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GCNET: graph-based prediction of stock price movement using graph convolutional network

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  • Alireza Jafari
  • Saman Haratizadeh

Abstract

The importance of considering related stocks data for the prediction of stock price movement has been shown in many studies, however, advanced graphical techniques for modeling, embedding and analyzing the behavior of interrelated stocks have not been widely exploited for the prediction of stocks price movements yet. The main challenges in this domain are to find a way for modeling the existing relations among an arbitrary set of stocks and to exploit such a model for improving the prediction performance for those stocks. The most of existing methods in this domain rely on basic graph-analysis techniques, with limited prediction power, and suffer from a lack of generality and flexibility. In this paper, we introduce a novel framework, called GCNET that models the relations among an arbitrary set of stocks as a graph structure called influence network and uses a set of history-based prediction models to infer plausible initial labels for a subset of the stock nodes in the graph. Finally, GCNET uses the Graph Convolutional Network algorithm to analyze this partially labeled graph and predicts the next price direction of movement for each stock in the graph. GCNET is a general prediction framework that can be applied for the prediction of the price fluctuations of interacting stocks based on their historical data. Our experiments and evaluations on a set of stocks from the NASDAQ index demonstrate that GCNET significantly improves the performance of SOTA in terms of accuracy and MCC measures.

Suggested Citation

  • Alireza Jafari & Saman Haratizadeh, 2022. "GCNET: graph-based prediction of stock price movement using graph convolutional network," Papers 2203.11091, arXiv.org, revised Aug 2022.
  • Handle: RePEc:arx:papers:2203.11091
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    References listed on IDEAS

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    1. Andrew Skabar, 2013. "Direction‐of‐Change Financial Time Series Forecasting using a Similarity‐Based Classification Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 409-422, August.
    2. Raehyun Kim & Chan Ho So & Minbyul Jeong & Sanghoon Lee & Jinkyu Kim & Jaewoo Kang, 2019. "HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction," Papers 1908.07999, arXiv.org, revised Nov 2019.
    3. Akhter Mohiuddin Rather & V. N. Sastry & Arun Agarwal, 2017. "Stock market prediction and Portfolio selection models: a survey," OPSEARCH, Springer;Operational Research Society of India, vol. 54(3), pages 558-579, September.
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    Cited by:

    1. Alireza Jafari & Saman Haratizadeh, 2022. "NETpred: Network-based modeling and prediction of multiple connected market indices," Papers 2212.05916, arXiv.org.
    2. Kassiani Papasotiriou & Srijan Sood & Shayleen Reynolds & Tucker Balch, 2024. "AI in Investment Analysis: LLMs for Equity Stock Ratings," Papers 2411.00856, arXiv.org.

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