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

TL;DR

Meta-Stock introduces a task-difficulty-adaptive meta-learning framework that enhances sub-new stock price prediction by leveraging old stock data, adaptive strategies, and volatility analysis, significantly improving prediction accuracy in data-scarce scenarios.

Contribution

The paper proposes a novel meta-learning approach with adaptive difficulty weighting and volatility-based task difficulty quantification for sub-new stock prediction.

Findings

Meta-Stock outperforms previous methods on multiple datasets.

Adaptive learning improves prediction accuracy for sub-new stocks.

Volatility-based task difficulty quantification is effective.

Abstract

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.