q-fin updates on arXiv.org
Fri, 06 Mar 2020 06:01:22 GMT language
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
non-stationary. 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 applied the proposed algorithm to the stock return
prediction problem studied in Gu et al. (2019) and achieved mean rank
correlation of 4.69%, almost twice as high as the expanding window approach. We
also show that prominent factors, such as the size effect and momentum, exhibit
time varying stock return predictiveness.