Cat Swarm Optimization Algorithm Tuned Multilayer Perceptron for Stock Price Prediction

S. Kumar Chandar; Hitesh Punjabi

doi:10.24423/cames.394

S. Kumar Chandar School of Business & Management, CHRIST
Hitesh Punjabi K. J. Somaiya Institute of Management & Research

DOI: http://dx.doi.org/10.24423/cames.394

Abstract

Due to the nonlinear and dynamic nature of stock data, prediction is one of the most challenging 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 the hybrid stock prediction model using the multilayer perceptron (MLP) and cat swarm optimization (CSO) algorithm. The CSO algorithm is a bio-inspired algorithm inspired by the 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 variables for the proposed model. The model’s performance is validated using historical data not used 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 by various bio-inspired algorithms presented in the literature to prove its efficiency. The empirical findings confirm that the proposed CSO-MLP prediction model provides the best performance compared to other models taken for analysis.

Keywords

bio-inspired algorithm, particle swarm optimization, cat swarm optimization, MAE, MAPE, multilayer perceptron and stock prediction,

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