{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T08:33:13Z","timestamp":1765960393382,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Blockchain-based cryptocurrency markets present unique analytical challenges due to their decentralized nature, continuous operation, and extreme volatility. Traditional price prediction models often struggle with the binary trade execution problem in these markets. This study introduces a confidence-based classification framework that separates directional prediction from execution decisions in cryptocurrency trading. We develop a neural network system that processes multi-scale market data, combining daily macroeconomic indicators with a high-frequency order book microstructure. The model trains exclusively on directional movements (up versus down) and uses prediction confidence levels to determine trade execution. We evaluate the framework across 11 major cryptocurrency pairs over 12 months. Experimental results demonstrate 82.68% direction accuracy on executed trades with 151.11-basis point average net profit per trade at 11.99% market coverage. Order book features dominate predictive importance (81.3% of selected features), validating the critical role of blockchain microstructure data for short-term price prediction. The confidence-based execution strategy achieves superior risk-adjusted returns compared to traditional classification approaches while providing natural risk management capabilities through selective trade execution. These findings contribute to blockchain technology applications in financial markets by demonstrating how a decentralized market microstructure can be leveraged for systematic trading strategies. The methodology offers practical implementation guidelines for cryptocurrency algorithmic trading while advancing the understanding of machine learning applications in blockchain-based financial systems.<\/jats:p>","DOI":"10.3390\/app152011145","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:07:22Z","timestamp":1760706442000},"page":"11145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Machine Learning Analytics for Blockchain-Based Financial Markets: A Confidence-Threshold Framework for Cryptocurrency Price Direction Prediction"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2331-6326","authenticated-orcid":false,"given":"Oleksandr","family":"Kuznetsov","sequence":"first","affiliation":[{"name":"Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, 22060 Novedrate, CO, Italy"},{"name":"Department of Intelligent Software Systems and Technologies, School of Computer Science and Artificial Intelligence, V.N. Karazin Kharkiv National University, 4 Svobody Sq., 61022 Kharkiv, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2131-0281","authenticated-orcid":false,"given":"Oleksii","family":"Kostenko","sequence":"additional","affiliation":[{"name":"State Scientific Institution \u201cInstitute of Information, Security and Law of the National Academy of Legal Sciences of Ukraine\u201d, 3, Pylypa Orlyka Street, 01024 Kyiv, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5227-2329","authenticated-orcid":false,"given":"Kateryna","family":"Klymenko","sequence":"additional","affiliation":[{"name":"Institute of Compliance in Financial Markets, Chicago Kent College of Law, 565 West Adams Street, Chicago, IL 60661, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4536-2438","authenticated-orcid":false,"given":"Zoriana","family":"Hbur","sequence":"additional","affiliation":[{"name":"Department of Economics, Hryhorii Skovoroda University in Pereiaslav, 30, Sukhomlynsky Street, 08401 Pereiaslav, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9244-0238","authenticated-orcid":false,"given":"Roman","family":"Kovalskyi","sequence":"additional","affiliation":[{"name":"Department of Post-Graduate and Doctoral Courses, State University \u201cKyiv Aviation Institute\u201d, 1, Liubomyra Huzara Ave., 03058 Kyiv, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ariffin, N., and Ismail, A.Z. (2019, January 5\u20136). The Design and Implementation of Trade Finance Application Based on Hyperledger Fabric Permissioned Blockchain Platform. Proceedings of the 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Yogyakarta, Indonesia.","DOI":"10.1109\/ISRITI48646.2019.9034576"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3881","DOI":"10.1109\/ACCESS.2023.3349019","article-title":"On the Integration of Artificial Intelligence and Blockchain Technology: A Perspective about Security","volume":"12","author":"Kuznetsov","year":"2023","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104512","DOI":"10.1016\/j.iref.2025.104512","article-title":"Cryptocurrency Dynamics during Global Crises: Insights from Bitcoin\u2019s Interplay with Traditional Markets","volume":"103","author":"Ballis","year":"2025","journal-title":"Int. Rev. Econ. Financ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103218","DOI":"10.1016\/j.irfa.2024.103218","article-title":"Cryptocurrency Anomalies and Economic Constraints","volume":"94","author":"Fieberg","year":"2024","journal-title":"Int. Rev. Financ. Anal."