Calendar Anomalies in NFT Coins

Abstract

This study examines the effect of day-of-the-week, month-of-the-year, and turn-of-the-month anomalies on NFT coins (Stacks, Tezos, and Decentraland) and Bitcoin. To this end, the generalized autoregressive conditional heteroscedasticity (GARCH) model was employed over the period 2019–2023. Based on the day-of-the-week anomaly results, Bitcoin has lower returns on Thursdays and Fridays, and Stacks has lower returns on Wednesdays. The remaining coins do not exhibit that anomaly. According to the month-of-the-year effect results, all evaluated coins generate abnormal returns in January. Moreover, positive returns are also reported in February for Tezos, Decentraland, and Bitcoin. Additionally, Bitcoin has positive returns in March as well. Furthermore, besides January, Stacks has significantly positive returns in April and May. Finally, the results of the turn-of-the-month anomaly suggest that only Stacks has statistically significant and positive returns on the last day of the month and the next three days. The remaining cryptocurrencies do not have such an anomaly. Overall, the findings of this study suggest the existence of calendar anomalies in the cryptocurrency market that contradict the assumptions of market efficiency. By using these outcomes, investors may develop trading strategies for their portfolio selection; hence, by taking advantage of the market, they could earn unusual profits.

Keywords

• Calendar Anomalies
• Efficient Markets
• Cryptocurrencies

NFT Paralarında Takvim Anomalileri

Abstract

Bu çalışma, haftanın günü, yılın ayı ve ayın dönüşü anomalilerinin NFT coinleri (Stacks, Tezos ve Decentraland) ve Bitcoin üzerindeki etkisini incelemektedir. Bu amaçla, 2019-2023 dönemi için genelleştirilmiş otoregresif koşullu değişen varyans (GARCH) modeli kullanılmıştır. Haftanın günü anomalisi sonuçlarına göre, Bitcoin perşembe ve cuma günleri, Stacks ise çarşamba günleri daha düşük getiri sağlamaktadır. Diğer kripto paralarda bu anomali görülmemektedir. Yılın ayı etkisi sonuçlarına göre, değerlendirilen tüm kripto paralar ocak ayında anormal getiri sağlamaktadır. Ayrıca, Tezos, Decentraland ve Bitcoin için şubat ayında da pozitif getiriler rapor edilmiştir. Ek olarak, Bitcoin mart ayında da pozitif getiriye sahiptir. Ayrıca, ocak ayının yanı sıra, Stacks Nisan ve Mayıs aylarında önemli ölçüde pozitif getiriye sahiptir. Son olarak, ay dönümü anomalisinin sonuçları, yalnızca Stacks'ın ayın son gününde ve sonraki üç günde istatistiksel olarak anlamlı ve pozitif getirilere sahip olduğunu göstermektedir. Geri kalan kripto para birimlerinde böyle bir anomali bulunmamaktadır. Genel olarak, bu çalışmanın bulguları kripto para piyasasında piyasa etkinliği varsayımlarını ihlal eden takvim anomalilerinin varlığına işaret etmektedir. Yatırımcılar bu sonuçları kullanarak portföy seçimleri için alım satım stratejileri geliştirebilir; dolayısıyla piyasadan faydalanarak olağandışı kârlar elde edebilirler.

Keywords

• Takvim Anomalileri
• Etkin Piyasalar
• Kripto Paralar

References

1. Aharon, D.Y. and Qadan, M. (2019). Bitcoin and the day-of-the-week effect. Finance Research Letters, 31, 415-424. https://doi.org/10.1016/j.frl.2018.12.004
2. Almosfi, A. (2023). Calendar anomalies in the cryptocurrency markets (Unpublished doctoral dissertation). University of Radboud, Nijmegen, The Netherlands.
3. Ante, L. (2022). Non-fungible token (NFT) markets on the Ethereum blockchain: Temporal development, cointegration and interrelations. Economics of Innovation and New Technology, 32(8), 1216-1234. https://doi.org/10.1080/10438599.2022.2119564
4. Ariss, R.T., Rezvanian, R. and Mehdian, S.M. (2011). Calendar anomalies in the Gulf Cooperation Council stock markets. Emerging Markets Review, 12(3), 293-307. https://doi.org/10.1016/j.ememar.2011.04.002
5. Barone, E. (1990). The Italian stock market: Efficiency and calendar anomalies. Journal of Banking & Finance, 14(2-3), 483-510. https://doi.org/10.1016/0378-4266(90)90061-6
6. Baur, D.G., Cahill, D., Godfrey, K. and Liu, Z.F. (2019). Bitcoin time-of-day, day-of-week and month-of-year effects in returns and trading volume. Finance Research Letters, 31, 78-92. https://doi.org/10.1016/j.frl.2019.04.023
7. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
8. Brooks, C. (2014). Introductory econometrics for finance. UK: Cambridge University Press.

