IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v68y2022ics0160791x22000665.html
   My bibliography  Save this article

Asymmetric connectedness between Google-based investor attention and the fourth industrial revolution assets: The case of FinTech and Robotics & Artificial intelligence stocks

Author

Listed:
  • Adekoya, Oluwasegun B.
  • Oliyide, Johnson A.
  • Saleem, Owais
  • Adeoye, Habeeb A.

Abstract

As the global economy is fast applying and appreciating modern technology in different sectors, some of the fourth industrial revolution assets are increasingly considered for investment purposes. Unfortunately, only cryptocurrency among the fourth industrial revolution assets has enjoyed massive empirical consideration in terms of the relationship between its performance and investor attention. Nothing is yet known about the connection of investor attention with FinTech and Robotics & Artificial Intelligence stocks. Against the limited empirical evidence, this study examines the dynamic relationship between different measures of investor attention and the returns of FinTech and robotics & artificial intelligence stocks while accounting for spillover effects, asymmetries and varying market states. In summary, our findings reveal, contrary to expectations, that the investor attention indicators are the net recipients of spillovers from the asset returns, regardless of whether asymmetry is accounted for or not. However, the connectedness is stronger between investor attention and the negative asset returns, and the net spillover for each asset is close to zero after 2016/2017. The causality results further show that investor attention affects the aggregate asset returns when the market is normal, but affect both asymmetric asset returns when the market is bearish and normal.

