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.

翻译：股票趋势预测是量化投资中的一项基本任务，其中精确的价格趋势预测不可或缺。作为在线服务，股票数据随时间持续到达。利用最新数据（这些数据可能揭示未来股市中重复出现的新模式）增量更新预测模型既实用又高效。然而，由于分布漂移（又称概念漂移）的挑战，股票趋势预测的增量学习仍未得到充分探索。随着股市动态变化，未来数据的分布与增量数据可能产生轻微或显著差异，从而阻碍增量更新的有效性。为应对这一挑战，我们提出DoubleAdapt——一个包含两个适配器的端到端框架，能够有效调整数据和模型以减轻分布漂移的影响。我们的核心见解是自动学习如何将股票数据调整为局部平稳分布，以利于有利可图的更新。通过数据适配的补充，我们可以在缓解后的分布漂移下自信地调整模型参数。我们将每个增量学习任务视为元学习任务，并自动优化适配器以实现理想的数据适配和参数初始化。在真实股票数据集上的实验表明，DoubleAdapt达到了最先进的预测性能，并展现出可观的效率。