Share:


Are there digital tech bubbles in China?

    Meng Qin Affiliation
    ; Chi-Wei Su Affiliation
    ; Lianhong Qiu Affiliation
    ; Oana-Ramona Lobonţ Affiliation

Abstract

This exploration employs the generalized supremum augmented Dickey-Fuller (GSADF) approach to explore whether there are digital tech bubbles in China. The empirical results suggest the existence of multiple digital tech bubbles, which are mostly accompanied by an excessive rise. However, the appearance of digital tech bubbles is curbed since 2016, mainly due to the increasing mature regulations in relevant fields. Besides, bubbles in different digital technologies are similar during the same period, which could be attributed to the close relationships among them. Additionally, we further investigate the factors influencing the explosive behaviours, and find that the Chinese stock market positively affects digital tech bubbles, while economic policy uncertainties and situations negatively influence such explosive behaviors. In the context of the new round of scientific and technological revolution and industrial transformation, these conclusions provide valuable implications to achieve the target of constructing a “Digital China” by becoming moderately cautious about potential bubbles in the digital tech industry.


First published online 11 October 2023

Keyword : digital technology, explosive bubbles, generalized supremum ADF, China

How to Cite
Qin, M., Su, C.-W., Qiu, L., & Lobonţ, O.-R. (2024). Are there digital tech bubbles in China?. Technological and Economic Development of Economy, 30(3), 603–626. https://doi.org/10.3846/tede.2023.19417
Published in Issue
May 22, 2024
Abstract Views
975
PDF Downloads
571
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Baker, S., Bloom, N., & Davis, S.-J. (2016). Measuring economic policy uncertainty. Quarterly Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qjw024

Baker, S., Bloom, N., Davis, S.-J., & Wang, X.-X. (2013). Economic policy uncertainty in China (Working Paper). University of Chicago.

Caferra, R., Tedeschi, G., & Morone, A. (2021). Bitcoin: Bubble that bursts or gold that glitters? Economics Letters, 205, 109942. https://doi.org/10.1016/j.econlet.2021.109942

Campello, M., & Graham, J.-R. (2013). Do stock prices influence corporate decisions? Evidence from the technology bubble. Journal of Financial Economics, 107(1), 89–110. https://doi.org/10.1016/j.jfineco.2012.08.002

Chaim, P., & Laurini, M.-P. (2019). Is Bitcoin a bubble? Physica A: Statistical Mechanics and its Applications, 517, 222–232. https://doi.org/10.1016/j.physa.2018.11.031

Chan, Y.-C. (2014). How does retail sentiment affect IPO returns? Evidence from the internet bubble period. International Review of Economics & Finance, 29, 235–248. https://doi.org/10.1016/j.iref.2013.05.016

Chang, H.-Y., Liang, W.-L., & Wang, Y.-Z. (2019). Do institutional investors still encourage patent-based innovation after the tech bubble period? Journal of Empirical Finance, 51, 149–164. https://doi.org/10.1016/j.jempfin.2019.02.003

Cheah, E., & Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32–36. https://doi.org/10.1016/j.econlet.2015.02.029

Chen, X., Guo, M., & Shangguan, W.-Y. (2022). Estimating the impact of cloud computing on firm performance: An empirical investigation of listed firms. Information & Management, 59(3), 103603. https://doi.org/10.1016/j.im.2022.103603

Chen, Y.-W., Chou, R.-K., & Lin, C.-B. (2019). Investor sentiment, SEO market timing, and stock price performance. Journal of Empirical Finance, 51, 28–43. https://doi.org/10.1016/j.jempfin.2019.01.008

Chiang, M.-C., Tsai, I.-C., & Lee, C.-F. (2011). Fundamental indicators, bubbles in stock returns and investor sentiment. The Quarterly Review of Economics and Finance, 51(1), 82–87. https://doi.org/10.1016/j.qref.2010.11.001

Choi, J.-J., Kedar-Levy, H., & Yoo, S.-S. (2015). Are individual or institutional investors the agents of bubbles? Journal of International Money and Finance, 59, 1–22. https://doi.org/10.1016/j.jimonfin.2015.09.004

