses the GARCH model (1.1). The results showed a volatility transmission from the entire futures market to the Medan spot market. Thus, the CPO commodity futures market influences price volatility and the price formation process in the Medan spot market. Therefore, CPO commodity market players must be alert to the information occurring in the futures market to determine investment strategies.

ANALYSIS OF THE DEPENDENCIES COMMODITY PRICES AND STOCK MARKET INDEXES USING COPULA

Indonesia is rich in natural resources and occupies an important position in the global raw materials market. The country's rich resources such as oil, coal, nickel, and crude palm oil (CPO) have a significant impact on the economic situation. As one of the world's leading producers and exporters of these raw materials, Indonesia's economic fate is closely linked to price fluctuations. This study uses the copula method to model the dependence between stock and commodity returns and calculates the dependence between commodity prices (oil, coal, nickel, CPO) and Indonesian stock market index (IHSG) The data used for this analysis was sourced from Bloomberg.com, covering the period from 29 September 2021 to 29 September 2023. This study investigates the dynamic dependencies between commodity price returns and the Indonesian stock market index. The results show that the correlations between oil prices and the Indonesian stock index, and between CPO prices and the stock index are generally weak. However, there are exceptions to stock index returns, such as their relatively high dependence on coal and nickel. This diverse research provides valuable insight into the complex interdependencies in Indonesia's financial landscape. Understanding dependence between commodity prices and stock indexes is of great value to investors and policymakers, as it is the basis for making informed decisions to navigate the complex global economy.

Advanced Machine Learning Techniques for Accurate Forecasting of Crude Palm Oil Price

Advanced Machine Learning Techniques for Accurate Forecasting of Crude Palm Oil Price

THE ROLE OF EXTENDED VALUE CHAIN ACTIVITIES IN ENHANCING ECONOMIC VALUE ADDED PLANTATION COMPANIES IN MALAYSIA

The palm oil industry in Malaysia has faced issues of lower palm oil price and limited land area, and these issues have created a challenging environment for plantation companies to operate in. Plantation companies are constrained in maximizing their profits. Thus, this paper aims to examine the Economic Value Added (EVA) and the factors that influence the EVA of different value chain activities. The dynamic generalized method of moments (DGMM) estimators were adopted for the analysis and the focus was on the -40 Malaysian plantation companies that were listed in the Bursa Malaysia from 2010 to 2018. The results revealed that both downstream integrated activities, namely oleo-chemicals/biodiesel and refineries activities had a significant influence on the plantation companies’ EVA, with the coefficient value 1.12 and 0.09 respectively. Other factors such as the gross margin, crude palm oil price, and exchange rate also significantly influenced the plantation companies’ EVA. The empirical results which were based on the plantation companies’ EVA were influenced by different value chain activities as expected. The findings show that the downstream integrated activities like refineries, and oleo-chemicals/biodiesel activities have played a significant role in increasing the EVA of plantation companies.

Implementation of Artificial Neural Network in Predicting CPO Prices Using Backpropagation

This research examines the development of a forecasting model for Crude Palm Oil (CPO) prices using artificial neural network algorithms, particularly the backpropagation algorithm. CPO, as Indonesia's main export commodity, has significant economic impacts and affects the income of oil palm farmers. Data on CPO prices taken from CIF Rotterdam from January 2019 to December 2023 were used in this study. The research method involved several stages, including data collection, pre-processing, model design, and model implementation using Python programming. The results of training the model using the backpropagation algorithm showed an error value of 0.537829578 after 1000 epochs, while evaluation using Mean Squared Error (MSE) showed an MSE value of 0.022709 during the training process and 0.017604 during the testing process. The model also produced predictions for CPO prices in the next three months: 932.578 for the first month, 949.568 for the second month, and 774.855 for the third month. These findings indicate that the developed model can predict future CPO prices with adequate accuracy, which can assist companies in making better financial decisions and managing risks associated with CPO price fluctuations.

