IDEAS home Printed from https://ideas.repec.org/a/fru/finjrn/190107p76-87.html
   My bibliography  Save this article

Cryptoeconomics: Pilot Study on Investments in ICO Startups Using Neural Networks

Author

Listed:
  • Andrey A. Kozlov

    (National Research University Higher School of Economics (HSE), Moscow 101000, Russia)

  • Andrey V. Vlasov

    (National Research University Higher School of Economics (HSE), Moscow 101000, Russia
    DeMontroyal, Penza 440015, Russia)

Abstract

In the field of cryptoeconomics the Ethereum (Ethereum Foundation) project gave opportunity to create “own” cryptocurrency – new token based on its smart-contract platform to everyone without lowlevel programming skills. Then it became obvious that tokens could be used for crowdfunding as the Ethereum did in 2014. Unregulated and easy to access such scheme became popular among related to the blockchain tech startups. It was named Initial Coin Offering (ICO/or ITO). Despite its name, which is similar to IPO, this scheme is usually used for venture funding of a new project instead of expanding already well-established working business. The authors use machine-learning algorithms to classify ICOs and estimate ROI based on public digital data and web-sources. The goal of the research is to develop sustainable and efficient model, which will predict target profit ROI (profit trends) of ICO startup. Data collection and analysis period: Feb-Mar 2018. The prediction model and the application (service) of ICO startups’ selection are developed as the result of the study. Results. There were over 3000 samples of ICO-startups in the research dataset. After cleaning and elimination of outliers, it contained only 518. The number of samples with positive ROI (which means that these ICOs were profitable) was 234. Cross validation metric was confirmed to be accurate. The model achieved 79 % accuracy (average value). To prove this score separated prediction was executed the metrics: for test dataset AUC is 0.78; for profitable samples Precision: 0.76; Recall: 0.9 for profitable; F1-score: 0.82. Discussions. In order to achieve the objectives of this study, various IT components of the service architecture (applications) were developed to monitor, analyze and predict the risks of ICO startups. An artificial neural network was developed to solve the problem of ROI classification and prediction. The average ROI among profitable ICOS was 47 %. Taking into consideration that the crypto market is highly volatile and that there is a possibility that such investments will not bring any profit, this model of monitoring, analysis and prediction can be very valuable for the purposes of critical selection (exclusion) of a number of ICO projects from potential investment. Conclusion. The developed components can be used as a basis of monitoring service of ICO startups. The risk-forecasting model can be improved, foremost, by using the most complete (and wider) set of data. In this case, individual data collection and processing tasks can be performed manually, which will require additional resources. It should be noted that other types of neural networks can be developed for both text analysis and trading data analysis. This may lead to the logic of using a combination of models, which will potentially help to provide the most accurate predictions.

Suggested Citation

  • Andrey A. Kozlov & Andrey V. Vlasov, 2019. "Cryptoeconomics: Pilot Study on Investments in ICO Startups Using Neural Networks," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 1, pages 76-87, February.
    • Handle: RePEc:fru:finjrn:190107:p:76-87
    DOI: 10.31107/2075-1990-2019-1-76-87
    as

