Modelling and Forecasting the Trend in Cryptocurrency Prices

Authors

  • Nurazlina Abdul Rashid School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia and Universiti Teknologi Mara (UiTM) Cawangan Kedah, Kampus Sungai Petani, Malaysia
  • Mohd Tahir Ismail School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia

DOI:

https://doi.org/10.32890/jict2023.22.3.6

Keywords:

Bitcoin, cryptocurrency, linear, nonlinear, trend

Abstract

The prediction of cryptocurrency prices is a hot topic among academics. Nevertheless, predicting the cryptocurrency price accurately can be
challenging in the real world. Numerous studies have been undertaken to determine the best model for successful prediction. However,
they lacked correct results because they avoided identifying the critical features. It is important to remember that trends are critical
features in time series to obtain data information. A dearth of research demonstrates that the cryptocurrency trend comprises linear and
nonlinear patterns. Therefore, this study attempted to fill this gap and focused on modelling and forecasting trends in cryptocurrency. This
study examined the linear and nonlinear dependency trend patterns of the top five cryptocurrency closing prices. The weekly historical data of each cryptocurrency were taken at different periods due to the availability of data on the system. In achieving its goal, this study examined the results by plotting based on residual trend and diagnostic statistic checking using three deterministic methods: linear trend regression, quadratic trend, and exponential trend. Based on the minimum Akaike Information Criterion (AIC), the result showed that the top five cryptocurrency closing price data series contained nonlinear and linear trend patterns. The information of this study will assist traders and investors in comprehending the trend of the top five cryptocurrencies and choosing the suitable model to predict cryptocurrency prices. Additionally, accurately measuring the forecast will protect investors from losing their investment.

References

Aditya Pai, B., Devareddy, L., Hegde, S., & Ramya, B. S. (2022). A time series cryptocurrency price prediction using LSTM. In N. R. Shetty, L. M. Patnaik, H. C. Nagaraj, P. N. Hamsavath, & N. Nalini (Eds.), Emerging research in computing, information, communication and applications (pp. 653–662). Springer. https://doi.org/10.1007/978-981-16-1342-5_50

Ahamed, S. A., & Ravi, C. (2021). Study of swarm intelligence algorithms for optimising deep neural network for bitcoin prediction. International Journal of Swarm Intelligence Research (IJSIR), 12(2), 22–38. https://doi.org/10.4018/ IJSIR.2021040102

Akyildirim, E., Goncu, A., & Sensoy, A. (2021). Prediction of cryptocurrency returns using machine learning. Annals of Operations Research, 297(1), 3–36. https://doi.org/10.1007/ s10479-020-03575-y

Al to Ameer, A., & Al to Sunni, F. (2021). A methodology for securities and cryptocurrency trading using exploratory data analysis and artificial intelligence. In T. Saba (Ed.), 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA) (pp. 54–61). IEEE. https://doi.org/10.1109/ CAIDA51941.2021.9425223

Aleksandra, P., Dmitry, Z., & Vladimir, K. (2021). A study of uncertainty contribution to cryptocurrency investment dynamics. International Journal of Technology, 12(7), 1529–1536. https://doi.org/10.14716/ijtech.v12i7.5348

Antonopoulos, A. M., & Wood, G. (2018). Mastering Ethereum: Building smart contracts and dapps. O’reilly Media, Inc. Journal of ICT, 22, No. 3 (July) 2023, pp: 449-

Cahyani, N. D. W., & Nuha, H. H. (2021). Ransomware detection on Bitcoin transactions using artificial neural network methods. In W. Maharani (Ed.), 2021 9th International Conference on Information and Communication Technology (ICoICT) (pp. 1–5). IEEE Indonesia Section. https://doi.org/10.1109/ ICoICT52021.2021.9527414

Cavalli, S., & Amoretti, M. (2021). CNN to based multivariate data analysis for Bitcoin trend prediction. Applied Soft Computing, 101, 107065. https://doi.org/10.1016/j.asoc.2020.107065

