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1
Pham, Nhien T. "Application of SARIMA model to forecasting the natural rubber price in the world market." Journal of Agriculture and Development 17, no. 06 (December 31, 2018): 1–7. http://dx.doi.org/10.52997/jad.1.03.2018.
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This study was conducted to develop a forecasting model to predict the price natural rubber in the world market by using the Seasonal Autoregressive Integrated Moving Average (SARIMA). The dataset for model development was collected from series data of average monthly closing average prices in the natural rubber - Ribbed Smoked Sheet No.3 (RSS3) on the Tokyo Commodity Exchange (TOCOM) for the period of January 2007 - September 2018. The RSS3 price on the TOCOM provided the reference price for natural rubber in the world market. It resulted SARIMA(2,1,2)(1,1,1)12 model was selected as the best-fit model. The model achieved 0.000 for Probability value (P-value). 8.86 for Akaike Information Criterion (AIC) and 9.01 for Schwarz Information Criterion (SIC); 6.68% for Mean Absolute Percentage Error (MAPE) and 21.43 for Root Mean Square Error (RMSE). This model was used to forecast the world's natural rubber price during October 2018 - December 2020. This study may be helpful to the farmers, traders, and the governments of the world's important natural rubber producing countries to plan policies to reduce natural rubber production costs and stabilize the natural rubber price in the future, such as by setting suitable areas for natural rubber plantation in each country and defining appropriate and sustainable alternative crop areas in each country.
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Bubshait, Abdulaziz K. "Butadiene Rubber in the Petrochemical Industry." International Annals of Science 11, no. 1 (December 31, 2021): 22–26. http://dx.doi.org/10.21467/ias.11.1.22-26.
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The Butadiene is a raw material used in the petrochemical industry. The use of Butadiene has risen with petrochemical market growth. The Global market is forecasting a demand growth for butadiene applications, especially for rubber materials. The estimated synthetic rubber market is $19.1 billion in 2021 and forecasted to reach $23.2 billion in five years. The dynamic growth in butadiene applications will introduce new products used in many things from the food industry to sports and goods. Also, the rubber materials have different applications in the automotive industry, oil and gas, medical products, and plastics. Companies’ strategic planning to increase the production of synthetic rubber for the global market. The demand increased as new applications were introduced to the market. The stability of oil prices will have the rubber market steady which always leads to optimal pricing. The diver for Butadiene rubber applications is to maximize production by having different kind of materials that applied for several products. The global business development indicated the ability to increases the synthetic rubber market rubber and capacities, which will enhance the chemical process techniques, new technology design, and efficiency that will maximize production and minimize product cost. Looking into the price difference between synthetic and natural rubber, many fluctuation variables were introduced in the price of each type. For example, synthetic rubber price is high, depending on crude oil, natural gasoline and naphtha prices, since those feedstocks are fed to the cracking units, as C4 is one of the cracking products. Therefore, any change in the oil prices will influence the butadiene price, which is the feed for most rubber plants. In addition, the utilities required for those plants to operate have a major impact on overall price. On the other hand, Natural rubber is an agricultural product and dependent on soil type and weather.
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MATHEW, SHYJU, and RAMASAMY MURUGESAN. "Indian natural rubber price forecast–An Autoregressive Integrated Moving Average (ARIMA) approach." Indian Journal of Agricultural Sciences 90, no. 2 (November 15, 2022): 418–22. http://dx.doi.org/10.56093/ijas.v90i2.103067.
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The objective of this study was to forecast the price of natural rubber in India during April 2019 to March 2020 by employing autoregressive integrated moving average (ARIMA). The monthly pricing data for the period from April 2008 to March 2018 was used for the study. The analysis was carried out during the year 2018–19. RSS4 (Ribbed Smoked Sheets), latex (60% DRC (Dry Rubber Content)) and ISNR 20 (Indian Standard Natural Rubber) are the different types of Indian natural rubber that are competitive in international rubber market. The prices of these types of natural rubber were taken for modelling. AIC was used as a selection criterion for the best-fitted model. ARIMA(3,1,2) for RSS 4, ARIMA (3,1,2) for Latex 60% DRC, and ARIMA (4,1,3) for ISNR20were the most suited modelsto forecast the price.The evaluation metrics were R2, Adjusted R2, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). These were employed for validating the forecasting model. The price forecasting of natural rubber in India can be a better-suited tool for the policymakers to decide on their investment in natural rubber cultivation.
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Cheong Fu, Mong, and Shariffah Suhaila Syed Jamaludin. "Forecasting Malaysia Bulk Latex Prices Using Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing." Malaysian Journal of Fundamental and Applied Sciences 18, no. 1 (February 28, 2022): 70–81. http://dx.doi.org/10.11113/mjfas.v18n1.2404.
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Natural rubber is an important component of many developed countries' socioeconomic structures because it is frequently used to manufacture essential consumer goods such as tires and latex gloves. The natural rubber industry is heavily affected by the volatility and unpredictability of the natural bulk latex markets. Forecasting natural rubber prices is critical for rubber industry in procurement decisions and marketing strategies. This study aims to model monthly bulk latex prices in Malaysia using Autoregressive Integrated Moving Averages (ARIMA) and Exponential Smoothing. The models performance are measured using the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The Malaysian Rubber Board has 132 historical prices for the latex in Malaysia from January 2010 to December 2020. They are used for training and testing in determining the forecasting accuracy. Overall finding show that ARIMA (1,1,0) provides the most accurate prediction. With a MAPE of 8.59 percent and an RMSE of 69.78 sen per kilogram, this model is considered the best and highly accurate.
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Mohamad Norizan, Nor Farah Hanim Binti, and Zahayu Binti Md Yusof. "Forecasting Natural Rubber Price in Malaysia by 2030." Malaysian Journal of Social Sciences and Humanities (MJSSH) 6, no. 9 (September 10, 2021): 382–90. http://dx.doi.org/10.47405/mjssh.v6i9.986.
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Natural rubber (NR) has recently become one of Malaysia's most important economic sectors. Despite, the price of Standard Malaysia Rubber 20 changes frequently. That is why it is important to develop a NR price forecasting model. Because there was a significant time lag between making output decisions and the actual output of the commodity in the market. The aim of this study is to determine the time series pattern for natural rubber price in Malaysia within 1995 until 2020 and to forecast the natural rubber price in Malaysia for 10 years ahead. The data used is from year 1995 until 2020 that were obtained from Malaysian Rubber Board (MRB). This study also used univariate forecasting like Naïve with Trend, Double Exponential Smoothing, Holt’s Winter and Autoregressive Integrated Moving Average (ARIMA). Then, the measurement error is used to determine the best method to forecast the future data. The measurement error that used in this study are Mean Absolute Error, Mean Squared Error, Root Mean Square Error, Mean Absolute Percentage Error and The Theil Inequality Coefficient. Result: The natural rubber price in Malaysia showed a trend pattern. Then, ARIMA is used to determine the forecast of natural rubber price for next 10 years since it has the lowest measurement error. Conclusion: There are volatility in the price of natural rubber in Malaysia over the next 10 years.
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Khin, Aye Aye, Seethaletchumy Thambiah, and Kevin Low Lock Teng. "Short-term and long-term price forecasting models for the future exchange of Malaysian natural rubber market." International Journal of Agricultural Resources, Governance and Ecology 13, no. 1 (2017): 21. http://dx.doi.org/10.1504/ijarge.2017.084032.
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Erni, Nofi, M. Syamsul Maarif, Nastiti S.Indrasti, Machfud Machfud, and Soeharto Honggokusumo. "Model Prakiraan Harga dan Permintaan pada Rantai Pasok Karet Spesifikasi Teknis Menggunakan Jaringan Syaraf Tiruan." JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI 1, no. 3 (April 4, 2012): 116. http://dx.doi.org/10.36722/sst.v1i3.49.
