Is the Metaverse Dead? Insights from Financial Bubble Analysis
Abstract
:1. Introduction
1.1. Literature Overview
1.1.1. Forecasting Using (Seasonal) ARIMA
1.1.2. Theory on Bubbles
1.1.3. Bubble Identification Using LPPLS
2. Materials and Methods
2.1. Data
2.1.1. Current State of the Metaverse
2.1.2. Data Sample and Descriptive Statistics
2.2. Methodology
2.2.1. Seasonal ARIMA Model
- p = autoregressive—allows incorporating the effect of past values;
- d = integrated—allows incorporating differencing (i.e., the number of past time points to subtract from current values);
- q = moving average—allows setting the error of the model as a linear combination of the error values observed at previous time points in the past;
- “s” = the subscripted letter shows the length of the seasonal period. P, D, Q follow the exact definition only for the seasonal component of the time series.
- B = backward shift operator
- = level of differencing
- = notated constant
- = autoregressive operator
- a = random shock corresponding to time t
- = moving-average operator
2.2.2. Log-Periodic Power Law Singularity (LPPLS)
- = expected log price at a given time t
- A = expected log price at the peak at
- B = amplitude of power law acceleration
- C = amplitude of log-periodic oscillations
- m = degree of super-exponential growth
- = scaling ratio of the temporal hierarchy of oscillations
- = time scale of oscillations
- = critical time, transitions to another regime
3. Results
3.1. Results from SARIMA Model
3.2. Result from LPPLS Model
3.3. Contextual Considerations: COVID-19
3.4. Practical Relevance
4. Discussion
4.1. Discussion of SARIMA Results
4.2. Discussion on Bubbles
4.3. Discussion of LPPLS Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
- Joshua, J. Information Bodies: Computational Anxiety in Neal Stephenson’s Snow Crash. In Interdisciplinary Literary Studies; Penn State University Press: University Park, PA, USA, 2017; Volume 19, pp. 17–47. [Google Scholar] [CrossRef]
- Lee, L.H.; Braud, T.; Zhou, P.; Wang, L.; Xu, D.; Lin, Z.; Kumar, A.; Bermejo, C.; Hui, P. All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda. arXiv 2021, arXiv:2110.05352. [Google Scholar] [CrossRef]
- Bodó, B.; Brekke, J.K.; Hoepman, J.H. Decentralisation: A multidisciplinary perspective. Internet Policy Rev. 2021, 10, 1–21. [Google Scholar] [CrossRef]
- Isaac, M. Facebook Changes Corporate Name to Meta—The New York Times. The New York Times, 29 October 2021. [Google Scholar]
- Ordano, E.; Meilich, A.; Jardi, Y.; Araoz, M. A blockchain-Based Virtual World. Decentraland, White Paper. 2017. Available online: https://decentraland.org/whitepaper.pdf (accessed on 3 September 2023).
- Henry, J. Metaverse Group Buys Digital Land ’Decentraland’ For a Whopping $2.43 Million|Tech Times. Tech Times, 25 November 2021. [Google Scholar]
- Madrid, A.; Borget, S. Play. Create. Own. Govern. Earn. Welcome to the Metaverse. The Sandbox White Paper. 2020. Available online: https://www.coursehero.com/file/120337453/The-Sandbox-Whitepaper-2020pdf/ (accessed on 3 September 2023).