Corbet, S., Lucey, B., & Yarovaya, L. (2018). Datestamping the Bitcoin and Ethereum bubbles. Finance Research Letters, 26, 81–88. https://doi.org/10.1016/j.frl.2017.12.006

Cross, J.-L., Hou, C.-H., & Trinh, K. (2021). Returns, volatility and the cryptocurrency bubble of 2017-18. Economic Modelling, 104, 105643. https://doi.org/10.1016/j.econmod.2021.105643

Dai, Z.-F., Zhu, J.-X., & Zhang, X.-H. (2022). Time-frequency connectedness and cross-quantile dependence between crude oil, Chinese commodity market, stock market and investor sentiment. Energy Economics, 114, 106226. https://doi.org/10.1016/j.eneco.2022.106226

Davis, S.-J., Liu, D.-Q., & Sheng, X.-G.-S. (2019). Economic policy uncertainty in China since 1949: The view from mainland newspapers (Working paper).

Diba, B.-T., & Grossman, H.-I. (1988). Explosive rational bubbles in stock prices? American Economic Review, 78(3), 520–530.

Dickey, D.-A., & Fuller, W.-A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057–1072. https://doi.org/10.2307/1912517

Enoksen, F.-A., Landsnes, C.-J., Lučivjanská, K., & Molnár, P. (2020). Understanding risk of bubbles in cryptocurrencies. Journal of Economic Behavior & Organization, 176, 129–144. https://doi.org/10.1016/j.jebo.2020.05.005

Evans, G.-W. (1991). Pitfalls in testing for explosive bubbles in asset prices. American Economic Review, 81(4), 922–930.

Geuder, J., Kinateder, H., & Wagner, N.-F. (2019). Cryptocurrencies as financial bubbles: The case of Bitcoin. Finance Research Letters, 31, 179–184. https://doi.org/10.1016/j.frl.2018.11.011

Griffin, J. M., Harris, J. H., Shu, T., & Topaloglu, S. (2011). Who drove and burst the tech bubble? Journal of Finance, American Finance Association, 66(4), 1251–1290. https://doi.org/10.1111/j.1540-6261.2011.01663.x

Gu, X., Zhang, W.-Q., & Cheng, S. (2021). How do investors in Chinese stock market react to external uncertainty? An event study to the Sino-US disputes. Pacific-Basin Finance Journal, 68, 101614. https://doi.org/10.1016/j.pacfin.2021.101614

Gui, J.-X., Pu, J.-C., Naktnasukanjn, N., Yu, X., Mu, L., & Pan, H.-P. (2022). Measuring investor sentiment of China’s growth enterprises market with ERNIE. Procedia Computer Science, 202, 1–8. https://doi.org/10.1016/j.procs.2022.04.001

Gürkaynak, R.-S. (2008). Econometric tests of asset price bubbles: Taking stock. Journal of Economic Surveys, 22(1), 166–186. https://doi.org/10.1016/j.procs.2022.04.001

Haddad, V., Ho, P., & Loualiche, E. (2022). Bubbles and the value of innovation. Journal of Financial Economics, 145(1), 69–84. https://doi.org/10.1016/j.jfineco.2022.04.006

Kassouri, Y., Kacou, K.-Y.-T., & 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

Khan, K., Su, C.-W., Umar, M., & Yue, X.-G. (2021). Do crude oil price bubbles occur? Resources Policy, 71, 101936. https://doi.org/10.1016/j.resourpol.2020.101936

Kyriazis, N., Papadamou, S., & 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

Le, T.-H., & Luong, A.-T. (2022). Dynamic spillovers between oil price, stock market, and investor sentiment: Evidence from the United States and Vietnam. Resources Policy, 78, 102931. https://doi.org/10.1016/j.resourpol.2022.102931

Lee, C.-K., & Yu, L.-M. (2022). A multi-level perspective on 5G transition: The China case. Technological Forecasting and Social Change, 182, 121812. https://doi.org/10.1016/j.techfore.2022.121812