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<p style="text-align: justify;">Karet spesifikasi teknis (TSR) merupakan jenis karet alam yang penting, dengan pertumbuhan permintaan yang tinggi dibanding jenis karet alam yang diproduksi dan diekspor oleh Indonesia. TSR paling banyak digunakan sebagai bahan baku untuk industri ban, sehingga dengan tumbuhnya indutri otomotif mendorong peningkatan permintaan terhadap TSR. Namun permasalahan muncul dalam produksi TSR, dimana tingkat fluktuasi baik karena kelebihan maupun kekurangan produksi sangat berpengaruh terhadap perubahan harga TSR di pasar Internasional. Untuk mengurangi fluktuasi tersebut diperlukan suatu metode untuk memperkirakan tingkat permintaan dan harga. Penelitian ini bertujuan untuk merancang suatu metode prakiraan yang dapat merperkirakan tingkat harga dan volume permintaan untuk TSR 20. Prakiraan dilakukan dengan Jaringan Syaraf Tiruan (JST) dengan algoritma propagasi balik, menggunakan data perkembangan pasar TSR di bursa berjangka SICOM. Model JST yang dirancang mempertimbangkan pola harga, pola permintaan dan interaksi kedua faktor. Hasil simulasi menunjukkan penggunaan 5 input neuron yaitu: 1) harga tertinggi, 2) harga terendah, 3) harga penutupan, 4) volume permintaan awal, 5) volume permintaan penutupan, 15 neuron pada lapisan tersembunyi dan 2 output yaitu harga dan volume permintaan pada lapisan output. Tingkat akurasi hasil prakiraan harga mencapai 91% dan akurasi prakiraan permintaan 87%. Berdasarkan hasil prakiraan ditentukan status harga dan permintaan. Harga tinggi jika perbedaan antara nilai maksimum dan nilai tengah lebih tinggi dari 47%, harga rendah jika perbedaan antara nilai minimum dan nilai tengah lebih dari 20%. Prakiraan permintaan dinyatakan tinggi atau rendah jika terjadi peningkatan maupun penurunan sebesar 50 % dari rata-rata permintaan<em>.</em></p><h6 style="text-align: center;"><strong><em>Abstract</em> </strong></h6><p style="text-align: justify;">Technically Specified Rubber (TSR) is the most important of natural rubber type which has a high demand growth which is produced and exported by Indonesia. TSR is mostly used as raw material for tire industries, as the world’s automotive industries grow up the demand for TSR is also rise up. However, the problem appears in the production of TSR, which is fluctuative production rate in the form of over and under production correlated to the price change in International market. Therefore, a method to forecast the price and demand level is needed to design in order to reduce fluctuation. The result is a forecasting that used as an input for preparing and adjusting TSR rubber production planning that working adaptively with market condition by utilising the expert knowledge. This research aimed to design a method that can forecast the changes in price level and demand volume. Artificial Neural Network (ANN) which is backpropagation algorithm that has been designed according to data TSR market condition in SICOM is used in this research, the ANN model is modified by observing the price pattern, demand pattern and the connection between both of them together. Experiments have shown that the optimal architecture network for price and demand forecasting can be obtained by using 5 different neuron parameter, there are: 1) the highest price, 2) the lowest price, 3) the closing price, 4) demand volume interest, 5) demand volume close for input layer, 15 neuron for hidden layer and 2 different neuron there are price and demand volume for output layer. The accuracy of forecasting price had reached 91% and 87% for forecasting demand. Based on forecasting result had determined the state of price and demand. The price is high if the differences between maximum and mean score is higher than 47% and the price is low if the differences between the minimum and mean score is higher than 20%. The demand is high if the demand forecasting is higher than 50% and it is low if smaller than 50% of average demand volume.</p>
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Pramananda, Penggawa Pietra, Amzul Rifin, and Dahlia Nauly. "The Effect of Domestic Consumption on Natural Rubber Farmgate Price in Indonesia." AGRARIS: Journal of Agribusiness and Rural Development Research 8, no. 2 (December 28, 2022): 248–60. http://dx.doi.org/10.18196/agraris.v8i2.12480.
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The decline in natural rubber farmgate prices in recent years directly impacted Indonesia’s natural rubber market. In response to this phenomenon, the government plans to increase natural rubber domestic consumption to raise Indonesia’s rubber price. This study aimed to determine the effect of increasing natural rubber domestic consumption on natural rubber farmgate prices and analyze other factors those influence it. The Error Correction Model was used to identify the variables that significantly affect Indonesia’s natural rubber farmgate price. The data used in this study were monthly data from January 2012 to December 2017. Results showed that natural rubber domestic consumption did not significantly affect the Indonesia natural rubber farmgate price. However, in the long run, Indonesia’s natural rubber farmgate price was influenced by the previous period of Indonesia’s natural rubber prices, world natural rubber prices, world crude oil prices, and exchange rates. While in the short run, Indonesia’s natural rubber farmgate price was influenced by the previous period of Indonesia’s natural rubber prices, world natural rubber prices, and exchange rate.
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Su, Chi-Wei, Lu Liu, Ran Tao, and Oana-Ramona Lobonţ. "Do natural rubber price bubbles occur?" Agricultural Economics (Zemědělská ekonomika) 65, No. 2 (February 27, 2019): 67–73. http://dx.doi.org/10.17221/151/2018-agricecon.
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In this paper, we employ the Generalized Supremum Augmented Dickey-Fuller test in order to identify the existence of multiple bubbles in natural rubber. This approach is practical for the using of time series and identifies the beginning and end points of multiple bubbles. The results reveal that there are five bubbles, where exist the divergences between natural rubber prices and their basic values on account of market fundamentals. The five bubbles are related to imbalance between supply and demand, inefficiencies of smallholders market, oil prices, exchange rate and climatic changes through analyses. Thus, the corresponding authorities are supposed to identify bubbles and consider their evolutions, which is beneficial to the stability of natural rubber price.
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Rani, Velpula Jhansi. "Forecasting the Prices of Indian Natural Rubber using ARIMA Model." International Journal of Pure & Applied Bioscience 6, no. 2 (March 28, 2018): 217–21. http://dx.doi.org/10.18782/2320-7051.5464.
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Khin, A. A., Zainalabidin M, and Mad Nasir S. "Comparative Forecasting Models Accuracy of Short-term Natural Rubber Prices." Trends in Agricultural Economics 4, no. 1 (January 1, 2011): 1–17. http://dx.doi.org/10.3923/tae.2011.1.17.
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Mouchtaris, Dimitrios, Emmanouil Sofianos, Periklis Gogas, and Theophilos Papadimitriou. "Forecasting Natural Gas Spot Prices with Machine Learning." Energies 14, no. 18 (September 14, 2021): 5782. http://dx.doi.org/10.3390/en14185782.
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The ability to accurately forecast the spot price of natural gas benefits stakeholders and is a valuable tool for all market participants in the competitive gas market. In this paper, we attempt to forecast the natural gas spot price 1, 3, 5, and 10 days ahead using machine learning methods: support vector machines (SVM), regression trees, linear regression, Gaussian process regression (GPR), and ensemble of trees. These models are trained with a set of 21 explanatory variables in a 5-fold cross-validation scheme with 90% of the dataset used for training and the remaining 10% used for testing the out-of-sample generalization ability. The results show that these machine learning methods all have different forecasting accuracy for every time frame when it comes to forecasting natural gas spot prices. However, the bagged trees (belonging to the ensemble of trees method) and the linear SVM models have superior forecasting performance compared to the rest of the models.
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Festić, Mejra, and France Križanič. "The Impact of Natural Gas Prices on Production by Industries in Slovenia." Lex localis - Journal of Local Self-Government 7, no. 1 (September 4, 2009): 65–81. http://dx.doi.org/10.4335/67.
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Article empirically investigates how intensive is the impact of natural gas prices on production by industries in Slovenian economy. Natural gas price movements can help us in forecasting the movements in electricity, natural gas, steam, hot water supplies, the production of metals, textiles, leather, footwear, leather and fur products, clothes, the production of pulp, paper, cardboard and products from paper and cardboard, the production of products from rubber and plastic materials, processing industry and the production of furniture, the production of intermediary consumption products and recycling. We proved that natural gas prices increase for 1 % point contributes to higher prices of living necessaries for 0,005 % points.
KEYWORDS: • natural gas prices • gas quantities • production by industries • Slovenia
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Thazhugal Govindan Nair, Saji. "Recession effect in pricing efficiency of rubber futures: the emerging market’s experience." Journal of Agribusiness in Developing and Emerging Economies 9, no. 5 (October 14, 2019): 503–19. http://dx.doi.org/10.1108/jadee-06-2018-0075.
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Purpose The purpose of this paper is to investigate the recession effects in market efficiency of natural rubber futures contracts traded in India. Design/methodology/approach The research draws inferences from Granger causality and Engle–Granger cointegration tests, which are administered separately on 14 year daily price data spanning into two distinct, non-overlapping time series of 2004–2008 and 2009–2017. Findings Analysis shows that rubber futures market is informationally efficient in price discovery. The results of cointegartion tests indicate that a long-term relationship does exist between futures and spot prices of the natural rubber in India. The recession effects in the market efficiency of rubber futures contracts are evident from the increase in optimal hedge ratios estimated with the cointegration methodology. Research limitations/implications The study pursues a simple cointegration methodology to assess the causal relations between spot and futures market prices in the Indian context. Future studies investigating the long-run causal relations, with error correction framework, between spot and future prices of rubber from other leading rubber producing countries can validate the findings more on this issue. Practical implications The research expects to pass on vital information inputs on the implications of future contracts to rubber traders for managing their portfolios. The study of this kind definitely will be a great help to farmers and exporters who are potentially interested in gaining access to a hedging vehicle. Originality/value The paper is unique in terms of understanding the effects of economic recession in information efficiency of futures market. Moreover, a limited number of studies have explored the functional utilities of rubber futures in emerging market context.
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Kremser, Thomas, and Margarethe Rammerstorfer. "Predictive Performance and Bias: Evidence from Natural Gas Markets." Journal of Management and Sustainability 7, no. 2 (May 30, 2017): 1. http://dx.doi.org/10.5539/jms.v7n2p1.
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This paper sheds light on the differences and similarities in natural gas trading at the National Balancing Point in the UK and the Henry Hub located in the US. For this, we analyze traders’ expectations and implement a mechanical forecasting model that allows traders to predict future spot prices. Based on this, we compute the deviations between expected and realized spot prices and analyze possible reasons and dependencies with other market variables. Overall, the mechanical predictor performs well, but a small forecast error remains which can not be characterized by the explanatory variables included.
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Ghani, I. M. Md, and H. A. Rahim. "Modeling and Forecasting of Volatility using ARMA-GARCH: Case Study on Malaysia Natural Rubber Prices." IOP Conference Series: Materials Science and Engineering 548 (August 27, 2019): 012023. http://dx.doi.org/10.1088/1757-899x/548/1/012023.
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van Rijswijk, K., S. Koussios, and O. K. Bergsma. "Filament wound container made of natural fibres and rubber: Conceptual design." Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 217, no. 4 (October 1, 2003): 277–86. http://dx.doi.org/10.1177/146442070321700403.
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In the transportation sector, the search for cost reduction has led to the introduction of composite materials. This paper focuses on filament wound containers made of natural fibres and rubber. These containers show great potential for application in developing countries, where usage of locally grown materials is often justified considering the high prices and mediocre quality of the locally available synthetic fibres, if already available. Where containers reinforced with synthetic fibres will not be able to compete with existing steel containers, which are often imported, the ones made of sustainable natural fibres show great potential. The cost reduction is primarily based on the philosophy of making use of the local situation: local manufacturing of locally designed products with locally grown materials for transportation of local consumables for the local market. In this article, the integrated design procedure of a natural fibre wound container taking these local aspects into account is discussed. A feasibility study on such a container for the Vietnamese market is described, showing significant improvements in economy and sustainability.
