Exploring the Effectiveness of ARIMA and GARCH Models in Stock Price Forecasting: An Application in the IT Industry

Abstract

his study aims to develop a predictive model for stock prices using time-series analysis. The primary objective is to identify volatility patterns through the implementation of the GARCH model and forecast future stock prices for Microsoft company utilizing the ARIMA model based on historical data. The findings of this study contribute to the literature on stock price forecasting and provide insights for investors in making informed investment decisions. Moreover, the effectiveness of the proposed methodology is assessed through a comprehensive set of tests, indicating highly positive results when compared to other similar approaches.

Keywords

• machine learning
• autoregressive integrated moving average
• generalized autoregressive conditional heteroskedasticity
• arima
• garch