Leone, V., & de Medeiros, O.-R. (2015). Signalling the Dotcom bubble: A multiple changes in persistence approach. The Quarterly Review of Economics and Finance, 55, 77–86. https://doi.org/10.1016/j.qref.2014.08.006

Li, Y., Chevallier, J., Wei, Y.-G., & Li, J. (2020). Identifying price bubbles in the US, European and Asian natural gas market: Evidence from a GSADF test approach. Energy Economics, 87, 104740. https://doi.org/10.1016/j.eneco.2020.104740

Li, Y., Zhang, W., Urquhart, A., & Wang, P.-F. (2022a). The role of media coverage in the bubble formation: Evidence from the Bitcoin market. Journal of International Financial Markets, Institutions and Money, 80, 101629. https://doi.org/10.1016/j.intfin.2022.101629

Li, Z.-Z., Su, C.-W., Chang, T.-Y., & Lobonţ, O.-R. (2022b). Policy-driven or market-driven? Evidence from steam coal price bubbles in China. Resources Policy, 78, 102878. https://doi.org/10.1016/j.resourpol.2022.102878

Li, Z.-Z., Tao, R., Su, C.-W., & Lobonţ, O.-R. (2019). Does Bitcoin bubble burst? Quality & Quantity, 53(1), 91–105. https://doi.org/10.1007/s11135-018-0728-3

Lin, A.-J., Peng, Y.-L., & Wu, X. (2022). Digital finance and investment of micro and small enterprises: Evidence from China. China Economic Review, 75, 101846. https://doi.org/10.1016/j.chieco.2022.101846

Liu, N., Gu, X.-H., & Lei, C.-K. (2022). The equilibrium effects of digital technology on banking, production, and employment. Finance Research Letters, 49, 103196. https://doi.org/10.1016/j.frl.2022.103196

Lucas, R.-E. (1978). Asset prices in an exchange economy. Econometrica, 46(6), 1429–1445. https://doi.org/10.2307/1913837

Lundvall, B., & Rikap, C. (2022). China’s catching-up in artificial intelligence seen as a co-evolution of corporate and national innovation systems. Research Policy, 51(1), 104395. https://doi.org/10.1016/j.respol.2021.104395

Lyu, W.-J., & Liu, J. (2021). Artificial Intelligence and emerging digital technologies in the energy sector. Applied Energy, 303, 117615. https://doi.org/10.1016/j.apenergy.2021.117615

Maouchi, Y., Charfeddine, L., & Montasser, G.-E. (2022). Understanding digital bubbles amidst the COVID-19 pandemic: Evidence from DeFi and NFTs. Finance Research Letters, 47, 102584. https://doi.org/10.1016/j.frl.2021.102584

Meng X.-Y., Zhang, Y.-Y., & Wei, X.-H. (2015). Market value of innovation: An Empirical analysis on China’s stock market. Procedia Computer Science, 55, 1275–1284. https://doi.org/10.1016/j.procs.2015.07.138

Nguyen, Q.-N., & Waters, G.-A. (2022). Detecting periodically collapsing bubbles in the S&P 500. The Quarterly Review of Economics and Finance, 83, 83–91. https://doi.org/10.1016/j.qref.2021.11.005

Özdurak, C., & Alcan, G. (2021). Is “the return of the tech bubble” next dystopian movie of Netflix? A DCC-GARCH analysis. In Contemporary approaches in the field of economy, finance and management (pp. 59–83). Nobel Akademik Yayıncılık.

Pan, L., & Mishra, V. (2018). Stock market development and economic growth: Empirical evidence from China. Economic Modelling, 68, 661–673. https://doi.org/10.1016/j.econmod.2017.07.005

Pavlidis, E., Paya, I., & Peel, D. (2012). A new test for rational speculative bubbles using forward exchange rates: The case of the interwar German hyperinflation (Working paper No. 09-2012).

