New IJF now accepts submissions exclusively via the Editorial Scholar portal — a dedicated submission & review system.
Go to Editorial Scholar →
Associated Management Consultants Pvt. Ltd. | COPE Member
Scopus Q2 UGC-CARE |
Indian Journal of Finance logo

Indian Journal of Finance

ISSN: 0973-8711 Frequency: Monthly Peer Review: Double-blind Published since: 2007 Language: English
A publication of AMCPL
amcon.co.in
New Delhi, India
Indexed in: Scopus Q2 UGC-CARE Group II Google Scholar J-Gate NISCAIR Crossref

Original Article

Open Access Original Article

Cryptocurrency in the Light of Sentiments : A Bibliometric Approach

Diya Sharma1,  Renu Ghosh2,  Chandra Shekhar Sharma3

1 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

2 Assistant Professor , Department of Management Studies, Netaji Subhas University of Technology, Azad Hind Fauj Marg, Dwarka Sector-3, Dwarka, Delhi - 110 078

3 Professor (Retd.), Shri Ram College of Commerce, University of Delhi, Maurice Nagar, Roop Nagar, New Delhi - 110 007

Volume 18
Issue 2
Pages 60–75
Year 2024
Received: Oct. 1, 2023 Accepted: Jan. 10, 2024 Published: Feb. 1, 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.

Keywords Cryptocurrency Sentiments Bibliometric Behavioral Finance PRISMA
How to Cite

Diya Sharma, Renu Ghosh, Chandra Shekhar Sharma (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

References
  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. López-Cabarcos, M. Á., Pérez-Pico, A. M., Piñeiro-Chousa, J., & Šević, 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
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. 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
  52. 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
  53. 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
  54. 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
  55. 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
  56. 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.
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. 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
  63. 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
  64. 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
  65. 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
  66. 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
Editorial Scholar

Submit your Research to IJF

Submit your Manuscript → Author Guidelines