Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8622
Title: A Deep Neural Network Model for Stock Investment Recommendation by Considering the Stock Market as a Time Graph
Authors: Keskin, Mustafa Mert
Yılmaz, Muhammed
Özbayoğlu, Ahmet Murat
Keywords: Convolutional neural networks
Deep learning
Deep neural networks
Financial forecasting
Graphs
Stock market
Commerce
Convolutional neural networks
Financial markets
Investments
Time series
Convolutional neural network
Deep learning
Financial forecasting
Graph representation
Learning methods
Neural network model
Series representations
Stock market
Time-series data
Times series
Deep neural networks
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Keskin, M. M., Yilmaz, M., & Ozbayoglu, A. M. (2021, December). A Deep Neural Network Model for Stock Investment Recommendation by Considering the Stock Market as a Time Graph. In 2021 2nd International Informatics and Software Engineering Conference (IISEC) (pp. 1-6). IEEE.
Abstract: Financial forecasting from raw time series data is one of the challenging problems in the literature for which satisfying results generally cannot be obtained even with deep learning methods. There is only limited information that can be extracted from the time series data. However, this can be compensated by using additional representations one of which is the graph representation. Graphs are better suited to represent relational data which can be essential for financial applications. Additionally, the stock market can be analyzed as a whole easily with graph representation which can unravel information that cannot be obtained with time series representation. We propose some graph representations that can be obtained from the financial data and show that using graph representation and time series representation together with deep neural networks (DNNs) improves the annual return significantly compared to using only time series data. © 2021 IEEE.
Description: 2nd International Informatics and Software Engineering Conference, IISEC 2021 -- 16 December 2021 through 17 December 2021 -- -- 176423
URI: https://doi.org/10.1109/IISEC54230.2021.9672444
https://hdl.handle.net/20.500.11851/8622
ISBN: 9781665407595
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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