Crude Palm Oil Price Prediction Using Simulated Annealing-based Support Vector Regression (SA-SVR)

Crude Palm Oil Price Prediction Using Simulated Annealing-based Support Vector Regression (SA-SVR)

Selection of Modelling for Forecasting Crude Palm Oil Prices Using Deep Learning (GRU & LSTM)

The unstable crude palm oil (CPO) prices have an impact on assessments of economic growth and environmental sustainability, as well as market strategies, international trade discussions, and consumer pricing expectations for products made from CPO. Therefore, it is crucial to identify the best prediction method to accurately forecast this cost. This research aims to develop an accurate time series data prediction model for crude palm oil prices using GRU and LSTM methods. The study also aims to identify the best-performing model by comparing their performance. This study uses LSTM and GRU methods as well as Bi-LSTM as a comparison, using crude palm oil price data from the Indonesian Ministry of Trade (August 1, 2018–August 31, 2023) from Medan (1064 data points) and Rotterdam SPOT (617 data), Rotterdam Forward 1 Month (1,022 data), and Rotterdam Forward 2 Month (950 data). Each dataset is then split into training and testing data with a 70:30 ratio. The hyperparameters used are (a) learning rate: (0.001); (b) batch size: 100; (c) node: (512); (d) optimizer: Adam; (e) and epoch: 50. The results of the forecast are highly accurate with a MAPE of less than 10%. Overall, both LSTM and GRU techniques demonstrate excellent performance in forecasting crude palm oil prices, but they may need to be modified based on data features. The ARIMA method can also be considered for forecasting. Future studies may consider optimizing parameters and model structures. Doi: 10.28991/ESJ-2024-08-03-05 Full Text: PDF

Study on Palm Oil Cultivation and Processing Technology to Support The Biofuel Program (Optimization for The Development of Palm Oil-Based Energy Plantation in Indonesia)

Abstract Crude Palm Oil (CPO) is an important commodity for Indonesia, after fossil oil is no longer the main component of export products. The bioenergy product that has been well developed and applied is biodiesel. The development of CPO for bioenergy has some obstacles, such the amount of CPO needed is large so that it will affect the capacity of CPO used for food. In addition, the price of CPO which is considered high is around US$ 700-900/ton, whereas the ideal price of CPO as a bioenergy raw material is a maximum of US$ 400/ton. Several approaches that are recommended as a solution to overcome these problems are optimization of palm oil cultivation, harvesting management, and also processing technology. The methodology used in this research includes collecting data from palm oil industry players, palm oil mills (PKS), academics involved in the development of the palm oil industry, and professional organizations related to palm oil. The data was obtained by conducting discussions and knowledge sharing with them. The results of this study will assist policy makers, in this case the Ministry of Industry, Ministry of Energy and Mineral Resources (ESDM) and the Ministry of Agriculture to determine bioenergy development policies that involve aquaculture ecosystems, land selection, industrial location and optimal process technology. The Ministry of Industry and Ministry of Energy and Mineral Resources (ESDM) can partner to develop palm oil-based energy, the Ministry of Agriculture will develop palm cultivation which is sufficient to support mustard products for energy, palm land can be restructured according to energy needs. To support the energy plantation program, it is recommended that the government set up a palm plantation devoted to energy covering an area of 250,000 hectares on state land or state-owned enterprises (BUMN). Procurement of palm oil plantations can be done in stages from 25 hectares, according to land potential and budget.

