Testing Semi-Strong Market Efficiency for Leading Altcoins

Authors

  • Rajnesh Shahani Government College University Hyderaba
  • Abdur Rahman Aleemi Institute of Business and Health Management. Dow University of Health Sciences. Karachi
  • Naeem Ahmed Qureshi University of Sindh, Jamshoro
  • Abdul Majid Memon , University of Sindh, Jamshoro, Pakistan

DOI:

https://doi.org/10.47743/saeb-2025-0032

Keywords:

altcoins, event study, semi-strong market efficiency, regulatory and international events.

Abstract

This study probes semi-strong market efficiency in leading altcoins by examining how various regulatory and international events impact the daily returns of altcoins. We aspire to contribute valuable insights into the behavior of altcoins market in response to external stimuli, highlighting the implications for investors and market analysts in the rapidly evolving landscape of digital currencies. Several events over the period of 2018 to 2024 are considered categorized in two distinct groups namely, crypto-regulatory events and international events, ranging from outbreak of global pandemics, geo-political events and wars, including COVID-19 waves, vaccines authorizations, imposition of lockdowns, BREXIT post 2018, US withdrawal from Afghanistan, Russia-Ukraine war and Israel-Palestine conflict. Subsequently the impact of these events on the daily returns of five leading altcoins is assessed using the Auto-Regressive Component GARCH-Mean model. Altcoins have been responding to both positive and negative regulatory as well as international events. However, the significance of cumulative abnormal returns in the event window indicates signs of semi-strong market inefficiency. The findings provide new insights into the response of cryptocurrencies to various events at a global level, contributing to the understanding of market behavior and market efficiency, particularly, in the leading crypto-assets other than bitcoin. The findings can help altcoin investors devise trading strategies and build investment portfolios in an optimal manner, thereby minimizing the risks involved.

Author Biographies

Rajnesh Shahani, Government College University Hyderaba

Department of Business Administration

Abdur Rahman Aleemi, Institute of Business and Health Management. Dow University of Health Sciences. Karachi

Associate Professor 

Naeem Ahmed Qureshi, University of Sindh, Jamshoro

Department of Statistics

Full Professor and Chairman

 

Abdul Majid Memon, , University of Sindh, Jamshoro, Pakistan

Department of Statistics

Student 

 

References

Abraham, M. (2021). An Event Study Analysis of Bitcoin and Altcoins under COVID-19. African Review of Economics and Finance, 13(2), 7-24.

Abreu, D. P. A. D., Coaguila, R. A. I., & Camargos, M. A. D. (2022). Evolution of the Degree of Efficiency of the Cryptocurrency Market from 2014 to 2020: An Analysis Based on its Fractal Components. Revista de Administração da UFSM, 15(2), 216-235. http://dx.doi.org/10.5902/1983465965639

Agosto, A., & Cafferata, A. (2020). Financial Bubbles: A Study of Co-Explosivity in the Cryptocurrency Market. Risks, 8(2), 34. Risks, 8(2), 1-14. http://dx.doi.org/10.3390/risks8020034

Alvarez-Ramirez, J., Rodriguez, E., & Ibarra-Valdez, C. (2018). Long-Range Correlations and Asymmetry in the Bitcoin Market. Physica A: Statistical Mechanics and its Applications, 492(February), 948-955. http://dx.doi.org/10.1016/j.physa.2017.11.025

Bartos, J. (2015). Does Bitcoin follow the Hypothesis of Efficient Market? International Journal of Economic Sciences, 4(2), 10-23. http://dx.doi.org/10.20472/ES.2015.4.2.002

Benninga, S. (2014). Financial modeling (fourth ed.). London, England: MIT press.

Caporale, G. M., & Plastun, A. (2019). Price Overreactions in the Cryptocurrency Market. Journal of Economic Studies (Glasgow, Scotland), 46(5), 1137-1155. http://dx.doi.org/10.1108/JES-09-2018-0310

Cheah, E. T., & Fry, J. (2015). Speculative Bubbles in Bitcoin Markets? An Empirical Investigation into the Fundamental Value of Bitcoin. Economics Letters, 130(May), 32-36. http://dx.doi.org/10.1016/j.econlet.2015.02.029

Cheah, E. T., Mishra, T., Parhi, M., & Zhang, Z. (2018). Long Memory Interdependency and Inefficiency in Bitcoin Markets. Economics Letters, 167(C), 18-25. http://dx.doi.org/10.1016/j.econlet.2018.02.010

Cheung, A., Roca, E., & Su, J. J. (2015). Crypto-Currency Bubbles: An Application of the Phillips–Shi–Yu (2013) Methodology on Mt. Gox Bitcoin Prices. Applied Economics, 47(23), 2348-2358. http://dx.doi.org/10.1080/00036846.2015.1005827

CoinDesk. (2024). Bitcoin BTC. Retrieved 2024 https://www.coindesk.com/price/bitcoin

CoinMarketCap. (2024). Cyptocurrency Prices. Retrieved 2024, from CoinMarketCap https://coinmarketcap.com/

Crippen, A. (2014, 14 March 2014) Buffett Blasts Bitcoin as ‘Mirage’: ‘Stay away!’/Interviewer: A. Crippen. (Vol 2), CNBC, CNBC.

Dhar, R., & Goetzmann, W. N. (2006). Bubble Investors: What Were They Thinking? Yale ICF Working Paper(06-22).

