A Hybrid Approach by Integrating Brain Storm Optimization Algorithm with Grey Neural Network for Stock Index Forecasting

Abstract

Stock index forecasting is an important tool for both the investors and the government organizations. However, due to the inherent large volatility, high noise, and nonlinearity of the stock index, stock index forecasting has been a challenging task for a long time. This paper aims to develop a novel hybrid stock index forecasting model named BSO-GNN based on the brain storm optimization (BSO) approach and the grey neural network (GNN) model by taking full advantage of the grey model in dealing with data with small samples and the neural network in handling nonlinear fitting problems. Moreover, the new developed BSO-GNN, which initializes the parameters in grey neural network with the BSO algorithm, has great capability in overcoming the deficiencies of the traditional GNN model with randomly initialized parameters through solving the local optimum and low forecasting accuracy problems. The performance of the proposed BSO-GNN model is evaluated under the normalization and nonnormalization preprocessing situations. Experimental results from the Shanghai Stock Exchange (SSE) Composite Index, the Shenzhen Composite Index, and the HuShen 300 Index opening price forecasting show that the proposed BSO-GNN model is effective and robust in the stock index forecasting and superior to the individual GNN model.

A Robust Design for Aperture-Level Simultaneous Transmit and Receive with Digital Phased Array

“…We found that the swarm intelligence optimization methods have the potential to achieve these goals. They have been successful in the fields such as beamforming-based pattern synthesis [ 12 ], array optimization [ 13 , 14 ], DC brushless motor efficiency problems [ 15 ], Loney’s solenoid problem [ 16 ], and stock index forecasting [ 17 ]. Extensive literature reveals that compared to traditional particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE), the brainstorm optimization (BSO) algorithm [ 18 ] has the characteristics of fast convergence, excellent robustness, and a strong global optimization ability in solving non-convex, multi-objective, and multi-modal optimization problems.…”

Adaptive Self-Scaling Brain-Storm Optimization via a Chaotic Search Mechanism

“…Since it was proposed in 2011, BSO has been a focus of swarm intelligence research due to its novelty and efficiency. BSO has been successfully applied to various scenarios such as function optimization, engineering optimization, and financial prediction [4,[36][37][38][39]. However, as with other swarm intelligence optimization algorithms, BSO is prone to falling into local optima and premature convergence.…”

An Agglomerative Greedy Brain Storm Optimization Algorithm for Solving the TSP

“…Compared with other intelligent optimization algorithms, BSO has the advantages of a compact mathematical model , simple operation, clear process, fast convergence speed and high optimization efficiency. Therefore, it is considered to be a very promising method, and it has been favored by many researchers and widely applied in practical optimization problems in different fields such as power systems [9], [10], [11], [12], [13], machine learning [14], [15], [16], [17], [18], [19], combinatorial optimization problems [20], [21], [22] and image processing [23], [24], [25] and prediction [15], [26], [27].…”