{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T06:04:06Z","timestamp":1768802646691,"version":"3.49.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T00:00:00Z","timestamp":1609977600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T00:00:00Z","timestamp":1609977600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s00500-020-05516-0","type":"journal-article","created":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T11:20:18Z","timestamp":1610018418000},"page":"5151-5161","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Deep learning-based investment strategy: technical indicator clustering and residual blocks"],"prefix":"10.1007","volume":"25","author":[{"given":"Anuar","family":"Maratkhan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ibrakhim","family":"Ilyassov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Madiyar","family":"Aitzhanov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7002-7506","authenticated-orcid":false,"given":"M. Fatih","family":"Demirci","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A. Murat","family":"Ozbayoglu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,7]]},"reference":[{"issue":"10","key":"5516_CR1","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1109\/TASLP.2014.2339736","volume":"22","author":"O Abdel-Hamid","year":"2014","unstructured":"Abdel-Hamid O, Mohamed A, Jiang H, Deng L, Penn G, Yu D (2014) Convolutional neural networks for speech recognition. IEEE\/ACM Trans Audio Speech Lang Process 22(10):1533\u20131545. https:\/\/doi.org\/10.1109\/TASLP.2014.2339736","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"5516_CR2","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/978-3-319-93034-3_22","volume-title":"Deep learning for forecasting stock returns in the cross-section. Advances in knowledge discovery and data mining","author":"M Abe","year":"2018","unstructured":"Abe M, Nakayama H (2018) Deep learning for forecasting stock returns in the cross-section. Advances in knowledge discovery and data mining. Springer International Publishing, Berlin, pp 273\u2013284. https:\/\/doi.org\/10.1007\/978-3-319-93034-3_22"},{"key":"5516_CR3","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1007\/978-3-319-60438-1_69","volume-title":"Predicting stock trends based on expert recommendations using GRU\/LSTM neural networks, Lecture notes in computer science","author":"P Buczkowski","year":"2017","unstructured":"Buczkowski P (2017) Predicting stock trends based on expert recommendations using GRU\/LSTM neural networks, Lecture notes in computer science. Springer International Publishing, Berlin, pp 708\u2013717. https:\/\/doi.org\/10.1007\/978-3-319-60438-1_69"},{"key":"5516_CR4","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.eswa.2016.02.006","volume":"55","author":"RC Cavalcante","year":"2016","unstructured":"Cavalcante RC, Brasileiro RC, Souza VL, Nobrega JP, Oliveira AL (2016) Computational intelligence and financial markets: a survey and future directions. Expert Syst Appl 55:194\u2013211. https:\/\/doi.org\/10.1016\/j.eswa.2016.02.006","journal-title":"Expert Syst Appl"},{"key":"5516_CR5","doi-asserted-by":"publisher","first-page":"48625","DOI":"10.1109\/access.2018.2859809","volume":"6","author":"L Chen","year":"2018","unstructured":"Chen L, Qiao Z, Wang M, Wang C, Du R, Stanley HE (2018) Which artificial intelligence algorithm better predicts the chinese stock market? IEEE Access 6:48625\u201348633. https:\/\/doi.org\/10.1109\/access.2018.2859809","journal-title":"IEEE Access"},{"key":"5516_CR6","doi-asserted-by":"publisher","unstructured":"Chen J, Chen W, Huang C, Huang S, Chen A (2016) Financial time-series data analysis using deep convolutional neural networks. In: 2016 7th International conference on cloud computing and big data (CCBD), pp 87\u201392. https:\/\/doi.org\/10.1109\/CCBD.2016.027","DOI":"10.1109\/CCBD.2016.027"},{"key":"5516_CR7","doi-asserted-by":"publisher","unstructured":"Chen K, Zhou Y, Dai F (2015) A LSTM-based method for