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  .
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 .
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non-stationary
time-varying
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applied
compare
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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
current approaches on predicting monthly U.S. stock returns and show its superiority
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,
(
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effect and momentum
and momentum effects) and industry indicators
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. 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