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.

翻译：次新股价格预测（即预测上市不足一年的股票价格趋势）对有效量化交易至关重要。尽管深度学习方法在预测老股价格方面已展现出有效性，但这些方法需要海量训练数据，而次新股难以获取此类数据。本文提出Meta-Stock：一种面向次新股价格预测的任务难度自适应元学习方法。通过利用老股构建的预测任务，我们的元学习方法旨在获取快速泛化能力，该能力可进一步适配至次新股价格预测任务，从而解决次新股数据稀缺问题。此外，我们通过引入对任务难度变化敏感的自适应学习策略来增强元学习过程。借助小波变换，我们提取高频系数以表征股票价格波动性，使得元学习模型能够基于波动性量化的任务难度分配梯度权重。在覆盖三个股票市场、跨越二十二年的数据集上进行的广泛实验证明，Meta-Stock显著优于以往方法，并在实际股票交易中展现出强适用性。同时，我们评估了任务难度量化的合理性及自适应学习策略的有效性。