Suggested Citation

  • Adekoya, Oluwasegun B. & Oliyide, Johnson A. & Saleem, Owais & Adeoye, Habeeb A., 2022. "Asymmetric connectedness between Google-based investor attention and the fourth industrial revolution assets: The case of FinTech and Robotics & Artificial intelligence stocks," Technology in Society, Elsevier, vol. 68(C).
    • Handle: RePEc:eee:teinso:v:68:y:2022:i:c:s0160791x22000665
    DOI: 10.1016/j.techsoc.2022.101925
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X22000665
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techsoc.2022.101925?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Le, TN-Lan & Abakah, Emmanuel Joel Aikins & Tiwari, Aviral Kumar, 2021. "Time and frequency domain connectedness and spill-over among fintech, green bonds and cryptocurrencies in the age of the fourth industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    2. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    3. Yao, Ting & Zhang, Yue-Jun & Ma, Chao-Qun, 2017. "How does investor attention affect international crude oil prices?," Applied Energy, Elsevier, vol. 205(C), pages 336-344.
    4. Johnson A. Oliyide & Oluwasegun B. Adekoya & Muhammad A. Khan, 2021. "Economic policy uncertainty and the volatility connectedness between oil shocks and metal market: An extension," International Economics, CEPII research center, issue 167, pages 136-150.
    5. Galaz, Victor & Centeno, Miguel A. & Callahan, Peter W. & Causevic, Amar & Patterson, Thayer & Brass, Irina & Baum, Seth & Farber, Darryl & Fischer, Joern & Garcia, David & McPhearson, Timon & Jimenez, 2021. "Artificial intelligence, systemic risks, and sustainability," Technology in Society, Elsevier, vol. 67(C).
    6. Nabila Jawadi & Fredj Jawadi & Abdoulkarim Idi Cheffou, 2020. "Computing the Time-Varying Effects of Investor Attention in Islamic Stock Returns," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 131-143, June.
    7. Mehmet Balcilar & Stelios Bekiros & Rangan Gupta, 2017. "The role of news-based uncertainty indices in predicting oil markets: a hybrid nonparametric quantile causality method," Empirical Economics, Springer, vol. 53(3), pages 879-889, November.
    8. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    9. Melody Y. Huang & Randall R. Rojas & Patrick D. Convery, 2020. "Forecasting stock market movements using Google Trend searches," Empirical Economics, Springer, vol. 59(6), pages 2821-2839, December.
    10. Huynh, Toan Luu Duc & Hille, Erik & Nasir, Muhammad Ali, 2020. "Diversification in the age of the 4th industrial revolution: The role of artificial intelligence, green bonds and cryptocurrencies," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    11. Han, Liyan & Lv, Qiuna & Yin, Libo, 2017. "Can investor attention predict oil prices?," Energy Economics, Elsevier, vol. 66(C), pages 547-558.
    12. Jeong, Kiho & Härdle, Wolfgang K. & Song, Song, 2012. "A Consistent Nonparametric Test For Causality In Quantile," Econometric Theory, Cambridge University Press, vol. 28(4), pages 861-887, August.
    13. Smales, L.A., 2021. "Investor attention and global market returns during the COVID-19 crisis," International Review of Financial Analysis, Elsevier, vol. 73(C).
    14. Kostopoulos, Dimitrios & Meyer, Steffen & Uhr, Charline, 2020. "Google search volume and individual investor trading," Journal of Financial Markets, Elsevier, vol. 49(C).
    15. 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.
    16. Tang, Wenbin & Zhu, Lili, 2017. "How security prices respond to a surge in investor attention: Evidence from Google Search of ADRs," Global Finance Journal, Elsevier, vol. 33(C), pages 38-50.
    17. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    18. Swamy, Vighneswara & Dharani, M. & Takeda, Fumiko, 2019. "Investor attention and Google Search Volume Index: Evidence from an emerging market using quantile regression analysis," Research in International Business and Finance, Elsevier, vol. 50(C), pages 1-17.
    19. Li, Xin & Ma, Jian & Wang, Shouyang & Zhang, Xun, 2015. "How does Google search affect trader positions and crude oil prices?," Economic Modelling, Elsevier, vol. 49(C), pages 162-171.
    20. Adekoya, Oluwasegun B. & Oliyide, Johnson A., 2020. "The hedging effectiveness of industrial metals against different oil shocks: Evidence from the four newly developed oil shocks datasets," Resources Policy, Elsevier, vol. 69(C).
    21. Adachi, Yuta & Masuda, Motoki & Takeda, Fumiko, 2017. "Google search intensity and its relationship to the returns and liquidity of Japanese startup stocks," Pacific-Basin Finance Journal, Elsevier, vol. 46(PB), pages 243-257.
    22. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    23. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    24. Dong, Xuefan & Lian, Ying, 2021. "A review of social media-based public opinion analyses: Challenges and recommendations," Technology in Society, Elsevier, vol. 67(C).
    25. Al Guindy, Mohamed, 2021. "Cryptocurrency price volatility and investor attention," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 556-570.
    26. Wang, Hua & Xu, Liao & Sharma, Susan Sunila, 2021. "Does investor attention increase stock market volatility during the COVID-19 pandemic?," Pacific-Basin Finance Journal, Elsevier, vol. 69(C).
    27. Hyuk Chung, 2021. "Adoption and Development of the Fourth Industrial Revolution Technology: Features and Determinants," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    28. Khaliq, Abdul & Waqas, Ali & Nisar, Qasim Ali & Haider, Shahbaz & Asghar, Zunaina, 2022. "Application of AI and robotics in hospitality sector: A resource gain and resource loss perspective," Technology in Society, Elsevier, vol. 68(C).
    29. Liyan Han & Ziying Li & Libo Yin, 2018. "Investor Attention and Stock Returns: International Evidence," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(14), pages 3168-3188, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gunay, Samet & Goodell, John W. & Muhammed, Shahnawaz & Kirimhan, Destan, 2023. "Frequency connectedness between FinTech, NFT and DeFi: Considering linkages to investor sentiment," International Review of Financial Analysis, Elsevier, vol. 90(C).
    2. Yousaf, Imran & Youssef, Manel & Goodell, John W., 2024. "Tail connectedness between artificial intelligence tokens, artificial intelligence ETFs, and traditional asset classes," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    3. Urom, Christian, 2023. "Time–frequency dependence and connectedness between financial technology and green assets," International Economics, Elsevier, vol. 175(C), pages 139-157.
    4. Mahdi Ghaemi Asl & Oluwasegun B. Adekoya & Muhammad Mahdi Rashidi, 2023. "Quantiles dependence and dynamic connectedness between distributed ledger technology and sectoral stocks: enhancing the supply chain and investment decisions with digital platforms," Annals of Operations Research, Springer, vol. 327(1), pages 435-464, August.
    5. Dong, Xueqin & Huang, Lilong, 2024. "Exploring ripple effect of oil price, fintech, and financial stress on clean energy stocks: A global perspective," Resources Policy, Elsevier, vol. 89(C).
    6. Pandey, Dharen Kumar & Hassan, M.Kabir & Kumari, Vineeta & Zaied, Younes Ben & Rai, Varun Kumar, 2024. "Mapping the landscape of FinTech in banking and finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 67(PA).
    7. Emmanuel Joel Aikins Abakah & Aviral Kumar Tiwari & Chi‐Chuan Lee & Matthew Ntow‐Gyamfi, 2023. "Quantile price convergence and spillover effects among Bitcoin, Fintech, and artificial intelligence stocks," International Review of Finance, International Review of Finance Ltd., vol. 23(1), pages 187-205, March.
    8. Ali, Omar & Murray, Peter A. & Momin, Mujtaba & Al-Anzi, Fawaz S., 2023. "The knowledge and innovation challenges of ChatGPT: A scoping review," Technology in Society, Elsevier, vol. 75(C).
    9. Rabbani, Mustafa Raza & Billah, Syed Mabruk & Shaik, Muneer & Rahman, Mashuk & Boujlil, Rhada, 2023. "Dynamic connectedness, spillover, and optimal hedging strategy among FinTech, Sukuk, and Islamic equity markets," Global Finance Journal, Elsevier, vol. 58(C).

