The Effect of Cryptocurrency Ecosystem and Global Indicators on Bitcoin Price
Year 2025,
Volume: 33 Issue: 63, 115 - 142
Ahmet Akusta
,
Mehmet Nuri Salur
Abstract
This research aims to forecast the price of Bitcoin by identifying the factors that influence its price movements. The study combines 396 variables, categorised into data concerning the cryptocurrency ecosystem and data about significant global indices. The analysis utilises a dataset spanning 90 days from October 2022 to December 2022. The dataset is divided into 85% for training and 15% for testing. Among the 18 machine learning methods, the model demonstrating the highest accuracy is selected. The findings show the solid overall performance of the model, as indicated by an R2 score of 0.909.
References
- Adhikary, S. & S. Banerjee (2022), “Introduction to Distributed Nearest Hash: On Further Optimizing Cloud Based Distributed kNN Variant”, Procedia Computer Science, 218, 1571-1580.
- Aggarwal, A. et al. (2019), “Deep Learning Approach to Determine the Impact of Socio Economic Factors on Bitcoin Price Prediction”, 2019 12th International Conference on Contemporary Computing, IC3 2019.
- Aghashahi, M. & S. Bamdad (2022), “Analysis of different artificial neural networks for Bitcoin price prediction”, International Journal of Management Science and Engineering Management, 18(2), 126-133.
- Akinduko, A.A. & A.N. Gorban (2014), “Multiscale principal component analysis”, Journal of Physics: Conference Series, 490, 012081.
- American Institute of Certified Public Accountants (n.d.), Audit guide : analytical procedures, <https://books.google.com/books/about/Audit_Guide.html?hl=tr&id=0vlotgEACAAJ>, 08.03.2023.
- Andrean, G. (2020), “Determinant of the Bitcoin Prices as Alternative Investment in Indonesia”, Indicators - Journal of Economic and Business, 1(1), 22-29.
- Aung, S.S. et al. (2018), “A high-performance classifier from k-dimensional tree-based Dual-kNN”, IEIE Transactions on Smart Processing and Computing, 7(3), 184-194.
- Azari, A. (2019), Bitcoin Price Prediction: An ARIMA Approach, <https://arxiv.org/abs/1904.05315v1>, 08.03.2023.
- Bouoiyour, J. & R. Selmi (2017), The Bitcoin price formation: Beyond the fundamental sources, <https://arxiv.org/abs/1707.01284v1>, 08.03.2024.
- Bouri, E. et al. (2017), “On the Return-Volatility Relationship in the Bitcoin Market Around the Price Crash of 2013”, Economics the Open-Access Open-Assessment E-Journal, 11(1), 2.
- Bouri, E. et al. (2018), “Testing for Asymmetric Nonlinear Short- And Long-Run Relationships Between Bitcoin, Aggregate Commodity and Gold Prices”, Resources Policy, 57, 224-235.
- Charilaou, P. & R. Battat (2022), “Machine learning models and over-fitting considerations”, World Journal of Gastroenterology, 28(5), 605-607.
- Chen, J. (2023), “Analysis of Bitcoin Price Prediction Using Machine Learning”, Journal of Risk and Financial Management, 16(1), 51.
- Ciaian, P. et al. (2015), “The Economics of BitCoin Price Formation”, Applied Economics, 48(19), 1799-1815.
- Cretarola, A. et al. (2017), “A Sentiment-Based Model for the Bitcoin: Theory, Estimation and Option Pricing”, SSRN Electronic Journal, <https://doi.org/10.2139/ssrn.3042029>.
- Critien, J.V. et al. (2022), “Bitcoin price change and trend prediction through twitter sentiment and data volume”, Financial Innovation, 8(1), 1-20.
- Dhande, A. et al. (2022), “Cryptocurrency Price Prediction Using Linear Regression and Long Short-Term Memory (LSTM)”, International Journal for Research in Applied Science and Engineering Technology, 10(12), 1591-1598.
- Dutta, A. et al. (2020), “A Gated Recurrent Unit Approach to Bitcoin Price Prediction”, Journal of Risk and Financial Management, 13(2), 23.
- Dyhrberg, A.H. (2016), “Bitcoin, Gold and the Dollar - A GARCH Volatility Analysis”, Finance Research Letters, 16, 85-92.
- Fil, M. & L. Krištoufek (2020), “Pairs Trading in Cryptocurrency Markets”, Ieee Access, 8, 172644-172651.
- García, D. et al. (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.
- Georgoula, I. et al. (2015), “Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices”, SSRN Electronic Journal, <https://doi.org/10.2139/SSRN.2607167>.
