A comprehensive study of cotton price fluctuations using multiple Econometric and LSTM neural network models

TL;DR

This study combines econometric models and LSTM neural networks to analyze and predict cotton price fluctuations from 1990 to 2020, identifying key regimes and the most effective predictive models.

Contribution

It introduces a comprehensive approach integrating multiple econometric methods with LSTM neural networks for cotton price analysis.

Findings

AR(2) with Markov switching best models price trends

Regime shifts from decreasing to increasing prices are more significant

LSTM neural networks provide accurate price predictions

Abstract

This paper proposes a new coherent model for a comprehensive study of the cotton price using econometrics and Long-Short term memory neural network (LSTM) methodologies. We call a simple cotton price trend and then assumed conjectures in structural method (ARMA), Markov switching dynamic regression, simultaneous equation system, GARCH families procedures, and Artificial Neural Networks that determine the characteristics of cotton price trend duration 1990-2020. It is established that in the structural method, the best procedure is AR (2) by Markov switching estimation. Based on the MS-AR procedure, it concludes that tending to regime change from decreasing trend to an increasing one is more significant than a reverse mode. The simultaneous equation system investigates three procedures based on the acreage cotton, value-added, and real cotton price. Finally, prediction with the GARCH families TARCH procedure is the best-fitting model, and in the LSTM neural network, the results show an accurate prediction by the training-testing method.