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Kim, Kyunghwan, Sangseop Lim, Chang-hee Lee, Won-Ju Lee, Hyeonmin Jeon, Jinwon Jung, and Dongho Jung. "Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management." Journal of Marine Science and Engineering 10, no. 12 (November 24, 2022): 1814. http://dx.doi.org/10.3390/jmse10121814.
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The LNG price is basically determined based on the oil price, but other than that, it is also determined by the influence of the method of LNG transportation; storage; processes; and political, economic, and geographical instability. Liquefied natural gas (LNG) may not reflect its market value if the destination of the purchase is restricted or the purchase contract includes a take-or-pay clause. Furthermore, it is difficult for the buyer to flexibly manage procurement, resulting in the decoupling of oil and natural gas prices. Therefore, as the LNG bunker price is expected to be more volatile than the marine bunker price in the future, shipping companies need to prepare countermeasures based on scientific forecasting techniques. This study aims to be the first to analyze the forecasting of short-term LNG bunker prices using recurrent neural network (RNN) models suitable for highly volatile data such as time series. Predictive analysis was performed using simple RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) models, which effectively forecast time-series data, and the prediction performance of LSTM among the three models was excellent. LSTM had relatively excellent prediction performance of outliers and beyond. In addition, it was possible to effectively manage ship operating costs with improved forecasting in practice. Furthermore, this study contributes to establishing a systematic strategy for supervisors in global shipping companies, port authorities, and LNG bunkering companies.
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., Zainuddin. "Dampak Penurunan Tarif Impor, Investasi dan Relokasi Industri Ban Terhadap Perdagangan Karet Alam dan Ban Indonesia di Pasar Dunia." Buletin Ilmiah Litbang Perdagangan 13, no. 1 (July 31, 2019): 71–98. http://dx.doi.org/10.30908/bilp.v13i1.341.
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Abstrak
Penelitian ini bertujuan menganalisis dampak penurunan tarif impor karet alam dan ban, peningkatan investasi dan relokasi industri ban dari USA, Jepang, Republik Rakyat Tiongkok (RRT) ke Indonesia terhadap perdagangan karet alam dan ban Indonesia. Kajian ini menggunakan model sistem persamaan simultan. Deregulasi perdagangan melalui penurunan tarif impor ban telah meningkatkan ekspor karet alam Indonesia ke pasar Jepang dan RRT yang mendorong peningkatan produksi dan ekspor ban Indonesia. Kebijakan tersebut telah memberikan dampak tidak menguntungkan bagi ekspor karet alam Thailand dan Malaysia. Kombinasi antara penurunan tarif impor ban dengan tarif impor karet alam RRT memberikan dampak tidak menguntungkan terhadap produksi dan ekspor karet alam Indonesia ke pasar RRT dan tidak berdampak signifikan terhadap harga karet alam tingkat petani domestik. Selanjutnya peningkatan investasi dan relokasi industri ban dari USA, Jepang, RRT ke domestik memberikan dampak terhadap peningkatan produksi dan ekspor ban Indonesia, konsumsi karet alam domestik, peningkatan produksi dan harga karet alam di tingkat petani domestik. Perubahan positif neraca perdagangan juga terjadi ketika semakin besarnya peningkatan investasi dan relokasi industri ban ke domestik. Penelitian ini merekomendasikan agar pemerintah dan asosiasi industri melakukan industrial lobbying ke negara-negara besar pelaku industri ban dunia khususnya Asia Timur dan USA dalam kerangka kerja sama PTA atau FTA.
Kata Kunci: Karet Alam, Ban, Perdagangan, Sistem Persamaan Simultan
Abstract
This study aims to analyze the impact of the reduction in import tariff on natural rubber and tires, increase investment and relocate of tire industry from the USA, Japan, China to Indonesia to trade in natural rubber and Indonesian tires. The analysis of the Indonesian natural rubber and tires trade used simultaneous equation system models. Trade deregulation through a reduction in tire import tariff had increased Indonesia's natural rubber exports to Japanese and Chinese markets, which has encouraged to increase Indonesian tire production. However, this policy had unfavorable impact on Thailand and Malaysia's natural rubber exports. The combination of the reduction in tire import tariff and the tariff for importing Chinese natural rubber had an unfavorable impact on the production and export of Indonesian natural rubber to the Chinese market and had a weak impact on the natural rubber prices of domestic farmers.Furthermore, increased investment and relocation of the tire industry from the USA, Japan, China to Indonesia had increased Indonesian tire production and exports, domestic consumption of natural rubber, production and prices of natural rubber at the level of domestic farmers. A positive change in the trade balance also occurred when the increasing investment and relocation of the tire industry to the domestic market grew. This study recommended the government and industrial association to conduct industrial lobbying to big tire-industry players particularly in East Asia and USA under PTA and FTA Framework.
Keywords: Natural Rubber, Tire, Trade and Simultaneous Equations System
JEL Classification: F13, F17, Q17
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Manowska, Anna, Aurelia Rybak, Artur Dylong, and Joachim Pielot. "Forecasting of Natural Gas Consumption in Poland Based on ARIMA-LSTM Hybrid Model." Energies 14, no. 24 (December 20, 2021): 8597. http://dx.doi.org/10.3390/en14248597.
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Natural gas is one of the main energy sources in Poland and accounts for about 15% of the primary energy consumed in the country. Poland covers only 1/5 of its demand from domestic deposits. The rest is imported from Russia, Germany, Norway, the Czech Republic, Ukraine, and Central Asia. An important issue concerning the market of energy resources is the question of the direct impact of the prices of energy resources on the income of exporting and importing countries. It should also be remembered that unexpected and large fluctuations are detrimental to both exporters and importers of primary fuels. The article analyzes natural gas deposits in Poland, raw material trade and proposes a model for forecasting the volume of its consumption, which takes into account historical consumption, prices of energy resources and assumptions of Poland’s energy policy until 2040. A hybrid model was built by combining ARIMA with LSTM artificial neural networks. The validity of the constructed model was assessed using ME, MAE, RMSE and MSE. The average percentage error is 2%, which means that the model largely represents the real gas consumption course. The obtained forecasts indicate an increase in consumption by 2040.
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Khin, Aye Aye, Jom Jacob, Kevin Low Lock Teng, Raymond Ling Leh Bin, and Wong Hong Chau. "Forecasting Technology Using for Dynamic Natural Rubber Production Models in Selected Asean Countries and World Market." Advanced Science Letters 24, no. 5 (May 1, 2018): 3368–73. http://dx.doi.org/10.1166/asl.2018.11377.
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Junaidi, J. "THE ANCILLARY PRODUCTS OF RUBBER (Hevea brasiliensis Muell. Arg.): POTENTIAL RESOURCES TO ENHANCE SUSTAINABILITY." Agricultural Socio-Economics Journal 22, no. 3 (July 31, 2022): 169. http://dx.doi.org/10.21776/ub.agrise.2022.022.3.3.
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Indonesia is one of the major natural rubber-producing countries. The low rubber prices severely affect rubber agribusiness as farmers and rubber companies depend on latex as the only source of income. This condition leads to an unprecedented challenge to rubber agribusiness sustainability. This systematic review aims to encourage the use of part of rubber plants as a source of revenue for rubber plantations to maintain their sustainability. Non-latex parts of the rubber plant can be utilized, including latex serum and skim waste, parts of rubber seeds, and rubberwood. The strength of the ancillary product of <em>Hevea brasiliensis </em>is that the raw materials are abundant, yet the weakness is that the rubber companies have no experience exploiting them. The opportunity for utilizing is widely open, as many methods have been researched; however, the main thread is how to compete with the existing products. Therefore, careful market research and feasibility studies are recommended.
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Devyatkin, Dmitry, and Yulia Otmakhova. "Methods for Mid-Term Forecasting of Crop Export and Production." Applied Sciences 11, no. 22 (November 19, 2021): 10973. http://dx.doi.org/10.3390/app112210973.
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A vast number of studies are devoted to the short-term forecasting of agricultural production and market. However, those results are more helpful for market traders than producers and agricultural policy regulators because any structural change in that field requires a while to be implemented. The mid and long-term predictions (from one year and more) of production and market demand seem more helpful. However, this problem requires considering long-term dependencies between various features. The most natural way of analyzing all those features together is with deep neural networks. The paper presents neural network models for mid-term forecasting of crop production and export, which considers heterogeneous features such as trade flows, production levels, macroeconomic indicators, fuel pricing, and vegetation indexes. They also utilize text-mining to assess changes in the news flow related to the state agricultural policy, sanctions, and the context in the local and international food markets. We collected and combined data from various local and international providers such as UN FAOSTAT, UN Comtrade, social media, the International Monetary Fund for 15 of the world’s top wheat exporters. The experiments show that the proposed models with additive regularization can accurately predict grain export and production levels. We also confirmed that vegetation indexes and fuel prices are crucial for export prediction. Still, the fuel prices seem to be more important for predicting production than the NDVI indexes from past observations.
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Zhao, Lu-Tao, Li-Na Liu, Zi-Jie Wang, and Ling-Yun He. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach." Sustainability 11, no. 14 (July 17, 2019): 3892. http://dx.doi.org/10.3390/su11143892.
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The rapid fluctuations in global crude oil prices are one of the important factors affecting both the sustainable development and the green transformation of the global economy. To accurately measure the risks of crude oil prices, in the context of big data, this study introduces the two-layer non-negative matrix factorization model, a kind of natural language processing, to extract the dynamic risk factors from online news and assign them as weighted factors to historical data. Finally, this study proposes a giant information history simulation (GIHS) method which is used to forecast the value-at-risk (VaR) of crude oil. In conclusion, this paper shows that considering the impact of dynamic risk factors from online news on the VaR can improve the accuracy of crude oil VaR measurement, providing an effective tool for analyzing crude oil price risks in oil market, providing risk management support for international oil market investors, and providing the country with a sense of risk analysis to achieve sustainable and green transformation.
