  From a complex network perspective, investigating the stock market holds
paramount significance as it enables the systematic revelation of topological
features inherent in the market. This approach is crucial in exploring market
interconnectivity, systemic risks, portfolio management, and structural
evolution. However, prevailing methodologies for constructing networks based on
stock data rely on threshold filtering, often needing help to uncover intricate
underlying associations among stocks. To address this, we introduce the Stock
Network Inference Framework (SNIF), which leverages a self-encoding mechanism.
Specifically, the Stock Network Inference Encoder (SNIE) facilitates network
construction, while the Movement Prediction Decoder (MPD) enhances movement
forecasting. This integrated process culminates in the inference of a stock
network, exhibiting remarkable performance across applications such as market
structure analysis, stock movement prediction, portfolio construction, and
community evolution analysis. Our approach streamlines the automatic
construction of stock networks, liberating the process from threshold
dependencies and eliminating the need for additional financial indicators.
Incorporating Graph Convolutional Network (GCN) and Long Short-Term Memory
(LSTM) models within the SNIF framework, we effectively unearth deep-seated
associations among stocks, augmenting the toolset available for comprehensive
financial market research. This integration empowers our methodology to
automatically construct stock networks without threshold dependencies or
reliance on additional economic indicators.