Terms of trade or market power? Further evidence from dynamic spillovers in return and volatility between Malaysian crude palm oil and foreign exchange markets

This study examines whether the Malaysian ringgit (MYR) provides terms of trade or market power for crude palm oil (CPO). We consider MYR exchange rates against three currencies of major CPO-importing countries, including the Indian rupee (INR), euro (EUR) and Chinese yuan (CNY). Using daily data on Malaysian CPO prices (spot and futures) and foreign exchange rates (INRMYR, EURMYR and CNYMYR) from 2000 to 2018, our results provide three notable findings. First, volatility spillovers from EURMYR to CPO spot and futures are more significant during the global financial crisis of 2008–2009. Second, the return spillovers from CPO spot and futures to INRMYR and EURMYR exchange rates experience significant bursts after the crisis. Third, the volatility of CPO spot price changes shows the predictive ability of the volatility of INRMYR exchange rate changes at the end of 2018. Overall, our findings support that the terms of trade are a driver of the CPO market after the crisis.

Dampak Penghapusan Pupuk Subsidi Bagi Petani Kelapa Sawit Swadaya Setelah Disahkannya Peraturan Menteri Pertanian No 10 Tahun 2022 tentang Tata Cara Penetapan Alokasi Dan Harga Eceran Tertinggi Pupuk Bersubsidi Sektor Pertanian

Palm oil or crude palm oil (CPO) is one of Indonesia's leading export commodities. The high level of exports is not accompanied by the provision of subsidized fertilizer for independent community farmers. Through the Ministry of Agriculture (Kementan) it has decided to remove subsidized fertilizer for oil palm plantations. With the elimination of fertilizer subsidies for oil palm farming, it makes it difficult for farmers to meet their fertilizer needs. The policy of eliminating subsidized fertilizer assistance has a direct impact on the productivity of oil palm farmers. The problems studied are limited to: What impact will there be after the ratification of the Minister of Agriculture Regulation No. 10 of 2022 concerning Procedures for Determining the Allocation and Highest Retail Prices for Subsidized Fertilizers in the Agricultural Sector related to the elimination of fertilizer subsidies for oil palm plantations? and How to overcome the problem of no subsidized fertilizer for oil palm farmers? The research method used is the Empirical Juridical method. The research results show that there are many impacts that can occur with the elimination of subsidized fertilizers by the government, especially for oil palm plants. Fluctuating CPO prices tend to make farmers unable to predict their income and set aside to purchase non-subsidized fertilizer which is unaffordable. Then the countermeasure initiated by the government is to use organic fertilizer, but it can take a long time to process organic fertilizer and the results can only be seen one year later. This is different from chemical fertilizers where the results can be seen immediately in a matter of months.

A Crude Palm Oil Industry Concentration and Influencing Factors: A Case Study of Indonesia as the World's Largest Producer

The Crude Palm Oil (CPO) industry is one of the plantation commodities that has a strategic role in Indonesia's economic development. The number of companies and CPO production is always increasing, but the concentration of the industry that always decreases every year makes it important to analyze the factors that affect the concentration of the CPO industry in Indonesia. The data period used in this study was from 2001 to 2020. In this study, data sources were obtained from the Central Bureau of Statistics (BPS) Indonesia, Word Bank, and UN Comtrade. The data analysis method used is regression analysis of the Error Correction Model (ECM). The results showed that technical efficiency has a negative and significant relationship both in the long and short term to the concentration of the CPO industry. Competitiveness in the long term has a positive and significant relationship, while in the short term it is negative but not significant to the concentration of the Indonesian CPO industry. In the long run, the relationship between RSPO and Indonesia's CPO industry concentration tends to be negative but not significant, while in the short term it shows a significant influence. World CPO prices, both long-term and short-term, provide a positive and significant correlation to the concentration of the Indonesian CPO Industry. Indonesia's CPO exports in the long and short term have a negative and significant effect on the concentration of the Indonesian CPO industry.

