Cat swarm optimization algorithm tuned multilayer perceptron for stock price prediction

• Autorzy: Chandar S. Kumar , Punjabi Hitesh

Identyfikatory

DOI

10.24423/cames.394

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

bioinspired algorithm; particle swarm optimization; cat swarm optimization; MAE; MAPE; multilayer perceptron; stock prediction

PL

algorytm inspirowany biologią; optymalizacja roju cząstek; optymalizacja roju kotów; MAE; MAPA; perceptron wielowarstwowy; prognoza giełdowa

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Methods in Engineering and Science
• Rocznik: 2022
• Tom: Vol. 29, no. 1-2 spec.
• Strony: 145--160
• Opis fizyczny: Bibliogr. 24 poz., rys., tab., wykr.

Twórcy

autor

Chandar S. Kumar

• School of Business & Management, CHRIST (Deemed to be University), Bangalore, India

autor

Punjabi Hitesh

• K. J. Somaiya Institute of Management & Research, Mumbai, India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).