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Sub-new stock price prediction, forecasting the price trends of stocks listedless than one year, is crucial for effective quantitative trading. While deeplearning methods have demonstrated effectiveness in predicting old stockprices, they require large training datasets unavailable for sub-new stocks. Inthis paper, we propose Meta-Stock: a task-difficulty-adaptive meta-learningapproach for sub-new stock price prediction. Leveraging prediction tasksformulated by old stocks, our meta-learning method aims to acquire the fastgeneralization ability that can be further adapted to sub-new stock priceprediction tasks, thereby solving the data scarcity of sub-new stocks.Moreover, we enhance the meta-learning process by incorporating an adaptivelearning strategy sensitive to varying task difficulties. Through wavelettransform, we extract high-frequency coefficients to manifest stock pricevolatility. This allows the meta-learning model to assign gradient weightsbased on volatility-quantified task difficulty. Extensive experiments ondatasets collected from three stock markets spanning twenty-two years provethat our Meta-Stock significantly outperforms previous methods and manifestsstrong applicability in real-world stock trading. Besides, we evaluate thereasonability of the task difficulty quantification and the effectiveness ofthe adaptive learning strategy.

Meta-Stock: Task-Difficulty-Adaptive Meta-learning for Sub-new Stock Price Prediction