Analisis Pengaruh Harga Minyak Kelapa Sawit (CPO), Inflasi, Kurs Dan Suku Bunga Terhadap Harga Saham (Studi Empiris Pada Perusahaan Crude Palm Oil Di BEI Periode 2017-2021)

The purpose of this study was to analyze how the effect of crude palm oil prices, inflation rates, exchange rates and interest rates on stock prices in oil palm plantation companies listed on the Indonesia Stock Exchange for the period 2017-2021. The results of this study are expected to contribute in addition to researchers, practitioners and investors in helping them to make decisions, whether to sell, buy or hold their shares. The data used is secondary data in the form of annual reports of each issuer and statistical publications from the Indonesia Stock Exchange, Bank Indonesia and IndexMundi from 2017 to 2021. The sampling technique is purposive sampling with certain criteria. A sample of 10 Palm Oil Plantation (CPO) companies listed on the IDX during the observation period 2017-2021 was obtained. The analysis technique used is panel data regression analysis. The hypothesis was tested using t statistics to test the significance of the regression coefficient partially at a significance level of 95%. From the test results, all independent variables, namely palm oil prices, inflation, exchange rates, and interest rates, have an effect on stock prices in palm oil-producing plantation companies listed on the IDX in 2017-2021. Keywords: Palm Oil Prices (CPO), Inflation, Exchange Rates, Interest Rates and Stock Prices

Volatility and dependence in crude oil and agricultural commodity markets

ABSTRACT This paper studies the evolution, volatility dynamics, and nonlinear dynamics between crude oil prices and major agricultural commodity prices. Semiparametric GARCH-in-Mean copula models are applied to monthly crude oil, corn, soybean, palm oil, rice, sugar, and wheat prices from January 1990 to May 2023. We find that the Clayton copula is the best copula to describe the (bivariate) dependence structures between the crude oil and soybean, crude oil and palm oil, crude oil and sugar, and crude oil and wheat markets, suggesting the statistically significant lower tail dependence between crude oil and major agricultural commodity returns. Moreover, the tail dependence is strongest between crude oil and palm oil.

The Wax and Wane of CPO Prices in India: A DCC Approach to Understanding Market Sentiment

This study provides a detailed analysis of Crude Palm Oil (CPO) price dynamics, focusing on the relationship between spot and future prices during significant global events, notably the COVID-19 pandemic and the Ukraine-Russia conflict. Utilizing Dynamic Conditional Correlation (DCC) models, the research examines conditional returns and covariance, as well as the correlation trends in the Crude Palm Oil (CPO) market. The study reveals critical insights into market behavior during these tumultuous times. For instance, sharp increases in covariance during key periods suggest strong movement synchronicity between spot and future prices, potentially driven by market-altering events. The onset of COVID-19 and the escalation of the Ukraine-Russia conflict are identified as significant influencers, disrupting global supply chains and impacting global food supplies, which in turn affect both spot and future Crude Palm Oil (CPO) prices. Additionally, the research finds a dynamic correlation trend between the future and spot prices of Crude Palm Oil (CPO). Starting with a strong positive correlation in 2018, the study observes a gradual decrease in this correlation, especially after 2020. This trend indicates diverging market forces or perceptions, particularly in response to the COVID-19 pandemic and the ongoing Ukraine-Russia conflict. Such fluctuations in correlation suggest varying market sentiments and expectations, which have implications for market risk assessment, hedging strategies, and speculative activities

Forecasting PT Triputra Agro Persada Tbk (TAPG) Share Prices Using Multivariate Time Series Analysis

An increase in the price of crude palm oil (CPO) positively affects the share prices of companies engaged in the palm oil industry. PT Triputra Agro Persada Tbk (TAPG) 2021 was recorded as one of the companies with the CPO business that received the most significant capital gain. Prediction or forecasting of stock prices in the future is crucial for investors as a consideration before deciding to invest. Many kinds of research on stock price prediction have been carried out previously using univariate methods. Univariate modeling cannot represent the influence of other variables on stock prices. Forecasting with the influence of other variables can be done with multivariate time series analysis. This study aims to analyze the multivariate time series of TAPG stock prices and the factors that influence them. Based on the research results, data on TAPG stock prices and CPO prices are cointegrated, so the multivariate time series model used is the vector error correction model (VECM). In the VECM model, the optimum lag used is lag 11. In the long run, CPO prices significantly affect TAPG stock prices.