9. Caporale, G.M. and Plastun, A. (2019). The day of the week effect in the cryptocurrency market. Finance Research Letters, 31, 258-269. https://doi.org/10.1016/j.frl.2018.11.012
10. Cilingirturk, A.M., Polat, M.U. and Gogus, H.S. (2020). Does Borsa Istanbul incorporate herding and calendar anomalies? An empirical evidence. Journal of Business Economics and Finance, 9(1), 12-27. https://doi.org/10.17261/Pressacademia.2020.1189
11. Cimen, A. (2019). Calendar anomalies in cryptocurrencies. Turkish Studies—Social Sciences, 14(5), 2097-2116. http://dx.doi.org/10.7827/TurkishStudies.
12. Coinmarket. (2023). NFTs [Dataset]. Retrieved from https://coinmarketcap.com/view/collectibles-nfts
13. Cross, F. (1973). The behavior of stock prices on Fridays and Mondays. Financial Analysts Journal, 29(6), 67-69. https://doi.org/10.2469/faj.v29.n6.67
14. Dorfleitner, G. and Lung, C. (2018). Cryptocurrencies from the perspective of euro investors: A re-examination of diversification benefits and a new day-of-the-week effect. Journal of Asset Management, 19, 472-494. https://doi.org/10.1057/s41260-018-0093-8
15. Dumrongwong, K. (2021). Calendar effects on cryptocurrencies: Not so straightforward. Southeast Asian Journal of Economics, 9(1), 1-26. Retrieved from https://so05.tci-thaijo.org/
16. Engle, R.F. (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica 50, 987–1007. https://doi.org/10.2307/1912773
17. Ergun, Z.C. (2023). The impact of seasonal affective disorder on green cryptocurrencies. PressAcademia Procedia, 17(1), 173-177. https://doi.org/10.17261/Pressacademia.2023.1773
18. Ergun, Z.C. and Karabiyik, B.K. (2021). Forecasting Monero prices with a machine learning algorithm. Eskişehir Osmangazi University Journal of Economics and Administrative Sciences, 16(3), 651-663. https://doi.org/10.17153/oguiibf.932839
19. Eyuboglu, K. (2018). Examining day of the week and month of the year effects in Bitcoin and Litecoin markets. Çankırı Karatekin University Journal of the Faculty of Economics and Administrative Sciences, 8(1), 165-183. Retrieved from https://dergipark.org.tr/en/pub/ckuiibfd/
20. Fraz, A., Hassan, A. and Chughtai, S. (2019). Seasonality in Bitcoin market. NICE Research Journal, 12(1), 1-11. https://doi.org/10.51239/nrjss.v0i0.78
21. Gunay, S. and Muhammed, S. (2022). Identifying the role of investor sentiment proxies in NFT market: Comparison of Google Trend, Fear-Greed Index and VIX. Paper presented at the Annual Event of Finance Research Letters, CEMLA Conference: New Advances in International Finance. Mexico City, Mexico. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4120176
22. İmre Bıyıklı, S. and Özaydın, O. (2023). Can cryptocurrency markets be beaten? Calendar anomalies aspect. Journal of Management and Economics Research, 21(4), 17-36. https://doi.org/10.11611/yead.1211806
23. Jay, P., Kalariya, V., Parmar, P., Tanwar, S., Kumar, N. and Alazab, M. (2020). Stochastic neural networks for cryptocurrency price prediction. IEEE Access, 8, 82804-82818. https://doi.org/10.1109/ACCESS.2020.2990659
24. Kahraman, İ.K. (2023). Kripto para piyasasındaki volatilitenin davranışsal finans teorisi kapsamında incelenmesi (Yayımlanmamış doktora tezi). Pamukkale Üniversitesi, Sosyal Bilimler Enstitüsü, Denizli.
25. Kaiser, L. (2019). Seasonality in cryptocurrencies. Finance Research Letters, 31, 232-238. https://doi.org/10.1016/j.frl.2018.11.007
26. Khuntia, S. and Pattanayak, J.K. (2022). Adaptive calendar effects and volume of extra returns in the cryptocurrency market. International Journal of Emerging Markets, 17(9), 2137-2165. https://doi.org/10.1108/IJOEM-06-2020-0682
27. Kinateder, H. and Papavassiliou, V.G. (2021). Calendar effects in bitcoin returns and volatility. Finance Research Letters, 38, 101420. https://doi.org/10.1016/j.frl.2019.101420
28. Kumar, S. (2022). Turn-of-the-month effect in cryptocurrencies. Managerial Finance, 48(5), 821-829. https://doi.org/10.1108/MF-02-2022-0084