David, S. A., Inacio Jr, C. M. C., Nunes, R., & Machado, J. A. T. (2021). Fractional and fractal processes applied to cryptocurrencies price series. Journal of Advanced Research, 32, 85–98. https://doi.org/10.1016/j.jare.2020.12.012

Fadil, I., Helmiawan, M. A., & Sofiyan, Y. (2021). Optimisation parameters support vector regression using grid search method. In H. T. Sukmana & D. Khairani (Eds.), 2021 9th International Conference on Cyber and I.T. Service Management (CITSM) (pp. 1–5). IEEE Indonesia Section. https://doi.org/10.1109/ CITSM52892.2021.9589028

Fama, E. F. (1965). The behavior of stock to market prices. The Journal of Business, 38(1), 34–105. http://www.jstor.org/ stable/2350752

Gandal, N., Hamrick, J. T., Moore, T., & Vasek, M. (2021). The rise and fall of cryptocurrency coins and tokens. Decisions in Economics and Finance, 44(2), 981–1014. https://doi.org/10.1007/s10203-021-00329-8

Gao, Z., He, Y., & Kuruoglu, E. E. (2021, October). A hybrid model integrating LSTM and GARCH for Bitcoin price prediction. In 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1–6). https://doi.org/10.1109/MLSP52302.2021.9596429

Guo, T., Bifet, A., & Antulov to Fantulin, N. (2018). Bitcoin volatility forecasting with a glimpse into buy and sell orders. In L. O’Conner (Ed.), Proceedings to IEEE International Conference on Data Mining (ICDM) (pp. 989–994). IEEE. https://doi.org/10.1109/ICDM.2018.00123

Hossain, M. J., & Ismail, M. T. (2021). Is there any influence of other cryptocurrencies on Bitcoin? Asian Academy of Management Journal of Accounting and Finance, 17(1), 125–152. https://doi.org/10.21315/aamjaf2021.17.1.5 Journal of ICT, 22, No. 3 (July) 2023, pp: 449-

Hossain, M. R., & Ismail, M. T. (2020). Empirical mode decomposition based on Theta method for forecasting daily stock price. Journal of Information and Communication Technology, 4(4), 533–558. https://doi.org/10.32890/jict2020.19.4.4

Huynh, T. L. D., Burggraf, T., & Wang, M. (2020). Gold, platinum, and expected Bitcoin returns. Journal of Multinational Financial Management, 56, 100628. https://doi.org/10.1016/j. mulfin.2020.100628

Ibrahim, A. (2021). Forecasting the early market movement in bitcoin using Twitter’s sentiment analysis: An ensemble to based prediction model. In S. Chakrabarti (Ed.), 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (pp. 1–5). IEEE. https://doi.org/10.1109/ IEMTRONICS52119.2021.9422647

Jalan, A., Matkovskyy, R., & Urquhart, A. (2021). What effect did the introduction of Bitcoin futures have on the Bitcoin spot market? The European Journal of Finance, 27(13), 1251–1281. https://doi.org/10.1080/1351847X.2020.1869992

Khedr, A. M., Arif, I., El-Bannany, M., Alhashmi, S. M., & Sreedharan, M. (2021). Cryptocurrency price prediction using traditional statistical and machine-learning techniques: A survey. Intelligent Systems in Accounting, Finance and Management, 28(1), 3–34. https://doi.org/10.1002/isaf.1488

Knight, R. (2021, 07 Dec). The 4 phases of a crypto market cycle. Data Driven Investor. https://medium.datadriveninvestor.com/ the-4-phases-of-a-crypto-market-cycle-6eb396097b9c

Koki, C., Leonardos, S., & Piliouras, G. (2020). Do cryptocurrency prices camouflage Latent economic effects? A Bayesian hidden Markov approach. Future Internet, 12(3), 59. https://doi.org/10.3390/fi12030059

Lahmiri, S., & Bekiros, S. (2020). Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market. Chaos, Solitons & Fractals, 133, 109641. https://doi.org/10.1016/j.chaos.2020.109641

