IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i12p5170-d375874.html
   My bibliography  Save this article

Empirical Research on the Fama-French Three-Factor Model and a Sentiment-Related Four-Factor Model in the Chinese Blockchain Industry

Author

Listed:
  • Ziyang Ji

    (International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China)

  • Victor Chang

    (School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK)

  • Hao Lan

    (International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China)

  • Ching-Hsien Robert Hsu

    (Department of Computer Science and Information Engineering, Asia University, Taichung 400-439, Taiwan
    Department of Medical Research, China Medical University, Taichung 400-439, Taiwan
    School of Mathematics and Big Data, Foshan University, Foshana 528000, China)

  • Raul Valverde

    (John Molson School of Business, Concordia University, Montreal, QC G1X 3X4, Canada)

Abstract

As one of the most significant components of financial technology (FinTech), blockchain technology arouses the interests of numerous investors in China, and the number of companies engaged in this field rises rapidly. The emotion of investors has an effect on stock returns, which is a hot topic in behavioral finance. Blockchain is an essential part of FinTech, and with the fast development of this technology, investors’ sentiment varies as well. The online information that directly reflects investors’ mood could be utilized for mining and quantifying to construct a sentiment index. For a better understanding of how well some factors adequately explain the return of stocks related to blockchain companies in the Chinese stock market, the Fama-French three-factor model (FFTFM) will be introduced in this paper. Furthermore, sentiment could be a new independent variable to enhance the explanatory power of the FFTFM. A comparison between those two models reveals that the sentiment factor could raise the explanatory power. The results also indicate that the Chinses blockchain industry does not own the size effect and book-to-market effect.

