Skip to main content
main-content

Tipp

Weitere Artikel dieser Ausgabe durch Wischen aufrufen

Erschienen in: Neural Processing Letters 4/2022

10.03.2022

A New CNN-Based Model for Financial Time Series: TAIEX and FTSE Stocks Forecasting

verfasst von: Melih Kirisci, Ozge Cagcag Yolcu

Erschienen in: Neural Processing Letters | Ausgabe 4/2022

Einloggen, um Zugang zu erhalten
share
TEILEN

Abstract

Financial time series forecasting has been becoming one of the most attractive topics in so many aspects owing to its broad implementation areas and substantial impact. Because of this reason in particular recent decades, various kinds of computational intelligence techniques like convolutional neural networks (CNNs) have been used for financial time series forecasting. However, in experiments reported so far, the number of applications of CNNs for the forecasting of financial time series seems quite a few and also in almost all-studies time sequence effect of time series is not preserved on forecasts because of image transformation. From this point of view, in this paper, by aiming to get better forecasting results and avoiding information loss which may occur the process of image transformation, we suggest a new CNN-based forecasting model that can be applied on some time series and, can successfully extract the features of them in the forecasting process. The proposed CNN forecasting model is composed of three convolutional layers and five full connected layers, also to be able to determine the nonlinear relation between input and output Relu and Elu activation functions have also been used. The suggested framework has been applied to some of the most evaluated financial time series, which are Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Financial Time Stock Exchange for London stock market data (FTSE). The results have been evaluated on different aspects as an error criterion, a regression analyses and also a visual demonstration. It has been clearly observed that CNN structure has produced outstanding forecasts compared to some other state-of-the-art forecasting tools such as different kinds of ANN, LSTM, fuzzy-based approaches, and some traditional methods.
Literatur
1.
Zurück zum Zitat Krollner B, Vanstone B, Finnie G (2010) Financial time series forecasting with machine learning techniques: a survey. In: Proceedings of the 18th European symposium on artificial neural networks - computational intelligence and machine learning, ESANN 2010 Krollner B, Vanstone B, Finnie G (2010) Financial time series forecasting with machine learning techniques: a survey. In: Proceedings of the 18th European symposium on artificial neural networks - computational intelligence and machine learning, ESANN 2010
21.
Zurück zum Zitat Egrioglu E, Aladag CH, Yolcu U, Bas E (2015) A new adaptive network based fuzzy inference system for time series forecasting. Aloy J Soft Comput Appl 2:25–32 Egrioglu E, Aladag CH, Yolcu U, Bas E (2015) A new adaptive network based fuzzy inference system for time series forecasting. Aloy J Soft Comput Appl 2:25–32
23.
Zurück zum Zitat Sezer OB, Ozbayoglu M, Dogdu E (2017) A deep neural-network based stock trading system based on evolutionary optimized technical analysis parameters. Procedia Computer Science 114:473–80 CrossRef Sezer OB, Ozbayoglu M, Dogdu E (2017) A deep neural-network based stock trading system based on evolutionary optimized technical analysis parameters. Procedia Computer Science 114:473–80 CrossRef
27.
Zurück zum Zitat Karpathy A, Toderici G, Shetty S, et al (2014) Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition Karpathy A, Toderici G, Shetty S, et al (2014) Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition
28.
Zurück zum Zitat Kim Y (2014) Convolutional neural networks for sentence classification. In: EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference Kim Y (2014) Convolutional neural networks for sentence classification. In: EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
29.
Zurück zum Zitat Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. In: 52nd Annual meeting of the association for computational linguistics, ACL 2014 - proceedings of the conference Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. In: 52nd Annual meeting of the association for computational linguistics, ACL 2014 - proceedings of the conference
31.
