Presenter(s)

Sydney Dobyns

Comments

1:15-2:30, Kennedy Union Ballroom

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Description

This study analyzes the historical closing stock price of Skechers to develop a predictive model for future price movements. The dataset spans from the early 2000s to December 31, 2019, and we will forecast stock closing price for the period 2020-2023. Analyzing the dataset reveals that the original time series is non-stationary which requires transformation before applying forecasting models. Various time series models are evaluated based on performance metrics such as Akaike Information Criterion (AIC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Percentage Error (MPE), and Mean Absolute Percentage Error (MAPE). By comparing these models we aim to determine the most accurate approach for predicting Skechers' future stock prices. Given the ever-changing nature of the retail industry, where consumer trends, economic conditions, and competition continuously fluctuates. It is important to develop a reliable forecasting method. Accurate predictions can assist investors, business leaders, and analysts in making informed decisions, allowing them to better navigate market uncertainties and strategize for future growth.

Publication Date

4-23-2025

Project Designation

Capstone Project

Primary Advisor

Thilini M. Jayasinghe

Primary Advisor's Department

Mathematics

Keywords

Stander Symposium, College of Arts and Sciences

Institutional Learning Goals

Scholarship

Skechers Closing Stock Price Forecasting Using Time Series Analysis

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