Journal Article

International carbon financial market prediction using particle swarm optimization and support vector machine

Ma, Shufan, Chen, Junhua, Wu, Ying

2022

Overview

Carbon financial futures have both the characteristics of commodity futures and environmental protection and its price is affected by many factors. It is hard and complex for traditional analysis methods to get precise prediction results effectively. How to effectively predict the price trend of carbon financial futures has been focused on by both academia and traders. This study addresses the high prediction error of European allowance (EUA) futures price by constructing a novel approach by combining the support vector machine (SVM) and particle swarm optimization (PSO) algorithm. This article introduces a parameters optimization method, which provides the best parameters for SVM to improve the prediction performance of the EUA futures price. Furthermore, this research uses the realistic trading dataset containing 30,762 EUA futures closing prices to verify the effectiveness and efficiency of the PSO-SVM prediction model. The empirical results show that the prediction performance of the model, especially the radial kernel function, is significantly improved. And this approach can determine the parameters according to the characteristics of the dataset and input the parameters for training and prediction automatically. The PSO-SVM algorithm can effectively predict extreme price fluctuations and overcome the problem of high prediction error caused by parameter constraints.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Algorithms / Artificial Intelligence / Brownian motion / Carbon / Carbon dioxide / Computational Intelligence / Crude oil / Datasets / Developing countries / Econometrics / Economic development / Empirical analysis / Energy / Engineering / Environmental impact / Environmental protection / Error analysis / Financial futures / Futures trading / Hedging / Industrial production / Kernel functions / LDCs / Mathematical models / Neural networks / Original Research / Parameters / Particle swarm optimization / Performance prediction / Prediction models / Prices / Robotics and Automation / Securities markets / Stochastic models / Support vector machines / Technology adoption / Time series / User Interfaces and Human Computer Interaction