Analysis of Stock Market Prices Based on ARIMA Model: Evidence from NASDAQ 100 Index

In recent years, Fluctuations in the stock market has become one of the hotspots in the field of finance. As one of the indicators reflecting the stock market situation in the United States, the volatility of the NASDAQ Industrial Index has attracted academic attention. This study uses time series modeling techniques to analyze NDX100 data representing the NASDAQ 100 index to gain insight into market trends and make informed predictions. Using historical price and volume data, the authors built robust models using statistical tools, time series models, and machine learning algorithms. The analysis included autocorrelation and stationary testing, and the ARIMA model was selected. The results show that there are clear autocorrelations and patterns in the data, indicating market inefficiency. This research contributes to understanding market dynamics and highlights the importance of data-driven analytics in financial decision making. Future research could focus on refining the model to improve prediction accuracy.