Cryptocurrency in the Light of Sentiments : A Bibliometric Approach

Authors

  •   Diya Sharma Research Scholar, Department of Management Studies, Netaji Subhas University of Technology, Azad Hind Fauj Marg, Dwarka Sector-3, Dwarka, Delhi - 110 078. & Assistant Professor, Dyal Singh College (M), University of Delhi, Lodhi Road, Pragati Vihar, New Delhi - 110 003
  •   Renu Ghosh Assistant Professor (Corresponding Author), Department of Management Studies, Netaji Subhas University of Technology, Azad Hind Fauj Marg, Dwarka Sector-3, Dwarka, Delhi - 110 078
  •   Chandra Shekhar Sharma Professor (Retd.), Shri Ram College of Commerce, University of Delhi, Maurice Nagar, Roop Nagar, New Delhi - 110 007

DOI:

https://doi.org/10.17010/ijf/2024/v18i2/173521

Keywords:

Cryptocurrency

, Sentiments, Bibliometric, Behavioral Finance, PRISMA.

JEL Classification Codes

, G11, G15, G41, O3

Paper Submission Date

, October 1, 2023, Paper sent back for Revision, December 10, Paper Acceptance Date, January 10, 2024, Paper Published Online, February 15, 2024

Abstract

Purpose : Cryptocurrency has drawn interest from academia and business, particularly amid notable spikes in the price of Bitcoin. The presence of cryptocurrencies and their technology-focused investments are intimately related to investor behavior. Due to its unpredictability, mood has a greater impact on Bitcoin investing than technical factors. The purpose of this study was to perform a bibliometric review in this field.

Methodology : Using VOSviewer software, this article attempted to do a bibliometric study of papers on sentiments and cryptocurrencies included in the Scopus database. The study complied with the PRISMA framework, which is the recommended practice for systematic reviews.

Findings : The findings showed that this field is seeing a massive increase in research activity. A total of 483 distinct authors wrote 151 articles. Studies that examined changes in the Bitcoin industry have received the attention they deserve in finance publications. Three connected clusters were discovered by co-citation analysis, indicating that studies are looking at how cryptocurrencies work as financial market investments using social media sentiments.

Practical Implications : The study on cryptocurrency sentiments offered valuable insights for investors, policymakers, and the market. It informed decision-making on investments, risk management, and regulations, providing a foundation for practical tools in the dynamic cryptocurrency market. These insights will contribute to a resilient and sustainable ecosystem, guiding public awareness for responsible cryptocurrency use.

Originality : To the best of our knowledge, this work is the first in this developing field to do a bibliometric literature evaluation on Scopus articles.

Downloads

Download data is not yet available.

Downloads

Published

2024-02-01

How to Cite

Sharma, D., Ghosh, R., & Sharma, C. S. (2024). Cryptocurrency in the Light of Sentiments : A Bibliometric Approach. Indian Journal of Finance, 18(2), 60–75. https://doi.org/10.17010/ijf/2024/v18i2/173521

Issue

Volume 18, Issue 2, February 2024

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

References

Abdul-Rahim, R., Khalid, A., Karim, Z. A., & Rashid, M. (2022). Exploring the driving forces of stock-cryptocurrency comovements during COVID-19 pandemic: An analysis using wavelet coherence and seemingly unrelated regression. Mathematics, 10(12), 2116. https://doi.org/10.3390/math10122116

Almeida, J., & Gonçalves, T. C. (2023). A systematic literature review of investor behavior in the cryptocurrency markets. Journal of Behavioral and Experimental Finance, 37, 100785. https://doi.org/10.1016/j.jbef.2022.100785

Anamika, A., & Subramaniam, S. (2022). Do news headlines matter in the cryptocurrency market? Applied Economics, 54(54), 6322–6338. https://doi.org/10.1080/00036846.2022.2061904

Anbaee Farimani, S., Vafaei Jahan, M., Milani Fard, A., & Tabbakh, S. R. (2022). Investigating the informativeness of technical indicators and news sentiment in financial market price prediction. Knowledge-Based Systems, 247, 108742. https://doi.org/10.1016/j.knosys.2022.108742