- Chowdhury, H. We’re about to find out whether AI might replace those 200,000 laid-off tech workers as Silicon Valley prioritizes efficiency. Business Insider, 17 March 2023. [Google Scholar]
- Zitron, E. RIP Metaverse. Business Insider, 8 May 2023. [Google Scholar]
- Dwivedi, Y.K.; Hughes, L.; Baabdullah, A.M.; Ribeiro-Navarrete, S.; Giannakis, M.; Al-Debei, M.M.; Dennehy, D.; Metri, B.; Buhalis, D.; Cheung, C.M.K.; et al. Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manag. 2022, 66, 102542. [Google Scholar] [CrossRef]
- Gao, H.; Chong, A.Y.L.; Bao, H. Metaverse: Literature Review, Synthesis and Future Research Agenda. J. Comput. Inf. Syst. 2023, 0, 1–21. [Google Scholar] [CrossRef]
- Sornette, D. Why Stock Markets Crash: Critical Events in Complex Financial Systems; Princeton University Press: Princeton, NJ, USA, 2003. [Google Scholar]
- Blanchard, O.J.; Watson, M.W. Bubbles, Rational Expectations and Financial Markets. In Crisis in the Economic and Financial Structure; Wachtel, P., Ed.; National Bureau of Economic Research, Inc.: Cambridge, MA, USA, 1982; p. 0945. [Google Scholar]
- Tirole, J. On the Possibility of Speculation under Rational Expectations. Econometrica 1982, 50, 1163–1181. [Google Scholar] [CrossRef]
- Flood, R.P.; Hodrick, R.J. On Testing for Speculative Bubbles. J. Econ. Perspect. 1990, 4, 85–101. [Google Scholar] [CrossRef]
- Abreu, D.; Brunnermeier, M.K. Bubbles and Crashes. Econometrica 2003, 71, 173–204. [Google Scholar] [CrossRef]
- Brunnermeier, M.K. Bubbles. In Banking Crises: Perspectives from The New Palgrave Dictionary; Jones, G., Ed.; Palgrave Macmillan: London, UK, 2016; pp. 28–36. [Google Scholar] [CrossRef]
- Fama, E.F. Efficient Capital Markets: A Review of Theory and Empirical Work. J. Financ. 1970, 25, 383–417. [Google Scholar] [CrossRef]
- Kaizoji, T.; Sornette, D. Bubbles and Crashes. In Encyclopedia of Quantitative Finance; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2010. [Google Scholar] [CrossRef]
- Dowd, K. New Private Monies: A Bit-Part Player? Hobart Paper; Institute of Economic Affairs: London, UK, 2014; p. 174. [Google Scholar]
- Dale, R.S.; Johnson, J.E.V.; Tang, L. Financial markets can go mad: Evidence of irrational behaviour during the South Sea Bubble1. Econ. Hist. Rev. 2005, 58, 233–271. [Google Scholar] [CrossRef]
- Kumar, A.; Ajaz, T. Co-movement in crypto-currency markets: Evidences from wavelet analysis. Financ. Innov. 2019, 5, 33. [Google Scholar] [CrossRef]
- Mondal, P.; Shit, L.; Goswami, S. Study of Effectiveness of Time Series Modeling (Arima) in Forecasting Stock Prices. Int. J. Comput. Sci. Eng. Appl. 2014, 4, 13–29. [Google Scholar] [CrossRef]
- Poongodi, M.; Vijayakumar, V.; Chilamkurti, N. Bitcoin price prediction using ARIMA model. Int. J. Internet Technol. Secur. Trans. 2020, 10, 396. [Google Scholar] [CrossRef]
- Sahay, S.; Mahajan, N.; Malik, S.; Kaur, J. Metaverse: Research Based Analysis and Impact on Economy and Business. In Proceedings of the 2022 2nd Asian Conference on Innovation in Technology (ASIANCON), Ravet, India, 26–28 August 2022; pp. 1–8. [Google Scholar] [CrossRef]
- Dybvig, P.H.; Ross, S.A. Tax Clienteles and Asset Pricing. J. Financ. 1986, 41, 751–762. [Google Scholar] [CrossRef]
- LeRoy, S.F. Rational Exuberance. J. Econ. Lit. 2004, 42, 783–804. [Google Scholar] [CrossRef]
- Kindleberger, C.P. Manias, Panics, and Crashes. A History of Financial Crisis; Macmillan: London, UK, 1978. [Google Scholar]