Phillips, P.-C.-B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335

Phillips, P.-C.-B., Shi, S., & Yu, J. (2012). Testing for multiple bubbles (Working Paper, Yale University, New Haven, CT. No. 1843). https://doi.org/10.2139/ssrn.1981976

Phillips, P.-C.-B., Shi, S., & Yu, J. (2013). Testing for multiple bubbles: Historical episodes of exuberance and collapse in the S&P 500 (Working Paper, No. 04-2013). Singapore Management University

Phillips, P.-C.-B., & Yu, J. (2011). Dating the timeline of financial bubbles during the subprime crisis. Quantitative Economics, 2(3), 455–491. https://doi.org/10.3982/QE82

Phillips, P.-C.-B., Shi, S., & 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

Qin, M., Su, C.-W., Zhong, Y.-F., Song, Y.-R., & Lobont, O.-R. (2022), Sustainable finance and renewable energy: Promoters of carbon neutrality in the United States. Journal of Environmental Management, 324, 116390. https://doi.org/10.1016/j.jenvman.2022.116390

Qin, M., Su, C.-W., Qi, X.-Z., & Hao, L.-N. (2020a). Should gold be stored in chaotic eras? Ekonomska Istrazivanja-Economic Research, 33(1), 224–242. https://doi.org/10.1080/1331677X.2019.1661789

Qin, M., Su, C.-W., & Tao, R. (2021). BitCoin: A new basket for eggs? Economic Modelling, 94(C), 896–907. https://doi.org/10.1016/j.econmod.2020.02.031

Qin, M., Su, C.-W., Pirtea, M.-G., & Peculea, A.-D. (2023). The essential role of Russian geopolitics: A fresh perception into the gold market. Resources Policy, 81, 103310. https://doi.org/10.1016/j.resourpol.2023.103310

Qin, M., Su, C.-W., Tao, R., & Umar, M. (2020b). Is factionalism a push for gold price? Resources Policy, 67, 101679. https://doi.org/10.1016/j.resourpol.2020.101679

Qin, M., Su, C.-W., Xiao, Y.-D., & Zhang, S. (2020c). Should gold be held under global economic policy uncertainty? Journal of Business Economics and Management, 21(3), 725–742. https://doi.org/10.3846/jbem.2020.12040

Sargen, N.-P. (2016). The tech bubble: Some lessons for rational investors. In Global Shocks (pp. 121–135). Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-41105-7_9

Sestino, A., Prete, M.-I., Piper, L., & Guido, G. (2020). Internet of things and Big Data as enablers for business digitalization strategies. Technovation, 98, 102173. https://doi.org/10.1016/j.technovation.2020.102173

Shen, W., & Hou, L.-Y. (2021). China’s central bank digital currency and its impacts on monetary policy and payment competition: Game changer or regulatory toolkit? Computer Law & Security Review, 41, 105577. https://doi.org/10.1016/j.clsr.2021.105577

Shiller, R.-J. (1984). Stock prices and social dynamics. Brookings Papers on Economic Activity, 1984(2), 457–498. https://doi.org/10.2307/2534436

Shu, M., & Zhu, W. (2020). Detection of Chinese stock market bubbles with LPPLS confidence indicator. Physica A: Statistical Mechanics and its Applications, 557, 124892. https://doi.org/10.1016/j.physa.2020.124892

Singh, V. (2013). Did institutions herd during the internet bubble? Review of Quantitative Finance and Accounting, 41, 513–534. https://doi.org/10.1007/s11156-012-0320-1

Su, C.-W., Pang, L.-D., Umar, M., Lobonţ, O.-R., & Moldovan, N.-C. (2022a). Does gold’s hedging uncertainty aura fade away? Resources Policy, 77, 102726. https://doi.org/10.1016/j.resourpol.2022.102726

Su, C.-W., Rizvi, S.-K.-A., Naqvi, B., Mirza, N., & Umar, M. (2022b). COVID19: A blessing in disguise for European stock markets? Finance Research Letters, 49, 103135. https://doi.org/10.1016/j.frl.2022.103135