- Greaves, A. & B. Au (2015), Using the Bitcoin Transaction Graph to Predict the Price of Bitcoin.
- Gronwald, M. (2019), “Is Bitcoin a Commodity? On Price Jumps, Demand Shocks, and Certainty of Supply”, Journal of International Money and Finance, 97, 86-92.
- Hayashi, S. et al. (2020), “Long-term prediction of small time-series data using generalized distillation”, Transactions of the Japanese Society for Artificial Intelligence, 35(5), 1-9.
- Hong, D. et al. (2018), “Asymptotic Performance of PCA for High-Dimensional Heteroscedastic Data”, Journal of Multivariate Analysis, 167, 435-452.
- Huang, W. et al. (2022), “Time Series Analysis and Prediction on Bitcoin”, BCP Business & Management, 34, 1223-1234.
- Hung, H. et al. (2012), “On Multilinear Principal Component Analysis of Order-Two Tensors”, Biometrika, 99(3), 56-583.
- Jana, R.K. et al. (2021), “A differential evolution-based regression framework for forecasting Bitcoin price”, Annals of Operations Research, 306(1-2), 295-320.
- Ji, S. et al. (2019), “A Comparative Study of Bitcoin Price Prediction Using Deep Learning”, Mathematics, 7(10), 898.
- Johnson, J. (2020), “Bitcoin, Corruption and Economic Freedom”, Journal of Financial Crime, 27(1), 58-66.
- Katubi, K.M. et al. (2023), “Machine learning assisted designing of organic semiconductors for organic solar cells: High-throughput screening and reorganization energy prediction”, Inorganic Chemistry Communications, 151, 110610.
- Kayal, P. & G. Balasubramanian (2021), “Excess Volatility in Bitcoin: Extreme Value Volatility Estimation”, Iim Kozhikode Society & Management Review, 10(2), 222-231.
- Kervancı, I.S. & F. Akay (2020), “Review on Bitcoin Price Prediction Using Machine Learning and Statistical Methods”, Sakarya University Journal of Computer and Information Sciences, 3(3), 272-282.
- Khatun, M. & S. Siddiqui (2023), “Estimating Conditional Event Probabilities with Mixed Regressors: a Weighted Nearest Neighbour Approach”, Statistika, 103(2), 226-234.
- Liu, X. & X. Zhang (2023), “The Analysis of the Influencing Factors of Virtual Currency Price Based on Multiple Regression Method”, Frontiers in Business Economics and Management, 7(1), 156-159.
- Mladenova, T. & I. Valova (2023), “Classification with K-Nearest Neighbors Algorithm: Comparative Analysis between the Manual and Automatic Methods for K-Selection”, International Journal of Advanced Computer Science and Applications, 14(4), 396-404.
- Mudassir, M. et al. (2020), “Time-Series Forecasting of Bitcoin Prices Using High-Dimensional Features: A Machine Learning Approach”, Neural Computing and Applications, https://doi.org/10.1007/s00521-020-05129-6.
- Munim, Z.H. et al. (2019), “Next-Day Bitcoin Price Forecast”, Journal of Risk and Financial Management, 12(2), 103.
- Oğuzlar, A. (2003), “Veri Ön İşleme”, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21, 67-76.
- Oyeyemi, G. et al. (2015), “Comparison of Outlier Detection Procedures in Multiple Linear Regressions”. American Journal of Mathematics and Statistics, 5(1), 37-41.
- Pano, T. & R. Kashef (2020), “A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets During the Era of COVID-19”, Big Data and Cognitive Computing, 4(4), 33.
- Pele, D.T. & M. Mazurencu-Marinescu-Pele (2019), “Metcalfe’s Law and Log-Period Power Laws in the Cryptocurrencies Market”, Economics the Open-Access Open-Assessment E-Journal, 13(29), 1-26.
- Richardson, M. (2009), Principal component analysis, <http://aurora.troja.mff.cuni.cz/nemec/idl/09bonus/pca.pdf>, 02.03.2024.
- Roy, S. et al. (2019), “Bitcoin Price Forecasting Using Time Series Analysis”, 21st International Conference of Computer and Information Technology, ICCIT 2018.
- Rubio, G. et al. (2009), “Parallelization of the nearest-neighbour search and the cross-validation error evaluation for the kernel weighted k-nn algorithm applied to large data dets in matlab”, Proceedings of the 2009 International Conference on High Performance Computing & Simulation, Leipzig, Germany, 145-152.
- 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.
- Tappin, B.M. et al. (2021), “Rethinking the Link Between Cognitive Sophistication and Politically Motivated Reasoning”, Journal of Experimental Psychology General, 150(6), 1095-1114.