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Su, Moting, Zongyi Zhang, Ye Zhu, Donglan Zha, and Wenying Wen. "Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods." Energies 12, no. 9 (May 3, 2019): 1680. http://dx.doi.org/10.3390/en12091680.
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Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR.
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Bobinaite, Viktorija, and Jānis Zuters. "Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia." Economics and Business 29, no. 1 (August 1, 2016): 12–26. http://dx.doi.org/10.1515/eb-2016-0017.
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AbstractThe paper aims at modelling the electricity generator’s expectations about price development in the Latvian day-ahead electricity market. Correlation and sensitivity analysis methods are used to identify the key determinants of electricity price expectations. A neural network approach is employed to model electricity price expectations. The research results demonstrate that electricity price expectations depend on the historical electricity prices. The price a day ago is the key determinant of price expectations and the importance of the lagged prices reduces as the time backwards lengthens. Nine models of electricity price expectations are prepared for different natural seasons and types of the day. The forecast accuracy of models varies from high to low, since errors are 7.02 % to 59.23 %. The forecasting power of models for weekends is reduced; therefore, additional determinants of electricity price expectations should be considered in the models and advanced input selection algorithms should be applied in future research. Electricity price expectations affect the generator’s loss through the production decisions, which are made considering the expected (forecasted) prices. The models allow making the production decision at a sufficient level of accuracy.
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Katterbauer, Klemens, and Philippe Moschetta. "An Innovative Artificial Intelligence and Natural Language Processing Framework for Asset Price Forecasting Based on Islamic Finance: A Case Study of the Saudi Stock Market." Econometric Research in Finance 6, no. 2 (December 1, 2021): 183–96. http://dx.doi.org/10.2478/erfin-2021-0009.
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Abstract Artificial intelligence has transformed the forecasting of stock prices and the evaluation of companies. Novel techniques, allowing the real-time processing of large amounts of data, have enabled the use of data on various external factors to improve the forecasting of the company’s value and stock price. Although conventional approaches solely focus on the use of quantitative data, history has shown that news announcements and statements may significantly affect the performance of the stock value of companies. We present an innovative framework for integrating a nonlinear autoregressive network with a natural language processing approach to analyze stock price movements and forecast stock prices. The framework analyzes and processes the company’s financial statements, determining indicative factors and transforming them into categorical parameters which are then integrated into a nonlinear autoregressive network to estimate and forecast the company’s stock price. The analysis of several Saudi companies listed in the Tadawul index affirms the improved estimation of the stock price and the possibility of a more precise prediction of long-term stock price evolution.
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Katterbauer, Klemens, and Philippe Moschetta. "An Innovative Artificial Intelligence and Natural Language Processing Framework for Asset Price Forecasting Based on Islamic Finance: A Case Study of the Saudi Stock Market." Econometric Research in Finance 6, no. 2 (December 1, 2021): 183–96. http://dx.doi.org/10.2478/erfin-2021-0009.
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Abstract Artificial intelligence has transformed the forecasting of stock prices and the evaluation of companies. Novel techniques, allowing the real-time processing of large amounts of data, have enabled the use of data on various external factors to improve the forecasting of the company’s value and stock price. Although conventional approaches solely focus on the use of quantitative data, history has shown that news announcements and statements may significantly affect the performance of the stock value of companies. We present an innovative framework for integrating a nonlinear autoregressive network with a natural language processing approach to analyze stock price movements and forecast stock prices. The framework analyzes and processes the company’s financial statements, determining indicative factors and transforming them into categorical parameters which are then integrated into a nonlinear autoregressive network to estimate and forecast the company’s stock price. The analysis of several Saudi companies listed in the Tadawul index affirms the improved estimation of the stock price and the possibility of a more precise prediction of long-term stock price evolution.
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Pongsayaporn, Pimnapa, Thanwadee Chinda, and Veeris Ammarapala. "Key Factors Influencing Multimodal Transportation of Natural Block Rubber in Thailand." MATEC Web of Conferences 312 (2020): 02008. http://dx.doi.org/10.1051/matecconf/202031202008.
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Thailand is the world’s largest natural rubbers exporter. The major type of natural rubbers exported is block rubber. Currently, the transportation of natural rubbers in Thailand depends mainly on truck mode because of the convenience and extensive network of roads. However, Thailand confronts road transportation limitations including maximum acceptable weight, traffic congestion and high fuel prices. Dealing with these problems, the multimodal connection from roads to container yards (CY) and ports is expected to decrease logistics cost, and increase various opportunities to trade with neighbouring countries. This paper, thus, aims at examining key factors influencing the use of multimodal transportation utilizing the exploratory factor analysis (EFA). The principal components analysis (PCA) method, together with the eigenvalue over 1, factor loading of 0.40, and varimax rotation method, extracted five key factors with a total of 20 associated attributes, which are the Multimodal Operation, Service Operation, Multimodal Service Provider, Market Consideration, and Road Constraints factors. The result also pinpoints the capacity of container yard (CY) or port, accessibility to container yard (CY) or port, documentation process, logistics cost, adequacy of a multimode service provider, and law enforcement on truck driving hours, as crucial attributes in planning for multimodal transportation. This paper can be used as a guideline for a feasibility study of multimodal transportation of natural block rubber in Thailand.
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Gupta, Sumeet. "FUNDAMENTAL & TECHNICAL ANAYSIS OF CRUDE OIL PRICES." Journal of Global Economy 17, no. 1 (April 17, 2021): 3–20. http://dx.doi.org/10.1956/jge.v17i1.617.
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The human mind is not as good at processing large amounts of information as we might like. Psychologists have shown that human beings are only able to juggle small numbers of related and often conflicting pieces of information without making judgment errors. As a result, individuals faced with the vast amounts of information available to support investment decisions often find themselves swamped by the enormity of the task; unable to see the wood from the trees. Technical analysis is a field of financial markets research that works to address the above problem by focusing on a single, commonly available, data source that reflects all known information and activity relating to all monetary securities- Price history. Technical analysts argue that as markets are efficient, prices reflect all known information and that they move over time as participants react to new information and changing needs.
As a result, the technical analysis of these price changes can provide real insight into the market dynamics and be used to develop trade strategies that exhibit superior risk/reward characteristics. While technical analysis approaches have developed significantly over the past few decades, some techniques are far more ancient. While their real origins are anonymous, Japanese candlestick charts have been recorded as being employed in the rice markets as far back as the 1600s. What is particularly interesting is that various of these ancient approaches continue to provide highly effective trading signals when applied to modern markets and securities.
Crude oil price volatility is in the midst of the largest business risk that oil and gas companies face. This is followed by unstable policy regime, managing costs and risks emerging from technological advancements. The high levels and rapid fluctuations of petroleum prices have become a great concern to individual consumers, firms, policy makers and society. Technical Analysis is the forecasting of future financial price movements based on an examination of past price movements. Like weather forecasting, technical analysis does not result in absolute predictions about the future. Instead, technical analysis can help investors anticipate what is "likely" to happen to prices over time. Technical analysis uses a wide variety of charts that show price over time. Hence, to mitigate the negative impacts of price volatility and to predict about the future price movement of crude oil and natural gas we can use technical analysis.
Technical analysis is the study of market action, primarily through the use of charts, for the purpose of forecasting price trends. The term “market action” includes the three principal source of action available to the technician-price, volume and open interest. This research paper highlights fundamental factor which affects the Brent price and analysed the factor which are highly correlated with Brent price and on the basis of the results forecasted the Brent price for next five years. Fundamental analysis of Brent oil, price pattern & movement of crude oil has also been carried out using candlestick technical tool.
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Zhan, Linjie, and Zhenpeng Tang. "Natural Gas Price Forecasting by a New Hybrid Model Combining Quadratic Decomposition Technology and LSTM Model." Mathematical Problems in Engineering 2022 (December 5, 2022): 1–13. http://dx.doi.org/10.1155/2022/5488053.
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Research on the price prediction of natural gas is of great significance to market participants of all kinds. In order to predict natural gas prices more reliably, this paper introduces a quadratic decomposition technology based on the combination of variational modal decomposition (VMD) and ensemble empirical modal decomposition (EEMD), which decomposes the residual term (Res) after VMD by EEMD; then, a new hybrid model called VMD-EEMD-Res.-LSTM is constructed in combination with the long short-term memory (LSTM) prediction model. The contribution of this new hybrid model is that, unlike existing application research that combines existing decomposition technology with the LSTM model, it does not ignore the important information contained in the residual after the VMD. In order to verify the predictive performance of the proposed new model, this paper uses the data of the spot price of natural gas in the United States to conduct a multistep-ahead empirical comparative analysis. The results show that the new hybrid model constructed in this paper has significant predictive advantages.
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Ling, Pang Wen. "The Stock Price Forecasting Comparative Research of the Use of Fractal Theory at Taiwan Traditional Industry and Technology Industry." Applied Mechanics and Materials 274 (January 2013): 53–56. http://dx.doi.org/10.4028/www.scientific.net/amm.274.53.
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As natural phenomena, financial and securities markets are full of unpredictable changes. It is an interesting topic whether fluctuation of stock prices follows certain rules. This paper sources data from the stock market of Taiwan, and selects the stock of Uni-President Enterprises Corp. which is a representative stock with large capital share, and belongs to an traditional industry that is not affected by business cycles, and selects the stock of TSMC which is a representative stock with large capital share, and belongs to an technology industry that is easily affected by business cycles. This paper will use the Fractal theory to study the accuracy of prediction of the random stock price of these two industries.