- Scheinkman, J.A.; Xiong, W. Overconfidence and Speculative Bubbles. J. Political Econ. 2003, 111, 1183–1220. [Google Scholar] [CrossRef]
- Minsky, H.P. The Financial Instability Hypothesis: An Interpretation of Keynes and an Alternative to “Standard” Theory. Challenge 1977, 20, 20–27. [Google Scholar] [CrossRef]
- Blanchard, O.J. Speculative bubbles, crashes and rational expectations. Econ. Lett. 1979, 3, 387–389. [Google Scholar] [CrossRef]
- Flood, R.P.; Garber, P.M. Market Fundamentals versus Price-Level Bubbles: The First Tests. J. Political Econ. 1980, 88, 745–770. [Google Scholar] [CrossRef]
- Johansen, A.; Sornette, D.; Ledoit, O. Predicting Financial Crashes Using Discrete Scale Invariance. J. Risk 1999, 1, 5–32. [Google Scholar] [CrossRef]
- Sornette, D.; Johansen, A. Significance of log-periodic precursors to financial crashes. Quant. Financ. 2001, 1, 452. [Google Scholar] [CrossRef]
- Homm, U.; Breitung, J. Testing for Speculative Bubbles in Stock Markets: A Comparison of Alternative Methods. J. Financ. Econom. 2012, 10, 198–231. [Google Scholar] [CrossRef]
- Breitung, J.; Kruse, R. When bubbles burst: Econometric tests based on structural breaks. Stat. Pap. 2013, 54, 911–930. [Google Scholar] [CrossRef]
- Corbet, S.; Lucey, B.; Yarovaya, L. Datestamping the Bitcoin and Ethereum bubbles. Financ. Res. Lett. 2018, 26, 81–88. [Google Scholar] [CrossRef]
- Sharma, S.; Escobari, D. Identifying price bubble periods in the energy sector. Energy Econ. 2018, 69, 418–429. [Google Scholar] [CrossRef]
- Bianchetti, M.; Ricci, C.; Scaringi, M. Are Cryptocurrencies Real Financial Bubbles? Evidence from Quantitative Analyses; SSRN: Milan, Italy, 2018. [Google Scholar] [CrossRef]
- Yao, C.Z.; Li, H.Y. A study on the bursting point of Bitcoin based on the BSADF and LPPLS methods. N. Am. J. Econ. Financ. 2021, 55, 101280. [Google Scholar] [CrossRef]
- Shu, M.; Song, R.; Zhu, W. The 2021 Bitcoin Bubbles and Crashes—Detection and Classification. Stats 2021, 4, 950–970. [Google Scholar] [CrossRef]
- Ito, K.; Shibano, K.; Mogi, G. Bubble Prediction of Non-Fungible Tokens (NFTs): An Empirical Investigation. arXiv 2022, arXiv:2203.12587. [Google Scholar]
- Williams, B. Understanding the Metaverse Economy And How to Navigate It. Nasdaq Blog, 8 March 2022. [Google Scholar]
- Takahashi, D. Nvidia CEO Jensen Huang Weighs in on the Metaverse, Blockchain, and Chip Shortage; Games Beat: San Francisco, CA, USA, 2021. [Google Scholar]
- Sudan, R.; Petrov, O.; Gupta, G. Can the Metaverse Offer Benefits for Developing Countries? 2022. Available online: https://blogs.worldbank.org/en/digital-development/can-metaverse-offer-benefits-developing-countries (accessed on 3 September 2023).
- McKinsey&Company. Value Creation in the Metaverse; Technical Report; McKinsey&Company: Chicago, IL, USA, 2022. [Google Scholar]
- Metaverse Market Size to Surpass USD 1.3 Trillion by 2030, Precedence Research, 2021. Available online: https://www.precedenceresearch.com/press-release/metaverse-market (accessed on 3 September 2023).
- Gwertzman, J.; Lai, J.; Chen, A. GAMES FUND ONE: Building the Future of Games; Andreessen Horowitz: Menlo Park, CA, USA, 2022. [Google Scholar]
- Caulfield, B. NVIDIA, BMW Blend Reality, Virtual Worlds to Demonstrate Factory of the Future. The Official NVIDIA Blog, 13 April 2021. [Google Scholar]
- McDowell, M. Metaverse Fashion Week: The Hits and Misses; Section: Tags. Vogue Business, 29 March 2022. [Google Scholar]
