Enhancing Next-Day Stock Price Prediction Accuracy and Reliability: A Comparative Study of Bi-GRU, Transformer, and Hybrid Models

Ganta, Satish Kumar (2024) Enhancing Next-Day Stock Price Prediction Accuracy and Reliability: A Comparative Study of Bi-GRU, Transformer, and Hybrid Models. Masters thesis, Dublin, National College of Ireland.

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Abstract

This research project is an attempt at enhancing the accuracy and performance of nextday stock price predictions using various machine learning models. In this paper, we look at how well Linear Regression, Bidirectional Gated Recurrent Unit, Neural Networks, and Transformer models do against stocks taken from Google historical data from January 2015 to December 2023. We did extensive experimentation and found out that a Linear Regression model was actually the most fitting one when compared to the large capacity models such as Bi-GRU and Transformer. Evaluations were based on the mean squared error and mean absolute error. The results indicated that, on the one hand, more complex models do have the ability to capture non-linear trends very well, but on the other hand, less complicated models perform much better if there is a strong presence of linearity in data. This research contributes to the current body of literature and continuing research on financial forecasting since it gives insights into when some of these different machine-learning approaches have relative strengths and not only that—this work also emphasises model selection based on the characteristic of data.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Horn, Christian UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HG Finance > Investment Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HG Finance > Investment > Stock Exchange
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	18 Aug 2025 13:34
Last Modified:	18 Aug 2025 13:34
URI:	https://norma.ncirl.ie/id/eprint/8564

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