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Saadah, Maratun, and Agustiyara Agustiyara. "What Can Rubber Farmers and Institutions Do for Supply Chain Networks: The Political Economy Analysis." Jurnal Borneo Administrator 18, no. 2 (August 25, 2022): 171–86. http://dx.doi.org/10.24258/jba.v18i2.902.
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This research analysed the process of the supply chain of natural rubber between Tauke (collectors) and farmers and between farmers who have their rubber plantation and their tapper labour, with the institutional political economy analysis in Batanghari Regency, Jambi province. The research was descriptive research with a qualitative approach. This research used in-depth interviews with particular informants with purposive sampling and participative observation methods in certain activities. Data that had been obtained was analysed and qualitatively described. This research found that the collectors still overpowered the supply-chain process of natural rubber in the Batanghari Regency – trader, both at the village, sub-regency, and Regency levels. Other institutions, such as the auction market and crumb rubber factory, were located only in particular locations, so they were not accessible to the whole farmers in the Regency. As a result, several channels of marketing systems made different prices from what the farmers got. Institutional marketing indeed could be implemented. It has been proven by institutions such as auctions, joint venture groups, and other groups. Besides, some non–economic aspects can affect the way price parameter changes in the supply chain process, and they are; institution characteristics, marketing channel, and patronage relationship between farmers and traders. Batanghari Regency government should re-examine their plantation regulation about the marketing mechanism until the lowest level, villages. The government should have encouraged a mutual understanding among farmers so that similar efforts could occur elsewhere.
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Dey, Kushankur, and Debasish Maitra. "Can futures markets accommodate Indian farmers?" Journal of Agribusiness in Developing and Emerging Economies 6, no. 2 (November 14, 2016): 150–72. http://dx.doi.org/10.1108/jadee-08-2013-0029.
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Purpose It has become an ongoing debate whether Indian commodity futures markets can accommodate farmers. The purpose of this paper is to examine whether Indian commodity futures markets help rationalize farmers’ price expectation. The study starts with questions on the efficiency and other roles of commodity futures markets. Design/methodology/approach From a sectoral standpoint and economic importance, the study considers pepper, coffee, and natural rubber (NR) futures and spot markets. The efficiency of futures markets, divergence/convergence and causality between futures and spot markets have been studied by employing co-integrations, error correction and causality models. The sample period of the data are taken from the inception of futures trading. These three commodities are also compared on the basis of trading at the futures markets vs spot markets. Findings Analysis shows that though pepper futures market is informationally efficient in price discovery, while coffee and NR spot markets do the process faster. Pepper and coffee futures and spot prices exhibit the convergence; NR shows a sign of divergence. Unidirectional causality from pepper futures to spot market is observed wherein the former was weakly exogenous to the latter and while, bidirectional causality is observed in coffee and rubber. Coffee spot appears weakly exogenous while this remains inconclusive in the case of NR. Research limitations/implications The authors analyzed the futures markets in rationalizing the spot market price in three plantation crops in India. In order to make the study more generalizable, further research is warranted in other commodities including those prices of which are government regulated. Originality/value The paper is unique in terms of understanding the interaction or interrelationship between futures markets and spot markets and drawing inferences about the role of futures markets in price formation in plantation commodities like pepper, coffee and NR.
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Mbae, Ariel Mutegi, and Nnamdi I. Nwulu. "Day-ahead load forecasting using improved grey Verhulst model." Journal of Engineering, Design and Technology 18, no. 5 (April 15, 2020): 1335–48. http://dx.doi.org/10.1108/jedt-12-2019-0337.
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Purpose In the daily energy dispatch process in a power system, accurate short-term electricity load forecasting is a very important tool used by spot market players. It is a critical requirement for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The purpose of this study is to present an improved grey Verhulst electricity load forecasting model. Design/methodology/approach To test the effectiveness of the proposed model for short-term load forecast, studies made use of Kenya’s load demand data for the period from January 2014 to June 2019. Findings The convectional grey Verhulst forecasting model yielded a mean absolute percentage error of 7.82 per cent, whereas the improved model yielded much better results with an error of 2.96 per cent. Practical implications In the daily energy dispatch process in a power system, accurate short-term load forecasting is a very important tool used by spot market players. It is a critical ingredient for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The fact that the model uses actual Kenya’s utility data confirms its usefulness in the practical world for both economic planning and policy matters. Social implications In terms of generation and transmission investments, proper load forecasting will enable utilities to make economically viable decisions. It forms a critical cog of the strategic plans for power utilities and other market players to avoid a situation of heavy stranded investment that adversely impact the final electricity prices and the other extreme scenario of expensive power shortages. Originality/value This research combined the use of natural logarithm and the exponential weighted moving average to improve the forecast accuracy of the grey Verhulst forecasting model.
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Anik, Asif Reza, and Sanzidur Rahman. "Commercial Energy Demand Forecasting in Bangladesh." Energies 14, no. 19 (October 6, 2021): 6394. http://dx.doi.org/10.3390/en14196394.
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Although both aggregate and per capita energy consumption in Bangladesh is increasing rapidly, its per capita consumption is still one of the lowest in the world. Bangladesh gradually shifted from petroleum-based energy to domestically sourced natural-gas-based energy sources, which are predicted to run out within next two decades. The present study first identified the determinants of aggregate commercial energy and its three major components of oil, natural gas, and coal demand for Bangladesh using a simultaneous equations framework on an annual database covering a period of 47 years (1972–2018). Next, the study forecast future demand for aggregate commercial energy and its three major components for the period of 2019–2038 under the business-as-usual and ongoing COVID-19 pandemic scenarios with some assumptions. As part of a sensitivity analysis, based on past trends, we also hypothesized four alternative GDP and population growth scenarios and forecast corresponding changes in total energy demand forecast. The results revealed that while GDP and lagged energy demand are the major drivers of energy demand in the country, we did not see strong effects of own- and cross-price elasticities of energy sources, which we attributed to three reasons: subsidized low energy prices, time and cost required to switch between different energy-mix technologies, and suppressed energy demand. The aggregate energy demand is expected to increase by 400% by the end of the forecasting period in 2038 from its existing level in 2018 under the business-as-usual scenario, whereas the effect of COVID-19 could suppress it down to 300%. Under the business-as-usual scenario, the highest increase will occur for coal (3.94-fold), followed by gas (2.64-fold) and oil (2.37-fold). The COVID-19 pandemic will suppress the future demand of all energy sources at variable rates. The ex ante forecasting errors were small, varying within the range of 3.6–3.7% of forecast values. Sensitivity analysis of changes in GDP and population growth rates showed that forecast total energy demand will increase gradually from 3.58% in 2019 to 8.79% by 2038 from original forecast values. Policy recommendations include capacity building of commercial energy sources while ensuring the safety and sustainability of newly proposed coal and nuclear power installations, removing inefficiency of production and distribution of energy and its services, shifting towards renewable and green energy sources (e.g., solar power), and redesigning subsidy policies with market-based approaches.
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Dibrova, Anatolii, Larysa Dibrova, Maksym Dibrova, and Alla Chmil. "Forecasting the Consequences of the Cost of Mineral Fertilisers on the Development of the Corn Market in Ukraine Using AGMEMOD Models." Ekonomika APK 29, no. 3 (May 19, 2022): 23–41. http://dx.doi.org/10.32317/2221-1055.202203023.
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In the past decade, from 2012 to 2021, Ukraine has doubled its maize production. The increase in gross corn yields is associated with the use of modern cultivation technologies by farmers, which increase the resistance of plants to adverse environmental factors and adjust the mineral nutrition of plants with regard to weather conditions. One of the most important components of such technologies is the use of mineral fertilisers, the optimal amount of application of which depends not only on the level of grain yield, but also on the efficiency of production and grain quality. However, the rapid increase in world natural gas prices during 2021 has created new challenges and threats for the further development of the grain market in Ukraine. After all, world prices for mineral fertilisers have increased by 110% since 2021, according to the World Bank. Under these conditions, a radical increase in the cost of mineral fertilisers threatens to ensure the competitiveness of Ukrainian corn in the domestic and foreign markets, which would eventually lead to higher food prices and deterioration in the level of food security in the country. This may negatively affect the gross yields and export potential of the grain industry. The purpose of the study is to assess the current state of supply and demand in the corn market in Ukraine and predict the consequences of the impact of changes in the cost of mineral fertilisers on the main parameters of its development according to probable scenarios, using the AGMEMOD econometric partial equilibrium model for the period up to 2025, which creates prerequisites for improving the efficiency of making and implementing management decisions and contributes to achieving the goals of national agrarian policy. The following methods were applied: monographic, abstract and logical, comparative analysis and expert assessments, tabular, statistical and economic, factor analysis, economic and mathematical modelling. The result of the study is an assessment of the current state and identification of the main factors influencing supply and demand in the corn market. Using multiple linear regression, the influence of the main factors on the yield of corn for grain in agricultural enterprises of Ukraine for 2001-2020 is determined. The dynamics of the balance of supply and demand in the corn grain market in Ukraine is analysed. The consequences of changes in the cost of mineral fertilisers on the main parameters of the corn market development in Ukraine are predicted according to probable scenarios using the AGMEMOD econometric partial equilibrium model for the period up to 2025. Based on the calculations made, it is proved that the high yield of corn and the favourable current price environment for grain will ensure a sufficient level of profitability of this grain crop. Methods for improving the mechanism of reducing the cost or compensation of expenses for the purchase of mineral fertilisers for commodity producers are proposed. Methodological and practical aspects of forecasting the consequences of changes in the cost of mineral fertilisers on the main parameters of corn market development in Ukraine using the AGMEMOD econometric partial equilibrium model for the period up to 2025 have been further developed. The proposed methodological approaches and findings can be used by state and industry management bodies in the development of priority areas for improving the effectiveness of the grain industry in Ukraine.
