Time-varying neural network for stock return prediction
Steven Y. K. Wong,
Lamiae Azizi and
Richard Y. D. Xu
Additional contact information
Steven Y. K. Wong: University of Technology Sydney
Lamiae Azizi: University of Sydney
Richard Y. D. Xu: University of Technology Sydney
Papers from arXiv.org
We consider the problem of neural network training in a time-varying context. Machine learning algorithms have excelled in problems that do not change over time. However, problems encountered in financial markets are often time-varying. We propose the online early stopping algorithm and show that a neural network trained using this algorithm can track a function changing with unknown dynamics. We compare the proposed algorithm to current approaches on predicting monthly U.S. stock returns and show its superiority. We also show that prominent factors (such as the size and momentum effects) and industry indicators, exhibit time varying stock return predictiveness. We find that during market distress, industry indicators experience an increase in importance at the expense of firm level features. This indicates that industries play a role in explaining stock returns during periods of heightened risk.
Date: 2020-03, Revised 2021-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-fmk
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://arxiv.org/pdf/2003.02515 Latest version (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2003.02515
Access Statistics for this paper
More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().