The Relationship Between Bitcoin Prices and Ethereum Trading Volume
DOI:
https://doi.org/10.15291/oec.5013Keywords:
Bitcoin prices, Ethereum trading volume, cryptocurrencies, Spearman’s rank correlation, artificial neural networks (ANN)Abstract
The paper aimed to investigate the statistical relationship between Bitcoin prices and Ethereum trading volumes, as well as to create a simple predictive model for Ethereum trading volumes based on Bitcoin prices. To perform Spearman’s rank correlation analysis and to construct an artificial neural network (ANN) model, daily closing prices of Bitcoin in USD and daily trading volumes of Ethereum were utilized. The timeframe covered by the data starts May 1, 2020 and ends November 22, 2025. In this study, Ethereum volumes were treated as the dependent variable, while Bitcoin prices served as the independent variable. The findings indicate a significant, moderate, positive correlation between Bitcoin prices and Ethereum volumes, and the ANN model successfully predicted Ethereum volumes with a high level of accuracy. These results reinforce existing evidence regarding the relationships among cryptocurrencies. Furthermore, by confirming the efficacy of artificial neural networks (ANN) in predicting trends within the cryptocurrency market, the study also makes a methodological contribution. In addition, the study also offers a simpler modelling approach that highlights the significance of bilateral interactions among major cryptocurrencies through a single-input model. Based on the impressive performance of the ANN model, exchanges, fintech companies, and investment firms could incorporate lightweight machine-learning systems into their forecasting tools to provide real-time analytics with minimal processing requirements.
References
Aboody, D., Lehavy, R., & Trueman, B. (2010). Limited attention and the earnings announcement returns of past stock market winners. Review of Accounting Studies, 15(2), 317-344.
Abu Bakar, N., & Rosbi, S. (2017) Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Cryptocurrency Exchange Rate in High Volatility Environment: A New Insight of Bitcoin Transaction. International Journal of Advanced Engineering Research and Science (IJAERS), 4(11), 130-137.
Al-Yahyaee, K. H., Mensi, W., & Yoon, S. M. (2018). Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets. Finance Research Letters, 27, 228-234.
Aslanidis, N., Bariviera, A. F., & Perez-Laborda, A. (2021). Are cryptocurrencies becoming more interconnected? Economics Letters, 199, Article 109725.
Azari, A. (2019). Bitcoin price prediction: An ARIMA approach, arXiv pre-print arXiv:1904.05315, https://arxiv.org/pdf/1904.05315.pdf [29.3.2021]
Behera, S., Nayak, S. C., & Kumar, A. P. (2024). Evaluating the performance of metaheuristic based artificial neural networks for cryptocurrency forecasting. Computational Economics, 64(2), 1219-1258.
Bikhchandani, S., & Sharma, S. (2000). Herd behavior in financial markets. IMF Staff papers, 47(3), 279-310.
Bouri, E., Saeed, T., Vo, X. V., & Roubaud, D. (2021). Quantile connectedness in the cryptocurrency market. Journal of International Financial Markets Institutions and Money, 71, Article 101302.
Canh, N. P., Wongchoti, U., Thanh, S. D., & Thong, N. T. (2019). Systematic risk in cryptocurrency market: Evidence from DCC-MGARCH model. Finance Research Letters, 29, 90–100.
Chan, F. T. S., & Chong, A. Y. L. (2012). A SEM-neural network approach for understanding determinants of interorganizational system standard adoption and performances. Decision Support Systems, 54, 621–630.
Chen, Y., Wu, J., & Wu, Z. (2022). China’s commercial bank stock price prediction using a novel K-means-LSTM hybrid approach. Expert Systems with Applications, 202, 117370.
Chen, Y., Zhang, L., & Bouri, E. (2024). Co-bubble transmission across clean and dirty cryptocurrencies: Network and portfolio analysis. Journal of International Money and Finance, 145, Article 103108.
Chen, Z., Li, C., & Sun, W. (2020). Bitcoin price prediction using machine learning: An approach to sample dimension engineering. Journal of Computational and Applied Mathematics, 365.
