An Efficient Auto Regressive Integrated Moving Average Model for AAPL, MSFT, NTFX, and GOOGL Stock Price Prediction

Stock market prediction is an important facet of financial analysis since it affects the investment decisions. This study employs the AutoRegressive Integrated Moving Average (ARIMA) model to predict the future price movements of four prominent technology stocks: The abovementioned companies include Apple Inc. (AAPL), Microsoft Corporation (MSFT), Netflix Inc. ARIMA model is a steady time series forecasting process that introduced the autoregressive, differencing and moving average components for capturing temporal patterns in stock price data. The study presents the data collection and preprocessing, model selection, training end evaluation steps. The historical stock price data, trading volumes and relevant financial indicators are then collected from records which includes the processing of information in terms of quality control as well as stationarity. The study focuses on choosing proper parameters for the three hyperparameters (p, d and q) of ARIMA model via rigorous analysis of autocorrelation plot along with partial correlation plot as well Akaike Information Criterion AIC & Bayesian Information Criterion BIC. The chosen models are trained on a subset of historical data, and their performance is measured using metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square error(RMSE). The aim of this study is to shed light on the accuracy and efficiency of ARIMA model for stock prices forecasting AAPL, MSFT, NFLX & GOOGL in a given time frame. Although aware of the fundamental difficulties and limitations in stock market prediction, this study seeks to offer perspectives on using time series forecasting techniques for better decision-making within an uncertain environment such as financial markets. The results and conclusions of this study are relevant for investors, traders, financial analysts as well as researchers who believe in the use of quantitative methods to obtain an overview on stock market patterns and make evidence-based investment decisions.