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102524","DOI":"10.1016\/j.najef.2025.102524","article-title":"Risk Spillover and Hedging Effects between Stock Markets and Cryptocurrency Markets Depending upon Network Analysis","volume":"80","author":"Guo","year":"2025","journal-title":"N. Am. J. Econ. Financ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"104546","DOI":"10.1016\/j.irfa.2025.104546","article-title":"Exploring Resilience in the Cryptocurrency Market: Risk Transmission and Network Robustness","volume":"106","author":"Yin","year":"2025","journal-title":"Int. Rev. Financ. Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"113229","DOI":"10.1016\/j.asoc.2025.113229","article-title":"Time Series Prediction for Cryptocurrency Markets with Transformer and Parallel Convolutional Neural Networks","volume":"177","author":"Izadi","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"109368","DOI":"10.1016\/j.compeleceng.2024.109368","article-title":"Cryptocurrency Trend Forecast Using Technical Analysis and Trading with Randomness-Preserving","volume":"118","author":"Liu","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"101609","DOI":"10.1016\/j.intfin.2022.101609","article-title":"Time Horizon and Cryptocurrency Ownership: Is Crypto Not Speculative?","volume":"79","author":"Bonaparte","year":"2022","journal-title":"J. Int. Financ. Mark. Inst. Money"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103938","DOI":"10.1016\/j.iref.2025.103938","article-title":"Market Efficiency and Its Determinants: Macro-Level Dynamics and Micro-Level Characteristics of Cryptocurrencies","volume":"98","author":"Bouteska","year":"2025","journal-title":"Int. Rev. Econ. Financ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"104214","DOI":"10.1016\/j.iref.2025.104214","article-title":"Quantifying Systemic Risk in Cryptocurrency Markets: A High-Frequency Approach","volume":"102","author":"Franco","year":"2025","journal-title":"Int. Rev. Econ. Financ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108187","DOI":"10.1016\/j.frl.2025.108187","article-title":"Liquidity Commonality in Cryptocurrencies","volume":"85","author":"Liu","year":"2025","journal-title":"Financ. Res. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"115795","DOI":"10.1016\/j.apm.2024.115795","article-title":"An Innovative Method for Short-Term Forecasting of Blockchain Cryptocurrency Price","volume":"138","author":"Yang","year":"2025","journal-title":"Appl. Math. Model."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108356","DOI":"10.1016\/j.frl.2025.108356","article-title":"State Transitions and Momentum Effect in Cryptocurrency Market","volume":"86","author":"Hsieh","year":"2025","journal-title":"Financ. Res. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7046","DOI":"10.1016\/j.eswa.2015.05.013","article-title":"Evaluating Multiple Classifiers for Stock Price Direction Prediction","volume":"42","author":"Ballings","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.eswa.2019.01.012","article-title":"Literature Review: Machine Learning Techniques Applied to Financial Market Prediction","volume":"124","author":"Henrique","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"114309","DOI":"10.1016\/j.dss.2024.114309","article-title":"Generalized Visible Curvature: An Indicator for Bubble Identification and Price Trend Prediction in Cryptocurrencies","volume":"185","author":"Zhang","year":"2024","journal-title":"Decis. Support Syst."},{"key":"ref_18","unstructured":"(2025, October 11). Cryptocurrency Order Book Data: Asks and Bids. Kaggle: San Francisco, CA, USA. Available online: https:\/\/www.kaggle.com\/datasets\/ilyazawilsiv\/cryptocurrency-order-book-data-asks-and-bids."},{"key":"ref_19","unstructured":"(2025, October 11). Top 100 Cryptocurrency (2020\u20132025), Available online: https:\/\/www.kaggle.com\/datasets\/imtkaggleteam\/top-100-cryptocurrency-2020-2025."},{"key":"ref_20","first-page":"47","article-title":"The Price Impact of Order Book Events","volume":"12","author":"Cont","year":"2012","journal-title":"J. Financ. Econom."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.jedc.2011.09.012","article-title":"The Market Impact of a Limit Order","volume":"36","author":"Hautsch","year":"2012","journal-title":"J. Econ. Dyn. Control"},{"key":"ref_22","unstructured":"Cartea, \u00c1., Jaimungal, S., and Penalva, J. (2015). Algorithmic and High-Frequency Trading, Cambridge University Press."},{"key":"ref_23","first-page":"1605","article-title":"On the Foundations of Noise-Free Selective Classification","volume":"11","author":"Wiener","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_24","unstructured":"Geifman, Y., and El-Yaniv, R. (2017, January 4\u20139). Selective Classification for Deep Neural Networks. