An application of graph neural networks for stock market data Proceedings of the 12th International Conference on Information Society and Technology application/pdf 181091 bytes 2022 All rights reserved Radojičić, Dragana Radojičić, Nina info:eu-repo/semantics/conferenceProceedings Abstract—This research is developed in order to describe the behavior present in the market and Limit Order book dynamics, using the concepts of supervised and unsupervised learning. The main mathematical object of interest is the limit order book, whose job is to keep track of all incoming and outgoing orders. There is a wide variety of possibilities to be explored for how to use machine learning techniques to get insights into market behavior. More precisely, in order to develop a statistical arbitrage strategy, the leverage of machine learning techniques can be employed. Furthermore, the concept can be enhanced with the feature that interprets the relationship of different features previously extracted from the limit order book data. The main idea is to employ a Graph Neural Network in order to describe the relationship between different features, and that relationship can be seen as a new feature that is potentially informative and possesses the power to uncover hidden and unknown knowledge from the data set. This work studies the ability to use Graph Neural Networks in order to get more insights from the stock market data. More precisely, this work investigates the ability to use Graph Neural Networks to label the stock market data into one of the labels from the set S={sell, buy, idle}. The obtained results are examined by using the F-score measure and compared with the results obtained by using the recurrent neural networks. This study discusses the potential for using GNNs for stock market data. eng https://phaidrabg.bg.ac.rs/o:30207