Engle, R. (2001). GARCH 101: The use of ARCH/GARCH Models in Applied Econometrics. The Journal of Economic Perspectives, 15(4), 157-168. http://dx.doi.org/10.1257/jep.15.4.157

Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417. http://dx.doi.org/10.2307/2325486

Feng, W., Wang, Y., & Zhang, Z. (2018). Informed Trading in the Bitcoin Market. Finance Research Letters, 26(C), 63-70. http://dx.doi.org/10.1016/j.frl.2017.11.009

Garcia, D., Tessone, C. J., Mavrodiev, P., & Perony, N. (2014). The Digital Traces of Bubbles: Feedback Cycles between Socio-Economic Signals in the Bitcoin Economy. Journal of the Royal Society, Interface, 11(99), 20140623. http://dx.doi.org/10.1098/rsif.2014.0623

Hashemi Joo, M., Nishikawa, Y., & Dandapani, K. (2020). Announcement Effects in the Cryptocurrency Market. Applied Economics, 52(44), 4794-4808. http://dx.doi.org/10.1080/00036846.2020.1745747

Hassanzadeh Tavakkol, M. (2022). Efficiency in Cryptocurrency Market. (Master Thesis ), Utrecht University. (4866363)

Kang, H. J., Lee, S. G., & Park, S. Y. (2022). Information Efficiency in the Cryptocurrency Market: The Efficient-Market Hypothesis. Journal of Computer Information Systems, 62(3), 622-631. http://dx.doi.org/10.1080/08874417.2021.1872046

Katsiampa, P. (2017). Volatility Estimation for Bitcoin: A Comparison of GARCH Models. Economics Letters, 158(C), 3-6. http://dx.doi.org/10.1016/j.econlet.2017.06.023

Koutsoupakis, D. (2022). Market Efficiency and Volatility within and Across Cryptocurrency Benchmark Indexes. The Journal of Risk, 24(4). http://dx.doi.org/10.21314/JOR.2022.028

Kramer, M. (2019). An Overview of Blockchain Technology Based on a Study of Public Awareness. Global Journal of Business Research, 13(1), 83-91. http://dx.doi.org/ssrn.com/abstract=3381119

Krishnan, P., & Periasamy, M. N. (2022). Testing of Semi–Strong Form of Efficiency: An Empirical Study on Stock Market Reaction Around Dividend Announcement. International Journal of Professional Business Review, 7(2), 8. http://dx.doi.org/10.26668/businessreview/2022.v7i2.483

Lengyel-Almos, K. E., & Demmler, M. (2021). Is the Bitcoin Market Efficient? A Literature Review. Análisis económico, 36(93), 167-187. http://dx.doi.org/10.24275/uam/azc/dcsh/ae/2021v36n93/lengyel

Mallesha, L., & Archana, H. N. (2024). Evolving Efficiency of Cryptocurrency Market: Evidence from Leading Cryptocurrencies. The Review of Finance and Banking, 16(1), 73-84. http://dx.doi.org/10.24818/rfb.23.16.01.06

Nadarajah, S., & Chu, J. (2017). On the Inefficiency of Bitcoin. Economics Letters, 150(C), 6-9. http://dx.doi.org/10.1016/j.econlet.2016.10.033

Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. SSRN(March), 3440802. http://dx.doi.org/10.2139/ssrn.3440802

Nimalendran, M., Pathak, P., Petryk, M., & Qiu, L. (2025). Informational Efficiency of Cryptocurrency Markets. Journal of Financial and Quantitative Analysis, 60(3), 1427-1456. http://dx.doi.org/10.1017/S0022109024000310

Palamalai, S., Kumar, K. K., & Maity, B. (2021). Testing the Random Walk Hypothesis for Leading Cryptocurrencies. Borsa Istanbul Review, 21(3), 256-268. http://dx.doi.org/10.1016/j.bir.2020.10.006

Shiller, R. J. (2015). Irrational Exuberance Revised and Expanded third Edition. In R. J. Shiller (Ed.), Irrational exuberance (Third ed.): Princeton University Press. http://dx.doi.org/10.2307/j.ctt1287kz5

The Federation of Pakistan Chambers of Commerce & Industries. (2021). Prospects o Cryptocurrencies: A context of Pakistan. Retrieved from https://fpcci.org.pk/wp-content/uploads/2021/12/Prospects-of-Cryptocurrencies-A-Context-of-Pakistan_compressed.pdf

Urquhart, A. (2016). The Inefficiency of Bitcoin. Economics Letters, 148(November), 80-82. http://dx.doi.org/10.1016/j.econlet.2016.09.019

Vidal-Tomás, D., & Ibañez, A. (2018). Semi-Strong Efficiency of Bitcoin. Finance Research Letters, 27(C), 259-265. http://dx.doi.org/10.1016/j.frl.2018.03.013

Xiong, J., Liu, Q., & Zhao, L. (2020). A New Method to Verify Bitcoin Bubbles: Based on the Production Cost. The North American Journal of Economics and Finance, 51(C), 101095. http://dx.doi.org/10.1016/j.najef.2019.101095

Downloads

Published

2025-09-17

How to Cite

Shahani, R., Aleemi, A. R., Qureshi, N. A., & Memon, A. M. (2025). Testing Semi-Strong Market Efficiency for Leading Altcoins. Scientific Annals of Economics and Business. https://doi.org/10.47743/saeb-2025-0032

Issue

Section

Articles

Similar Articles

<< < 17 18 19 20 21 22 23 24 25 26 > >> 

You may also start an advanced similarity search for this article.