  Stock trend forecasting is a fundamental task of quantitative investment
where precise predictions of price trends are indispensable. As an online
service, stock data continuously arrive over time. It is practical and
efficient to incrementally update the forecast model with the latest data which
may reveal some new patterns recurring in the future stock market. However,
incremental learning for stock trend forecasting still remains under-explored
due to the challenge of distribution shifts (a.k.a. concept drifts). With the
stock market dynamically evolving, the distribution of future data can slightly
or significantly differ from incremental data, hindering the effectiveness of
incremental updates. To address this challenge, we propose DoubleAdapt, an
end-to-end framework with two adapters, which can effectively adapt the data
and the model to mitigate the effects of distribution shifts. Our key insight
is to automatically learn how to adapt stock data into a locally stationary
distribution in favor of profitable updates. Complemented by data adaptation,
we can confidently adapt the model parameters under mitigated distribution
shifts. We cast each incremental learning task as a meta-learning task and
automatically optimize the adapters for desirable data adaptation and parameter
initialization. Experiments on real-world stock datasets demonstrate that
DoubleAdapt achieves state-of-the-art predictive performance and shows
considerable efficiency.
