Trend following in financial time series with multi-objective optimization

Abstract Trend following (TF) is an investment strategy based on the technical analysis of market prices. Trend followers do not aim to forecast nor predict specific price levels. They simply jump on the uptrend and ride on it until the end of this uptrend. Most of the trend followers determine the establishment and termination of uptrend based on their own rules. In this paper, we propose a TF algorithm which employs multiple pairs of thresholds to determine the stock market timing. The optimal values of thresholds are obtained by particle swarm optimization (PSO) and simulated annealing (SA). The experimental result on 7 stock market indexes shows that the proposed multi-threshold TF algorithm with multi-objective optimization is superior when it is compared to static, dynamic, and float encoding genetic algorithm based TF.