Chu, J., Chan, S., Nadarajah, S., & Osterrieder, J. (2017). GARCH modelling of cryptocurrencies. Journal of Risk and Financial Management, 10, 17.
Dancey, C. P., & Reidy, J. (2007). Statistics without maths for psychology. Pearson education.
Dutta, A., Kumar, S., & Basu, M. (2020). A gated recurrent unit approach to bitcoin price prediction. Journal of risk and financial management, 13(2), 23.
Elsayed, A. H., Gozgor, G., & Lau, C. K. M. (2022). Causality and dynamic spillovers among cryptocurrencies and currency markets. International Journal of Finance and Economics, 27(2), 2026–2040.
Fagarazzi, A. (2025). Granger Causation between Bitcoin Prices and Prices of Older Cryptocurrencies. Ekonomski pregled, 76(6), 466-481.
Faghih Mohammadi Jalali, M., & Heidari, H. (2020). Predicting changes in Bitcoin price using grey system theory. Financial Innovation, 6(1), 13.
Fahmi, A.M, Samsudin, N.A., Mustapha, A., Razali, N., & Khalid, S.K.A. (2018). Regression based Analysis for Bitcoin Price Prediction. International Journal of Engineering & Technology, 7 (4.38), 1070-1073.
Gil-Alana, L. A., Abakah, E. J. A., & Rojo, M. F. R. (2020). Cryptocurrencies and stock market indices. Are they related? Research in International Business and Finance, 51, Article 101063.
Greaves, A., & Au, B. (2015). Using the bitcoin transaction graph to predict the price of bitcoin. No data, 8, 416-443.
Günther, F., & Fritsch, S. (2012). Neuralnet: Training of neural networks. RELC Journal, 2, 30–38.
Indera, N. I., Yassin, I. M., Zabidi, A., & Rizman, Z. I. (2017). Non-linear autoregressive with exogeneous input (NARX) Bitcoin price prediction model using PSO-optimized parameters and moving average technical indicators. Journal of fundamental and applied sciences, 9(3S), 791-808.
Jang, H., & Lee, J. (2017). An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information. IEEE access, 6, 5427-5437.
Ji, S., Kim, J., & Im, H. (2019). A comparative study of bitcoin price prediction using deep learning. Mathematics, 7(10), 898.
Kahneman, D. (1973). Attention and Effort. Prentice-Hall, Englewood Cliffs, NJ.
Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, 3–6.
Keynes J. M. (1930). In A treatise on money London, UK: Macmillan
Khashei, M., & Bijari, M. (2010). An artificial neural network (p,d,q) model for timeseries forecasting. Expert Systems with Applications, 37, 479- 489.
Klein, T., Thu, H. P., & Walther, T. (2018). Bitcoin is not the New Gold–A comparison of volatility, correlation, and portfolio performance. International Review of Financial Analysis, 59, 105–116.
Kraus, M., Feuerriegel, S., & Oztekin, A. (2020). Deep learning in business analytics and operations research: Models, applications and managerial implications. European Journal of Operational Research, 281, 628–641.
Kristoufek, L., & Vosvrda, M. (2019). Cryptocurrencies market efficiency ranking: Not so straightforward. Physica A: statistical mechanics and its applications, 531, 120853.
Kumar, A., Iqbal, N., Mitra, S. K., Kristoufek, L., & Bouri, E. (2022). Connectedness among major cryptocurrencies in standard times and during the COVID-19 outbreak. Journal of International Financial Markets Institutions and Money, 77, Article 101523.
Lahmiri, S., & Bekiros, S. (2019). Cryptocurrency forecasting with deep learning chaotic neural networks. Chaos, Solitons & Fractals, 118, 35-40.
Leong, L. Y., Hew, T. S., Tan, G. W. H., & Ooi, K. B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 40(14), 5604–5620.
Liébana-Cabanillas, F., Marinković, V., & Kalinić, Z. (2017). A SEM-neural network approach for predicting antecedents of m-commerce acceptance. International Journal of Information Management, 37(2), 14–24.
Liu, J., et al. (2016). Bitcoin literature: a co-word analysis. In 6th economics & finance conference, OECD: Paris (pp. 262–272).
