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Cat swarm optimization algorithm tuned multilayer perceptron for stock price prediction

Chandar S. Kumar, Punjabi Hitesh

Computer Assisted Methods in Engineering and Science

2022

Vol. 29, no. 1-2 spec.

145--160

Due to the nonlinear and dynamic nature of stock data, prediction is one of the mostchallenging tasks in the financial market. Nowadays, soft and bio-inspired computing algorithms are used to forecast the stock price. This article assesses the efficiency of thehybrid stock prediction model using the multilayer perceptron (MLP) and cat swarm optimization (CSO) algorithm. The CSO algorithm is a bio-inspired algorithm inspired bythe behavior traits of cats. CSO is employed to find the appropriate value of MLP parameters. Technical indicators calculated from historical data are used as input variablesfor the proposed model. The model’s performance is validated using historical data notused for training. The model’s prediction efficiency is evaluated in terms of MSE, MAPE, RMSE and MAE. The model’s results are compared with other models optimized byvarious bio-inspired algorithms presented in the literature to prove its efficiency. The empirical findings confirm that the proposed CSO-MLP prediction model provides the bestperformance compared to other models taken for analysis.