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Djaenudin, D., Indartik, E. Y. Suryandari, N. Parlinah, F. J. Salaka, A. S. Kurniawan, and M. Iqbal. "Business model for community featured products in peatlands: case study of Pulang Pisau Regency." IOP Conference Series: Earth and Environmental Science 917, no. 1 (November 1, 2021): 012040. http://dx.doi.org/10.1088/1755-1315/917/1/012040.
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Abstract Peatlands are one of resources for regional economic development in Pulang Pisau Regency. However, the condition of peatlands continues to be degraded due to uncontrolled use and fires on peatlands and forests areas. The socio-economic revitalization of community is one of the efforts to restore the degraded peatlands. This revitalization is required as the utilization of peatland by the community is still traditional, so the added value of land use products, until now has not been created optimally. Therefore, a land-use business approach that is appropriate with the capacity of the community and available natural capital is needed. This approach will improve community livelihoods and contribute positively to economic, social, and environmental development. This paper aims to identify the features of paludiculture products cultivated by the community and develop a featured product business model. Research locations are in Buntoi and Mantaren 1 Village. Data collection is carried out through in-depth interviews and focus group discussions. Respondents are farmers, traders, industry players, and the government. The data analysis used is descriptive data analysis and canvas business model (CBM). The dominant type of plant cultivated by the community is rubber. The community considers rubber could provide high economic benefits. Analysis results of rubber business practices carried out by the community are characterized by (i) low dry content of rubber latex (slab); (ii) rubber latex that is sold to a local trader or factories at lower prices than it is sold to the factory, (iii) the certainty of the slab supply to the factory is relatively high; and (iv) limited control of capital and technology. The development of the CBM model is needed to improve the rubber business through improving the quality of latex as a market segmentation requirement, expanding potential customers through collective marketing channels, improving productivity by selecting superior seeds and environmental engineering technology.
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NAZAROVA, Zinaida Mikhailovna. "Formation of a stable price for energy resources by solving a non-linear dynamic problem as an element of socio-economic stability of the economy." NEWS of the Ural State Mining University 1, no. 1 (March 23, 2020): 170–81. http://dx.doi.org/10.21440/2307-2091-2020-1-170-181.
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Relevance. The authors note that the formation of a stable price for natural resources and the possibility of smoothing the price during fluctuations not only in demand for them, but also changes in the supply schedule for economic and other reasons require the development of an economic model; they are very relevant. The purpose of the study: identifying the possibilities of forecasting energy prices in the event of price fluctuations and the formation of various levels of demand in the market and taking into account market and non-market methods for its regulation. Results of the study. Studying various processes and problems in the field of economics, the authors propose using various types of linear and nonlinear boundary value problems for ordinary differential equations. This paper shows that the theory of boundary value problems for nonlinear differential equations is one of relevant factors in predicting energy prices in the structure of the economic development program. The authors define modeling tasks for the purpose of adjusting energy demand for a long time. This paper notes that the formation of demand for energy resources in addition to the standard time lag, which determines seasonality, also requires adjustment due to the need to formulate the socio-economic task of smoothing demand. Conclusions. The economic and mathematical models presented in the paper are universal and can be used by business entities, state bodies which form the demand and consumption of energy resources in the country. This will contribute to increasing the efficiency of managing the country’s energy complex and ensuring integrated activities focused on making informed and timely decisions in other business facilities.
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Deepika, Nalabala, and Mundukur Nirupamabhat. "An Optimized Machine Learning Model for Stock Trend Anticipation." Ingénierie des systèmes d information 25, no. 6 (December 31, 2020): 783–92. http://dx.doi.org/10.18280/isi.250608.
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Security market is an economical-volatile in nature as it is driven by not only based on historical prices various unpredictable external factors like financial news, changes in socio-political issues and natural calamities happened in real world; hence its forecasting is a challenging task for traders. To gain profits and to overcome any crisis in financial market, it is essential to have a very accurate calculation of future trends by for the investors. The trend prediction results can be used as recommendations for investors as to whether they should buy or sell. Feature selection, dimensionality reduction and optimization techniques can be integrated with emerging advanced machine learning models, to get improvised prediction in terms of quality, performance, security and for effective assessment external factors role in stock market nonlinear signals. In this empirical research work, a set of hybrid models were built and their predictive abilities were compared to find consistent model. This work implies the base model, boosted model and deep learning model along with optimization techniques. From the experimental result, the optimized deep learning model, ABC-LSTM was turned out superior to all other considered financial models LSSVM, Gradient Boost, LSTM, ABC-LSSVM, ABC-Gradient Boost by showing best Mean Absolute Percentage Error (MAPE) value, which was low.
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Redko, Kateryna, and Oleksandra Furs. "THE CURRENT SITUATION AND WORLD TRENDS OF GREEN ENERGY DEVELOPMEN." Scientific Bulletin of Mukachevo State University. Series “Economics” 1(13) (2020): 55–60. http://dx.doi.org/10.31339/2313-8114-2020-1(13)-55-60.
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As demand for electricity grows significantly, the main drivers of new capacity are the disposal of older, less efficient fossil fuel units; the near-term prospect of having tax credits for renewable energy; and long-term reductions in capital costs for renewable energy, especially solar photovoltaic. Low natural gas prices and favorable renewable energy costs make natural gas and renewable energy the main sources of new generation capacity by 2050. The main purpose of the scientific article is to cover the main problems of the electricity market of Ukraine, to analyze the process of promotion of green energy, to highlight the cases when the transition to alternative sources is a profitable process, in the context of uncertainty and rising prices for traditional energy sources. The article uses a number of general scientific and specific research methods, including methods of analysis and synthesis, scientific deduction and induction. The practical significance of the research is to develop recommendations for improving the state's regulatory function in the field of alternative energy. The large-scale introduction of non-traditional renewable energy in Ukraine will make a significant step in reducing the country's energy dependency, protecting the environment and creating the conditions for a country to join the European community. The scientific novelty is to study the stimulation of energy production using alternative sources, to study the creation of favorable economic conditions for the construction of alternative energy facilities, the development of a "green" economy and to ensure sustainable development of Ukraine. Conclusions and prospects for further research. In Ukraine, the alternative energy sector is developing slowly, but some structural shifts are noticeable, though far from planned. Many small and medium-sized enterprises have already installed solar panels in order to reduce the cost of production and generate additional profits. Further research requires the search for tools and mechanisms in the RES incentive system, with an assessment of the economic impact of their use, using modeling and forecasting methods and models. Keywords: electricity market, energy efficiency, energy intensity of the economy, renewable energy, green tariff
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Deshmukh, Reena, Tanuj Kude, Nikita Godambe, and Sahil Gawade. "Agriculture 1O1." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 200–204. http://dx.doi.org/10.22214/ijraset.2022.40551.
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Abstract: Agriculture is the backbone of Asian nation and therefore the farmer may be a crucial component of it. Agriculture is primary and ancient occupation of Asian nation, and it's a supply of support for many of the population residing in India. Agriculture sector desires plenty of public sector support for a property growth. Government has issued varied schemes and incentives to the farmers to supply them social and monetary security. The introduction of contemporary techniques in agriculture sector has helped boost-up the productivity level, alongside an improvement in price and labor used. Trendy agricultural technique's area unit dynamic approach to agricultural innovations and farming practices that helps farmers increase potency and cut back the number of natural resources required to satisfy the world's food, fuel and fiber demands. Value prediction may be a huge issue for farmers WHO don't seem to be tuned in to the market costs. Foretelling value of agriculture commodities helps the farmers to foresee the market and grow appropriate crop to maximize profit. Forecasting weather API provides information regarding the longer term weather, so farmers will pre-plan their agricultural activities. For forecasting of weather, we tend to record the pattern of weather of past few days and so predict the longer-term weather. Farm Profit Calculation provides farmers a tool to grasp a way to maximize monetary potency for his or her operation. We wish farmers to urge the foremost bang for his or her buck, and learn wherever they'll cut back inputs while not touching their bottom line. Hoarding is an act of aggregation great amount of merchandise and keeping it to yourself to extend the market value. Farmers encounter high production prices in their efforts to spice up production however hardly get truthful rating of their merchandise from the middlemen. Therefore, farmers will keep track on activities of the middlemen. Thus, the system helps the farmers to ease their life. Keywords: Weather, Price Prediction, Hoarding, Profit.
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Gurrib, Ikhlaas. "Can energy commodities affect energy blockchain-based cryptos?" Studies in Economics and Finance 36, no. 4 (October 7, 2019): 682–99. http://dx.doi.org/10.1108/sef-10-2018-0313.
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Purpose The purpose of this paper is to shed fresh light into whether an energy commodity price index (ENFX) and energy blockchain-based crypto price index (ENCX) can be used to predict movements in the energy commodity and energy crypto market. Design/methodology/approach Using principal component analysis over daily data of crude oil, heating oil, natural gas and energy based cryptos, the ENFX and ENCX indices are constructed, where ENFX (ENCX) represents 94% (88%) of variability in energy commodity (energy crypto) prices. Findings Natural gas price movements were better explained by ENCX, and shared positive (negative) correlations with cryptos (crude oil and heating oil). Using a vector autoregressive model (VAR), while the 1-day lagged ENCX (ENFX) was significant in estimating current ENCX (ENFX) values, only lagged ENCX was significant in estimating current ENFX. Granger causality tests confirmed the two markets do not granger cause each other. One standard deviation shock in ENFX had a negative effect on ENCX. Weak forecasting results of the VAR model, support the two markets are not robust forecasters of each other. Robustness wise, the VAR model ranked lower than an autoregressive model, but higher than a random walk model. Research limitations/implications Significant structural breaks at distinct dates in the two markets reinforce that the two markets do not help to predict each other. The findings are limited by the existence of bubbles (December 2017-January 2018) which were witnessed in energy blockchain-based crypto markets and natural gas, but not in crude oil and heating oil. Originality/value As per the authors’ knowledge, this is the first paper to analyze the relationship between leading energy commodities and energy blockchain-based crypto markets.