- Taylan, P. (2019), “On foundations of estimation for nonparametric regression with continuous optimization”, in: F.P. Garcia Marquez (ed.), Handbook of Research on Big Data Clustering and Machine Learning (177-203), IGI Global Scientific Publishing.
- Vatcheva, K. et al. (2016), “Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies”, Epidemiology Open Access, 6(2), 227.
- Virk, D.S. (2017), “Prediction of Bitcoin Price using Data Mining”, Doctoral Dissertation, National College of Ireland, Dublin.
- Wamuyu, P.K. (2022), “Use of Cloud Computing Services in Micro and Small Enterprises: A Fit Perspective”, International Journal of Information Systems and Project Management, 5(2), 59-81.
- Wang, J. et al. (2016), “An Analysis of Bitcoin Price Based on VEC Model”, in: Proceedings of the 2016 International Conference on Economics and Management Innovations (180-186), Atlantis Press.
- Wu, C.H. et al. (2019), “A new forecasting framework for bitcoin price with LSTM”, IEEE International Conference on Data Mining Workshops, ICDMW (168-175), 2018-November.
- Ye, Z. et al. (2022), “A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin”, Mathematics, 10(8), 1307.
- Zaman, S. & B. Ahmed (2019), “Hybrid Subspace Detection Based on Spectral and Spatial Information for Effective Hyperspectral Image Classification”, International Journal of Computer Applications, 178(41), 37-43.
- Zhao, J. (2022), “Do Economic Crises Cause Trading in Bitcoin?”, Review of Behavioral Finance, 14(4), 465-490.
- Zhao, P. & L. Lai (2019), “Minimax Regression via Adaptive Nearest Neighbor”, IEEE International Symposium on Information Theory - Proceedings (1447-1451), 2019-July.
- Zhou, S. (2019), “Exploring the Driving Forces of the Bitcoin Currency Exchange Rate Dynamics: An EGARCH Approach”, Empirical Economics, 60, (557-606).
Kripto Para Ekosistemi ve Küresel Göstergelerin Bitcoin Fiyatı Üzerindeki Etkisi
Year 2025,
Volume: 33 Issue: 63, 115 - 142
Ahmet Akusta
,
Mehmet Nuri Salur
Abstract
Bu araştırma, Bitcoin’in fiyatını etkileyen faktörleri belirleyerek Bitcoin'in fiyatını tahmin etmeyi amaçlamaktadır. Çalışma, kripto para ekosistemiyle ilgili veriler ve önemli küresel endekslerle ilgili veriler olmak üzere toplamda 396 değişkeni bir araya getirmektedir. Analiz, Ekim 2022’den Aralık 2022’ye kadar olan 90 günlük bir veri setini kullanmaktadır. Veri seti, %85’i eğitim ve %15’i test için ayrılmıştır. 18 makine öğrenme yöntemi arasından en yüksek doğruluğa sahip olan model seçilmiştir. Bulgular, modelin, 0.909 R2 skoruyla iyi bir performans sergilediğini göstermektedir.
Ethical Statement
Çalışmamızda bilimsel etik kurallarına uyulduğunu beyan eder, etik kurul izni alınması gerektiren herhangi bir veri içermediğini teyit ederim.
Supporting Institution
Bu çalışma için herhangi bir kurumdan destek alınmamıştır.
Thanks
Bu çalışma için herhangi bir teşekkür açıklaması yoktur.
References
- Adhikary, S. & S. Banerjee (2022), “Introduction to Distributed Nearest Hash: On Further Optimizing Cloud Based Distributed kNN Variant”, Procedia Computer Science, 218, 1571-1580.
- Aggarwal, A. et al. (2019), “Deep Learning Approach to Determine the Impact of Socio Economic Factors on Bitcoin Price Prediction”, 2019 12th International Conference on Contemporary Computing, IC3 2019.
- Aghashahi, M. & S. Bamdad (2022), “Analysis of different artificial neural networks for Bitcoin price prediction”, International Journal of Management Science and Engineering Management, 18(2), 126-133.
- Akinduko, A.A. & A.N. Gorban (2014), “Multiscale principal component analysis”, Journal of Physics: Conference Series, 490, 012081.
- American Institute of Certified Public Accountants (n.d.), Audit guide : analytical procedures, <https://books.google.com/books/about/Audit_Guide.html?hl=tr&id=0vlotgEACAAJ>, 08.03.2023.
- Andrean, G. (2020), “Determinant of the Bitcoin Prices as Alternative Investment in Indonesia”, Indicators - Journal of Economic and Business, 1(1), 22-29.
