Multi-task Learning for Financial Forecasting

TL;DR

This paper introduces a multi-task learning framework with an attention mechanism for joint stock forecasting, leveraging shared and private information across related stocks to improve prediction accuracy.

Contribution

It proposes a novel multi-task learning approach with an attention mechanism based on CAPM for financial forecasting, addressing the limitation of single-stock models.

Findings

Improved forecasting accuracy over baseline methods.

Effective utilization of shared and private information among stocks.

Demonstrated robustness across various datasets.

Abstract

Financial forecasting is challenging and attractive in machine learning. There are many classic solutions, as well as many deep learning based methods, proposed to deal with it yielding encouraging performance. Stock time series forecasting is the most representative problem in financial forecasting. Due to the strong connections among stocks, the information valuable for forecasting is not only included in individual stocks, but also included in the stocks related to them. However, most previous works focus on one single stock, which easily ignore the valuable information in others. To leverage more information, in this paper, we propose a jointly forecasting approach to process multiple time series of related stocks simultaneously, using multi-task learning framework. Compared to the previous works, we use multiple networks to forecast multiple related stocks, using the shared and private information of them simultaneously through multi-task learning. Moreover, we propose an attention method learning an optimized weighted combination of shared and private information based on the idea of Capital Asset Pricing Model (CAPM) to help forecast. Experimental results on various data show improved forecasting performance over baseline methods.