IDEAS home Printed from https://ideas.repec.org/a/bla/joares/v60y2022i2p427-466.html
   My bibliography  Save this article

Coins for Bombs: The Predictive Ability of On‐Chain Transfers for Terrorist Attacks

Author

Listed:
  • DAN AMIRAM
  • BJØRN N. JØRGENSEN
  • DANIEL RABETTI

Abstract

This study examines whether we can learn from the behavior of blockchain‐based transfers to predict the financing of terrorist attacks. We exploit blockchain transaction transparency to map millions of transfers for hundreds of large on‐chain service providers. The mapped data set permits us to empirically conduct several analyses. First, we analyze abnormal transfer volume in the vicinity of large‐scale highly visible terrorist attacks. We document evidence consistent with heightened activity in coin wallets belonging to unregulated exchanges and mixer services—central to laundering funds between terrorist groups and operatives on the ground. Next, we use forensic accounting techniques to follow the trails of funds associated with the Sri Lanka Easter bombing. Insights from this event corroborate our findings and aid in our construction of a blockchain‐based predictive model. Finally, using machine‐learning algorithms, we demonstrate that fund trails have predictive power in out‐of‐sample analysis. Our study is informative to researchers, regulators, and market players in providing methods for detecting the flow of terrorist funds on blockchain‐based systems using accounting knowledge and techniques.