- Aanandhi, S.P.; Akhilaa, S.P.; Vardarajan, V.; Sathiyanarayanan, M. Cryptocurrency Price Prediction using Time Series Forecasting (ARIMA). In Proceedings of the 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Yogyakarta, Indonesia, 16–17 December 2021; pp. 598–602. [Google Scholar] [CrossRef]
- Box, G.E.P.; Jenkins, G.M. Time Series Analysis: Forecasting and Control, revised ed.; Holden-Day: San Francisco, CA, USA, 1976. [Google Scholar]
- Chan, S.; Treleaven, P. Chapter 5 - Continuous Model Selection for Large-Scale Recommender Systems. In Handbook of Statistics; Govindaraju, V., Raghavan, V.V., Rao, C.R., Eds.; Big Data Analytics; Elsevier: Amsterdam, The Netherlands, 2015; Volume 33, pp. 107–124. [Google Scholar] [CrossRef]
- Johansen, A.; Ledoit, O.; Sornette, D. Crashes as critical points. Int. J. Theor. Appl. Financ. 2000, 3, 219–255. [Google Scholar] [CrossRef]
- Sornette, D.; Johansen, A.; Bouchaud, J.P. Stock Market Crashes, Precursors and Replicas. Sci. Financ. Cap. Fund Manag. Sci. Financ. (CFM) Work. Pap. Arch. 1995, 6, 167–175. [Google Scholar] [CrossRef]
- Hüsler, A.; Sornette, D.; Hommes, C.H. Super-exponential bubbles in lab experiments: Evidence for anchoring over-optimistic expectations on price. J. Econ. Behav. Organ. 2013, 92, 304–316. [Google Scholar] [CrossRef]
- Leiss, M.; Nax, H.H.; Sornette, D. Super-exponential growth expectations and the global financial crisis. J. Econ. Dyn. Control 2015, 55, 1–13. [Google Scholar] [CrossRef]
- Filimonov, V.; Sornette, D. A stable and robust calibration scheme of the log-periodic power law model. Phys. A Stat. Mech. Its Appl. 2013, 392, 3698–3707. [Google Scholar] [CrossRef]
- Johansen, A.; Sornette, D. Shocks, Crashes and Bubbles in Financial Markets. Bruss. Econ. Rev. 2010, 53, 201–253. [Google Scholar]
- Sornette, D.; Demos, G.; Zhang, Q.; Cauwels, P.; Filimonov, V.; Zhang, Q. Real-Time Prediction and Post-Mortem Analysis of the Shanghai 2015 Stock Market Bubble and Crash; Research Paper No. 15-31; Swiss Finance Institute: Zürich, Switzerland, 2015. [Google Scholar] [CrossRef]
- von Bothmer, H.C.; Meister, C. Predicting critical crashes? A new restriction for the free variables. Phys. A Stat. Mech. Its Appl. 2003, 320, 539–547. [Google Scholar] [CrossRef]
- De Pace, P.; Rao, J. Comovement and instability in cryptocurrency markets. Int. Rev. Econ. Financ. 2023, 83, 173–200. [Google Scholar] [CrossRef]
- Wang, Y.; Horky, F.; Baals, L.J.; Lucey, B.M.; Vigne, S.A. Bubbles all the way down? Detecting and date-stamping bubble behaviours in NFT and DeFi markets. J. Chin. Econ. Bus. Stud. 2022, 20, 415–436. [Google Scholar] [CrossRef]
- Campello, M.; Kankanhalli, G.; Muthukrishnan, P. Corporate Hiring Under COVID-19: Financial Constraints and the Nature of New Jobs. J. Financ. Quant. Anal. 2023, 1–45. [Google Scholar] [CrossRef]
- Cortes, G.S.; Gao, G.P.; Silva, F.B.; Song, Z. Unconventional monetary policy and disaster risk: Evidence from the subprime and COVID–19 crises. J. Int. Money Financ. 2022, 122, 102543. [Google Scholar] [CrossRef]
- Winata, A.; Kumara, S.; Suhartono, D. Predicting Stock Market Prices using Time Series SARIMA. In Proceedings of the 2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI), Jakarta, Indonesia, 28–28 October 2021; pp. 92–99. [Google Scholar] [CrossRef]
- Cheah, E.T.; Fry, J. Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Econ. Lett. 2015, 130, 32–36. [Google Scholar] [CrossRef]
- Almeida, J.; Gonçalves, T.C. A systematic literature review of investor behavior in the cryptocurrency markets. J. Behav. Exp. Financ. 2023, 37, 100785. [Google Scholar] [CrossRef]
