Are there digital tech bubbles in China?
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
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
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