Enhancing Stock Market Forecasting on the Bombay Stock Exchange through an Evolutionary Approach and Deep Learning Techniques

Sahu, Ankit Kumar (2024) Enhancing Stock Market Forecasting on the Bombay Stock Exchange through an Evolutionary Approach and Deep Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

In addressing the research problem of improving BSE stock future prediction through deep learning models. the deployment of techniques for analyzing and forecasting stock prices on the Bombay Stock Exchange utilizing deep learning signifies a major leap forward from conventional approaches. By adeptly handling vast as well as complex dataset and uncovering intricate patterns within them, these advanced AI methodologies offer a more nuanced and accurate prediction of market trends. This not only improves investment decision-making but also promises to elevate the overall efficiency and stability of the market. As such, embracing deep learning in financial analysis on the BSE is pivotal for investors and analysts seeking to navigate the complexities of the modern financial landscape effectively. In the focused study on the prediction of Bombay Stock Exchange (BSE), LSTM, and GRU strategically employed to analyze market data, By tapping into their skill in capturing complex sequential patterns that are essential for predicting time series, we've harnessed the power of LSTM's ability to learn long-term dependencies and GRU's proficiency in handling intricacies inherent in time series data. The result has been remarkably successful in accurately forecasting the near-term movements of the stock market. The application of these complex models resulted in a significant improvement of predictive accuracy, showcasing their superiority over conventional methods. This breakthrough not only facilitates more informed investment decisions and effective risk management but also highlights the potential of advanced deep learning techniques in navigating the dynamic nature of financial markets, marking a notable stride in the realm of financial forecasting and the development of trading strategies.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Tomer, Vikas UNSPECIFIED
Uncontrolled Keywords:	LSTM; GRU; Stock price
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:	05 Jun 2025 13:27
Last Modified:	05 Jun 2025 13:27
URI:	https://norma.ncirl.ie/id/eprint/7759

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