Stock Predictions Based on Time Series Models - The Case of Pepsi Research Background

¹ Nottingham University Business School China, University of Nottinghan Ningbo China, Zhejiang, 315100, China
² Henan University, Shanghai University, Shanghai, 201800, China
³ Eurasia International School of Henan University, Henan University, Henan, 475004, China

^* Corresponding author: 2228200293@henu.edu.cn

Abstract

A statistical model widely used in time series analysis and prediction is Auto Regressive Integrated Moving Average (ARIMA) model. Through the closing price data of Pepsi’s stock during the period from January 1, 2024, to January 1, 2025, this model can be applied to conduct a time order analysis to project Pepsi’s closing price in the coming trading time. It provides investors some important information about the future trend of Pepsi’s stock price. This research has found that when the data is stable and there are no significant structural changes, ARIMA model performs better for predicting the stock market trend. And investors can also use ARIMA model if they want to avoid the risk of stock fluctuations. The limitations of this research mainly due to the limitations of this model itself (only suitable for short-term prediction), which can be solved through the combination of multiple models in the future study.