- Aung, S.S. et al. (2018), “A high-performance classifier from k-dimensional tree-based Dual-kNN”, IEIE Transactions on Smart Processing and Computing, 7(3), 184-194.
- Azari, A. (2019), Bitcoin Price Prediction: An ARIMA Approach, <https://arxiv.org/abs/1904.05315v1>, 08.03.2023.
- Bouoiyour, J. & R. Selmi (2017), The Bitcoin price formation: Beyond the fundamental sources, <https://arxiv.org/abs/1707.01284v1>, 08.03.2024.
- Bouri, E. et al. (2017), “On the Return-Volatility Relationship in the Bitcoin Market Around the Price Crash of 2013”, Economics the Open-Access Open-Assessment E-Journal, 11(1), 2.
- Bouri, E. et al. (2018), “Testing for Asymmetric Nonlinear Short- And Long-Run Relationships Between Bitcoin, Aggregate Commodity and Gold Prices”, Resources Policy, 57, 224-235.
- Charilaou, P. & R. Battat (2022), “Machine learning models and over-fitting considerations”, World Journal of Gastroenterology, 28(5), 605-607.
- Chen, J. (2023), “Analysis of Bitcoin Price Prediction Using Machine Learning”, Journal of Risk and Financial Management, 16(1), 51.
- Ciaian, P. et al. (2015), “The Economics of BitCoin Price Formation”, Applied Economics, 48(19), 1799-1815.
- Cretarola, A. et al. (2017), “A Sentiment-Based Model for the Bitcoin: Theory, Estimation and Option Pricing”, SSRN Electronic Journal, <https://doi.org/10.2139/ssrn.3042029>.
- Critien, J.V. et al. (2022), “Bitcoin price change and trend prediction through twitter sentiment and data volume”, Financial Innovation, 8(1), 1-20.
- Dhande, A. et al. (2022), “Cryptocurrency Price Prediction Using Linear Regression and Long Short-Term Memory (LSTM)”, International Journal for Research in Applied Science and Engineering Technology, 10(12), 1591-1598.
- Dutta, A. et al. (2020), “A Gated Recurrent Unit Approach to Bitcoin Price Prediction”, Journal of Risk and Financial Management, 13(2), 23.
- Dyhrberg, A.H. (2016), “Bitcoin, Gold and the Dollar - A GARCH Volatility Analysis”, Finance Research Letters, 16, 85-92.
- Fil, M. & L. Krištoufek (2020), “Pairs Trading in Cryptocurrency Markets”, Ieee Access, 8, 172644-172651.
- García, D. et al. (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.
- Georgoula, I. et al. (2015), “Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices”, SSRN Electronic Journal, <https://doi.org/10.2139/SSRN.2607167>.
- Greaves, A. & B. Au (2015), Using the Bitcoin Transaction Graph to Predict the Price of Bitcoin.
- Gronwald, M. (2019), “Is Bitcoin a Commodity? On Price Jumps, Demand Shocks, and Certainty of Supply”, Journal of International Money and Finance, 97, 86-92.
- Hayashi, S. et al. (2020), “Long-term prediction of small time-series data using generalized distillation”, Transactions of the Japanese Society for Artificial Intelligence, 35(5), 1-9.
- Hong, D. et al. (2018), “Asymptotic Performance of PCA for High-Dimensional Heteroscedastic Data”, Journal of Multivariate Analysis, 167, 435-452.
- Huang, W. et al. (2022), “Time Series Analysis and Prediction on Bitcoin”, BCP Business & Management, 34, 1223-1234.
- Hung, H. et al. (2012), “On Multilinear Principal Component Analysis of Order-Two Tensors”, Biometrika, 99(3), 56-583.
- Jana, R.K. et al. (2021), “A differential evolution-based regression framework for forecasting Bitcoin price”, Annals of Operations Research, 306(1-2), 295-320.
- Ji, S. et al. (2019), “A Comparative Study of Bitcoin Price Prediction Using Deep Learning”, Mathematics, 7(10), 898.
- Johnson, J. (2020), “Bitcoin, Corruption and Economic Freedom”, Journal of Financial Crime, 27(1), 58-66.
- Katubi, K.M. et al. (2023), “Machine learning assisted designing of organic semiconductors for organic solar cells: High-throughput screening and reorganization energy prediction”, Inorganic Chemistry Communications, 151, 110610.
- Kayal, P. & G. Balasubramanian (2021), “Excess Volatility in Bitcoin: Extreme Value Volatility Estimation”, Iim Kozhikode Society & Management Review, 10(2), 222-231.
- Kervancı, I.S. & F. Akay (2020), “Review on Bitcoin Price Prediction Using Machine Learning and Statistical Methods”, Sakarya University Journal of Computer and Information Sciences, 3(3), 272-282.
