Stock price prediction for IT, banking and health care using ARIMA model

Abstract

Predicting stock prices is crucial for making smart financial decisions, and aiding investors in understanding and navigating its effectiveness and limitations the ever-changing financial markets. This research explores using the Autoregressive Integrated Moving Average (ARIMA) model to predict stock prices, assessing its effectiveness and limitations. The study explains the ARIMA model's theory, highlighting its ability to identify patterns over time, making it useful for predicting future prices. Historical stock price data from various sectors, including IT, Banking, and Health Care, is used to apply the ARIMA model, and its performance in making accurate predictions is evaluated. The methodology involves several key steps, including data preprocessing, the ADF test, parameter estimation, model selection, and model evaluation. Data preprocessing involves cleaning and transforming raw stock price data to ensure consistency and reliability in the analysis. The ADF test helps determine whether a time series is stationary and can indicate if differencing is needed. Parameter estimation is done by evaluating the PACF and ACF plots. Model selection entails identifying the most appropriate ARIMA specifications, considering factors such as stationarity, autocorrelation, and partial autocorrelation in the data. Model evaluation is conducted using Mean Absolute Percentage Error (MAPE) to assess the accuracy and robustness of the ARIMA model's predictions. The analysis of the ARIMA model encompasses a comprehensive examination of its predictive capabilities across different stocks and time periods. This research evaluates the ARIMA model's performance in predicting stock prices from various sectors, including IT, Banking, and Health Care. By analyzing historical stock price data, the study assesses the model's accuracy and reliability in forecasting future stock prices. The results provide insights into the strengths and limitations of the ARIMA model in different market conditions, offering valuable information for investors and analysts in making informed decisions based on forecasted stock price trends. The findings contribute to the existing knowledge on stock price prediction methodologies, offering valuable insights and practical guidance for investors, financial analysts, and researchers. This research advances understanding of stock price prediction using the ARIMA model, paving the way for future research endeavors.