A PSO Approach to Search for Adaptive Trading Rules in the EUA Futures Market

摘要

The carbon emission futures markets become more and more important in worldwide. More and more counties begin to emphasize environmental protection in the economic development. Carbon emission trading has become an important part of the energy finance. How to make more profits in the carbon emission futures market is concern by more and more traders and scholars. This paper proposed an approach to search for optimal trading rules in the CO2 allowance futures markets. A group of different moving average trading rules with different weights are used to constitute an integrated trading rule. This is better than a single fixed moving average trading rule. Similarity of trading rules, a parameter we designed, is used to help select basic rules. The authors use static particle swarm optimization process to find the best weights distributions of the selected basic trading rules. After the initial weight distribution is determined, the weights of the basic trading rules will adjusted dynamically every day in the trading process using particle swarm optimization algorithms. Experiments using the EUA Futures Market price data were conducted to find out best adaptive trading rules in the carbon emission futures market. According to our results, it is not necessary to use two moving average trading rules that making same investment advice at a probability higher than 70%. The results show this approach have good performance in adjusting the weights according to the price changes. We found that the adaptive trading rules can help traders make profit in the EUA Futures Market except extreme special circumstances after price change significantly. This approach might be helpful for traders to make scientific decision in actual investments.