{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T05:04:19Z","timestamp":1779858259516,"version":"3.53.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s00521-026-11989-1","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T04:30:30Z","timestamp":1776054630000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advancing predictive analytics in cryptocurrencies: deep learning models for projecting Ripple (XRP) prices using technical indicator"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5678-051X","authenticated-orcid":false,"given":"Susrita","family":"Mahapatro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0460-9783","authenticated-orcid":false,"given":"Prabhat Kumar","family":"Sahu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3116-8562","authenticated-orcid":false,"given":"Asit Kumar","family":"Subudhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"11989_CR1","unstructured":"Nakamoto S (2008) Bitcoin: A peer-to-peer electronic cash system,\" Decentralized Business Review"},{"key":"11989_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113250","volume":"149","author":"S Alonso-Monsalve","year":"2020","unstructured":"Alonso-Monsalve S et al (2020) Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators. Expert Syst Appl 149:113250","journal-title":"Expert Syst Appl"},{"issue":"8","key":"11989_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/math10081307","volume":"10","author":"Z Ye","year":"2022","unstructured":"Ye Z et al (2022) A stacking ensemble deep learning model for bitcoin price prediction using Twitter comments on bitcoin. Mathematics 10(8):1307","journal-title":"Mathematics"},{"key":"11989_CR4","unstructured":"\"Cryptocurrency vs Gold A multicounty comparison of cryptocurrency vs gold: Portfolio optimization through generalized simulated annealing"},{"key":"11989_CR5","unstructured":"Coindesk. [Online]. Available: https:\/\/www.coindesk.com"},{"issue":"1","key":"11989_CR6","doi-asserted-by":"publisher","DOI":"10.3390\/ijfs11010005","volume":"11","author":"K Saetia","year":"2022","unstructured":"Saetia K, Yokrattanasak J (2022) Stock movement prediction using machine learning based on technical indicators and Google Trend searches in Thailand. Int J Financial Studies 11(1):5","journal-title":"Int J Financial Studies"},{"issue":"3","key":"11989_CR7","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.jfds.2018.10.001","volume":"5","author":"JZ Huang","year":"2019","unstructured":"Huang JZ, Huang W, Ni J (2019) Predicting bitcoin returns using high-dimensional technical indicators. J Finance Data Sci 5(3):140\u2013155","journal-title":"J Finance Data Sci"},{"key":"11989_CR8","unstructured":"S. Athey S, Parashkevov I, Sarukkai V, Xia J (2016) Bitcoin pricing, adoption, and usage: Theory and evidence, Working Paper, Stanford University"},{"key":"11989_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-019-03282-3","author":"P Giudici","year":"2019","unstructured":"Giudici P, Polinesi G (2019) Crypto price discovery through correlation networks. Ann Oper Res. https:\/\/doi.org\/10.1007\/s10479-019-03282-3","journal-title":"Ann Oper Res"},{"issue":"1","key":"11989_CR10","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10479-020-03575-y","volume":"297","author":"E Akyildirim","year":"2021","unstructured":"Akyildirim E, Goncu A, Sensoy A (2021) Prediction of cryptocurrency returns using machine learning. Ann Oper Res 297(1):3\u201336","journal-title":"Ann Oper Res"},{"issue":"2","key":"11989_CR11","doi-asserted-by":"publisher","DOI":"10.3390\/jrfm12020053","volume":"12","author":"CY-H Chen","year":"2019","unstructured":"Chen CY-H, Hafner CM (2019) Sentiment-induced bubbles in the cryptocurrency market. J Risk Financial Management 12(2):53","journal-title":"J Risk Financial Management"},{"key":"11989_CR12","doi-asserted-by":"crossref","unstructured":"Phillips RC, Gorse D (2018) Mutual-excitation of cryptocurrency market returns and social media topics,\" In Proceedings of the 4th International Conference on Frontiers of Educational Technologies, ACM, pp 80\u201386","DOI":"10.1145\/3233347.3233370"},{"issue":"1","key":"11989_CR13","doi-asserted-by":"publisher","DOI":"10.1140\/epjds\/s13688-020-00239-6","volume":"9","author":"S Bartolucci","year":"2020","unstructured":"Bartolucci S et al (2020) The butterfly \u2018affect\u2019: impact of development practices on cryptocurrency prices. EPJ Data Sci 9(1):21","journal-title":"EPJ Data Sci"},{"key":"11989_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116804","volume":"198","author":"M Ortu","year":"2022","unstructured":"Ortu M et al (2022) On technical trading and social media indicators for cryptocurrency price classification through deep learning. Expert Syst Appl 198:116804","journal-title":"Expert Syst Appl"},{"issue":"2","key":"11989_CR15","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/S0304-405X(98)00052-X","volume":"51","author":"F Allen","year":"1999","unstructured":"Allen F, Karjalainen R (1999) Using genetic algorithms to find technical trading rules. J Financ Econ 51(2):245\u2013271","journal-title":"J Financ Econ"},{"issue":"8","key":"11989_CR16","doi-asserted-by":"publisher","first-page":"10896","DOI":"10.1016\/j.eswa.2009.02.038","volume":"36","author":"M-C Lee","year":"2009","unstructured":"Lee M-C (2009) Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Syst Appl 36(8):10896\u201310904","journal-title":"Expert Syst Appl"},{"key":"11989_CR17","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.eswa.2017.04.030","volume":"83","author":"E Chong","year":"2017","unstructured":"Chong E, Han C, Park FC (2017) Deep learning networks for stock market analysis and prediction: methodology, data representations, and case studies. Expert Syst Appl 83:187\u2013205","journal-title":"Expert Syst Appl"},{"key":"11989_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115497","volume":"184","author":"E