  Most recent works model the market structure of the stock market as a
correlation network of the stocks. They apply pre-defined patterns to extract
correlation information from the time series of stocks. Without considering the
influences of the evolving market structure to the market index trends, these
methods hardly obtain the market structure models which are compatible with the
market principles. Advancements in deep learning have shown their incredible
modeling capacity on various finance-related tasks. However, the learned inner
parameters, which capture the essence of the finance time series, are not
further exploited about their representation in the financial fields. In this
work, we model the financial market structure as a deep co-investment network
and propose a Deep Co-investment Network Learning (DeepCNL) method. DeepCNL
automatically learns deep co-investment patterns between any pairwise stocks,
where the rise-fall trends of the market index are used for distance
supervision. The learned inner parameters of the trained DeepCNL, which encodes
the temporal dynamics of deep co-investment patterns, are used to build the
co-investment network between the stocks as the investment structure of the
corresponding market. We verify the effectiveness of DeepCNL on the real-world
stock data and compare it with the existing methods on several financial tasks.
The experimental results show that DeepCNL not only has the ability to better
reflect the stock market structure that is consistent with widely-acknowledged
financial principles but also is more capable to approximate the investment
activities which lead to the stock performance reported in the real news or
research reports than other alternatives.
