Modeling and Forecasting the Probability of Crypto-Exchange Closures: A Forecast Combination Approach

Magomedov, Said and Fantazzini, Dean (2025): Modeling and Forecasting the Probability of Crypto-Exchange Closures: A Forecast Combination Approach. Forthcoming in: Journal of Risk and Financial Management

Abstract

The popularity of cryptocurrency exchanges has surged in recent years, accompanied by the proliferation of new digital platforms and tokens. However, the issue of credit risk and the reliability of crypto exchanges remain critical, highlighting the need for indicators to assess the safety of investing through these platforms. This study examines a unique, hand-collected dataset of 228 cryptocurrency exchanges operating between April 2011 and May 2024. Using various machine learning algorithms, we identify the key factors contributing to exchange shutdowns, with trading volume, exchange lifespan, and cybersecurity scores emerging as the most significant predictors. Since individual machine learning models often capture distinct data characteristics and exhibit varying error patterns, we employ a forecast combination approach by aggregating multiple predictive distributions. Specifically, we evaluate several specifications of the generalized linear pool (GLP), beta-transformed linear pool (BLP), and beta-mixture combination (BMC). Our findings reveal that the beta-transformed linear pool and the beta-mixture combination achieve the best performances, improving forecast accuracy by approximately 4.1% based on a robust H-measure, which effectively addresses the challenges of misclassification in imbalanced datasets.

• Item Type: MPRA Paper
• Language: English
• Keywords: forecast combination; exchange; bitcoin; crypto assets; cryptocurrencies; credit risk; bankruptcy; default probability
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C35 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics; G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing ; Trading Volume ; Bond Interest Rates; G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation; G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill; G - Financial Economics > G3 - Corporate Finance and Governance > G33 - Bankruptcy ; Liquidation

References

Philippe Bracke, Anupam Datta, Carsten Jung, and Shayak Sen. Machine learning explainability in finance: an application to default risk analysis. Technical report, Bank of England, working paper n. 816, 2019.

Glenn Brier. Verification of forecasts expressed in terms of probability. Monthly weather review, 78(1):1–3, 1950.

Roberto Casarin, Giulia Mantoan, and Francesco Ravazzolo. Bayesian calibration of generalized pools of predictive distributions. Econometrics, 4(1):17, 2016.

Dean Fantazzini and Raffaella Calabrese. Crypto Exchanges and Credit Risk: Modeling and Forecasting the Probability of Closure. Journal of Risk and Financial Management, 14(11):516, 2021.

Pedro G Fonseca and Hugo D Lopes. Calibration of machine learning classifiers for probability of default modelling. arXiv preprint arXiv:1710.08901, 2017.

Tilmann Gneiting and Roopesh Ranjan. Combining predictive distributions. Electronic Journal of Statistics, 7:1747–1782, 2013.

David J Hand. Measuring classifier performance: a coherent alternative to the area under the roc curve. Machine learning, 77(1):103–123, 2009.

David J Hand and Christoforos Anagnostopoulos. A better beta for the h measure of classification performance. Pattern Recognition Letters, 40:41–46, 2014.

James Hanley and Barbara McNeil. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1):29–36, 1982.

Peter Hansen, Asger Lunde, and James Nason. The model confidence set. Econometrica, 79(2):453–497, 2011.

Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An introduction to statistical learning, volume 112. Springer, 2013.

Kajal Lahiri and Liu Yang. Forecasting binary outcomes. In Handbook of economic forecasting, volume 2, pages 1025–1106. Elsevier, 2013.

Kajal Lahiri, Huaming Peng, and Yongchen Zhao. Testing the value of probability forecasts for calibrated combining. International journal of forecasting, 31(1):113–129, 2015.

Cheng Few Lee and Hai-Chin Yu. Application of discriminant analysis, factor analysis, logistic regression, and KMV-Merton model in credit risk analysis. In Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning, pages 4313–4348. World Scientific, 2021.

Seung Ah Lee and George Milunovich. Digital exchange attributes and the risk of closure. Blockchain: Research and Applications, 4(2):100131, 2023.

Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems, pages 4768–4777, 2017.

Charles Metz. Basic principles of ROC analysis. In Seminars in nuclear medicine, volume 8, pages 283–298. Elsevier, 1978.

Charles Metz and Helen Kronman. Statistical significance tests for binormal ROC curves. Journal of Mathematical Psychology, 22(3):218–243, 1980.

George Milunovich and Seung Ah Lee. Cryptocurrency exchanges: Predicting which markets will remain active. Journal of forecasting, 41(5):945–955, 2022.

Tyler Moore, Nicolas Christin, and Janos Szurdi. Revisiting the risks of bitcoin currency exchange closure. ACM Transactions on Internet Technology, 18(4):1–18, 2018.

Mojtaba Nabipour, Pooyan Nayyeri, Hamed Jabani, S Shahab, and Amir Mosavi. Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. Ieee Access, 8:150199–150212, 2020.

Satoshi Nakamoto. A peer to peer electronic cash system. Technical report, 2008. URL https://bitcoin.org/bitcoin.pdf.

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. ” why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144, 2016.

Claude Sammut and Geoffrey Webb. Encyclopedia of machine learning. Springer, 2011.

Patrick Schueffel and Nikolaj Groeneweg. Evaluating crypto exchanges in the absence of governmental frameworks-a multiple criteria scoring model. Available at SSRN 3432798, 2019.

Nutcha Wattanachit, Evan L Ray, Thomas C McAndrew, and Nicholas G Reich. Comparison of combination methods to create calibrated ensemble forecasts for seasonal influenza in the us. Statistics in Medicine, 42(26):4696–4712, 2023.