Barucci","year":"2021","unstructured":"Barucci E, Bonollo M, Poli F, Rroji E (2021) A machine learning algorithm for stock picking built on information-based outliers. Expert Syst Appl 184:115497","journal-title":"Expert Syst Appl"},{"key":"11989_CR19","doi-asserted-by":"crossref","unstructured":"Jaquart P, Dann D, Martin C (2020) Machine learning for bitcoin pricing - a structured literature review, In Wirtschaftsinformatik (Zentrale Tracks), pp 174\u2013188","DOI":"10.30844\/wi_2020_b4-jaquart"},{"issue":"1","key":"11989_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jocs.2010.12.007","volume":"2","author":"J Bollen","year":"2011","unstructured":"Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1\u20138","journal-title":"J Comput Sci"},{"key":"11989_CR21","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.neucom.2016.10.103","volume":"264","author":"SY Yang","year":"2017","unstructured":"Yang SY, Mo SYK, Liu A, Kirilenko AA (2017) Genetic programming optimization for sentiment feedback strength-based trading strategy. Neurocomputing 264:29\u201341","journal-title":"Neurocomputing"},{"issue":"2","key":"11989_CR22","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1080\/15427560.2020.1772261","volume":"22","author":"S Duz Tan","year":"2021","unstructured":"Duz Tan S, Tas O (2021) Social media sentiment in international stock returns and trading activity. J Behav Finance 22(2):221\u2013234","journal-title":"J Behav Finance"},{"issue":"10","key":"11989_CR23","doi-asserted-by":"publisher","first-page":"6885","DOI":"10.1016\/j.eswa.2010.03.033","volume":"37","author":"LA Teixeira","year":"2010","unstructured":"Teixeira LA, De Oliveira ALI (2010) A method for automatic stock trading combining technical analysis and nearest neighbor classification. Expert Syst Appl 37(10):6885\u20136890","journal-title":"Expert Syst Appl"},{"issue":"1","key":"11989_CR24","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.jfds.2016.03.002","volume":"2","author":"R Dash","year":"2016","unstructured":"Dash R, Dash PK (2016) A hybrid stock trading framework integrating technical analysis with machine learning techniques. J Finance Data Sci 2(1):42\u201357","journal-title":"J Finance Data Sci"},{"key":"11989_CR25","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1016\/j.ins.2014.03.096","volume":"278","author":"Q Li","year":"2014","unstructured":"Li Q, Wang T, Li P, Liu L, Gong Q, Chen Y (2014) The effect of news and public mood on stock movements. Inf Sci 278:826\u2013840","journal-title":"Inf Sci"},{"issue":"2","key":"11989_CR26","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.jfds.2018.02.002","volume":"4","author":"A Atkins","year":"2018","unstructured":"Atkins A, Niranjan M, Gerding E (2018) Financial news predicts stock market volatility better than close price. The Journal of Finance and Data Science 4(2):120\u2013137","journal-title":"The Journal of Finance and Data Science"},{"issue":"6","key":"11989_CR27","first-page":"81","volume":"12","author":"SI Sabilla","year":"2019","unstructured":"Sabilla SI, Sarno R, Triyana K (2019) Optimizing threshold using Pearson correlation for selecting features of electronic nose signals. Int J Intell Eng Syst 12(6):81\u201390","journal-title":"Int J Intell Eng Syst"},{"key":"11989_CR28","unstructured":"H. Pabu\u00e7cu, S. Ongan, and A. Ongan, \"Forecasting the movements of Bitcoin prices: An application of machine learning algorithms,\" arXiv preprint arXiv:2303.04642, 2023."},{"issue":"10","key":"11989_CR29","doi-asserted-by":"publisher","first-page":"4741","DOI":"10.1007\/s00521-020-05532-z","volume":"33","author":"W Lu","year":"2021","unstructured":"Lu W, Li J, Wang J, Qin L (2021) A CNN-BiLSTM-AM method for stock price prediction. Neural Comput Appl 33(10):4741\u20134753","journal-title":"Neural Comput Appl"},{"key":"11989_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115199","volume":"182","author":"ML Shen","year":"2021","unstructured":"Shen ML et al (2021) Effective multinational trade forecasting using LSTM recurrent neural network. Expert Syst Appl 182:115199","journal-title":"Expert Syst Appl"},{"issue":"7","key":"11989_CR31","doi-asserted-by":"publisher","DOI":"10.3390\/app13074644","volume":"13","author":"H Song","year":"2023","unstructured":"Song H, Choi H (2023) Forecasting stock market indices using the recurrent neural network-based hybrid models: CNN-LSTM, GRU-CNN, and ensemble models. Appl Sci 13(7):4644","journal-title":"Appl Sci"},{"key":"11989_CR32","doi-asserted-by":"crossref","unstructured":"S. Mahapatro, P.K. Sahu, and A. Subudhi, \"Navigating XRP volatility: A deep learning perspective on technical indicators,\" International Journal of Advanced Computer Science & Applications, vol. 15, no. 6, 2024.","DOI":"10.14569\/IJACSA.2024.01506115"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-11989-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-026-11989-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-11989-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T04:43:51Z","timestamp":1779857031000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-026-11989-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":32,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["11989"],"URL":"https:\/\/doi.org\/10.1007\/s00521-026-11989-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"30 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We, the authors, declare that there are no financial, non-financial, or conflicts of interest influencing the publication of this paper. The submission is made purely in the interest of education and research. Additionally, we confirm that we have not received financial support in any form from any individual or corporate entity. We also confirm that there is no conflict of interest among the Authors or with any other parties.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"294"}}