THE SUSPICION OF MANIPULATION IN BITCOIN RETURNS: AN INVESTIGATION WITH BENFORD’S LAW

Abstract

Purpose- Bitcoin is a blockchain-based digital currency that can be generated via data mining. Several complex computational methods along with random processes have been utilized in the production of that currency. Nonetheless, it is an important research question whether this process, which have been taking place in a entirely digital platform, involves manipulation. Accordingly, the amount of Bitcoin in circulation would involve manipulation as much as the Bitcoin price and returns do. Methodology- Related tests are performed to detect compliance with Benford’s Law in the analyses conducted on the issue. Distribution frequency of the digits can be determined by Benford’s Law in order to determine whether the random digital database is manipulated. In order to detect any possible manipulation in Bitcoin returns, the Chi-Square test is performed Findings- With the daily Bitcoin price data obtained over the period between 02.02.2012 - 10.02.2020 its found out that Bİtcoin prices comply with Benford’s Law reference distribution. Conclusion- According to the results of the analysis, it is concluded that the Bitcoin returns comply with Benford’s Law. Therefore, there is no possible manipulation on the Bitcoin returns throughout the study period.

Keywords

• Benford’s Law
• Bitcoin
• Detection of Fraud
• Digital Analysis
• Manipulation

References

1. Akkas, M. E. 2015(a). Altın getirileri dağılımının Newcomb-Benford kanunu ile tespiti. Uluslararası Sosyal Araştırmalar Dergisi. 8(40), 577-584.
2. Akkas, M. E. 2015(b). ABD doları ve Avro döviz kurlarının nümerik desen analizi. Karabük Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. 5(2), 215-226.
3. Alagoz, A., Ay, M. (2001). Muhasebe denetiminde Benford Kanunu temelli dijital analiz. Selçuk Üniversitesi Sosyal ve Ekonomik Araştırmalar Dergisi.(4), 59-76.
4. Amiram, D, Lyandres, E., Rabetti, D. (2021). Competition and product quality: fake trading on crypto exchanges. Available at SSRN: https://ssrn.com/abstract=3745617 or http://dx.doi.org/10.2139/ssrn.3745617.
5. Benford, F., (1938). The law of anomalous numbers. American Philosophical Society.78(4), 551-572.
6. Chan, J., Liu, T., Xue, E., (2013). Analyzing the Bitcoin transaction graph: a look at mixers and traceability. https://css.csail.mit.edu/6.858/2013/projects/jeffchan-exue-tanyaliu.pdf
7. Cipp, C., Phillips J., Mai, A, (2013). The hacker’s code: finding Bitcoin thieves through the similarity and status claims between users. Stanford CS 224W - Team 39C – S 224W Final Report.
8. Cong, L. Li, X. Tang, K., Yang, Y. (2019). Crypto wash trading. Available at SSRN: https://ssrn.com/abstract=3530220 or http://dx.doi.org/10.2139/ssrn.3530220.

9. Carkacıoglu, A., (2016). Kripto-para Bitcoin. Sermaye Piyasası Kurulu Araştırma Raporu. Ankara.
10. Dodgson, M., Gann, D. M. , George, G., (2015). Managing digital money. The Academy of Management Journal.58 (2), 325-333.
11. Doğan, Z., Buyrukoglu, S. and Kutbay, H. (2018). Türkiye’de Bitcoin işlemlerinin vergilendirilmesi ve muhasebeleştirilmesine ilişkin oneriler. Vergi Sorunları Dergisi (361), 32-40.
12. Houben, R., Snyers, A. (2018). Cryptocurrencies and blockchain legal context and ımplications for financial crime, money laundering and tax evasion. The European Parliament’s Special Committee on Financial Crimes. http://www.europarl.europa.eu/cmsdata/150761/TAX3%20Study%20on%20cryptocurrencies%20and%20blockchain.pdf,
13. Jermann, U. J. (2018). Bitcoin, Cagan’s model of hyperinflation. Available at SSRN:https://ssrn.com/abstract=3132050 or http://dx.doi.org/10.2139/ssrn.3132050
14. Gandal, N., Hamrick, JT, Moore, T., Oberman, T. (2017). Price manipulation in the Bitcoin ecosystem. Journal of Monetary Economics. DOI:10.1016 https://tylermoore.utulsa.edu/jme17appendix.pdf
15. Ley, E. (1996). On the peculiar distribution of the U.S. stock indexes digits. The American Statistician.50(4), 311-313.
16. Newcomb, S. (1881). Note on the frequency of the use of digits in natural numbers. American Journal of Mathematics.(4), 39-40.
17. Pearson, K.(1900). On a criterion that a given system of deviations from the probable ın the case of a correlated system of variables ıs such that ıt can be reasonably supposed to have arisen from random sampling. Philosophical Magazine. 5(50), 157–175.
18. Poyser, O. (2017). Exploring the determinants of Bitcoin’s price: an application of bayesian structural time series. Dissertation. https://arxiv.org/ftp/arxiv/papers/1706/1706.01437.pdf
19. Peterson, T. (2020). To the moon: a history of Bitcoin price manipulation. Journal of Forensic and Investigative Accounting. 13(2), Available at SSRN: https://ssrn.com/abstract=3639431 or http://dx.doi.org/10.2139/ssrn.3639431
20. Sermaye Piyasası Kurulu (2003). Hisse senedi piyasasında manipülasyon: kullanılan yöntem örnekleri, manipülatif işlem kalıbı örnekleri, korunma yolları. http://www.spk.gov.tr/Duyuru/Dosya/20031215/0
21. Shengmin, Z., Wenchao, W. (2010). Does Chinese Stock Indices agree with Benford’s Law?. Management and Service Science (MAASS). 2010 International Conference on Management and Service Science. DOI:10.1109/ICMSS.2010.5575999
22. Uzuner, M.T.(2014). Benford Yasasının Borsa İstanbul’da işlem gören bankaların konsolide bilançolarına uygulanması. Finansal Araştırmalar ve Çalışmalar Dergisi. 5(10), 73-82.
23. Veres, I. (2019). Identification of unusual transactions in Blockchain networks. Master’s Thesis. Slovak University of Technology in Bratislava. Faculty of Informatics and Information Technologies. Institute of Computer Engineering and Applied Informatics.
24. Wist, H. (2019). Assessing the conformity of cryptocurrency market data with Benford’s Law. Master´s Thesis, University of Vaasa. Faculty of Business Studies. Department of Accounting and Finance