Aslam, N., Rustam, F., Lee, E., Washington, P. B., & Ashraf, I. (2022). Sentiment analysis and emotion detection on cryptocurrency related tweets using ensemble LSTM-GRU Model. IEEE Access, 10, 39313–39324. https://doi.org/10.1109/ACCESS.2022.3165621

Bahamazava, K., & Nanda, R. (2022). The shift of DarkNet illegal drug trade preferences in cryptocurrency: The question of traceability and deterrence. Forensic Science International: Digital Investigation, 40, 301377. https://doi.org/10.1016/j.fsidi.2022.301377

Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61(4), 1645–1680. https://doi.org/10.1111/j.1540-6261.2006.00885.x

Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129–152. https://doi.org/10.1257/jep.21.2.129

Ballis, A., & Verousis, T. (2022). Behavioural finance and cryptocurrencies. Review of Behavioral Finance, 14(4), 545–562. https://doi.org/10.1108/RBF-11-2021-0256

Bianchi, D., Guidolin, M., & Pedio, M. (2023). The dynamics of returns predictability in cryptocurrency markets. The European Journal of Finance, 29(6), 583–611. https://doi.org/10.1080/1351847X.2022.2084343

Bouri, E., Roubaud, D., & Shahzad, S. J. (2020). Do Bitcoin and other cryptocurrencies jump together? The Quarterly Review of Economics and Finance, 76, 396–409. https://doi.org/10.1016/j.qref.2019.09.003

Caferra, R. (2022). Sentiment spillover and price dynamics: Information flow in the cryptocurrency and stock market. Physica A: Statistical Mechanics and its Applications, 593, 126983. https://doi.org/10.1016/j.physa.2022.126983

Chen, M., Qin, C., & Zhang, X. (2022). Cryptocurrency price discrepancies under uncertainty: Evidence from COVID-19 and lockdown nexus. Journal of International Money and Finance, 124, 102633. https://doi.org/10.1016/j.jimonfin.2022.102633

Chopra, M., & Saldi, R. (2022). Investment in Bitcoin : A delusion or diligence? Indian Journal of Finance, 16(7), 8–22. https://doi.org/10.17010/ijf/2022/v16i7/170632

Chu, J., Zhang, Y., & Chan, S. (2019). The adaptive market hypothesis in the high frequency cryptocurrency market. International Review of Financial Analysis, 64, 221–231. https://doi.org/10.1016/j.irfa.2019.05.008

Chursook, A., Yahya Dawod, A., Chanaim, S., Naktnasukanjn, N., & Chakpitak, N. (2022). Twitter sentiment analysis and expert ratings of initial coin offering fundraising: Evidence from Australia and Singapore markets. TEM Journal, 11(1), 44–55. https://doi.org/10.18421/TEM111-06

Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402. https://doi.org/10.1002/asi.21525

Corbet, S., Goodell, J. W., & Günay, S. (2022). What drives DeFi prices? Investigating the effects of investor attention. Finance Research Letters, 48, 102883. https://doi.org/10.1016/j.frl.2022.102883

Corbet, S., Hou, Y., Hu, Y., Larkin, C., & Oxley, L. (2020). Any port in a storm: Cryptocurrency safe-havens during the COVID-19 pandemic. Economics Letters, 194, 109377. https://doi.org/10.1016/j.econlet.2020.109377

Corbet, S., Hou, Y., Hu, Y., Oxley, L., & Xu, D. (2021). Pandemic-related financial market volatility spillovers: Evidence from the Chinese COVID-19 epicentre. International Review of Economics & Finance, 71, 55–81. https://doi.org/10.1016/j.iref.2020.06.022

Corbet, S., Larkin, C., Lucey, B. M., Meegan, A., & Yarovaya, L. (2020a). The impact of macroeconomic news on Bitcoin returns. The European Journal of Finance, 26(14), 1396–1416. https://doi.org/10.1080/1351847X.2020.1737168

