KKT

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====== Multi-task Learning for Financial Forecasting ======

  - https://arxiv.org/abs/1809.10336
  - https://eyun.baidu.com/s/3o9KXvaa

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.