Speculative Bubbles in Artificial Intelligence Investments: Analysis of the “Magnificent Seven” Technology Stocks and Volatility Spillover Effects

Abstract

This paper comprehensively analyzes the speculative bubble risks of investments in artificial intelligence (AI) technologies in financial markets. A GSADF test and volatility spillover analysis on the stocks of the so-called “Magnificent Seven,” namely Meta, Microsoft, Apple, Amazon, Google, Nvidia, and Tesla is conducted. The test results reveal significant bubbles, especially in Nvidia and Tesla stocks, and these bubbles spread volatility to other technology stocks. The fact that Nvidia plays a central role in volatility spillovers suggests that overpricing in AI investments can create a domino effect across the sector, leading to severe volatility in global markets. Investors should diversify portfolios and adopt long-term strategies against speculative bubble risks. At the same time, policymakers should increase market efficiency by tightening financial regulations.

Keywords

• Speculative Bubbles
• Frequency Connectedness
• Tech Stock
• GSADF Test

Yapay Zekâ Yatırımlarında Spekülatif Balonlar: “Muhteşem Yedi” Teknoloji Hisse Senetlerinin Analizi ve Volatilite Taşma Etkileri

Abstract

BÇalışmada finans piyasalarında yapay zeka (YZ) teknolojilerine yapılan yatırımların spekülatif balon risklerini kapsamlı bir şekilde analiz edilmektedir. Meta, Microsoft, Apple, Amazon, Google, Nvidia ve Tesla olmak üzere "Muhteşem Yedili" hisseleri üzerinde bir GSADF testi ve volatilite taşması analizi yapılmıştır. Test sonuçları, özellikle Nvidia ve Tesla hisselerinde önemli balonlar olduğunu ortaya koymakta ve bu balonlar diğer teknoloji hisselerine volatilite yaymaktadır. Nvidia'nın volatilite taşmalarında merkezi bir rol oynaması, YZ yatırımlarında aşırı fiyatlandırmanın sektör genelinde domino etkisi yaratarak küresel piyasalarda ciddi volatiliteye yol açabileceğini göstermektedir. Yatırımcılar portföylerini çeşitlendirmeli ve spekülatif balon risklerine karşı uzun vadeli stratejiler benimsemelidir. Aynı zamanda, politika yapıcılar finansal düzenlemeleri sıkılaştırarak piyasa verimliliğini artırmalıdır.

Keywords

• Spekülatif Balonlar
• Frekans Bağlantısı
• Teknoloji Hisseleri
• GSADF Testi

References

1. Almudhaf, F. (2017). Speculative Bubbles and Irrational Exuberance in African Stock Markets. Journal of Behavioral and Experimental Finance, 13: 28–32. https://doi.org/10.1016/j.jbef.2016.11.002
2. Baker, M. and Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2): 129–151. https://doi.org/10.1257/jep.21.2.129
3. Baruník, J. and Křehlík, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2): 271–296. https://doi.org/10.1093/jjfinec/nby001
4. Brunnermeier, M.K. and Nagel, S. (2005). Hedge Funds and the Technology Bubble. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.423940
5. Crain, M. (2014). Financial markets and online advertising: Reevaluating the dotcom investment bubble. Information Communication and Society, 17(3): 371–384. https://doi.org/10.1080/1369118X.2013.869615
6. Fama, E.F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2): 383–417.
7. Giorgis, V., Huber, T.A. and Sornette, D. (2024). ‘Salvation and profit’: Deconstructing the clean-tech bubble. Technology Analysis and Strategic Management, 36(4): 827–839. https://doi.org/10.1080/09537325.2022.2060809
8. Hays, P. and Schreiber, M. (2010). Evidence of long memory in U.S. stock returns : The case of the 1990s bubble. Quarterly Journal of Finance and Accounting, 49(1): 5–19.

9. Johansen, A. and Sornette, D. (2000). The Nasdaq crash of April 2000: Yet another example of log-periodicity in a speculative bubble ending in a crash. European Physical Journal B, 17(2): 319–328. https://doi.org/10.1007/s100510070147
10. Kassouri, Y., Kacou, K.Y.T. and Alola, A.A. (2021). Are oil-clean energy and high technology stock prices in the same straits? Bubbles speculation and time-varying perspectives. Energy, 232: 121021. https://doi.org/10.1016/j.energy.2021.121021
11. Keynes, J.M. (1936). General Theory of employment. Quarterly Journal of Economics, 209–223. http://qje.oxfordjournals.org/
12. Kindleberger, C.P. (1978). Manias, Panics and Crashes. In Manias, Panics and Crashes, 4(2): 103-112.
13. Kyriazis, N., Papadamou, S. and Corbet, S. (2020). A systematic review of the bubble dynamics of cryptocurrency prices. Research in International Business and Finance, 54: 101254. https://doi.org/10.1016/j.ribaf.2020.101254
14. Mcenary, K.W. (1995). The internet, world-wide web, and mosaic: An overview. American Journal of Roentgenology, 164(2): 469-473.
15. Minsky, H.P. (1986). Stabilizing an unstable economy (Hyman P. Minsky Archive, 144). http://digitalcommons.bard.edu/hm_archive/144
16. Mitcham, K. (2024). ESG factors and profitability performance for the Dow Jones industrial average stock index. Available at SSRN 4793201. https://doi.org/10.2139/ssrn.4793201
17. Phillips, P.C.B., Shi, S. and Yu, J. (2015). Testing for multiple bubbles: Historical episodes of exuberance and collapse in the S&P 500. International Economic Review, 56(4): 1043–1078. https://doi.org/10.1111/iere.12132
18. Phillips, P.C.B., Wu, Y. and Yu, J. (2011). Explosive behavior in the 1990s Nasdaq: When did exuberance escalate asset values? International Economic Review, 52(1): 201–226.
19. Schumpeter, J. (1942). Capitalism, Socialism and Democracy (3rd edition). London: George Allen and Unwin.
20. Shiller, R.J. (2000). Measuring bubble expectations and investor confidence. Journal of Psychology and Financial Markets, 1(1): 49–60. https://doi.org/10.1207/S15327760JPFM0101_05
21. Teti, E. and Maroni, D. (2021). The new great bubble in the technology industry? Technology Analysis and Strategic Management, 33(5): 520–534. https://doi.org/10.1080/09537325.2020.1828577
22. Wheale, P.R. and Amin, L.H. (2003). Bursting the dot.com “Bubble”: A case study in investor behaviour. Technology Analysis and Strategic Management, 15(1): 117–136. https://doi.org/10.1080/0953732032000046097
23. Zhao, Z., Wen, H. and Li, K. (2021). Identifying bubbles and the contagion effect between oil and stock markets: New evidence from China. Economic Modelling, 94: 780–788. https://doi.org/10.1016/j.econmod.2020.02.018