Corbet, S., Larkin, C., Lucey, B., Meegan, A., & Yarovaya, L. (2020b). Cryptocurrency reaction to FOMC Announcements: Evidence of heterogeneity based on blockchain stack position. Journal of Financial Stability, 46, 100706. https://doi.org/10.1016/j.jfs.2019.100706

Coulter, K. A. (2022). The impact of news media on Bitcoin prices: Modelling data driven discourses in the crypto-economy with natural language processing. Royal Society Open Science, 9(4), 220276. https://doi.org/10.1098/rsos.220276

Ding, W., Mazouz, K., & Wang, Q. (2019). Investor sentiment and the cross-section of stock returns: New theory and evidence. Review of Quantitative Finance and Accounting, 53(2), 493–525. https://doi.org/10.1007/s11156-018-0756-z

Fang, T., Su, Z., & Yin, L. (2020). Economic fundamentals or investor perceptions? The role of uncertainty in predicting long-term cryptocurrency volatility. International Review of Financial Analysis, 71, 101566. https://doi.org/10.1016/j.irfa.2020.101566

Gaies, B., Nakhli, M. S., Sahut, J. M., & Guesmi, K. (2021). Is Bitcoin rooted in confidence? – Unraveling the determinants of globalized digital currencies. Technological Forecasting and Social Change, 172, 121038. https://doi.org/10.1016/j.techfore.2021.121038

Garcia, D., & Schweitzer, F. (2015). Social signals and algorithmic trading of Bitcoin. Royal Society Open Science, 2(9), 150288. https://doi.org/10.1098/rsos.150288

Geng, J.-B., Du, Y.-J., Ji, Q., & Zhang, D. (2021). Modeling return and volatility spillover networks of global new energy companies. Renewable and Sustainable Energy Reviews, 135, 110214. https://doi.org/10.1016/j.rser.2020.110214

Gill, K., Kumar, H., & Singh, A. K. (2023). Evaluating the efficiency of the global cryptocurrency market. Indian Journal of Finance, 17(11), 8–25. https://doi.org/10.17010/ijf/2023/v17i11/173325

Hasan, S. H., Hasan, S. H., Ahmed, M. S., & Hasan, S. H. (2022). A novel cryptocurrency prediction method using optimum CNN. Computers, Materials & Continua, 71(1), 1051–1063. https://doi.org/10.32604/cmc.2022.020823

Hassan, M. K., Hudaefi, F. A., & Caraka, R. E. (2022). Mining netizen's opinion on cryptocurrency: Sentiment analysis of Twitter data. Studies in Economics and Finance, 39(3), 365–385. https://doi.org/10.1108/SEF-06-2021-0237

Hussain, S. J., Bouri, E., Roubaud, D., & Kristoufek, L. (2020). Safe haven, hedge and diversification for G7 stock markets: Gold versus bitcoin. Economic Modelling, 87, 212–224. https://doi.org/10.1016/j.econmod.2019.07.023

Huynh, T. L. (2023). When Elon Musk changes his tone, does Bitcoin adjust its tune? Computational Economics, 62(2), 639–661. https://doi.org/10.1007/s10614-021-10230-6

Kalariya, V., Parmar, P., Jay, P., Tanwar, S., Raboaca, M. S., Alqahtani, F., Tolba, A., & Neagu, B.-C. (2022). Stochastic neural networks-based algorithmic trading for the cryptocurrency market. Mathematics, 10(9), 1456. https://doi.org/10.3390/math10091456

Karim, S., Lucey, B. M., Naeem, M. A., & Uddin, G. S. (2022). Examining the interrelatedness of NFTs, DeFi tokens and cryptocurrencies. Finance Research Letters, 47(Part B), 102696. https://doi.org/10.1016/j.frl.2022.102696

Kim, K., Lee, S.-Y., & Assar, S. (2022). The dynamics of cryptocurrency market behavior: Sentiment analysis using Markov chains. Industrial Management & Data Systems, 122(2), 365–395. https://doi.org/10.1108/IMDS-04-2021-0232

