Stock Trading Volume Prediction with Dual-Process Meta-Learning

TL;DR

This paper introduces a dual-process meta-learning approach for stock volume prediction that captures both common and stock-specific patterns, improving prediction accuracy over traditional methods.

Contribution

The paper proposes a novel dual-process meta-learning framework that models shared and individual stock patterns, addressing data sparsity and cold start issues.

Findings

Improved volume prediction accuracy over baseline models.

Effective modeling of both common and stock-specific patterns.

Meta-learning framework enhances prediction robustness.

Abstract

Volume prediction is one of the fundamental objectives in the Fintech area, which is helpful for many downstream tasks, e.g., algorithmic trading. Previous methods mostly learn a universal model for different stocks. However, this kind of practice omits the specific characteristics of individual stocks by applying the same set of parameters for different stocks. On the other hand, learning different models for each stock would face data sparsity or cold start problems for many stocks with small capitalization. To take advantage of the data scale and the various characteristics of individual stocks, we propose a dual-process meta-learning method that treats the prediction of each stock as one task under the meta-learning framework. Our method can model the common pattern behind different stocks with a meta-learner, while modeling the specific pattern for each stock across time spans with stock-dependent parameters. Furthermore, we propose to mine the pattern of each stock in the form of a latent variable which is then used for learning the parameters for the prediction module. This makes the prediction procedure aware of the data pattern. Extensive experiments on volume predictions show that our method can improve the performance of various baseline models. Further analyses testify the effectiveness of our proposed meta-learning framework.