- Fry, J.; Cheah, E.T. Negative bubbles and shocks in cryptocurrency markets. Int. Rev. Financ. Anal. 2016, 47, 343–352. [Google Scholar] [CrossRef]
MANA | SAND | ETH | BTC | |
---|---|---|---|---|
n = | 1868 | 944 | 1868 | 1868 |
Minimum | 0.0179 | 0.0308 | 84.31 | 3228.700 |
Mean | 0.5625 | 1.2402 | 1163.9481 | 20,509.0342 |
Maximum | 5.1950 | 8.4022 | 4812.0900 | 67,527.9000 |
Median | 0.0960 | 0.7165 | 557.6200 | 11,337.8499 |
Standard Deviation | 0.8783 | 1.5007 | 1189.0905 | 16,762.4430 |
Skewness | 2.3304 | 1.9036 | 1.1213 | 1.0097 |
Kurtosis | 5.3721 | 3.1073 | 0.1991 | −0.2605 |
Coefficient | Standard Error | Z-Value | p-Value | 0.025 | 0.975 | |
---|---|---|---|---|---|---|
ar.L1 | 0.9985 | 0.002 | 514.157 | 0.000 | 0.995 | 1.002 |
ar.S.L4 | 0.9936 | 0.021 | 46.338 | 0.000 | 0.952 | 1.036 |
ma.S.L4 | −0.9904 | 0.023 | −43.214 | 0.000 | −1.035 | −0.945 |
sigma2 | 0.0063 | 81.816 | 0.000 | 0.006 | 0.006 |
A | B | C | m | |||||
---|---|---|---|---|---|---|---|---|
MANA | −12.1341 | 143.1350 | −1.2134 | −0.3516 | 8.1304 | −6.0181 | 17.1913 | 5 July 2025 |
SAND | −0.4327 | 2.0597 | 4.8706 | 1.6765 | 3.2276 | 4.8597 | 3.2634 | 23 April 2023 |
ETH | 7.3582 | 2.0270 | 0.0015 | 1.0310 | 3.1468 | 0.0014 | 0.0005 | 12 April 2023 |
BTC | −4.3041 | 23.1065 | 1.3118 | −0.0688 | 5.4534 | 1.2861 | 0.2583 | 20 July 2024 |
Bubble (neg) | Start | End | Days | Mean Price | Price Decrease | Bubble Category |
---|---|---|---|---|---|---|
1 | 19-08-2018 | 26-08-2018 | 8 Days | 0.0680 | −7.31% | market dip |
2 | 30-11-2018 | 12-12-2018 | 13 Days | 0.0593 | −7.63% | market dip |
3 | 30-12-2018 | 11-02-2019 | 44 Days | 0.0386 | −27.51% | financial bubble |
4 | 18-08-2019 | 15-09-2019 | 29 Days | 0.0332 | −13.82% | market correction |
5 | 29-11-2019 | 07-12-2019 | 9 Days | 0.0246 | −4.60% | market dip |
6 | 01-05-2022 | 23-06-2022 | 54 Days | 1.0511 | −41.38% | financial bubble |
7 | 14-10-2022 | 24-10-2022 | 11 Days | 0.0621 | −3.21% | market dip |
8 | 12-11-2022 | 27-11-2022 | 16 Days | 0.4126 | −13.93% | market correction |
Bubble (pos) | Start | End | Days | Mean Price | Price Increase | Bubble Category |
---|---|---|---|---|---|---|
1 | 12-03-2019 | 04-04-2019 | 24 Days | 0.0525 | 26.60% | financial bubble |
2 | 30-07-2020 | 06-08-2020 | 8 Days | 0.0482 | 22.03% | financial bubble |
3 | 12-01-2021 | 15-02-2021 | 35 Days | 0.1833 | 169.48% | financial bubble |
4 | 04-03-2021 | 21-03-2021 | 18 Days | 0.7260 | 134.64% | financial bubble |
5 | 02-04-2021 | 23-04-2021 | 22 Days | 1.1491 | 19.47% | market correction |
6 | 18-08-2021 | 06-09-2021 | 20 Days | 0.9355 | 34.26% | financial bubble |
7 | 13-11-2021 | 01-12-2021 | 19 Days | 4.1629 | 40.29% | financial bubble |
8 | 21-01-2023 | 04-02-2023 | 15 Days | 0.7354 | 9.12% | market dip |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Frank, P.; Rudolf, M. Is the Metaverse Dead? Insights from Financial Bubble Analysis. FinTech 2024, 3, 302-323. https://doi.org/10.3390/fintech3020017
Frank P, Rudolf M. Is the Metaverse Dead? Insights from Financial Bubble Analysis. FinTech. 2024; 3(2):302-323. https://doi.org/10.3390/fintech3020017
Chicago/Turabian StyleFrank, Pascal, and Markus Rudolf. 2024. "Is the Metaverse Dead? Insights from Financial Bubble Analysis" FinTech 3, no. 2: 302-323. https://doi.org/10.3390/fintech3020017
APA StyleFrank, P., & Rudolf, M. (2024). Is the Metaverse Dead? Insights from Financial Bubble Analysis. FinTech, 3(2), 302-323. https://doi.org/10.3390/fintech3020017