Date of Award
3-2024
Document Type
Thesis
Degree Name
Master of Science in Logistics and Supply Chain Management
Department
Department of Operational Sciences
First Advisor
Frank W. Ciarallo, PhD
Abstract
This thesis explores the integration of Autoregressive Integrated Moving Average (ARIMA) models and technical analysis to forecast stock prices, with a focus on Coca-Cola's (KO) and Netflix’s (NFLX) stocks. It examines the effectiveness of combining ARIMA models, known for their predictive accuracy in time-series analysis, with technical indicators, particularly moving averages. The study evaluates whether this integrated approach can enhance the predictive capability of stocks prices beyond traditional methods. The predictive capability is evaluated using error metrics from the ARIMA models, as well as by assessing the return earned using simple rules based on the technical indicators. Utilizing data spanning 2003 to 2023, the research applies a rolling forecast method to predict future prices over multiple periods. It also introduces novel investment strategies based on moving average cross over technique signals. The analysis includes a comprehensive literature review on forecasting, time series analysis, ARIMA models, and technical analysis, alongside related works demonstrating the practical application of these methods in diverse market conditions.
AFIT Designator
AFIT-ENS-MS-24-M-064
Recommended Citation
Almaiman, Muath I., "Forecasting Stock Prices Using ARIMA Models And Technical Analysis" (2024). Theses and Dissertations. 7806.
https://scholar.afit.edu/etd/7806
Comments
A 12-month embargo was observed for posting this work on AFIT Scholar.
Distribution Statement A, Approved for Public Release. PA case number on file.