Levulytė, L., & Šapkauskienė, A. (2021). Cryptocurrency in context of fiat money functions. The Quarterly Review of Economics and Finance, 82, 44–54. https://doi.org/10.1016/j.qref.2021.07.003

Loginova, E., Tsang, W. K., van Heijningen, G., Kerkhove, L. P., & Benoit, D. F. (2021). Forecasting directional Bitcoin price returns using aspect to based sentiment analysis on online text data. Machine Learning, 1–24. https://doi.org/10.1007/s10994-021-06095-3 Journal of ICT, 22, No. 3 (July) 2023, pp: 449-

Mahdi, E., Leiva, V., Mara’Beh, S., & Martin to Barreiro, C. (2021). A new approach to predicting cryptocurrency returns based on the gold prices with support vector machines during the COVID to 19 pandemic using sensor to related data. Sensors, 21(18), 6319. https://doi.org/10.3390/s21186319

Mohammed, A. J., Ghathwan, K. I., & Yusof, Y. (2020). A hybrid least squares support vector machine with bat and cuckoo search algorithms for time series forecasting. Journal of Information and Communication Technology, 19(3), 351–379. https://doi.org/10.32890/jict2020.19.3.3

MA Lazim. (2005). Introductory business forecasting. Pusat Penerbitan UPENA Universiti Teknologi Mara.

Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Bitcoin. https://bitcoin.org/bitcoin.pdf.

Neslihanoglu, S. (2021). Linearity extensions of the market model: A case of the top 10 cryptocurrency prices during the pre to COVID to 19 and COVID to 19 periods. Financial Innovation, 7(1), 1–27. https://doi.org/10.1186/s40854-021-00247-z

Park, M., & Chai, S. (2020). The effect of information asymmetry on investment behavior in cryptocurrency market. In T. X. Bui (Ed.), Proceedings of the 53rd Hawaii International Conference on System Sciences, HICSS 2020 (pp. 4043–4052). IEEE Computer Society. https://doi.org/10.24251/hicss.2020.

Serrano, W. (2022). The random neural network in price predictions. Neural Computing and Applications, 34(2), 855–873. https://doi.org/10.1007/s00521-021-05903-0

Trozze, A., Davies, T., & Kleinberg, B. (2022). Explaining prosecution outcomes for cryptocurrency to based financial crimes. Journal of Money Laundering Control, 26(1), 172–188. https://doi.org/10.1108/JMLC-10-2021-0119

Wang, Z., Jin, H., Dai, W., Choo, K. K. R., & Zou, D. (2021). Ethereum smart contract security research: survey and future research opportunities. Frontiers of Computer Science, 15(2). https://doi.org/10.1007/s11704-020-9284-9

Zhang, Z., Dai, H. N., Zhou, J., Mondal, S. K., García, M. M., & Wang, H. (2021). Forecasting cryptocurrency price using convolutional neural networks with weighted and attentive memory channels. Expert Systems with Applications, 183, 115378. https://doi.org/10.1016/j.eswa.2021.115378

Zhu, P., Zhang, X., Wu, Y., Zheng, H., & Zhang, Y. (2021). Investor attention and cryptocurrency: Evidence from the Bitcoin market. PLoS One, 16(2), e0246331. https://doi.org/10.1371/ journal.pone.0246331

Downloads

Published

24-07-2023

Issue

Vol. 22 No. 3 (2023): Journal of Information and Communication Technology (JICT) Vol.22, No.3, July 2023

Section

Articles

License

Copyright (c) 2023 Journal of Information and Communication Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Abdul Rashid, N., & Ismail, M. T. (2023). Modelling and Forecasting the Trend in Cryptocurrency Prices. Journal of Information and Communication Technology, 22(3), 449-501. https://doi.org/10.32890/jict2023.22.3.6

Research impact

Harvested 2026-09-06
9 citations, from Semantic Scholar — the highest of the sources checked

Counts differ between services because each indexes a different body of literature. None of them is the whole picture.

Identifiers DOI 10.32890/jict2023.22.3.6 OpenAlex W4386103473 Semantic Scholar CorpusID 261134256 Scopus 85168592024

Most read articles by the same author(s)