stock returns prediction: a case study of china stock market. In: 2015 IEEE international conference on big data (Big Data). https:\/\/doi.org\/10.1109\/bigdata.2015.7364089","DOI":"10.1109\/bigdata.2015.7364089"},{"issue":"4.1","key":"5516_CR8","first-page":"54","volume":"11","author":"E Dezsi","year":"2016","unstructured":"Dezsi E, Nistor IA (2016) Can deep machine learning outsmart the market? A comparison between econometric modelling and long-short term memory. Rom Econ Bus Rev 11(4.1):54\u201373","journal-title":"Rom Econ Bus Rev"},{"key":"5516_CR9","doi-asserted-by":"publisher","unstructured":"Doering J, Fairbank M, Markose S (2017) Convolutional neural networks applied to high-frequency market microstructure forecasting. In: 2017 9th Computer science and electronic engineering (CEEC), pp 31\u201336. https:\/\/doi.org\/10.1109\/CEEC.2017.8101595","DOI":"10.1109\/CEEC.2017.8101595"},{"key":"5516_CR10","unstructured":"Feng G, He J, Polson NG, (2018)Deep learning for predicting asset returns. arXiv:arXiv:1804.09314"},{"key":"5516_CR11","doi-asserted-by":"publisher","unstructured":"Gudelek MU, Boluk SA, Ozbayoglu AM (2017) A deep learning based stock trading model with 2-d CNN trend detection. In: 2017 IEEE symposium series on computational intelligence (SSCI). https:\/\/doi.org\/10.1109\/ssci.2017.8285188","DOI":"10.1109\/ssci.2017.8285188"},{"key":"5516_CR12","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.knosys.2017.09.023","volume":"137","author":"H Gunduz","year":"2017","unstructured":"Gunduz H, Yaslan Y, Cataltepe Z (2017) Intraday prediction of borsa istanbul using convolutional neural networks and feature correlations. Knowl Based Syst 137:138\u2013148. https:\/\/doi.org\/10.1016\/j.knosys.2017.09.023","journal-title":"Knowl Based Syst"},{"key":"5516_CR13","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: The IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5516_CR14","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1016\/j.procs.2018.05.050","volume":"132","author":"M Hiransha","year":"2018","unstructured":"Hiransha M, Gopalakrishnan EA, Menon VK, Soman KP (2018) Nse stock market prediction using deep-learning models. Procedia Comput Sci 132:1351\u20131362. https:\/\/doi.org\/10.1016\/j.procs.2018.05.050","journal-title":"Procedia Comput Sci"},{"key":"5516_CR15","unstructured":"Jia H (2016) Investigation into the effectiveness of long short term memory networks for stock price prediction. arXiv:arXiv:1603.07893"},{"key":"5516_CR16","doi-asserted-by":"publisher","unstructured":"Khare K, Darekar O, Gupta P, Attar VZ (2017) Short term stock price prediction using deep learning. In: 2017 2nd IEEE international conference on recent trends in electronics, information communication technology (RTEICT), pp 482\u2013486. https:\/\/doi.org\/10.1109\/RTEICT.2017.8256643","DOI":"10.1109\/RTEICT.2017.8256643"},{"key":"5516_CR17","doi-asserted-by":"publisher","unstructured":"Kim Y (2014)Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), association for computational linguistics, pp 1746\u20131751. https:\/\/doi.org\/10.3115\/v1\/D14-1181. http:\/\/aclweb.org\/anthology\/D14-1181","DOI":"10.3115\/v1\/D14-1181"},{"key":"5516_CR18","unstructured":"Kingma DP, Ba J (2017) Adam: a method for stochastic optimization, CoRR abs\/1412.6980. arxiv.org\/abs\/1412.6980"},{"key":"5516_CR19","first-page":"1097","volume-title":"Advances in neural information processing systems","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems, vol 25. Curran Associates Inc, Red Hook, pp 1097\u20131105"},{"key":"5516_CR20","first-page":"276","volume":"261","author":"Y LeCun","year":"1995","unstructured":"LeCun Y, Jackel L, Bottou L, Cortes C, Denker JS, Drucker H, Guyon I, Muller UA, Sackinger E, Simard P et al (1995) Learning algorithms for classification: a comparison on handwritten digit recognition. Neural Netw Stat Mech Perspect 261:276","journal-title":"Neural Netw Stat Mech Perspect"},{"key":"5516_CR21","doi-asserted-by":"publisher","unstructured":"Liang