• Login
    View Item 
    •   DBS eSource Home
    • Masters Dissertations
    • Business & Management
    • View Item
    •   DBS eSource Home
    • Masters Dissertations
    • Business & Management
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Portfolio Management For Asset Forecasting Using Recurrent Neural Network

    View/Open
    DanishFrancis10569277.pdf (2.059Mb)
    Author
    Francis, Danish
    Date
    2022
    Degree
    MSc in Business Analytics
    URI
    https://esource.dbs.ie/handle/10788/4346
    Publisher
    Dublin Business School
    Rights holder
    http://esource.dbs.ie/copyright
    Rights
    Items in eSource are protected by copyright. Previously published items are made available in accordance with the copyright policy of the publisher/copyright holder.
    Metadata
    Show full item record
    Abstract
    This thesis examines the use of emerging neural networks to predict future financial asset price movements in a set of futures contracts. To help with our research, we compare ourselves to a simple set Feed Network. We do more research on different networks by considering the different functions that lose purpose and how they affect the performance of our networks. This discussion is expanded by considering the Mass Loss Network. The use of different law functions highlights the importance of feature selection. We learn about a set of simple and complex features and how they affect our model. This will enable us to take a closer look at the differences between our networks. Finally, we analyse our model gradients to provide more information about the features of our features. Our results show that repetitive networks offer higher specification performance than relay networks when considering sharpening ratings and accuracy. General features show better results when it comes to accuracy. While the goal of the network is to expand to shards, complex features are selected. Using high-loss networks is successful because we consider achieving high Sharp ratings as our main goal. Our results show better performance than the usual set of benchmarks.
    Collections
    • Business & Management

    Browse

    All of DBS eSourceCommunities & CollectionsBy Issue DateAuthorsSupervisorTitlesSubjectsThis CollectionBy Issue DateAuthorsSupervisorTitlesSubjects

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    DSpace software copyright © 2002-2022  DuraSpace
    Contact Us | Send Feedback
    DSpace Express is a service operated by 
    Atmire NV