IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2305.12739.html
   My bibliography  Save this paper

The Influence of ChatGPT on Artificial Intelligence Related Crypto Assets: Evidence from a Synthetic Control Analysis

Author

Listed:
  • Aman Saggu
  • Lennart Ante

Abstract

The introduction of OpenAI's large language model, ChatGPT, catalyzed investor attention towards artificial intelligence (AI) technologies, including AI-related crypto assets not directly related to ChatGPT. Utilizing the synthetic difference-in-difference methodology, we identify significant 'ChatGPT effects' with returns of AI-related crypto assets experiencing average returns ranging between 10.7% and 15.6% (35.5% to 41.3%) in the one-month (two-month) period after the ChatGPT launch. Furthermore, Google search volumes, a proxy for attention to AI, emerged as critical pricing indicators for AI-related crypto post-launch. We conclude that investors perceived AI-assets as possessing heightened potential or value after the launch, resulting in higher market valuations.

Suggested Citation

  • Aman Saggu & Lennart Ante, 2023. "The Influence of ChatGPT on Artificial Intelligence Related Crypto Assets: Evidence from a Synthetic Control Analysis," Papers 2305.12739, arXiv.org.
  • Handle: RePEc:arx:papers:2305.12739
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2305.12739
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Aslanidis, Nektarios & Bariviera, Aurelio F. & López, Óscar G., 2022. "The link between cryptocurrencies and Google Trends attention," Finance Research Letters, Elsevier, vol. 47(PA).
    2. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
    3. Nikolay Doudchenko & Guido W. Imbens, 2016. "Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis," NBER Working Papers 22791, National Bureau of Economic Research, Inc.
    4. Adam Zaremba & Ender Demir, 2023. "ChatGPT: Unlocking the future of NLP in finance," Modern Finance, Modern Finance Institute, vol. 1(1), pages 93-98.
    5. Azi Ben-Rephael & Zhi Da & Ryan D. Israelsen, 2017. "It Depends on Where You Search: Institutional Investor Attention and Underreaction to News," The Review of Financial Studies, Society for Financial Studies, vol. 30(9), pages 3009-3047.
    6. Ante, Lennart, 2023. "How Elon Musk's Twitter activity moves cryptocurrency markets," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    7. Damian Clarke & Daniel Paila~nir & Susan Athey & Guido Imbens, 2023. "Synthetic Difference In Differences Estimation," Papers 2301.11859, arXiv.org, revised Feb 2023.
    8. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    9. Wu, Chih-Chiang & Chen, Wei-Peng, 2022. "What's an AI name worth? The impact of AI ETFs on their underlying stocks," Finance Research Letters, Elsevier, vol. 46(PB).
    10. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    11. Sowmya Subramaniam & Madhumita Chakraborty, 2020. "Investor Attention and Cryptocurrency Returns: Evidence from Quantile Causality Approach," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 21(1), pages 103-115, January.
    12. Philippas, Dionisis & Rjiba, Hatem & Guesmi, Khaled & Goutte, Stéphane, 2019. "Media attention and Bitcoin prices," Finance Research Letters, Elsevier, vol. 30(C), pages 37-43.
    13. Dowling, Michael & Lucey, Brian, 2023. "ChatGPT for (Finance) research: The Bananarama Conjecture," Finance Research Letters, Elsevier, vol. 53(C).
    14. Shahzad, Syed Jawad Hussain & Anas, Muhammad & Bouri, Elie, 2022. "Price explosiveness in cryptocurrencies and Elon Musk's tweets," Finance Research Letters, Elsevier, vol. 47(PB).
    15. Dastgir, Shabbir & Demir, Ender & Downing, Gareth & Gozgor, Giray & Lau, Chi Keung Marco, 2019. "The causal relationship between Bitcoin attention and Bitcoin returns: Evidence from the Copula-based Granger causality test," Finance Research Letters, Elsevier, vol. 28(C), pages 160-164.
    16. Markus K. Brunnermeier, 2005. "Information Leakage and Market Efficiency," The Review of Financial Studies, Society for Financial Studies, vol. 18(2), pages 417-457.
    17. Michael Spence, 1973. "Job Market Signaling," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 87(3), pages 355-374.
    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. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
    2. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    3. Chen, Conghui & Liu, Lanlan, 2022. "How effective is China's cryptocurrency trading ban?," Finance Research Letters, Elsevier, vol. 46(PB).
    4. Jason Poulos & Andrea Albanese & Andrea Mercatanti & Fan Li, 2021. "Retrospective causal inference via matrix completion, with an evaluation of the effect of European integration on cross-border employment," Papers 2106.00788, arXiv.org.
    5. Lea Bottmer & Guido Imbens & Jann Spiess & Merrill Warnick, 2021. "A Design-Based Perspective on Synthetic Control Methods," Papers 2101.09398, arXiv.org, revised Jul 2023.
    6. Dallas Dotter & Duncan Chaplin & Maria Bartlett, "undated". "Impacts of School Reforms in Washington, DC on Student Achievement," Mathematica Policy Research Reports 44e95d7566434a21b8d57f951, Mathematica Policy Research.
    7. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Mar 2024.
    8. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
    9. Blankespoor, Elizabeth & deHaan, Ed & Marinovic, Iván, 2020. "Disclosure processing costs, investors’ information choice, and equity market outcomes: A review," Journal of Accounting and Economics, Elsevier, vol. 70(2).
    10. Florian Gunsilius, 2020. "Distributional synthetic controls," Papers 2001.06118, arXiv.org, revised Dec 2021.
    11. Claudia Shi & Dhanya Sridhar & Vishal Misra & David M. Blei, 2021. "On the Assumptions of Synthetic Control Methods," Papers 2112.05671, arXiv.org, revised Dec 2021.
    12. Parast Layla & Hunt Priscillia & Griffin Beth Ann & Powell David, 2020. "When is a Match Sufficient? A Score-based Balance Metric for the Synthetic Control Method," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 209-228, January.
    13. Davide Viviano & Jelena Bradic, 2021. "Dynamic covariate balancing: estimating treatment effects over time with potential local projections," Papers 2103.01280, arXiv.org, revised Jan 2024.
    14. Kamal, Javed Bin & Hassan, M. Kabir, 2022. "Asymmetric connectedness between cryptocurrency environment attention index and green assets," The Journal of Economic Asymmetries, Elsevier, vol. 25(C).
    15. Yi‐Ting Chen, 2020. "A distributional synthetic control method for policy evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 505-525, August.
    16. Jason Poulos & Shuxi Zeng, 2021. "RNN‐based counterfactual prediction, with an application to homestead policy and public schooling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1124-1139, August.
    17. Ozdamar, Melisa & Sensoy, Ahmet & Akdeniz, Levent, 2022. "Retail vs institutional investor attention in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    18. Alberto Abadie & Jinglong Zhao, 2021. "Synthetic Controls for Experimental Design," Papers 2108.02196, arXiv.org, revised Dec 2023.
    19. Alberto Abadie & Jaume Vives-i-Bastida, 2022. "Synthetic Controls in Action," Papers 2203.06279, arXiv.org.
    20. Bruno Ferman & Cristine Pinto, 2021. "Synthetic controls with imperfect pretreatment fit," Quantitative Economics, Econometric Society, vol. 12(4), pages 1197-1221, November.

    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2305.12739. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.