29. Kurihara, Y. and Fukushima, A. (2017). The market efficiency of Bitcoin: A weekly anomaly perspective. Journal of Applied Finance and Banking, 7(3), 57. Retrieved from https://www.scienpress.com/journal_focus.asp?Main_Id=56
30. Lakonishok, J. and Smidt, S. (1988). Are seasonal anomalies real? A ninety-year perspective. The Review of Financial Studies, 1(4), 403-425. https://doi.org/10.1093/rfs/1.4.403
31. Lopez-Martin, C. (2022a). Ramadan effect in the cryptocurrency markets. Review of Behavioral Finance, 14(4), 508-532. https://doi.org/10.1108/RBF-09-2021-0173
32. Lopez-Martín, C. (2022b). Dynamic analysis of calendar anomalies in cryptocurrency markets: Evidences of adaptive market hypothesis. Spanish Journal of Finance and Accounting/Revista Española de Financiación y Contabilidad, 52(4), 559-592. https://doi.org/10.1080/02102412.2022.2131239
33. Ma, D. and Tanizaki, H. (2019). The day-of-the-week effect on Bitcoin return and volatility. Research in International Business and Finance, 49, 127-136. https://doi.org/10.1016/j.ribaf.2019.02.003
34. Mbanga, C.L. (2019). The day-of-the-week pattern of price clustering in Bitcoin. Applied Economics Letters, 26(10), 807-811. https://doi.org/10.1080/13504851.2018.1497844
35. Naz, F., Sayyed, M., Rehman, R.U., Naseem, M.A., Abdullah, S.N. and Ahmad, M.I. (2023). Calendar anomalies and market volatility in selected cryptocurrencies. Cogent Business & Management, 10(1), 2171992. https://doi.org/10.1080/23311975.2023.2171992
36. Ossola, D. (2022). Seasonality anomalies in the cryptocurrency market (Unpublished doctoral dissertation). University of Catolica, Lisbon, Portugal.
37. Ozic, H.C. (2023). Finansal anomaliler. Ankara: Gazi Kitabevi.
38. Pintelas, E., Livieris, I.E., Stavroyiannis, S., Kotsilieris, T. and Pintelas, P. (2020). Investigating the problem of cryptocurrency price prediction: A deep learning approach. In I. Maglogiannis, L. Iliadis and E. Pimenidis (Eds.), Artificial intelligence applications and innovations (pp. 99-110). Papers presented at the IFIP International Conference on Artificial Intelligence Applications and Innovations, Greece: Springer.
39. Qadan, M., Aharon, D.Y. and Eichel, R. (2022). Seasonal and calendar effects and the price efficiency of cryptocurrencies. Finance Research Letters, 46, 102354. https://doi.org/10.1016/j.frl.2021.102354
40. Susana, D., Sreejith, S. and Kavisanmathi, J.K. (2020). A study on calendar anomalies in the cryptocurrency market. Paper presented at the International Working Conference on Transfer and Diffusion of IT (TDIT). Tiruchirappalli, India. Retrieved from https://inria.hal.science/hal-03701815/file/497052_1_En_16_Chapter.pdf
41. Taylor, S.J. (1986). Modelling financial time series. Chichester: Wiley.
42. Tosunoglu, N., Abaci, H., Ates, G. and Saygili Akkaya, N. (2023). Artificial neural network analysis of the day of the week anomaly in cryptocurrencies. Financial Innovation, 9, 88. https://doi.org/10.1186/s40854-023-00499-x
43. Vasileiou, E. (2023). Is the turn of the month an anomaly on which an investment strategy could be based? Evidence from Bitcoin and Ethereum. International Journal of Banking, Accounting and Finance, 13(3), 388-402. https://doi.org/10.1504/IJBAAF.2023.129336
44. Verma, R., Sharma, D. and Sam, S. (2023). Cryptocurrency market anomaly: The day-of-the-week-effect. Finance India, 37(1), 301-316. Retrieved from https://financeindia.org/
45. Wachtel, S.B. (1942). Certain observations on seasonal movements in stock prices. The Journal of Business of the University of Chicago, 15(2), 184-193. Retrieved from https://www.jstor.org/
46. Yaya, O.S. and Ogbonna, E.A. (2019). Do we experience day-of-the-week effects in returns and volatility of cryptocurrency? (MPRA Working Paper No. 91429). Retrieved from https://mpra.ub.uni-muenchen.de/91429/