NETpred: Network-based modeling and prediction of multiple connected market indices

• URL: http://arxiv.org/abs/2212.05916v1
• Date: Fri, 2 Dec 2022 17:23:09 GMT
• Title: NETpred: Network-based modeling and prediction of multiple connected market indices
• Authors: Alireza Jafari and Saman Haratizadeh
• Abstract summary: We introduce a framework called NETpred that generates a novel graph representing multiple related indices and their stocks. It then thoroughly selects a diverse set of representative nodes that cover different parts of the state space and whose price movements are accurately predictable. The resulting model is then used to predict the stock labels which are finally aggregated to infer the labels for all the index nodes in the graph.
• Score: 8.122270502556372
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Market prediction plays a major role in supporting financial decisions. An emerging approach in this domain is to use graphical modeling and analysis to for prediction of next market index fluctuations. One important question in this domain is how to construct an appropriate graphical model of the data that can be effectively used by a semi-supervised GNN to predict index fluctuations. In this paper, we introduce a framework called NETpred that generates a novel heterogeneous graph representing multiple related indices and their stocks by using several stock-stock and stock-index relation measures. It then thoroughly selects a diverse set of representative nodes that cover different parts of the state space and whose price movements are accurately predictable. By assigning initial predicted labels to such a set of nodes, NETpred makes sure that the subsequent GCN model can be successfully trained using a semi-supervised learning process. The resulting model is then used to predict the stock labels which are finally aggregated to infer the labels for all the index nodes in the graph. Our comprehensive set of experiments shows that NETpred improves the performance of the state-of-the-art baselines by 3%-5% in terms of F-score measure on different well-known data sets.

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