Empirical Analysis of the Predicting Future Trend in Nasdaq Using ARIMA Model

Abstract

The paper delves into a detailed empirical analysis leveraging the ARIMA model, with data gleaned from the weekly return trends of the Nasdaq Index. Using diagnostic tools such as ACF, PACF, and the AIC information criteria, the study zeroes in on the optimal lag period for the model. Subsequently, this refined model is employed to forecast potential trajectories for the Nasdaq Index's weekly returns. Through a thorough examination of analytical outcomes, especially AIC values, the study gauges the margin of prediction errors, culminating in the selection of the most proficient forecasting model: ARIMA (2,0,2). Importantly, these analytical insights serve as a valuable compass for investors aiming to capitalize on lucrative opportunities or mitigate potential risks within the Nasdaq stock exchange. Furthermore, it underscores the importance of empirical data analysis in guiding informed decision-making, ensuring optimal returns in the volatile Nasdaq market landscape. This work acts as a beacon for those navigating the intricate nuances of stock market investments.