Portfolio Management Using Particle Swarm Optimization on GPU

Abstract

Mathematical models like the Black-Scholes-Merton model used to price options approximately for simple and plain options in the form of closed form solution. The market is flooded with various styles of options, which are difficult to price. Numerical techniques used for pricing take exorbitant time for reasonable accuracy in pricing results. Heuristic approaches such as Particle swarm optimization (PSO) have been proposed for option pricing, which provide same or better results for simple options than that of numerical techniques at much less computational cost (time). In this work, we first investigate the characteristics of PSO for option pricing and propose improvements to PSO modeling, which reduces the number of PSO parameters without loss of generality of the financial application under study. We have used our improved PSO (called NPSO) model to price complex chooser option, one of the complicated options in the market. Cooperation among particles of the NPSO helps reach the solution in less time. Interest in diversifying investments stems from the necessity to avert risk involved in any single type of investments. The complex chooser option is shown to exhibit the characteristics of a financial portfolio. As a further study, we have used NPSO for portfolio optimization. We have implemented our NPSO model in the state-of-the-art multi-core Graphics processing units (GPU) platform and show that the computational time can be significantly reduced.

Parallelizing Comprehensive Learning Particle Swarm Optimization by Open Computing Language on an Integrated Graphical Processing Unit

“…A CPU consists of only a few processing elements optimized for sequential processing, whereas a GPU consists of a large number of compute units, with each compute unit in turn containing many processing elements, thereby constituting a massively parallel architecture for handling multiple computing tasks simultaneously. People have recently studied leveraging the massively parallel architectures of GPUs for accelerating nongraphical general-purpose computing in a wide range of areas [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. GPU-based parallel computing is implemented by a host program and kernel(s).…”

“…Papadakis and Bakrtzis [7] investigated developing an all-GPU model of CLPSO by OpenCL. Kilic et al [8], Ouyang et al [9], Souza et al [10], Sharma et al [11,12], and Rabinovich [30] studied parallelizing other PSO variants on GPUs.…”

An Evolutionary Optimization Approach to Risk Parity Portfolio Selection

“…Evolutionary optimization approaches have been shown to be useful for solving a wide range of different portfolio optimization problems, see e.g. [15] or [8] and the references therein. See also the series of books on Natural Computing in Finance for more examples [2], [3], [4].…”

High-accuracy Optimization by Parallel Iterative Discrete Approximation and GPU Cluster Computing

“…Particle Swarm Optimization is a method of optimization with scanning the search space by a group of candidate solutions named particle, and these particles are suitable for parallelization. Particle Swarm Optimization is well parallelized by GPU computing [1][2][3][4][5][6] with application to computer sciences [7][8][9][10][11][12][13][14], finance [15,16], physics [17], biology [18], etc.…”