    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. Ding, Qian & Huang, Jianbai & Zhang, Hongwei, 2022. "Time-frequency spillovers among carbon, fossil energy and clean energy markets: The effects of attention to climate change," International Review of Financial Analysis, Elsevier, vol. 83(C).
    2. Chen, Qitong & Zhu, Huiming & Yu, Dongwei & Hau, Liya, 2022. "How does investor attention matter for crude oil prices and returns? Evidence from time-frequency quantile causality analysis," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    3. Pham, Linh & Cepni, Oguzhan, 2022. "Extreme directional spillovers between investor attention and green bond markets," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 186-210.
    4. Adekoya, Oluwasegun B. & Oliyide, Johnson A., 2021. "How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques," Resources Policy, Elsevier, vol. 70(C).
    5. Goodell, John W. & Kumar, Satish & Li, Xiao & Pattnaik, Debidutta & Sharma, Anuj, 2022. "Foundations and research clusters in investor attention: Evidence from bibliometric and topic modelling analysis," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 511-529.
    6. Zhu, Xuehong & Zhang, Shishi & Ding, Qian, 2024. "Does extreme climate change drive the connectedness among global gold markets? Evidence from TVP-VAR and causality-in-quantiles techniques," Resources Policy, Elsevier, vol. 91(C).
    7. Zhao, Xin & Ghaemi Asl, Mahdi & Rashidi, Muhammad Mahdi & Vasa, László & Shahzad, Umer, 2023. "Interoperability of the revolutionary blockchain architectures and Islamic and conventional technology markets: Case of Metaverse, HPB, and Bloknet," The Quarterly Review of Economics and Finance, Elsevier, vol. 92(C), pages 112-131.
    8. Wang, Yizhi, 2022. "Volatility spillovers across NFTs news attention and financial markets," International Review of Financial Analysis, Elsevier, vol. 83(C).
    9. Maher Abida & Emna Mnif, 2023. "Investor Attention in Cryptocurrency Markets: Examining the Effects of Vaccination and COVID-19 Spread through a Wavelet Approach," International Journal of Economics and Financial Issues, Econjournals, vol. 13(5), pages 43-51, September.
    10. Zou, Fei & Huang, Lingyu & Ghaemi Asl, Mahdi & Delnavaz, Mohammad & Tiwari, Sunil, 2023. "Natural resources and green economic recovery in responsible investments: Role of ESG in context of Islamic sustainable investments," Resources Policy, Elsevier, vol. 86(PA).
    11. Wan, Jieru & Yin, Libo & Wu, You, 2024. "Return and volatility connectedness across global ESG stock indexes: Evidence from the time-frequency domain analysis," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 397-428.
    12. Abakah, Emmanuel Joel Aikins & Adeabah, David & Tiwari, Aviral Kumar & Abdullah, Mohammad, 2023. "Effect of Russia–Ukraine war sentiment on blockchain and FinTech stocks," International Review of Financial Analysis, Elsevier, vol. 90(C).
    13. Umar, Zaghum & Hadhri, Sinda & Abakah, Emmanuel Joel Aikins & Usman, Muhammad & Umar, Muhammad, 2024. "Return and volatility spillovers among oil price shocks and international green bond markets," Research in International Business and Finance, Elsevier, vol. 69(C).
    14. Oguzhan Cepni, Duc Khuong Nguyen, and Ahmet Sensoy, 2022. "News Media and Attention Spillover across Energy Markets: A Powerful Predictor of Crude Oil Futures Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
    15. Duan, Kun & Zhao, Yanqi & Urquhart, Andrew & Huang, Yingying, 2023. "Do clean and dirty cryptocurrencies connect with financial assets differently? The role of economic policy uncertainty," Energy Economics, Elsevier, vol. 127(PA).
    16. Adekoya, Oluwasegun B. & Oliyide, Johnson A. & Noman, Ambreen, 2021. "The volatility connectedness of the EU carbon market with commodity and financial markets in time- and frequency-domain: The role of the U.S. economic policy uncertainty," Resources Policy, Elsevier, vol. 74(C).
    17. Xiao, Jihong & Wen, Fenghua & He, Zhifang, 2023. "Impact of geopolitical risks on investor attention and speculation in the oil market: Evidence from nonlinear and time-varying analysis," Energy, Elsevier, vol. 267(C).
    18. Wang, Xuetong & Fang, Fang & Ma, Shiqun & Xiang, Lijin & Xiao, Zumian, 2024. "Dynamic volatility spillover among cryptocurrencies and energy markets: An empirical analysis based on a multilevel complex network," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    19. Matteo Foglia & Vasilios Plakandaras & Rangan Gupta & Qiang Ji, 2024. "Long-Span Multi-Layer Spillovers between Moments of Advanced Equity Markets: The Role of Climate Risks," Working Papers 202415, University of Pretoria, Department of Economics.
    20. Nguyen, Linh Hoang & Chevapatrakul, Thanaset & Yao, Kai, 2020. "Investigating tail-risk dependence in the cryptocurrency markets: A LASSO quantile regression approach," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 333-355.

    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:eee:teinso:v:68:y:2022:i:c:s0160791x22000665. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/technology-in-society .

    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.