    Download full text from publisher

    File URL: http://www.nifi.ru/images/FILES/Journal/Archive/2019/1/statii/fm_2019_1_07.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.31107/2075-1990-2019-1-76-87?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. Laura Alessandretti & Abeer ElBahrawy & Luca Maria Aiello & Andrea Baronchelli, 2018. "Anticipating cryptocurrency prices using machine learning," Papers 1805.08550, arXiv.org, revised Nov 2018.
    2. Christian Catalini & Joshua S. Gans, 2018. "Initial Coin Offerings and the Value of Crypto Tokens," NBER Working Papers 24418, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    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. Magnus Schückes & Tobias Gutmann, 2021. "Why do startups pursue initial coin offerings (ICOs)? The role of economic drivers and social identity on funding choice," Small Business Economics, Springer, vol. 57(2), pages 1027-1052, August.
    2. Yannis Bakos & Hanna Halaburda, 2022. "Overcoming the Coordination Problem in New Marketplaces via Cryptographic Tokens," Information Systems Research, INFORMS, vol. 33(4), pages 1368-1385, December.
    3. Chowdhury, Reaz & Rahman, M. Arifur & Rahman, M. Sohel & Mahdy, M.R.C., 2020. "An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    4. Viktor Manahov, 2024. "The rapid growth of cryptocurrencies: How profitable is trading in digital money?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2214-2229, April.
    5. Lin William Cong & Ye Li & Neng Wang, 2021. "Tokenomics: Dynamic Adoption and Valuation [The demand of liquid assets with uncertain lumpy expenditures]," The Review of Financial Studies, Society for Financial Studies, vol. 34(3), pages 1105-1155.
    6. Sudersan Behera & Sarat Chandra Nayak & A. V. S. Pavan Kumar, 2024. "Evaluating the Performance of Metaheuristic Based Artificial Neural Networks for Cryptocurrency Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1219-1258, August.
    7. Reuter, Marco, 2022. "The value of decentralization using the blockchain," ZEW Discussion Papers 22-056, ZEW - Leibniz Centre for European Economic Research.
    8. Paola Cerchiello & Anca Mirela Toma & Marco Caluzzi, 2021. "ICOs White Papers: identity card or lark mirror?," DEM Working Papers Series 197, University of Pavia, Department of Economics and Management.
    9. Duygu Ider & Stefan Lessmann, 2022. "Forecasting Cryptocurrency Returns from Sentiment Signals: An Analysis of BERT Classifiers and Weak Supervision," Papers 2204.05781, arXiv.org, revised Mar 2023.
    10. Jiri Chod & Evgeny Lyandres, 2021. "A Theory of ICOs: Diversification, Agency, and Information Asymmetry," Management Science, INFORMS, vol. 67(10), pages 5969-5989, October.
    11. Anton Miglo, 2022. "Choice between IEO and ICO: Speed vs. Liquidity vs. Risk," FinTech, MDPI, vol. 1(3), pages 1-18, September.
    12. Miglo, Anton, 2022. "Theories of financing for entrepreneurial firms: a review," MPRA Paper 115835, University Library of Munich, Germany.
    13. Martin Cimiterra & Jackie Krafft & Lionel Nesta, 2021. "Blockchain as Schumpeter Mark 1 or Mark 2? An empirical analysis of blockchain job offers in France and Germany [Innovation: mapping the winds of creative destruction]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(6), pages 1388-1402.
    14. Miglo, Anton, 2020. "ICO vs. Equity Financing Under Imperfect, Complex and Asymmetric Information," MPRA Paper 99598, University Library of Munich, Germany.
    15. Volodymyr Babich & Gilles Hilary, 2020. "OM Forum—Distributed Ledgers and Operations: What Operations Management Researchers Should Know About Blockchain Technology," Manufacturing & Service Operations Management, INFORMS, vol. 22(2), pages 223-245, March.
    16. Garratt, Rodney J. & van Oordt, Maarten R.C., 2022. "Entrepreneurial incentives and the role of initial coin offerings," Journal of Economic Dynamics and Control, Elsevier, vol. 142(C).
    17. Ju‐Chun Yen & Tawei Wang & Yu‐Hung Chen, 2021. "Different is better: how unique initial coin offering language in white papers enhances success," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(4), pages 5309-5340, December.
    18. Dulani Jayasuriya Daluwathumullagamage & Alexandra Sims, 2020. "Blockchain-Enabled Corporate Governance and Regulation," IJFS, MDPI, vol. 8(2), pages 1-41, June.
    19. Helder Sebastião & Pedro Godinho, 2021. "Forecasting and trading cryptocurrencies with machine learning under changing market conditions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
    20. Suhwan Ji & Jongmin Kim & Hyeonseung Im, 2019. "A Comparative Study of Bitcoin Price Prediction Using Deep Learning," Mathematics, MDPI, vol. 7(10), pages 1-20, September.

    More about this item

    Keywords

    cryptocurrency; tokens; investment; machine learning; neural networks; ICO; ITO; ROI; risk; cryptoeconomics;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • M13 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - New Firms; Startups
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • P49 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Other

    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:fru:finjrn:190107:p:76-87. 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: Gennady Ageev (email available below). General contact details of provider: https://edirc.repec.org/data/frigvru.html .

    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.