Kini, A. N., & Basri, S. (2022). A framework for customer engagement behaviour in the financial services industry : A critical review of evidence. Indian Journal of Finance, 16(6), 27–43. https://doi.org/10.17010/ijf/2022/v16i6/169925

Kraaijeveld, O., & De Smedt, J. (2020). The predictive power of public Twitter sentiment for forecasting cryptocurrency prices. Journal of International Financial Markets, Institutions and Money, 65, 101188. https://doi.org/10.1016/j.intfin.2020.101188

Ledwani, S., Chakraborty, S., & Digal, S. K. (2022). The evolution of Indian Journal of Finance : A retrospective review and future directions. Indian Journal of Finance, 16(4), 8–30. https://doi.org/10.17010/ijf/2022/v16i4/169172

Li, T. R., Chamrajnagar, A. S., Fong, X. R., Rizik, N. R., & Fu, F. (2019). Sentiment-based prediction of alternative cryptocurrency price fluctuations using gradient boosting tree model. Frontiers in Physics, 7. https://doi.org/10.3389/fphy.2019.00098

Liao, H., Tang, M., Luo, L., Li, C., Chiclana, F., & Zeng, X.-J. (2018). A bibliometric analysis and visualization of medical big data research. Sustainability, 10(1), 166. https://doi.org/10.3390/su10010166

Lohan, S., Sidhu, A., & Kakran, S. (2023). Impact of investors' attention on the global stock market: A bibliometric analysis. Indian Journal of Finance, 17(10), 21–42. https://doi.org/10.17010/ijf/2023/v17i10/170881

López-Cabarcos, M. Ã., Pérez-Pico, A. M., Piñeiro-Chousa, J., & Å ević, A. (2021). Bitcoin volatility, stock market and investor sentiment. Are they connected? Finance Research Letters, 38, 101399. https://doi.org/10.1016/j.frl.2019.101399

Mahdi, E., & Al-Abdulla, A. (2022). Impact of COVID-19 pandemic news on the cryptocurrency market and gold returns: A quantile-on-quantile regression analysis. Econometrics, 10(2), 26. https://doi.org/10.3390/econometrics10020026

Malik, S., & Kaur, S. (2021). Rationales and advantages of financing infrastructure through public – private partnerships: A systematic literature review. Indian Journal of Finance, 15(10), 28–43. https://doi.org/10.17010/ijf/2021/v15i10/166510

Mohan, A., & Bohra, N. S. (2023). Academic research on Indian mutual funds: A systematic review. Indian Journal of Finance, 17(5), 53–65. https://doi.org/10.17010/ijf/2023/v17i5/170161

Naeem, M. A., Mbarki, I., Suleman, M. T., Vo, X. V., & Shahzad, S. J. H. (2021). Does Twitter happiness sentiment predict cryptocurrency? International Review of Finance, 21(4), 1529–1538. https://doi.org/10.1111/irfi.12339

Naik, P. K., & Padhi, P. (2016). Investor sentiment, stock market returns and volatility: Evidence from National Stock Exchange of India. International Journal of Management Practice, 9(3), 213–237. https://doi.org/10.1504/IJMP.2016.077816

Narayanaswamy, T., & Karthika, P. (2018). Cryptocurrency: Invisible or invincible currency–answers for unanswered questions. Indian Journal of Finance, 12(6), 63–73. https://doi.org/10.17010/ijf/2018/v12i6/128140

Ngo, V. M., & Nguyen, H. H. (2022). Are fear and hope of the COVID-19 pandemic responsible for the V-shaped behaviour of global financial markets? A text-mining approach. Applied Economics Letters, 29(11), 1005–1015. https://doi.org/10.1080/13504851.2021.1904105

Ortu, M., Uras, N., Conversano, C., Bartolucci, S., & Destefanis, G. (2022). On technical trading and social media indicators for cryptocurrency price classification through deep learning. Expert Systems with Applications, 198, 116804. https://doi.org/10.1016/j.eswa.2022.116804