Q, Rong W, Zhang J, Liu J, Xiong Z (2017) Restricted Boltzmann machine based stock market trend prediction. In: 2017 International joint conference on neural networks (IJCNN). https:\/\/doi.org\/10.1109\/ijcnn.2017.7966014","DOI":"10.1109\/ijcnn.2017.7966014"},{"key":"5516_CR22","doi-asserted-by":"publisher","unstructured":"Liu S, Zhang C, Ma J (2017) CNN-LSTM neural network model for quantitative strategy analysis in stock markets. In: Neural information processing, Springer International Publishing, pp 198\u2013206. https:\/\/doi.org\/10.1007\/978-3-319-70096-0_21","DOI":"10.1007\/978-3-319-70096-0_21"},{"key":"5516_CR23","doi-asserted-by":"publisher","first-page":"838","DOI":"10.1109\/CEC.2019.8789932","volume":"2019","author":"A Maratkhan","year":"2019","unstructured":"Maratkhan A, Ilyassov I, Aitzhanov M, Demirci MF, Ozbayoglu M (2019) Financial forecasting using deep learning with an optimized trading strategy. IEEE Congr Evolut Comput (CEC) 2019:838\u2013844. https:\/\/doi.org\/10.1109\/CEC.2019.8789932","journal-title":"IEEE Congr Evolut Comput (CEC)"},{"issue":"2","key":"5516_CR24","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.aci.2017.09.001","volume":"14","author":"M Mareli","year":"2018","unstructured":"Mareli M, Twala B (2018) An adaptive Cuckoo search algorithm for optimisation. Appl Comput Inform 14(2):107\u2013115. https:\/\/doi.org\/10.1016\/j.aci.2017.09.001","journal-title":"Appl Comput Inform"},{"key":"5516_CR25","unstructured":"Ng JY, Hausknecht MJ, Vijayanarasimhan S, Vinyals O, Monga R, Toderici G (2015) Beyond short snippets: deep networks for video classification, CoRR abs\/1503.08909. arxiv.org\/abs\/1503.08909"},{"key":"5516_CR26","doi-asserted-by":"publisher","unstructured":"Samarawickrama A, Fernando T (2017) A recurrent neural network approach in predicting daily stock prices an application to the Sri Lankan stock market. In: 2017 IEEE international conference on industrial and information systems (ICIIS). https:\/\/doi.org\/10.1109\/iciinfs.2017.8300345","DOI":"10.1109\/iciinfs.2017.8300345"},{"issue":"1","key":"5516_CR27","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/0925-2312(90)90013-H","volume":"2","author":"E Sch\u00f6neburg","year":"1990","unstructured":"Sch\u00f6neburg E (1990) Stock price prediction using neural networks: a project report. Neurocomputing 2(1):17\u201327. https:\/\/doi.org\/10.1016\/0925-2312(90)90013-H","journal-title":"Neurocomputing"},{"key":"5516_CR28","doi-asserted-by":"publisher","unstructured":"Selvin S, Vinayakumar R, Gopalakrishnan EA, Menon VK, Soman KP (2017) Stock price prediction using LSTM, RNN and CNN-sliding window model. In: 2017 International conference on advances in computing, communications and informatics (ICACCI), pp 1643\u20131647. https:\/\/doi.org\/10.1109\/ICACCI.2017.8126078","DOI":"10.1109\/ICACCI.2017.8126078"},{"key":"5516_CR29","unstructured":"Sezer OB, Ozbayoglu AM (2019) Financial trading model with stock bar chart image time series with deep convolutional neural networks. arXiv preprint arXiv:1903.04610"},{"key":"5516_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2018.04.024","author":"O Sezer","year":"2018","unstructured":"Sezer O, Ozbayoglu M (2018) Algorithmic financial trading with deep convolutional neural networks: time series to image conversion approach. Appl Soft Comput. https:\/\/doi.org\/10.1016\/j.asoc.2018.04.024","journal-title":"Appl Soft Comput"},{"key":"5516_CR31","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/j.procs.2017.09.031","volume":"114","author":"OB Sezer","year":"2017","unstructured":"Sezer OB, Ozbayoglu M, Dogdu E (2017) A deep neural-network based stock trading system based on evolutionary optimized technical analysis parameters. Procedia Comput Sci 114:473\u2013480. https:\/\/doi.org\/10.1016\/j.procs.2017.09.031","journal-title":"Procedia Comput Sci"},{"key":"5516_CR32","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition, CoRR