Predicting Stock Market Trends and Analyzing Daily Returns Using Statistical Modeling: A Case Study on Google Stock

Abstract

A key component of financial planning is stock market forecasting, which assists investors in choosing how to allocate their assets and manage their risk. In particular, Auto Regressive Integrated Moving Average (ARIMA) and linear regression are two statistical and mathematical models that are being evaluated for their predictive power in relation to daily returns and stock prices. Accurate forecasting is difficult due to the inherent volatility and unpredictability of financial markets, which highlights the necessity for reliable models. To ensure accurate predictions, the methodology consists of data preprocessing, model implementation, and diagnostic assessments. Results indicate that the ARIMA model effectively captures long-term trends in stock prices, making it suitable for general forecasting. However, the linear regression model exhibits inconsistent performance when predicting daily returns, especially during periods of high volatility, as evidenced by increased residual errors. This research highlights the importance of selecting appropriate predictive models and integrating advanced techniques to enhance accuracy. The findings provide valuable insights into improving financial forecasting practices, ultimately contributing to better decision-making in investment strategies.