- Khatun, M. & S. Siddiqui (2023), “Estimating Conditional Event Probabilities with Mixed Regressors: a Weighted Nearest Neighbour Approach”, Statistika, 103(2), 226-234.
- Liu, X. & X. Zhang (2023), “The Analysis of the Influencing Factors of Virtual Currency Price Based on Multiple Regression Method”, Frontiers in Business Economics and Management, 7(1), 156-159.
- Mladenova, T. & I. Valova (2023), “Classification with K-Nearest Neighbors Algorithm: Comparative Analysis between the Manual and Automatic Methods for K-Selection”, International Journal of Advanced Computer Science and Applications, 14(4), 396-404.
- Mudassir, M. et al. (2020), “Time-Series Forecasting of Bitcoin Prices Using High-Dimensional Features: A Machine Learning Approach”, Neural Computing and Applications, https://doi.org/10.1007/s00521-020-05129-6.
- Munim, Z.H. et al. (2019), “Next-Day Bitcoin Price Forecast”, Journal of Risk and Financial Management, 12(2), 103.
- Oğuzlar, A. (2003), “Veri Ön İşleme”, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21, 67-76.
- Oyeyemi, G. et al. (2015), “Comparison of Outlier Detection Procedures in Multiple Linear Regressions”. American Journal of Mathematics and Statistics, 5(1), 37-41.
- Pano, T. & R. Kashef (2020), “A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets During the Era of COVID-19”, Big Data and Cognitive Computing, 4(4), 33.
- Pele, D.T. & M. Mazurencu-Marinescu-Pele (2019), “Metcalfe’s Law and Log-Period Power Laws in the Cryptocurrencies Market”, Economics the Open-Access Open-Assessment E-Journal, 13(29), 1-26.
- Richardson, M. (2009), Principal component analysis, <http://aurora.troja.mff.cuni.cz/nemec/idl/09bonus/pca.pdf>, 02.03.2024.
- Roy, S. et al. (2019), “Bitcoin Price Forecasting Using Time Series Analysis”, 21st International Conference of Computer and Information Technology, ICCIT 2018.
- Rubio, G. et al. (2009), “Parallelization of the nearest-neighbour search and the cross-validation error evaluation for the kernel weighted k-nn algorithm applied to large data dets in matlab”, Proceedings of the 2009 International Conference on High Performance Computing & Simulation, Leipzig, Germany, 145-152.
- 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.
- Tappin, B.M. et al. (2021), “Rethinking the Link Between Cognitive Sophistication and Politically Motivated Reasoning”, Journal of Experimental Psychology General, 150(6), 1095-1114.
- Taylan, P. (2019), “On foundations of estimation for nonparametric regression with continuous optimization”, in: F.P. Garcia Marquez (ed.), Handbook of Research on Big Data Clustering and Machine Learning (177-203), IGI Global Scientific Publishing.
- Vatcheva, K. et al. (2016), “Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies”, Epidemiology Open Access, 6(2), 227.
- Virk, D.S. (2017), “Prediction of Bitcoin Price using Data Mining”, Doctoral Dissertation, National College of Ireland, Dublin.
- Wamuyu, P.K. (2022), “Use of Cloud Computing Services in Micro and Small Enterprises: A Fit Perspective”, International Journal of Information Systems and Project Management, 5(2), 59-81.
- Wang, J. et al. (2016), “An Analysis of Bitcoin Price Based on VEC Model”, in: Proceedings of the 2016 International Conference on Economics and Management Innovations (180-186), Atlantis Press.
- Wu, C.H. et al. (2019), “A new forecasting framework for bitcoin price with LSTM”, IEEE International Conference on Data Mining Workshops, ICDMW (168-175), 2018-November.
- Ye, Z. et al. (2022), “A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin”, Mathematics, 10(8), 1307.
- Zaman, S. & B. Ahmed (2019), “Hybrid Subspace Detection Based on Spectral and Spatial Information for Effective Hyperspectral Image Classification”, International Journal of Computer Applications, 178(41), 37-43.
- Zhao, J. (2022), “Do Economic Crises Cause Trading in Bitcoin?”, Review of Behavioral Finance, 14(4), 465-490.
- Zhao, P. & L. Lai (2019), “Minimax Regression via Adaptive Nearest Neighbor”, IEEE International Symposium on Information Theory - Proceedings (1447-1451), 2019-July.
- Zhou, S. (2019), “Exploring the Driving Forces of the Bitcoin Currency Exchange Rate Dynamics: An EGARCH Approach”, Empirical Economics, 60, (557-606).