abs\/1409.1556. arxiv.org\/abs\/1409.1556"},{"issue":"18","key":"5516_CR33","doi-asserted-by":"publisher","first-page":"18569","DOI":"10.1007\/s11042-016-4159-7","volume":"76","author":"R Singh","year":"2016","unstructured":"Singh R, Srivastava S (2016) Stock prediction using deep learning. Multimed Tools Appl 76(18):18569\u201318584. https:\/\/doi.org\/10.1007\/s11042-016-4159-7","journal-title":"Multimed Tools Appl"},{"key":"5516_CR34","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3141294","author":"J Sirignano","year":"2019","unstructured":"Sirignano J, Cont R (2019) Universal features of price formation in financial markets: perspectives from deep learning. SSRN Electr J. https:\/\/doi.org\/10.2139\/ssrn.3141294","journal-title":"SSRN Electr J"},{"key":"5516_CR35","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: The IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"5516_CR36","unstructured":"Torres DG, Qiu H (2018) Applying Recurrent Neural Networks for Multivariate Time Series Forecasting of Volatile Financial Data. ResearchGate. Available: https:\/\/www.researchgate.net\/project\/Applying-Recurrent-Neural-Networks-for- Multivariate-Time-Series-Forecasting-of-Volatile-Financial-Data. Accessed 14 Dec 2020"},{"key":"5516_CR37","doi-asserted-by":"publisher","unstructured":"Tsantekidis A, Passalis A, Tefas A, Kanniainen J, Gabbouj M, Iosifidis A (2017) Forecasting stock prices from the limit order book using convolutional neural networks. In: 2017 IEEE 19th conference on business informatics (CBI). https:\/\/doi.org\/10.1109\/cbi.2017.23","DOI":"10.1109\/cbi.2017.23"},{"key":"5516_CR38","doi-asserted-by":"publisher","unstructured":"Tsantekidis A, Passalis N, Tefas A, Kanniainen J, Gabbouj M, Iosifidis A (2017) Using deep learning to detect price change indications in financial markets. In: IEEE 2017 25th European signal processing conference (EUSIPCO). https:\/\/doi.org\/10.23919\/eusipco.2017.8081663","DOI":"10.23919\/eusipco.2017.8081663"},{"key":"5516_CR39","doi-asserted-by":"publisher","unstructured":"Yuan Z, Zhang R, Shao X (2018) Deep and wide neural networks on multiple sets of temporal data with correlation. In: Proceedings of the 2018 international conference on computing and data engineering-ICCDE 2018, ACM Press. https:\/\/doi.org\/10.1145\/3219788.3219793","DOI":"10.1145\/3219788.3219793"},{"key":"5516_CR40","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1007\/978-3-319-93803-5_58","volume-title":"Deep stock ranker: a LSTM neural network model for stock selection. Data mining and big data","author":"X Zhang","year":"2018","unstructured":"Zhang X, Tan Y (2018) Deep stock ranker: a LSTM neural network model for stock selection. Data mining and big data. Springer International Publishing, Berlin, pp 614\u2013623. https:\/\/doi.org\/10.1007\/978-3-319-93803-5_58"},{"key":"5516_CR41","unstructured":"Zhang X, LeCun Y (2015) Text understanding from scratch, CoRR abs\/1502.01710. arxiv.org\/abs\/1502.01710"},{"issue":"2019","key":"5516_CR42","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.eswa.2018.07.065","volume":"115","author":"F Zhou","year":"2019","unstructured":"Zhou F, Min Zhou H, Yang Z, Yang L (2019) Emd2fnn: a strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction. Expert Syst Appl 115(2019):136\u2013151. https:\/\/doi.org\/10.1016\/j.eswa.2018.07.065","journal-title":"Expert Syst Appl"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-05516-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-020-05516-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-05516-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T14:18:58Z","timestamp":1615817938000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-020-05516-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,7]]},"references-count":42,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["5516"],"URL":"https:\/\/doi.org\/10.1007\/s00500-020-05516-0","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,7]]},"assertion":[{"value":"7 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal rights"}}]}}