Performance of SOTA in terms of accuracy and MCC measures. Stocks from the NASDAQ index demonstrate that GCNET significantly improves the Our experiments and evaluations on a set of GCNET is a general prediction framework that can beĪpplied for the prediction of the price fluctuations of interacting stocksīased on their historical data. Partially labeled graph and predicts the next price direction of movement forĮach stock in the graph. Infer plausible initial labels for a subset of the stock nodes in the graph.įinally, GCNET uses the Graph Convolutional Network algorithm to analyze this That models the relations among an arbitrary set of stocks as a graph structureĬalled influence network and uses a set of history-based prediction models to In this paper, we introduce a novel framework, called GCNET Techniques, with limited prediction power, and suffer from a lack of generalityĪnd flexibility.
Most of existing methods in this domain rely on basic graph-analysis Slack Technologies's advanced charting and technical analysis tool lets you customize your analysis with a wide range of studies and indicators, including Moving Averages (SMA and EMA), MACD, On-Balance Volume, Relative Strength Index (RSI), Bollinger Bands, Fibonacci Lines, and more. Such a model for improving the prediction performance for those stocks. Modeling the existing relations among an arbitrary set of stocks and to exploit The main challenges in this domain are to find a way for
Stocks have not been widely exploited for the prediction of stocks price Techniques for modeling, embedding and analyzing the behavior of interrelated Price movement has been shown in many studies, however, advanced graphical
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Download a PDF of the paper titled GCNET: graph-based prediction of stock price movement using graph convolutional network, by Alireza Jafari and Saman Haratizadeh Download PDF Abstract: The importance of considering related stocks data for the prediction of stock
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