A Novel Improved Particle Swarm Optimization With Long-Short Term Memory Hybrid Model for Stock Indices Forecast

Alan Wee‐Chung Liew · Lixia Yang

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Stock Market Forecasting Methods Hydrological Forecasting Using AI Time Series Analysis and Forecasting

Abstract

Stock market volatility has a significant impact on many economic and financial activities in the world. Forecasting stock price movement plays an important role in setting an investment strategy or determining the right timing for trading. However, stock price movements are noisy, nonlinear, and chaotic. It is difficult to forecast stock trends for improving return on investment. Here, we proposed a novel improved particle swarm optimization (IPSO) and long-short term memory (LSTM) hybrid model for stock price forecasting. An adaptive mutation factor was used as a parameter for model optimization to avoid premature convergence to a local optimum. Furthermore, we presented a nonlinear approach to improve the inertia weight of the particle swarm optimization and then used the IPSO model to optimize the hyperparameters of LSTM. The experimental results showed that the proposed model outperformed the other related baseline models: support-vector regression, LSTM and PSO-LSTM on the Australian stock market index. These results indicated the proposed model possesses high reliability and good forecasting capability.

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References

2017Deep learning with long short-term memory networks for financial market predictions 2003Financial time series forecasting using support vector machines 2001Application of support vector machines in financial time series forecasting 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory 2004Forecasting stock market movement direction with support vector machine 2017Stock market's price movement prediction with LSTM neural networks 2015A LSTM-based method for stock returns prediction: A case study of China stock market 2010Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm

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