DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting

TL;DR

DoubleAdapt is a meta-learning framework designed for incremental stock trend forecasting, effectively addressing distribution shifts to improve prediction accuracy and efficiency in dynamic stock markets.

Contribution

The paper introduces DoubleAdapt, a novel end-to-end meta-learning approach with two adapters that adapt data and models to mitigate distribution shifts in incremental stock trend forecasting.

Findings

Achieves state-of-the-art predictive performance on real-world datasets.

Effectively mitigates distribution shifts during incremental updates.

Demonstrates considerable efficiency in online stock trend prediction.

Abstract

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.