Abstract
ABSTRACTThe study has two objectives. The first, to develop an earnings movement prediction model to help investors in their decision process, the second, to explore the potential of Recurrent Neural Networks (RNN) in financial statement analysis and present a detailed model for its application. RNN's two major advantages are: they do not make assumptions regarding the data and allow users to search whatever functional form best describes the underlying relationship between financial data and changes in earnings; they dynamically account for time-series behavior, earnings of a certain time period are not independent of earnings in previous time periods. The paper utilizes the newly mandated XBRL data, whose benefits are that it is freely available, easily accessible and is more timely than traditional databases. The use of RNN is validated in the results by providing a higher accuracy prediction than neural networks and logistic regression.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