Su, C.-W., Meng, X.-L., Tao, R., & Umar, M. (2022c). Policy turmoil in China: A barrier for FDI flows? International Journal of Emerging Markets, 17(7), 1617–1634. https://doi.org/10.1108/IJOEM-03-2021-0314

Su, C.-W., Qin, M., Tao, R., Shao, X.-F., Albu, L.-L., & Umar, M. (2020a). Can Bitcoin hedge the risks of geopolitical events? Technological Forecasting & Social Change, 159, 120182. https://doi.org/10.1016/j.techfore.2020.120182

Su, C.-W., Qin, M., Tao, R., & Umar, M. (2020b). Financial implications of fourth industrial revolution: Can bitcoin improve prospects of energy investment? Technological Forecasting & Social Change, 158, 120178. https://doi.org/10.1016/j.techfore.2020.120178

Teixeira, J.-E., & Tavares-Lehmann, A.-T.-C.-P. (2022). Industry 4.0 in the European union: Policies and national strategies. Technological Forecasting and Social Change, 180, 121664. https://doi.org/10.1016/j.techfore.2022.121664

Tirole, J. (1982). On the possibility of speculation under rational expectations. Econometrica, 50(5), 1163–1181. https://doi.org/10.2307/1911868

Tirole, J. (1985). Asset bubbles and overlapping generations. Econometrica, 53(6), 1499–1528. https://doi.org/10.2307/1913232

Vernim, S., Krauel, M., & Reinhart, G. (2021). Identification of digitization trends and use cases in assembly. Procedia CIRP, 97, 136–141. https://doi.org/10.1016/j.procir.2020.05.215

Viana, C.-J.-P., Hernandez, R., & Ariza, M.-A. (2022). The joint effect of the internet of things and democracy on corruption: A cross-country study. Procedia Computer Science, 203, 544–548. https://doi.org/10.1016/j.procs.2022.07.077

Wang, J.-D., Wang, B., Dong, K.-Y., & Dong, X.-C. (2022a). How does the digital economy improve high-quality energy development? The case of China. Technological Forecasting and Social Change, 184, 121960. https://doi.org/10.1016/j.techfore.2022.121960

Wang, J.-Q., Ma, X.-W., Zhang, J., & Zhao, X. (2022b). Impacts of digital technology on energy sustainability: China case study. Applied Energy, 323, 119329. https://doi.org/10.1016/j.apenergy.2022.119329

Wang, J., Shao, W., Ma, C.-M., Chen, W.-B., & Kim, J. (2021). Co-movements between Shanghai Composite Index and some fund sectors in China. Physica A: Statistical Mechanics and its Applications, 573, 125981. https://doi.org/10.1016/j.physa.2021.125981

Wang, J., Xue, W.-N., & Song, J.-S. (2022c). Economic policy uncertainty and industry risk on China’s stock market. The North American Journal of Economics and Finance, 62, 101771. https://doi.org/10.1016/j.najef.2022.101771

Wang, K.-H., Su, C.-W., Lobonţ, O.-R., & Moldovan, N.-C. (2020). Chinese renewable energy industries’ boom and recession: Evidence from bubble detection procedure. Energy Policy, 138, 111200. https://doi.org/10.1016/j.enpol.2019.111200

Wu, Y.-L., & Huang, S.-L. (2022). The effects of digital finance and financial constraint on financial performance: Firm-level evidence from China’s new energy enterprises. Energy Economics, 112, 106158. https://doi.org/10.1016/j.eneco.2022.106158

Yao, C.-Z., & Li, H.-Y. (2021). A study on the bursting point of Bitcoin based on the BSADF and LPPLS methods. The North American Journal of Economics and Finance, 55, 101280. https://doi.org/10.1016/j.najef.2020.101280

Zhang, G.-F., & Du, Z.-P. (2017). Co-movements among the stock prices of new energy, high-technology and fossil fuel companies in China. Energy, 135, 249–256. https://doi.org/10.1016/j.energy.2017.06.103

Zhao, Z., Wen, H.-W., & 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