Liu, Y., & Tsyvinski, A. (2018). Risk and Returns of Cryptocurrency, NBER Working Paper, No. 24877, August, 1-25
McNally, S., Roche, J., & Caton, S. (2018). Predicting the price of bitcoin using machine learning. 2018 26th euromicro international conference on parallel, distributed and network-based processing (PDP), 339–343.
Moratis, G. (2021). Quantifying the spillover effect in the cryptocurrency market. Finance Research Letters, 38, 101534.
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Decentralized Business Review.
Nybo, C. (2021). Sector volatility prediction performance using GARCH models and artificial neural networks. arXiv preprint
Omanović, A., Arnaut-Berilo, A., & Zaimović, A. (2020). Effectiveness of cryptocurrency portfolio management before and during covid-19 pandemic. An international serial publication for theory and practice of Management ScienceXVI, 1, 319-331.
Pabuçcu, H., Ongan, S., & Ongan, A. (2020). Forecasting the movements of Bitcoin prices: an application of machine learning algorithms. Quantitative Finance and Economics, 4(4), 679–692.
Peters, E. E. (1994). Fractal Market Analysis: Applying Chaos Theory to Investment and Economics. John Wiley & Sons.
Platanakis, E., & Urquhart, A. (2019). Portfolio management with cryptocurrencies: The role of estimation risk. Economics Letters, 177, 76–80.
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.
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.
Poyser, O. (2017). Exploring the determinants of Bitcoin's price: an application of Bayesian Structural Time Series. Dissertation
Pretorius, E. (2002). Economic determinants of emerging stock market interdependence. Emerging Markets Review, 3(1), 84-105.
Salcedo, E., & Gupta, M. (2021). The effects of individual-level espoused national cultural values on the willingness to use bitcoin-like blockchain currencies. International Journal of Information Management, 60, 102388.
Salisu, A.A. and Ogbonna, A.E. (2021). The return volatility of cryptocurrencies during the COVID-19 pandemic: assessing the news effect. Global Finance Journal, 100641.
Šestanović, T. (2021). Bitcoin Price Direction Forecasting Using Neural Networks. In Proceedings of the 16 th International Symposium on Operational Research in Slovenia, SOR'21 (pp. 557-562). Ljubljana: Slovensko društvo informatika.
Šestanović, T. (2024). A comprehensive approach to Bitcoin forecasting using neural networks. Ekonomski pregled, 75(1), 62-85.
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.
Sila, J., Kocenda, E., Kristoufek, L., & Kukacka, J. (2024). Good vs. bad volatility in major cryptocurrencies: The dichotomy and drivers of connectedness. Journal of International Financial Markets Institutions and Money, 96, Article 102062.
Smales, L. A. (2022). Cryptocurrency as an alternative inflation hedge? Available at SSRN 3883123.
Sovbetov, Y. (2018). Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero. Journal of Economics and Financial Analysis, 2(2), 1-27.
Spilak, B. (2018). Deep Neural Networks for Cryptocurrencies Price Predic- tion, Master thesis, Humboldt University, Berlin. Reviewers: Härdle, W.K. and Lessmann, S.
Tu, Z., & Xue, C. (2019). Effect of bifurcation on the interaction between Bitcoin and Litecoin. Finance Research Letters, 31, 382–385.
Uras, N., Marchesi, L., Marchesi, M., & Tonelli, R. (2020). Forecasting Bitcoin closing price series using linear regression and neural networks models. PeerJ Computer Science, 6, e279.
Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82.
W.R. Martin, I., & Papadimitriou, D. (2022). Sentiment and speculation in a market with heterogeneous beliefs. American Economic Review, 112(8), 2465–2517.
Walther, T., Klein, T., & Bouri, E. (2019). Exogenous drivers of Bitcoin and Cryptocurrency volatility–A mixed data sampling approach to forecasting. Journal of International Financial Markets, Institutions and Money, 63, 101133.
Zhang, Y., He, M., Wen, D., & Wang, Y. (2021). Forecasting Bitcoin volatility: A new insight from the threshold regression model. Journal of Forecasting, 1– 20.
