  Sub-new stock price prediction, forecasting the price trends of stocks listed
less than one year, is crucial for effective quantitative trading. While deep
learning methods have demonstrated effectiveness in predicting old stock
prices, they require large training datasets unavailable for sub-new stocks. In
this paper, we propose Meta-Stock: a task-difficulty-adaptive meta-learning
approach for sub-new stock price prediction. Leveraging prediction tasks
formulated by old stocks, our meta-learning method aims to acquire the fast
generalization ability that can be further adapted to sub-new stock price
prediction tasks, thereby solving the data scarcity of sub-new stocks.
Moreover, we enhance the meta-learning process by incorporating an adaptive
learning strategy sensitive to varying task difficulties. Through wavelet
transform, we extract high-frequency coefficients to manifest stock price
volatility. This allows the meta-learning model to assign gradient weights
based on volatility-quantified task difficulty. Extensive experiments on
datasets collected from three stock markets spanning twenty-two years prove
that our Meta-Stock significantly outperforms previous methods and manifests
strong applicability in real-world stock trading. Besides, we evaluate the
reasonability of the task difficulty quantification and the effectiveness of
the adaptive learning strategy.