Zurück zum Zitat Ding X, Zhang Y, Liu T, Duan J (2015) Deep learning for event-driven stock prediction. In: IJCAI international joint conference on artificial intelligence Ding X, Zhang Y, Liu T, Duan J (2015) Deep learning for event-driven stock prediction. In: IJCAI international joint conference on artificial intelligence
37.
Zurück zum Zitat Di Persio L, Honchar O (2016) Artificial neural networks architectures for stock price prediction: comparisons and applications. Int J Circuits, Syst Signal Process 10:403–13 Di Persio L, Honchar O (2016) Artificial neural networks architectures for stock price prediction: comparisons and applications. Int J Circuits, Syst Signal Process 10:403–13
38.
39.
Zurück zum Zitat Yeh SH, Wang CJ, Tsai MF (2015) Deep belief networks for predicting corporate defaults. In: 2015 24th wireless and optical communication conference, WOCC 2015 Yeh SH, Wang CJ, Tsai MF (2015) Deep belief networks for predicting corporate defaults. In: 2015 24th wireless and optical communication conference, WOCC 2015
40.
Zurück zum Zitat Arévalo A, Niño J, Hernández G, Sandoval J (2016) High-frequency trading strategy based on deep neural networks. In: Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Arévalo A, Niño J, Hernández G, Sandoval J (2016) High-frequency trading strategy based on deep neural networks. In: Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
42.
Zurück zum Zitat Nelson DMQ, Pereira ACM, De Oliveira RA (2017) Stock market’s price movement prediction with LSTM neural networks. In: Proceedings of the international joint conference on neural networks Nelson DMQ, Pereira ACM, De Oliveira RA (2017) Stock market’s price movement prediction with LSTM neural networks. In: Proceedings of the international joint conference on neural networks
43.
Zurück zum Zitat Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems
44.
Zurück zum Zitat Lin M, Chen Q, Yan S (2014) Network in network. In: 2nd International conference on learning representations, ICLR 2014 - Conference track proceedings Lin M, Chen Q, Yan S (2014) Network in network. In: 2nd International conference on learning representations, ICLR 2014 - Conference track proceedings
45.
Zurück zum Zitat Szegedy C, Liu W, Jia Y, et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition Szegedy C, Liu W, Jia Y, et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition
46.
Zurück zum Zitat Lecun Y, Bengio Y (2010) Convolutional networks for images, Speech, and Time Series Variable-Size Convolutional Networks : SDNNs. Processing Lecun Y, Bengio Y (2010) Convolutional networks for images, Speech, and Time Series Variable-Size Convolutional Networks : SDNNs. Processing
48.
Zurück zum Zitat Chen C, Li K, Teo SG, et al (2018) Exploiting Spatio-temporal correlations with multiple 3D convolutional neural networks for citywide vehicle flow prediction. In: Proceedings - IEEE international conference on data mining, ICDM Chen C, Li K, Teo SG, et al (2018) Exploiting Spatio-temporal correlations with multiple 3D convolutional neural networks for citywide vehicle flow prediction. In: Proceedings - IEEE international conference on data mining, ICDM
49.
Zurück zum Zitat Chen C, Li K, Ouyang A, et al (2016) GFlink: An In-memory computing architecture on heterogeneous CPU-GPU clusters for big data. In: Proceedings of the international conference on parallel processing Chen C, Li K, Ouyang A, et al (2016) GFlink: An In-memory computing architecture on heterogeneous CPU-GPU clusters for big data. In: Proceedings of the international conference on parallel processing
52.
Zurück zum Zitat Shin Y, Ghosh J (1991) The pi-sigma network: an efficient higher-order neural network for pattern classification and function approximation. In: Proceedings. IJCNN-91-Seattle: international joint conference on neural networks Shin Y, Ghosh J (1991) The pi-sigma network: an efficient higher-order neural network for pattern classification and function approximation. In: Proceedings. IJCNN-91-Seattle: international joint conference on neural networks
54.
Zurück zum Zitat Broomhead D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2 Broomhead D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2
Metadaten
Titel
A New CNN-Based Model for Financial Time Series: TAIEX and FTSE Stocks Forecasting
verfasst von
Melih Kirisci
Ozge Cagcag Yolcu
Publikationsdatum
10.03.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 4/2022
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-10767-z

Weitere Artikel der Ausgabe 4/2022

Neural Processing Letters 4/2022 Zur Ausgabe