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Investor Driven Adaptive and Automated Stock Market Portfolio Management Platform with Stock Prices Prediction for Colombo Stock Exchange of Sri Lanka

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dc.contributor.author Nanayakkara, VSS
dc.contributor.author Wanniarachchi, WAAM
dc.contributor.author Vidanagama, DU
dc.date.accessioned 2022-01-17T14:06:32Z
dc.date.available 2022-01-17T14:06:32Z
dc.date.issued 2022-01-10
dc.identifier.uri http://ir.kdu.ac.lk/handle/345/5298
dc.description.abstract Over the past few years various studies have been conducted to develop an optimum stock market related portfolio management platform that will assist investors to actively perform the portfolio management process. Risk and level of investor participation is considered to be one of the challenging aspects identified for optimum portfolio management. Along with portfolio management, stock price prediction is one of the key contributing factors that helps an investor to make mid and long-term strategic investment decisions. Various concepts are evaluated and studied thoroughly to determine the most accurate algorithm to implement a stock price-based prediction system. Currently, Colombo Stock Exchange have identified a desperate requirement of a portfolio management system with prediction capabilities to support the local and foreign investors to actively engage in trading activities in different stock exchanges in different countries. A critical study has been conducted using supportive research papers, studying similar applications which are developed so far and using various requirement elicitation techniques to determine the functional requirements, non-functional requirements, investor requirements and User Interface/User Experience (UI/UX) considerations. The paper further describes various technological mechanisms implemented and system architectures used to develop the portfolio management and stock price prediction system. Accordingly, the implementation of Brownian Motion algorithm-based model and LSTM (Long Short-Term Memory) model are presented in detail by the author. Finally, evaluation and testing results of the completed system and stock price prediction models are presented to prove the successfulness of the completed application and accuracy of the models implemented en_US
dc.language.iso en en_US
dc.subject CSE en_US
dc.subject LSTM en_US
dc.subject Portfolio en_US
dc.subject Prediction en_US
dc.subject Stock en_US
dc.title Investor Driven Adaptive and Automated Stock Market Portfolio Management Platform with Stock Prices Prediction for Colombo Stock Exchange of Sri Lanka en_US
dc.type Article Full Text en_US
dc.identifier.journal International Journal of Research in Computing en_US
dc.identifier.issue 01 en_US
dc.identifier.volume 01 en_US
dc.identifier.pgnos 44-51 en_US


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