Suggested Citation

  • Ziyang Ji & Victor Chang & Hao Lan & Ching-Hsien Robert Hsu & Raul Valverde, 2020. "Empirical Research on the Fama-French Three-Factor Model and a Sentiment-Related Four-Factor Model in the Chinese Blockchain Industry," Sustainability, MDPI, vol. 12(12), pages 1-22, June.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:12:p:5170-:d:375874
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/12/5170/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/12/5170/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    2. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    3. Schwert, G. William, 2003. "Anomalies and market efficiency," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 15, pages 939-974, Elsevier.
    4. Mehra, Rajnish & Sah, Raaj, 2002. "Mood fluctuations, projection bias, and volatility of equity prices," Journal of Economic Dynamics and Control, Elsevier, vol. 26(5), pages 869-887, May.
    5. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    6. Ferikawita M. Sembiring, 2018. "Three-Factor and Five-Factor Models: Implementation of Fama and French Model on Market Overreaction Conditions," GATR Journals jfbr150, Global Academy of Training and Research (GATR) Enterprise.
    7. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    8. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    9. Romana Bangash & Faisal Khan & Zohra Jabeen, 2018. "Size, Value and Momentum in Pakistan Equity Market: Size and Liquidity Exposures," Global Social Sciences Review, Humanity Only, vol. 3(1), pages 374-392, March.
    10. Fama, Eugene F. & French, Kenneth R., 2012. "Size, value, and momentum in international stock returns," Journal of Financial Economics, Elsevier, vol. 105(3), pages 457-472.
    11. François-Eric Racicot & William F. Rentz & Alfred Kahl & Olivier Mesly, 2019. "Examining the dynamics of illiquidity risks within the phases of the business cycle," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 19(2), pages 117-131, June.
    12. François-Éric Racicot & William F Rentz & David Tessier & Raymond Théoret, 2019. "The conditional Fama-French model and endogenous illiquidity: A robust instrumental variables test," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-26, September.
    13. Banz, Rolf W., 1981. "The relationship between return and market value of common stocks," Journal of Financial Economics, Elsevier, vol. 9(1), pages 3-18, March.
    14. Fama, Eugene F & French, Kenneth R, 1995. "Size and Book-to-Market Factors in Earnings and Returns," Journal of Finance, American Finance Association, vol. 50(1), pages 131-155, March.
    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. Gaies, Brahim & Nakhli, Mohamed Sahbi & Sahut, Jean Michel & Guesmi, Khaled, 2021. "Is Bitcoin rooted in confidence? – Unraveling the determinants of globalized digital currencies," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    2. Ali, Mohd Helmi & Chung, Leanne & Kumar, Ajay & Zailani, Suhaiza & Tan, Kim Hua, 2021. "A sustainable Blockchain framework for the halal food supply chain: Lessons from Malaysia," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    3. Ante, Lennart & Fiedler, Ingo & Strehle, Elias, 2021. "The impact of transparent money flows: Effects of stablecoin transfers on the returns and trading volume of Bitcoin," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    4. Garg, Poonam & Gupta, Bhumika & Chauhan, Ajay Kumar & Sivarajah, Uthayasankar & Gupta, Shivam & Modgil, Sachin, 2021. "Measuring the perceived benefits of implementing blockchain technology in the banking sector," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    5. Hajek, Petr & Hikkerova, Lubica & Sahut, Jean-Michel, 2023. "How well do investor sentiment and ensemble learning predict Bitcoin prices?," Research in International Business and Finance, Elsevier, vol. 64(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. Sebastien Valeyre, 2020. "Refined model of the covariance/correlation matrix between securities," Papers 2001.08911, arXiv.org.
    2. Sebastien Valeyre & Denis Grebenkov & Sofiane Aboura & Francois Bonnin, 2016. "Should employers pay their employees better? An asset pricing approach," Papers 1602.00931, arXiv.org, revised Oct 2016.
    3. Peter S. Schmidt & Urs von Arx & Andreas Schrimpf & Alexander F. Wagner & Andreas Ziegler, 2019. "Common risk factors in international stock markets," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(3), pages 213-241, September.
    4. Cakici, Nusret & Zaremba, Adam, 2022. "Salience theory and the cross-section of stock returns: International and further evidence," Journal of Financial Economics, Elsevier, vol. 146(2), pages 689-725.
    5. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, April.
    6. Israel, Ronen & Moskowitz, Tobias J., 2013. "The role of shorting, firm size, and time on market anomalies," Journal of Financial Economics, Elsevier, vol. 108(2), pages 275-301.
    7. Walkshäusl, Christian, 2015. "Equity financing activities and European value-growth returns," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 27-40.
    8. De Moor, Lieven & Sercu, Piet, 2013. "The smallest firm effect: An international study," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 129-155.
    9. Muhammad Usman Arshad, 2021. "Forecasted E/P Ratio and ROE: Shanghai Stock Exchange (SSE), China," SAGE Open, , vol. 11(2), pages 21582440211, June.
    10. Anton Astakhov & Tomas Havranek & Jiri Novak, 2019. "Firm Size And Stock Returns: A Quantitative Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 33(5), pages 1463-1492, December.
    11. Cakici, Nusret & Zaremba, Adam, 2021. "Liquidity and the cross-section of international stock returns," Journal of Banking & Finance, Elsevier, vol. 127(C).
    12. Fletcher, Jonathan, 2018. "Betas V characteristics: Do stock characteristics enhance the investment opportunity set in U.K. stock returns?," The North American Journal of Economics and Finance, Elsevier, vol. 46(C), pages 114-129.
    13. Ekaterini Panopoulou & Sotiria Plastira, 2014. "Fama French factors and US stock return predictability," Journal of Asset Management, Palgrave Macmillan, vol. 15(2), pages 110-128, April.
    14. Tobek, Ondrej & Hronec, Martin, 2021. "Does it pay to follow anomalies research? Machine learning approach with international evidence," Journal of Financial Markets, Elsevier, vol. 56(C).
    15. Neharika Sobti, 2018. "Does Size, Value and Seasonal Effects Still Persist in Indian Equity Markets?," Vision, , vol. 22(1), pages 11-21, March.
    16. Ailie Charteris & Mukashema Rwishema & Tafadzwa-Hidah Chidede, 2018. "Asset Pricing and Momentum: A South African Perspective," Journal of African Business, Taylor & Francis Journals, vol. 19(1), pages 62-85, January.
    17. Harshita & Shveta Singh & Surendra S. Yadav, 2018. "Changing Nature of the Value Premium in the Indian Stock Market," Vision, , vol. 22(2), pages 135-143, June.
    18. repec:fau:fauart:v:65:y:2015:i:1:p:84-104 is not listed on IDEAS
    19. Sakemoto, Ryuta, 2023. "The long-run risk premium in the intertemporal CAPM: International evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
    20. Liebi, Luca J., 2022. "Is there a value premium in cryptoasset markets?," Economic Modelling, Elsevier, vol. 109(C).
    21. Kewei Hou & Chen Xue & Lu Zhang, 2017. "Replicating Anomalies," NBER Working Papers 23394, National Bureau of Economic Research, Inc.

    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:gam:jsusta:v:12:y:2020:i:12:p:5170-:d:375874. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.