Using Artificial Neural networks and Optimal Scaling Model to Forecast Agriculture Commodity Price: An Ecological-economic Approach

Abstract

This research paper employs input-output pricing model based on ecological-economic approach to investigate the impacts of internal factors as well as external forces on agriculture commodities. To empirically test our model, we select two different methodologies such as the optimal scaling regression with nonlinear transformations and feedforward artificial neural networks. Our sample includes data related to price of agriculture and energy commodities (cocoa, coffee and crude oil), production of crops and livestock, emissions of greenhouse gases (GHG) from agriculture from 1961 to 2019. Results find a bidirectional relationship between cocoa price and coffee price explaining by the fact that commodity-dependent countries often use kindred production landscapes and similar supply chain management when dealing with coffee and cocoa. Therefore, effect of supply side shocks may be transmitted from one market to another. We also present evidence that greenhouse gas emissions have strong effect on commodity price, thus we encourage an integrated approach including both concrete technological and proactive managerial measures in order to mitigate global warming impacts on the food system. We believe that these findings will be of interest to commodity producers, asset managers and academics who look a better understanding of the dynamics of commodity markets. JEL classification numbers: C50, Q02, Q57. Keywords: Agriculture commodity, Input-output pricing model, Ecological-economic approach, Artificial neural networks, Optimal scaling regression.

Scrap steel price forecasting with neural networks for east, north, south, central, northeast, and southwest China and at the national level

“…Original descriptions of the LM algorithm were given by Marquardt [270]. Applications of the LM algorithm for training neural network models were described by Hagan and Menhaj [287] and Hagan, Demuth and Beale [288] (Pages [12][13][14][15][16][17][18][19]. Original descriptions of the SCG algorithm were given by Møller [271].…”

“…Forecasting commodity prices is an important matter to policy makers and a wide spectrum of market participants in diverse varieties of economic sectors, which could include procedures, speculators, exporters, processors, hedgers, the media, and economists [1][2][3][4][5][6][7]. For example, producers would generally require price forecasting information for helping fix sales prices ahead of production, exporters and processors would need it for covering contractual requirements, speculators use it for generating profits, hedgers utilize it for risk management, economists analyse it for market evaluations, the media relies on it for transmitting market information, and policy makers take it into consideration for designing, monitoring, and assessing strategic plans and policies [8][9][10][11][12][13][14][15]. The importance of the commodity price forecasting problem for scrap steel of the metal sector in China should be of no exception, particularly when one considers its significant role to the general public [16][17][18][19][20][21][22], great influences from volatile macro-economic and policy factors and financial markets on prices [23][24][25][26][27][28], and close connections with many other economic sectors and industries [29][30][31][32][33][34][35][36][37][38][39].…”