Suggested Citation

  • Dan Amiram & Bjørn N. Jørgensen & Daniel Rabetti, 2022. "Coins for Bombs: The Predictive Ability of On‐Chain Transfers for Terrorist Attacks," Journal of Accounting Research, Wiley Blackwell, vol. 60(2), pages 427-466, May.
    • Handle: RePEc:bla:joares:v:60:y:2022:i:2:p:427-466
    DOI: 10.1111/1475-679X.12430
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1475-679X.12430
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1475-679X.12430?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Brown, Stephen J. & Warner, Jerold B., 1985. "Using daily stock returns : The case of event studies," Journal of Financial Economics, Elsevier, vol. 14(1), pages 3-31, March.
    2. Stefano Cascino & Maria Correia & Ane Tamayo, 2019. "Does Consumer Protection Enhance Disclosure Credibility in Reward Crowdfunding?," Journal of Accounting Research, Wiley Blackwell, vol. 57(5), pages 1247-1302, December.
    3. Piotroski, JD, 2000. "Value investing: The use of historical financial statement information to separate winners from losers," Journal of Accounting Research, Wiley Blackwell, vol. 38, pages 1-41.
    4. Makarov, Igor & Schoar, Antoinette, 2020. "Trading and arbitrage in cryptocurrency markets," LSE Research Online Documents on Economics 100409, London School of Economics and Political Science, LSE Library.
    5. Makarov, Igor & Schoar, Antoinette, 2020. "Trading and arbitrage in cryptocurrency markets," Journal of Financial Economics, Elsevier, vol. 135(2), pages 293-319.
    6. Sokolov, Konstantin, 2021. "Ransomware activity and blockchain congestion," Journal of Financial Economics, Elsevier, vol. 141(2), pages 771-782.
    7. Jonathan L. Rogers & Douglas J. Skinner & Sarah L. C. Zechman, 2016. "The role of the media in disseminating insider-trading news," Review of Accounting Studies, Springer, vol. 21(3), pages 711-739, September.
    8. Corrado, Charles J. & Zivney, Terry L., 1992. "The Specification and Power of the Sign Test in Event Study Hypothesis Tests Using Daily Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 27(3), pages 465-478, September.
    9. Campbell, Cynthia J. & Wesley, Charles E., 1993. "Measuring security price performance using daily NASDAQ returns," Journal of Financial Economics, Elsevier, vol. 33(1), pages 73-92, February.
    10. David Godsell & Michael Welker & Ning Zhang, 2017. "Earnings Management During Antidumping Investigations in Europe: Sample‐Wide and Cross‐Sectional Evidence," Journal of Accounting Research, Wiley Blackwell, vol. 55(2), pages 407-457, May.
    11. Copeland, Thomas E, 1979. "Liquidity Changes Following Stock Splits," Journal of Finance, American Finance Association, vol. 34(1), pages 115-141, March.
    12. Quinn McNemar, 1947. "Note on the sampling error of the difference between correlated proportions or percentages," Psychometrika, Springer;The Psychometric Society, vol. 12(2), pages 153-157, June.
    13. Abarbanell, JS & Bushee, BJ, 1997. "Fundamental analysis, future earnings, and stock prices," Journal of Accounting Research, Wiley Blackwell, vol. 35(1), pages 1-24.
    14. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    15. Bamber, Ls, 1986. "The Information-Content Of Annual Earnings Releases - A Trading Volume Approach," Journal of Accounting Research, Wiley Blackwell, vol. 24(1), pages 40-56.
    16. Beaver, Wh, 1968. "Market Prices, Financial Ratios, And Prediction Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 6(2), pages 179-192.
    17. Cascino, Stefano & Correia, Maria & Tamayo, Ane, 2019. "Does consumer protection enhance disclosure credibility in reward crowdfunding?," LSE Research Online Documents on Economics 102103, London School of Economics and Political Science, LSE Library.
    18. Sean Foley & Jonathan R Karlsen & Tālis J Putniņš, 2019. "Sex, Drugs, and Bitcoin: How Much Illegal Activity Is Financed through Cryptocurrencies?," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1798-1853.
    19. Paolo Tasca & Adam Hayes & Shaowen Liu, 2018. "The evolution of the bitcoin economy," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 19(2), pages 94-126, March.
    20. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    21. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    22. Zorn, Christopher, 2005. "A Solution to Separation in Binary Response Models," Political Analysis, Cambridge University Press, vol. 13(2), pages 157-170, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lin William Cong & Campbell R. Harvey & Daniel Rabetti & Zong-Yu Wu, 2023. "An Anatomy of Crypto-Enabled Cybercrimes," NBER Working Papers 30834, National Bureau of Economic Research, Inc.
    2. Cong, Lin William & Landsman, Wayne & Maydew, Edward & Rabetti, Daniel, 2023. "Tax-loss harvesting with cryptocurrencies," Journal of Accounting and Economics, Elsevier, vol. 76(2).
    3. Lars Hornuf & Paul P. Momtaz & Rachel J. Nam & Ye Yuan, 2023. "Cybercrime on the Ethereum Blockchain," CESifo Working Paper Series 10598, CESifo.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ken Li, 2024. "Liquidity ratios and corporate failures," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 1111-1134, March.
    2. Ahsan Habib & Mabel D' Costa & Hedy Jiaying Huang & Md. Borhan Uddin Bhuiyan & Li Sun, 2020. "Determinants and consequences of financial distress: review of the empirical literature," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 1023-1075, April.
    3. Aggarwal, Nidhi & Singh, Manish K. & Thomas, Susan, 2023. "Do decreases in Distance-to-Default predict rating downgrades?," Economic Modelling, Elsevier, vol. 129(C).
    4. Kumar, Rahul & Deb, Soumya Guha & Mukherjee, Shubhadeep, 2020. "Do words reveal the latent truth? Identifying communication patterns of corporate losers," Journal of Behavioral and Experimental Finance, Elsevier, vol. 26(C).
    5. Khushbu Agrawal, 2015. "Default Prediction Using Piotroski’s F-score," Global Business Review, International Management Institute, vol. 16(5_suppl), pages 175-186, October.
    6. Nikolaus Hautsch & Christoph Scheu & Stefan Voigt, 2024. "Building trust takes time: limits to arbitrage for blockchain-based assets," Review of Finance, European Finance Association, vol. 28(4), pages 1345-1381.
    7. Nguyen, Ha, 2023. "An empirical application of Particle Markov Chain Monte Carlo to frailty correlated default models," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 103-121.
    8. Sanjay Sehgal & Ritesh Kumar Mishra & Ajay Jaisawal, 2021. "A search for macroeconomic determinants of corporate financial distress," Indian Economic Review, Springer, vol. 56(2), pages 435-461, December.
    9. Foley, Sean & Frijns, Bart & Garel, Alexandre & Roh, Tai-Yong, 2022. "Who buys Bitcoin? The cultural determinants of Bitcoin activity," International Review of Financial Analysis, Elsevier, vol. 84(C).
    10. John W. Pacey & Toan M. Pham, 1990. "The Predictiveness of Bankruptcy Models: Methodological Problems and Evidence," Australian Journal of Management, Australian School of Business, vol. 15(2), pages 315-337, December.
    11. Li, Yuanzhi, 2013. "A nonlinear wealth transfer from shareholders to creditors around Chapter 11 filing," Journal of Financial Economics, Elsevier, vol. 107(1), pages 183-198.
    12. Qunfeng LIAO & Seyed MEHDIAN, 2016. "Measuring Financial Distress And Predicting Corporate Bankruptcy: An Index Approach," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 17, pages 33-51, June.
    13. Nidhi Aggarwal & Manish K. Singh & Susan Thomas, 2022. "Informational efficiency of credit ratings," Working Papers 14, xKDR.
    14. Hamadi Matoussi & Rim Mouelhi & Sayah Salah, 1999. "La Prediction De Faillite Des Entreprises Tunisiennes Par La Regression Logistique," Post-Print halshs-00587769, HAL.
    15. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    16. Dong, Manh Cuong & Tian, Shaonan & Chen, Cathy W.S., 2018. "Predicting failure risk using financial ratios: Quantile hazard model approach," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 204-220.
    17. Pope, Peter F., 2010. "Bridging the gap between accounting and finance," The British Accounting Review, Elsevier, vol. 42(2), pages 88-102.
    18. Manzaneque, Montserrat & Priego, Alba María & Merino, Elena, 2016. "Corporate governance effect on financial distress likelihood: Evidence from Spain," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 19(1), pages 111-121.
    19. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    20. Richardson, Scott & Tuna, Irem & Wysocki, Peter, 2010. "Accounting anomalies and fundamental analysis: A review of recent research advances," Journal of Accounting and Economics, Elsevier, vol. 50(2-3), pages 410-454, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:joares:v:60:y:2022:i:2:p:427-466. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0021-8456 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.