Predicting Apples future stock price using ARIMA model

Abstract

Recent explorations in the financial forecasting realm have increasingly leaned on technological and data-analytic tools to predict stock market trends. Nonetheless, traditional metrics and the intertwined global economic dynamics continue to be pertinent in understanding market behaviors. This study utilizes the ARIMA analytical framework to analyze data from the updated 2022 Stock Market Trend Database, which covers trend patterns of various enterprises including Apple Inc. The analysis reveals that basic financial metrics and certain technological indicators hold minimal sway over stock market oscillations. However, a notable linkage is observed with sentiment analysis derived from social platforms like Twitter and StockTwits, alongside the challenges posed by high-frequency and algorithmic trading. Intriguingly, emergent indicators such as algorithm-driven sentiment analysis and tech-based markers, hitherto unexplored in scholarly circles, surface as potential predictive tools. These findings not only bolster the existing comprehension but also unfold new pathways for subsequent investigations in the realm of financial forecasting, highlighting the potential for a more nuanced understanding of market dynamics through a blend of traditional and modern analytical lenses.