Abstract: This study simultaneously investigates the causality and dynamic links between international energy trade and economic price changes, especially in the Chinese commodity market. To get a causal route, it attempts to identify the linear and nonlinear causality among commodity prices, equities, and the exchange rate in China and the United States (US). Here, we adapt multilayer perceptron networks to obtain a nonlinear autoregressive model for causality discovery. After comparing methods without networks, this study proves that the nonlinear causality discovery method using machine learning performs best on simulated data. Subsequently, we apply that causality to actual data; we combine the causal routes, particularly from the machine learning methodology, to investigate the existence of a causal direct or indirect relationship among Chinese commodity prices, long-term interest rates, stock index, and exchange rates in China and the US. The steady-state accuracy of cmlpgranger is 99%. In most cases, the order of judgment accuracy of causality is cmlpgranger > HSICLasso > ARD > LinSVR. The results show that Energy trade as an element of the global economic system. The Chinese commodity price of energy has an interactive relationship with the Chinese commodity price of agricultural products. The significant transmission is from the commodity price of energy to equities, then to the exchange rate, and, finally, to the commodity price of agricultural products.

Yu Cui and Tianhao Ma, “Explore Chinese Energy Commodity Prices in Financial Markets using Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140896

@article{Cui2023,
title = {Explore Chinese Energy Commodity Prices in Financial Markets using Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140896},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140896},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Yu Cui and Tianhao Ma}
}