Analysis of the Comparison Between Linear Regression, Random Forest, and Logistic Regression Methods in Predicting Crude Palm Oil (CPO) Price

This study compared the predictive capabilities of linear regression, random forest, and logistic regression models for forecasting crude palm oil prices. Utilizing historical data from 02/11/2020 to 13/11/2023, the dataset underwent training and testing with three scenarios: 90:10, 80:20, and 70:30. Evaluation metrics, including RMSE, MSE, and MAPE, assessed model performance. Each method had unique strengths and weaknesses, and the choice depended on application needs. The goal was to improve decision accuracy in predicting crude palm oil price trends. In the 90:10 scenario, random forest outperformed linear and logistic regression, yielding smaller MSE (43948.56), MAE (80.37), and RMSE (209.64). Similarly, in the 80:20 scenario, random forest had smaller MSE (137787.61), MAE (106.38), and RMSE (371.20). In the 70:30 scenario, random forest showed smaller MSE (107582.32), MAE (104.13), and RMSE (328). Overall, random forest consistently demonstrated better performance than linear and logistic regression.

Macroeconomic Variables, Global Stock, and Financial Sector Stock Indices in Indonesia

This study aims to analyze the impact of macroeconomic variables and global stock indices on the financial sector stock index in Indonesia. The data used is secondary data in the form of monthly time series from January 2010 to December 2019, which comes from Bank Indonesia, the Central Bureau of Statistics (BPS), and Yahoo Finance. The variables of this study include the Jakarta Islamic Index (Indonesia), IDX Sectoral Index (Indonesia), Money Supply Inflation, Rupiah against USD Exchange Rate, Crude Palm Oil Price, Industrial Production Index, KL Financial Sectoral Index (Malaysia), SET Financial Sectoral Index (Thailand) and S&P 500 Stock Index (USA). The analysis method used is Autoregressive Distributed Lag (ARDL). The results concluded that the short-term estimation resulted in the exchange rate variable, the Industrial Production Index (IPI), and the Thailand SET financial sector stock index having a significant effect on the JII index, while the money supply variable, the Malaysia KL financial sector stock index, the Thailand SET financial sector stock index, and the US S&P 500 stock index had a significant effect on the IDX financial sector index. Then, the long-term estimation found that the Thailand SET financial sector stock index variable has a significant effect on the JII. Then, in the IDX financial sector index model, there are two significant variables, namely money supply and Industrial Production Index.

Strategy for the implementation of sustainable green fuels in Indonesia

Global climate change has triggered the implementation of energy transition programs in many countries where renewable fuels have received great attention. While Indonesia successfully im- plements biofuels-based crude palm oil (CPO) such as biodiesel (up to B30). The program proceeds towards implementing other green fuels (such as green diesel, gasoline, and bio jet fuel). However, the current price of CPO (as a raw material) is highly fluctuated depending on the global market mechanism and mostly more expensive than the price of fossil fuels. This situation leads to an unattractive business of green fuels. So, the government's policies are strongly required to make more attractive businesses with enhanced competitiveness of green fuels. A qualitative approach was conducted by using forum group discussion to gain information about the current status of biofuel policy in Indonesia and its challenges. The quantitative method was conducted by calculat- ing the economic analysis of palm oil-based energy crops and processing plants. The concept of integrated palm energy plantation was proposed based on the result. It may be a kind of integrated business entity owned by the government which aims at producing CPO and the following green fuels. Economic analysis shows that from the plantation side, the selling price of Fruit Fresh Bunch (FFB) is USD 0.068/kg with an IRR of 12.6%. While in the CPO processing industry, using the FFB price of USD 0.082/kg obtained an IRR of 14.4%. This result shows promising results from the calculation, keeps the sustainability of raw material supply and enhances the competitiveness of green fuels.