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From a complex network perspective, investigating the stock market holdsparamount significance as it enables the systematic revelation of topologicalfeatures inherent in the market. This approach is crucial in exploring marketinterconnectivity, systemic risks, portfolio management, and structuralevolution. However, prevailing methodologies for constructing networks based onstock data rely on threshold filtering, often needing help to uncover intricateunderlying associations among stocks. To address this, we introduce the StockNetwork Inference Framework (SNIF), which leverages a self-encoding mechanism.Specifically, the Stock Network Inference Encoder (SNIE) facilitates networkconstruction, while the Movement Prediction Decoder (MPD) enhances movementforecasting. This integrated process culminates in the inference of a stocknetwork, exhibiting remarkable performance across applications such as marketstructure analysis, stock movement prediction, portfolio construction, andcommunity evolution analysis. Our approach streamlines the automaticconstruction of stock networks, liberating the process from thresholddependencies 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-seatedassociations among stocks, augmenting the toolset available for comprehensivefinancial market research. This integration empowers our methodology toautomatically construct stock networks without threshold dependencies orreliance on additional economic indicators.

Stock network inference: A framework for market analysis from topology perspective