Parekh, R., Patel, N. P., Thakkar, N., Gupta, R., Tanwar, S., Sharma, G., Davidson, I. E., & Sharma, R. (2022). DL-GuesS: Deep learning and sentiment analysis-based cryptocurrency price prediction. IEEE Access, 10, 35398–35409. https://doi.org/10.1109/ACCESS.2022.3163305

Paul, J., Lim, W. M., O'Cass, A., Hao, A. W., & Bresciani, S. (2021). Scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR). International Journal of Consumer Studies, 45(4), 1–16. https://doi.org/10.1111/ijcs.12695

Polasik, M., Piotrowska, A. I., Wisniewski, T. P., Kotkowski, R., & Lightfoot, G. (2015). Price fluctuations and the use of Bitcoin: An empirical inquiry. International Journal of Electronic Commerce, 20(1), 9–49. https://doi.org/10.1080/10864415.2016.1061413

Raut, R. K., & Das, N. (2015). Behavioral prospects of individual investor decision making process: A review. Indian Journal of Finance, 9(4), 44–55. https://doi.org/10.17010/ijf/2015/v9i4/71457

Salamat, S., Lixia, N., Naseem, S., Mohsin, M., Zia-ur-Rehman., M., & Baig, S. A. (2020). Modeling cryptocurrencies volatility using GARCH models: A comparison based on normal and student's T-error distribution. Entrepreneurship and Sustainability Issues, 7(3), 1580–1596.

Serafini, G., Yi, P., Zhang, Q., Brambilla, M., Wang, J., Hu, Y., & Li, B. (2020). Sentiment-driven price prediction of the Bitcoin based on statistical and deep learning approaches. 2020 International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN48605.2020.9206704

Shahzad, S. J. H., Bouri, E., Roubaud, D., Kristoufek, L., & Lucey, B. (2019). Is Bitcoin a better safe-haven investment than gold and commodities? International Review of Financial Analysis, 63, 322–330. https://doi.org/10.1016/j.irfa.2019.01.002

Singh, R., & Bhar, C. (2015). DSS for cash management in Indian microfinance institutions: A brief review of literature. Indian Journal of Finance, 9(3), 38–48. https://doi.org/10.17010/ijf/2015/v9i3/71513

Trigka, M., Kanavos, A., Dritsas, E., Vonitsanos, G., & Mylonas, P. (2022). The predictive power of a Twitter user's profile on cryptocurrency popularity. Big Data and Cognitive Computing, 6(2), 59. https://doi.org/10.3390/bdcc6020059

Valencia, F., Gómez-Espinosa, A., & Valdés-Aguirre, B. (2019). Price movement prediction of cryptocurrencies using sentiment analysis and machine learning. Entropy, 21(6), 589. https://doi.org/10.3390/e21060589

van Raan, A. F. (2005). For your citations only? Hot topics in bibliometric analysis. Measurement: Interdisciplinary Research & Perspective, 3(1), 50–62. https://doi.org/10.1207/s15366359mea0301_7

Verma, R., & Verma, P. (2007). Noise trading and stock market volatility. Journal of Multinational Financial Management, 17(3), 231–243. https://doi.org/10.1016/j.mulfin.2006.10.003

Welch, V., Petticrew, M., Tugwell, P., Moher, D., O'Neill, J., Waters, E., & White, H. (2012). PRISMA-Equity 2012 extension: Reporting guidelines for systematic reviews with focus on health equity. PLoS Medicine, 9(10), e1001333. https://doi.org/10.1371/journal.pmed.1001333

Ye, Z., Wu, Y., Chen, H., Pan, Y., & Jiang, Q. (2022). A stacking ensemble deep learning model for Bitcoin price prediction using twitter comments on Bitcoin. Mathematics, 10(8), 1307. https://doi.org/10.3390/math10081307

Zhang, J., & Zhang, C. (2022). Do cryptocurrency markets react to issuer sentiments? Evidence from Twitter. Research in International Business and Finance, 61, 101656. https://doi.org/10.1016/j.ribaf.2022.101656