Data Analysis Approaches for Apple Stock Price Prediction and Financial Risk Management

Abstract

Financial markets are characterized by rapid volatility and dynamic fluctuations, which necessitate robust approaches to financial risk management. Traditional statistical techniques often fall short in capturing the nonlinear and complex interactions that influence stock price behavior. This study aims to develop and evaluate predictive models for Apple Inc.’s stock prices by integrating statistical analysis with machine learning approaches. Using 17 years of historical stock market data, we examine price dynamics, trading volume patterns, and volatility trends. Descriptive statistical analysis reveals a significant negative correlation between trading volume and stock price (r = –0.523), while daily return volatility is measured at 11.5369, underscoring inherent financial risks. Two predictive models—Linear Regression and Random Forest—are employed, utilizing features such as opening price, daily high and low prices, and trading volume. Model performance is assessed through mean absolute percentage error (MAPE), yielding error rates of 0.0143 and 0.0161, respectively, with Random Forest demonstrating slightly superior accuracy. The findings highlight the potential of data-driven approaches for enhancing stock price forecasting and financial decision-making. By combining traditional statistical methods with machine learning techniques, this study contributes to the literature on financial risk management and offers practical insights into how advanced predictive analytics can improve strategic responses to market uncertainty.