Predicting Stock Market Trends: A Hybrid Approach Using Time Series and Machine Learning Methods

Predicting stock market trends has always been a complex challenge, owing to the market's volatility and the numerous influencing factors such as economic indicators, geopolitical events, and company performance. This study explores the effectiveness of time series analysis and machine learning techniques in forecasting stock market trends, aiming to evaluate and compare the predictive power of traditional methods (like ARIMA) with more advanced algorithms (like Support Vector Machines and Random Forests). By examining the predictive accuracy, computational efficiency, and adaptability of these models across various market conditions, this research provides insights into their relative strengths and weaknesses. The findings suggest that while traditional time series models perform well in stable markets, machine learning models excel in capturing non-linear patterns and adapting to market volatility. Additionally, the study highlights the potential for hybrid models that combine both approaches for improved forecasting. This research offers valuable recommendations for investors and financial analysts seeking more accurate and robust methods for predicting stock market trends.