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Bublyk, Yevhen, Oleksandra Kurbet, and Roman Yukhymets. "Price convergence on the national gas markets of the Eastern European region." Problems and Perspectives in Management 20, no. 4 (December 30, 2022): 612–23. http://dx.doi.org/10.21511/ppm.20(4).2022.47.
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Establishing institutional arrangements for regulating gas markets toward price convergence is one of the crucial integrational factors. The strategy of the firm and economic development management depends on it. The paper aims to assess the characteristics of price convergence on the natural gas markets of the Eastern European region. This region is relevant for Ukraine in a number of parameters. The assessment was made based on Eurostat data for different groups of consumers, excluding taxes, using the standard deviation detection method of price convergence for 15 countries in 2007–2020. Despite the revealed generally positive price convergence on the natural gas markets in the considered countries after 2014, obtained results showed three points that highlight the heterogeneous structure of the process. First, an even movement toward a single price is detected in groups of large households (the standard price deviation of the price decreased in 2014–2020 from 2.7 to 1.9 euro per Giga Joule or 1.5 times) and medium industrial enterprises (the standard deviation decreased from 1.0-1.7 to 0.6-1.1 or 1.5-1.8 times). Second, the prices for the largest industrial enterprises in considered countries approached the fastest (the deviation decreased from 2.0 to 0.5). Third, in the segment of small enterprises, the deviation even increased from 2.1 to 2.2 (1.05 times). This result highlights the gap in the institutional mechanisms of European integration and sources of uncertainty for the small firms’ management. AcknowledgmentThe paper was funded as a part of the “Determination of institutional conditions for the development of the exchange segment of the gas market” research project (No. 0122U002205), conducted at the State Institution Institute for Economics and Forecasting of the NAS of Ukraine.
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Habibullah, Muzafar Shah. "The Rationality Of Economic Forecasts: The Cases Of Rubber, Oil Palm, Forestry And Mining Sector." Agro Ekonomi 10, no. 1 (November 29, 2016): 67. http://dx.doi.org/10.22146/agroekonomi.16788.
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Forecasts of economic variables is very important for planning and policy making purposes. Forecasts is an important input in decision making processes because obtaining reliable forecasts of some relevant macroeconomic variables is necessary for efficient management of funds, time and resources.Business has always recognised the need for a view of the future and has used explicit forecasts in the design and execution of their economic andJor business policies. For example, a firm trying to decide upon its investment programme will have to take into account not only the current known set of circumstances but also the unknown economic and business conditions in the future. The firm has to form a view about the future, such as the likely sales, costs, prices, competitors' reactions, labour requirements, government regulations and so on. These views about the future values of economic variables are frequently referred to as 'expectations', that is, what the firm expects to happen in the future.In recent years the performances of many microeconomics and macroeconomics series have been erratic. For example, rate of inflation, price of crude oil, prices of primary commodities, rate of interest and other pertinent economic variables have been fluctuating widely and have caused concern among the public, politicians, economists and also the businessmen. According to Mayes (l 981), with such non-uniformity of economic variables observed in the last two decades, the role of expectations has become more relevant in the economic agents' decision making process. Mayes (1981) further states that under the present conditions it has become more important to consider what expectations actually are and how they are formed.The value of economic forecasts of certain macroeconomic variables can be derived from several methods. The three main methods for deriving economic forecasts are (i) time series, (ii) econometric models, and (iii) survey of intentions of concerned agents and organizations. Time seriesanalysis and econometric modeling are the two most widely used methods in economic forecasting, but Holden and Peel (1983) had noted their drawbacks. Recently, economists have turned their direction of interest in evaluating the rationality of economic forecasts from surveys of market participants. The empirical literature on the direct tests of the rational expectations hypothesis is vast and growing. Holden et al. (1985), Lovell (1986), Wallis (1989), Maddala (1991) and Pesaran (1991) had reviewed some of these studies. The aim was to determine whether survey data on economic forecasts are accurate in the Muth's (1961) sense, that is, whether participating economic agents used all available information at the time forecasts are made. in other words, the rational expectations hypothesis of the economic forecast was put to test. In general, the empirical studies do not support the rational expectations hypothesis.Most of the studies carried out to evaluate the rationality of business firms' forecasts of economic variables were conducted on developed nations. Madsen (1993) studies the formation of output expectations in manufacturing industry in Japan, Denmark, Finland, France, Germany, Netherlands, Norway, Sweden and the United Kingdom. He found that the rational expectations hypothesis was weakly rejected. Williams (1988) and Chazelas (1988) found investment forecasts biased predictors of the actual investment value for firms in the United Kingdom and France. Meganck et a!. (1988) have concluded that investment forecasts of the manufacturing firm in Belgium were unbiased predictors of the actual values. However. Daub (1982) failed to find any rationality of the Canadian capital investment intention survey data. On the other hand. a study by Leonard (1982) on employment forecasts by the United States services sectors found that the forecasts were biased and the rationality of these employment forecasts rejected.The purpose of this paper is to present some empirical evidence on the rationality of agricultural firm managers' expectations using survey data. This study is important because it adds to the current literature on the testing of rationality of survey data, in particular, it provides empirical evidence from the perspective of a developing country. As for the country under study, the finding of the study could establish whether the forecasts documented by such survey are accurate or not; and if not, ways to produce more accurate forecasts must be found. 'Rationality' in this paper means that managers in agricultural firms have unbiased expectations and efficiently utilised available information at the time the forecasts are made.
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Afsahhosseini, Fatemehalsadat. "Forecasting housing units in Iran." International Journal of Housing Markets and Analysis 12, no. 4 (August 5, 2019): 644–60. http://dx.doi.org/10.1108/ijhma-06-2018-0041.
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Purpose The theory of competitiveness of cities is based on Porter’s Diamond Theory. There is a relation between housing and urban competitiveness. The adequacy of land supply and allocation of land for new housing development is integral. This paper aims to estimate the required number of housing units to secure housing needs in Tehran for the next four years in 1400 H.Sh (2021 A.D.). The research methodology is carried out using qualitative and quantitative approaches based on the given data. First, the population of Tehran in 1400 H.Sh was predicted using nonlinear quadratic polynomial, Gompertz and logistic models. Then, a Logistic model is proposed to estimate the number of housing units in Tehran. The calculations of residential units related to the population obtained from the Gompertz model equivalent to 663141 is suggested as a criterion for local authority to future decision making and planning for urban development. Design/methodology/approach The present research is an applied research in terms of the purpose a descriptive research in terms of the nature and methodology and a descriptive-analytical research in terms of attitude and approach toward the research problem (Hafeznia, 2013, 58, 63 and 71). To provide the required information for the analytical stage, a documentary method, related to the use of internal and external books and papers, has been applied. First, the population of Tehran in 1400 H.Sh is estimated using three nonlinear models of quadratic polynomials, Gompertz and logistic. Then, among them, the options that were more consistent with the estimation of the new comprehensive plan of Tehran (1386 H.Sh), which is the most important plan of this city, were chosen. After that, by using the logistic model, which is an appropriate expression of saturable phenomena and a suitable method of estimating the number of residential units in a city and based on the past trend, the future of housing is predicted, and the number of required residential units is determined. Findings Any city for competitiveness must seek the search and development of a set of unique strategies and practices that will shape its status from other cities. No single action for all cities is feasible. In fact, the most important challenge is to propose a unique value proposition and to formulate a strategy that distinguishes that city from the rest. Among the measures taken around the world is attention to infrastructure. From the point of view of competitiveness, different types of investment in infrastructure are important for different types of cities and in different stages of development of a city. Large cities need targeted investments in housing issues to overcome the segments associated with the poorer neighborhoods. Without investment in desirable housing, there will be holes in competitive advantage. In this paper, the number of residential units in Tehran was projected for 2021. The city’s population was originally estimated for 2021. In addition to the models used to predict and estimate necessary, it is necessary to consider the area, land use map, future development lines and […] city. To this end, the city can continue to meet the needs of residents’ diversification and the city’s needs. We cannot accept any predictions about the population and, consequently, the number of residential units. Providing predictions can provide the most predictive, or more prudent, and different scenarios that can emerge, which will lead to flexibility in the presentation of plans and programs. Among the models that were used to predict the population, the result obtained from second-order polynomial and Gompartz models was found to be appropriate for the estimation of the new comprehensive design of Tehran (2007). But the prediction of the population of the logistic model was beyond the prediction of the new comprehensive plan of Tehran (2007) and thus was not considered appropriate. The number of residential units required according to the predicted population of the second order polynomial models, Gompartz and the population considered in the new comprehensive plan of Tehran (2007). After the finalization of the proposed population, using the logistic model, the number of residential units needed in Tehran was projected for 2021. Since these three estimates are somewhat close to each other, it is suggested that Gompertz model calculations, equivalent to 663,141 residential units, are proposed, and according to that, local authorities are planning to supply land to achieve economic competitiveness (urban). As it is shown in the conceptual model of the paper in Figure 1, after determining the need for housing, it is necessary to ask whether the adequacy of the supply and allocation of land, as well as the importance of maintaining it for the development of housing by local authorities, is clear. Also, is there any suitable planning for that? Despite the severe shortage of ready-made land for the city of Tehran, a large volume of land is a large area owned by natural and legal persons, and, in particular, state-owned enterprises of semipublic and public institutions, which have been abandoned in cities for years without use and in the form of barren. According to municipal management laws, municipalities can receive land, taxes and fees that are included in the annual budget of the Tehran Municipality. According to the figure obtained from this study, which states that 663,141 residential units are needed for Tehran in 2021, large landowners in Tehran need to supply their land to the market. According to the Population and Housing Census in Tehran in 2011, there are 245,769 inhabited vacancies in Tehran; hence there are two scenarios for the provision of residential units in the city of Tehran in 2021, assuming that these units in the housing market require 417,372 units Another residence will be for Tehran, otherwise 663141 residential units will be needed for Tehran in 2021. Other possibl Originality/value Tehran is the largest city and the capital of Iran, and it is also the capital of the province Tehran. In the southern foothills of the Alborz Mountains within a longitude of 51 degrees and 2 minutes East to 51 degrees and 36 minutes East, with an approximate length of 50 kilometers and latitude 35 degrees and 34 minutes North to 35 degrees and 50 minutes North with an approximate width of 30 kilometers. The area of this city is 730 km2. This is one of the largest cities in West Asia, the 25th the most populous city, and the 27th greatest city to the world. The administrative structure of Iran has been concentrated in this city. The city has been divided into 22 zones, 134 areas (including Rey and Tajrish), and 370 districts (Wikipedia). The problem of housing in the city of Tehran has always been one of the important issues that less has been planned for it. The result is housing shortage, high housing prices and so on, due to the excessive expansion of the city, its population increase and so on.