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_25","unstructured":"Romano, Y., Patterson, E., and Cand\u00e8s, E.J. (2019, January 8\u201314). Conformalized Quantile Regression. Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5","DOI":"10.21314\/JOR.2001.041","article-title":"Optimal Execution of Portfolio Transactions","volume":"3","author":"Almgren","year":"2001","journal-title":"J. Risk"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.jfineco.2019.07.001","article-title":"Trading and Arbitrage in Cryptocurrency Markets","volume":"135","author":"Makarov","year":"2020","journal-title":"J. Financ. Econ."},{"key":"ref_28","unstructured":"(2025, October 11). Fees-Binance.US|Buy & Sell Crypto. Available online: https:\/\/www.binance.us\/fees."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108509","DOI":"10.1016\/j.isci.2023.108509","article-title":"A Survey of Deep Learning Applications in Cryptocurrency","volume":"27","author":"Zhang","year":"2024","journal-title":"iScience"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"124404","DOI":"10.1016\/j.eswa.2024.124404","article-title":"Probabilistic Deep Learning and Transfer Learning for Robust Cryptocurrency Price Prediction","volume":"255","author":"Golnari","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"121520","DOI":"10.1016\/j.eswa.2023.121520","article-title":"Attention-Based CNN\u2013LSTM for High-Frequency Multiple Cryptocurrency Trend Prediction","volume":"237","author":"Peng","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"113955","DOI":"10.1016\/j.dss.2023.113955","article-title":"LSTM-ReGAT: A Network-Centric Approach for Cryptocurrency Price Trend Prediction","volume":"169","author":"Zhong","year":"2023","journal-title":"Decis. Support Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"130359","DOI":"10.1016\/j.physa.2025.130359","article-title":"Harnessing Technical Indicators with Deep Learning Based Price Forecasting for Cryptocurrency Trading","volume":"660","author":"Kang","year":"2025","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"125457","DOI":"10.1016\/j.eswa.2024.125457","article-title":"CARROT: Simultaneous Prediction of Anomalies from Groups of Correlated Cryptocurrency Trends","volume":"260","author":"Pellicani","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"112088","DOI":"10.1016\/j.knosys.2024.112088","article-title":"Machine Learning Ethereum Cryptocurrency Prediction and Knowledge-Based Investment Strategies","volume":"299","author":"Santos","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1016\/j.ejor.2019.09.018","article-title":"Deep Learning in Business Analytics and Operations Research: Models, Applications and Managerial Implications","volume":"281","author":"Kraus","year":"2020","journal-title":"Eur. J. Oper. Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Campbell, J.Y., Lo, A.W., and MacKinlay, A.C. (1997). The Econometrics of Financial Markets, Princeton University Press.","DOI":"10.1515\/9781400830213"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1111\/j.1540-6261.1993.tb04702.x","article-title":"Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency","volume":"48","author":"Jegadeesh","year":"1993","journal-title":"J. Financ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"49","DOI":"10.3905\/jpm.1994.409501","article-title":"The Sharpe Ratio","volume":"21","author":"Sharpe","year":"1994","journal-title":"J. Portf. Manag."},{"key":"ref_40","first-page":"449","article-title":"Synthesis Concept of Information and Analytical Support for Bank Security System","volume":"11","author":"Trydid","year":"2014","journal-title":"Actual Probl. Econ."},{"key":"ref_41","first-page":"151","article-title":"Indicators-Markers for Assessment of Probability of Insurance Companies Relatedness in Implementation of Risk-Oriented Approach","volume":"29","author":"Vnukova","year":"2020","journal-title":"Econ. Stud. J."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kavun, S., Zamula, A., and Mikheev, I. (2017, January 10\u201313). Calculation of Expense for Local Computer Networks. Proceedings of the 2017 4th International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T), Kharkov, Ukraine.","DOI":"10.1109\/INFOCOMMST.2017.8246369"}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/15\/20\/11145\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T08:48:45Z","timestamp":1760950125000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/15\/20\/11145"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,17]]},"references-count":42,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["app152011145"],"URL":"https:\/\/doi.org\/10.3390\/app152011145","relation":{},"ISSN":["2076-3417"],"issn-type":[{"type":"electronic","value":"2076-3417"}],"subject":[],"published":{"date-parts":[[2025,10,17]]}}}