A PAA-PSO technique for investment strategies in the financial market

Abstract

Particle Swarm Optimization algorithm (PSO), when applied to problems with continuous variables, presents results with better quality at a lower computational cost when compared to the Genetic Algorithm (GA). Thus, the PSO becomes a very useful method to be applied with investment strategies used to optimize profit from operations made in the stock market, since investors seek quick and profitable results for their decision making. In this context, the Symbolic Aggregate Approximation (SAX) and Piecewise Aggregate Approximation (PAA) are time series representation techniques that, when used with optimization algorithms like PSO or GA, can help investors discover hidden and relevant patterns in financial time series data. The SAX uses discrete variables to represent their values, whereas PAA uses continuous values. Thus, this paper proposes the PAA-PSO technique, which combines the PAA with the optimization of PSO for discovery of patterns that will be used with a formulated investment strategy in order to maximize the profit from operations made in the stock market. Experiments that compare the proposed method to the results of the SAX-GA technique, which combines the techniques of SAX and GA in their investment strategy, are reported.