ORCID ID

0000-0003-3656-365X

Date of Award

5-2024

Degree Type

Thesis

Degree Name

M.S.

Degree Program

Computer Science

Department

Computer Science

Major Professor

Md. Tamjidul Hoque

Second Advisor

Abdullah Al Redwan Newaz

Third Advisor

Abdullah Yasin Nur

Abstract

This study compares the performance of deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer, in predicting stock prices across five companies (AAPL, CSCO, META, MSFT, and TSLA) from July 2019 to July 2023. Key findings reveal that GRU models generally exhibit the lowest Mean Absolute Error (MAE), indicating higher precision, particularly notable for CSCO with a remarkably low MAE. While LSTM models often show slightly higher MAE values, they outperform Transformer models in capturing broader trends and variance in stock prices, as evidenced by higher R-squared (R2) values. Transformer models generally exhibit higher MAE values and lower R2 values, suggesting lower predictive accuracy, especially for volatile stocks like TSLA. In conclusion, while all three types of models show promise in predicting stock prices, GRU models offer the highest precision, LSTM models capture broader trends effectively, and Transformer models may require further refinement for enhanced predictive accuracy. Financial practitioners and analysts should consider these factors when selecting models for stock price prediction and incorporate them into a broader analytical framework for informed investment decisions.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

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