Novel grey wolf optimizer based parameters selection for GARCH and ARIMA models for stock price prediction.

ABSTRACT: Stock price data often exhibit nonlinear patterns and dynamics in nature. The parameter selection in generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA) models is challenging due to stock price volatility. Most studies examined the manual method for parameter selection in GARCH and ARIMA models. These procedures are time-consuming and based on trial and error. To overcome this, we considered a GWO method for finding the optimal parameters in GARCH and ARIMA models. The motivation behind considering the grey wolf optimizer (GWO) is one of the popular methods for parameter optimization. The novel GWO-based parameters selection approach for GARCH and ARIMA models aims to improve stock price prediction accuracy by optimizing th

SUBMITTER: Bagalkot SS 

PROVIDER: S-EPMC10773900 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature