Stock Market Prediction using LSTM and Markov Chain Models: A Case Study of Royal Bank of Canada Stock

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dc.contributor.authorKumar, Amer
dc.contributor.supervisorGebali, Fayez
dc.contributor.supervisorEl-Kharashi, Mohamed Watheq
dc.date.accessioned2023-08-09T17:32:30Z
dc.date.available2023-08-09T17:32:30Z
dc.date.copyright2023en_US
dc.date.issued2023-08-09
dc.degree.departmentDepartment of Electrical and Computer Engineeringen_US
dc.degree.levelMaster of Engineering M.Eng.en_US
dc.description.abstractStock price prediction is one of the most important aspects of financial investment. This research aims to provide insights into the dynamics of stock prices, enabling more informed decision-making in financial investments by combining these two modeling approaches. Using a four-layer long short-term memory (LSTM) architecture and the Root Mean Square Error (RMSE) as the loss function, we aim to capture temporal dependencies and patterns to predict closing prices. Furthermore, we employ a threestate Markov chain to estimate the transition matrix, and metrics like steady-state distribution and mean hitting times have been used to calculate the matrix. The preliminary results indicate that this approach shows promising results for stock market prediction as LSTM has predictive power that caters more to long-term temporal trends while Markov Chain provides probabilistic values for staying and transitioning to states. The findings of the study highlight the effectiveness of combining LSTM and Markov Chain in capturing the intricate dynamics of the stock market data and predicting stock market prices.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/15242
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectLSTMen_US
dc.subjectMarkov Chainen_US
dc.subjectStock Marketen_US
dc.subjectRMSEen_US
dc.subjectRBCen_US
dc.titleStock Market Prediction using LSTM and Markov Chain Models: A Case Study of Royal Bank of Canada Stocken_US
dc.typeprojecten_US

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