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VOLONTYR, Ludmila, Nadiya POTAPOVA, and Oksana ZELINSKA. "ECONOMETRIC MODELING IN FORMATION OF OPTIMAL PRICE FOR IMPLEMENTATION OF AGRICULTURAL PRODUCTS." "EСONOMY. FINANСES. MANAGEMENT: Topical issues of science and practical activity", no. 5 (45) (May 2019): 83–93. http://dx.doi.org/10.37128/2411-4413-2019-5-9.
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Ukraine is a predominantly agricultural country, and this branch has been recently demonstrating relatively high efficiency. Vegetable growing is a specific branch of crop production, which includes a large set of vegetables grown according to different technologies, with different shelf life of vegetable products, their different cost and production efficiency. The analysis of the situation on the vegetable market of Ukraine showed that there is a certain correlation between production volumes, sales and products sales prices. The price market environment on the vegetable market in recent years is largely determined by the ratio of supply and demand on the market. Thus, sales volumes increase when the supply on the market is the highest and the price level on the market is the lowest. The absence of permanent wholesale distribution channels also leads to an increase in the hidden market for vegetable products. According to experts of the Ukrainian Agrarian Confederation, the hidden market for fruit and vegetables is about $ 14 billion, or about 60% of the total turnover of vegetable products in Ukraine. Due to the moratorium on the sale of agricultural land, businesses are not able to buy land on their own and develop their business in the long-term prospects. Today, government support in the vegetable sector is limited to preferential lending and to individual funding programs, most often in collaboration with international donors. Much of the support for agro-industrial farms goes to grain and pulse plant producers, which significantly limits the opportunity for developing crop producers with higher marginality. The conditions in which the agrarian sector operates have a high degree of changeable uncertainty, and this circumstance requires agricultural producers to find ways to obtain reliable information about the state of the agricultural market, organizational and functional links between the subjects of the agricultural market, prices for agricultural products. etc. The purpose of this study is to: analyze the price of vegetable sales in Ukraine; substantiation of the use of the AGMEMOD partial equilibrium model for forecasting vegetable production in Ukraine; establish dependence of demand and supply of vegetable production on their sales price; determine the point of equilibrium of supply and demand and calculate of the optimal selling price of vegetables in Ukraine; justify the optimal costs for vegetable production; analyze of the price of selling vegetables in Ukraine and determine the optimal price according to supply and demand, as well as the optimal cost of vegetable production. Now, there are 12 key vegetable crops in Ukraine. These are potatoes, cucumbers, tomatoes, cabbage, beets, carrots, onions, garlic, peppers, zucchini, eggplants and pumpkin. Of these 12 cultures, 9 showed an increase in the period 2010-2016, even without taking into account the uncontrolled Crimea and Donbass. This increase has been driven by two crucial factors: - yield increase. This was made possible due to improving the quality of the seed and natural technological progress in the processing and the use of crop protecting agents. - increase in export demand for products. The demand, for example, for Ukrainian carrots and onions has increased, and therefore the opportunities for their cultivation have become greater. Price is a complex economic category, practically the only element of marketing that enables an enterprise to earn real income. Without proper economic justification of the price level, the normal functioning of economic entities and entire sectors of the economy is impossible, which in turn has a significant impact on the material well-being of the population. The level of market price depends on the value of other marketing elements, as well as on the level of competition on the market and the general state of the economy. As a rule, other marketing elements also change (for example, with increasing product differentiation in order to maximize price or at least the difference between price and cost). The price formation strategy allows determining the price level and marginal prices for individual product groups. The price formation should always be carried out taking into account the nomenclature and quality of products, their usefulness, importance and purchasing power of consumers and prices of the competitors. The strategy of price formation management is a set of measures to maintain conditional prices while actually regulating them in accordance with the variety and characteristics of demand, competition in the market. The AGMEMOD model is an example of the partial equilibrium (PE) models used in agriculture. The main advantages of partial equilibrium models are: the simplicity of the implemented algorithms, the operation of which is quite easily traced; relative availability of necessary data; the calculations are amenable to adequate economic interpretation, making it possible to quickly analyze the consequences of making a decision in the agricultural sector. However, partial equilibrium models are not without their disadvantages. In particular, they do not permit to assess macroeconomic effects such as changes in national income or employment levels, the effects that may be obtained from the redistribution of resources (labor, capital, etc.) into more efficient sectors. For national researchers, it is advisable to use these models, because they have a module of Ukraine, but it is necessary to supplement the program with statistics on vegetables. The demand is a function of price changes in the current period, and the supply is a function of price changes in previous periods. Econometric models of supply and demand dependence of vegetable production on the price of their sale are constructed. The equilibrium of the system is observed at the price of 6558 UAH. for 1 ton of vegetables under the given conditions of consumption, the demand is equal to supply and is 9321 thousand tons. Econometric models of price dependence on material costs, labor costs and depreciation have been constructed. By the first model, it can be determined that the content of unaccounted factors is estimated at 99.82 UAH. per hectare; with an increase in material costs by 1 hectare by 1 UAH, selling price increases by 0.9 UAH. per ton. Based on the Fisher's ratio test, the model is adequate, the relationship between the indicators is tight. The relationship between the indicators of the second model is weak, the calculated correlation coefficient can be trusted, but in general, the adequacy of the model conclusion cannot be made. The model shows that with an increase in labor costs by 1 UAH per hectare, the price increases by 10.03 UAH per ton. The third model based on the Fisher's ratio test is adequate, the relationship between the indicators is average. With the increase in depreciation costs per hectare by 1 UAH, the selling price will increase by 12.42 UAH per ton. The value of the linear correlation coefficient other than zero is statistically significant. Based on the calculated models, we will determine the optimal cost per hectare: material – 7144.2 UAH, labor costs – 689.4 UAH, depreciation costs – 543.4 UAH.
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Arunwarakorn, Suratwadee, Kamonchanok Suthiwartnarueput, and Pongsa Pornchaiwiseskul. "Forecasting equilibrium quantity and price on the world natural rubber market." Kasetsart Journal of Social Sciences, September 2017. http://dx.doi.org/10.1016/j.kjss.2017.07.013.
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Abosedra, Salah, Khaled Elkhal, and Faisal Al-Khateeb. "Forecasting Performance Of Natural Gas Futures Market: An Assessment Of Recent Data." Journal of Business & Economics Research (JBER) 4, no. 11 (February 8, 2011). http://dx.doi.org/10.19030/jber.v4i11.2715.
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<p class="MsoNormal" style="text-align: justify; margin: 0in 34.2pt 0pt 1in;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">Natural gas has assumed increasing importance in the global energy market. This study evaluates the forecasting performance of futures prices of natural gas in the large market of the U.S. at various time horizons. The results indicate that futures prices are unbiased predictors at the 1-, 6-, and 12- month horizons, but not at the 3- and 9- month horizons. The results further suggest that futures prices of natural gas, although biased at some intervals, significantly outperform naïve forecasts in predicting future movements of spot prices. In addition, the information content of the 1-month ahead futures price proves especially useful as a forecasting device. Policy implications are also discussed.<span style="mso-bidi-font-style: italic; mso-bidi-font-weight: bold;"></span></span></span></p>
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"Export - Import Performance of Natural Rubber in India." International Journal of Recent Technology and Engineering 8, no. 4 (November 30, 2019): 4974–77. http://dx.doi.org/10.35940/ijrte.d7659.118419.
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This paper assesses the performance of rubber exports during the period of 2010 -2011 till 2017 - 2018. Indian Rubber is an export promotion strategy promoted by the Board since 2011 with an objective of distinguishing Indian rubber in international market with its discerning quality features. The board is promoting export as a market intervention strategy to adjust imbalances in the domestic market owing to unscrupulous imports of rubber. Exports during this peak production period are at high level from December 2016 to March 2017. The exports of rubber from India jumped to 650 tonnes during April 2018 to October 2018. The price of natural rubber (NR) in India had been ruling high over international market prices since December 2013. The surge in international rubber price was due to the amplified demand for rubber from China. United States and the European Union together contribute nearly 70 % of India's overall rubber products' exports. Indian exporters, however, are looking to raise India's share in the world market to 5 per cent in the next couple of years from the existing 1.48 per cent compared to China's market share of 11 per cent. This paper examines the export and import performance of rubber from India. Researcher has collected the secondary data from various sources such as DGCIS, EXIM Bank annual reports, etc. This paper brings out the relationship between the export and import performance of rubber from India and the trade performance of Natural rubber, ratio, Pearson's Correlation Coefficient and One - Way ANOVA has been used for analyzing appropriate results.