Kripto para tahminlemesi üzerine sistematik bir literatür taraması

Yıl 2025, Cilt: 7 Sayı: 2, 199 - 217, 29.12.2025

Mustafa Zihni Tunca , Mehmet Özsoy

https://doi.org/10.55580/oguzhan.1823903

Öz

Bu çalışmada kripto para piyasalarının tahminlenmesi ile ilgili bilimsel araştırmalar sistematik bir şekilde incelenmiştir. Web of Science veri tabanından elde edilen 790 makale analize dahil edilmiş ve literatürün yapısı bibliyometrik yöntemlerle değerlendirilmiştir. Yapılan ilk analiz sonucunda kripto paraların tahminlenmesi ile ilgili çalışmaların 2016 yılından itibaren oldukça hızlı arttığı gözlemlenmiştir. Yapılan çalışmaların birçoğu kripto paraların ilki olan Bitcoin ile ilgili iken yıllar içerisinde Ethereum gibi diğer kripto paraların da araştırmacıların ilgisini çektiği görülmüştür. Analizler doğrultusunda dört temel kategori belirlenmiştir: makine öğrenmesi tabanlı tahminleme yöntemleri, finansal risk ve volatilite analizleri, davranışsal ve teknik belirleyiciler ve son olarak gelişmiş derin öğrenme yöntemleri. Bu bağlamdaki çalışmalar kripto para tahminlemesinde özniteliklerin önemine ve kurulan modellerde geleneksel yöntemler ile birlikte makine öğrenmesi ve derin öğrenme tabanlı modellerin kullanılmasının tahmin performansına etkisine dikkat çekmektedir. Çalışma, gelecekteki araştırmalarda davranışsal göstergelerin entegrasyonu ve çoklu piyasa ilişkilerinin incelenmesi gibi konulara odaklanılması gerektiğini vurgulamaktadır.

Anahtar Kelimeler

Tahminleme , Bitcoin , Kripto Para , Literatür Taraması

A Systematic literature review on Cryptocurrency forecasting

Öz

This study systematically reviews scientific research on predicting cryptocurrency markets. A total of 790 articles obtained from the Web of Science database were included in the analysis, and the structure of the literature was evaluated using bibliometric methods. The preliminary investigation indicated that studies examining the prediction of cryptocurrencies have undergone a substantial increase since 2016. While a significant proportion of the extant literature pertains to Bitcoin, the first cryptocurrency, it is evident that other cryptocurrencies, such as Ethereum, have also attracted the attention of researchers over the years. The analysis yielded four primary categories: machine learning-based prediction methods, financial risk and volatility analyses, behavioral and technical determinants, and finally, advanced deep learning methods. In the context of cryptocurrency prediction, studies have underscored the significance of attributes, emphasizing their role in enhancing the efficacy of prediction models. These studies have also highlighted the impact of integrating machine learning and deep learning-based models with conventional methods in enhancing the performance of established models. The study emphasizes the necessity to direct future research towards the integration of behavioral indicators and the examination of multiple market relationships.

Anahtar Kelimeler

Forecasting , Bitcoin , Cryptocurrency , Literature Review

Kaynakça

• Abakah, E. J. A., Tiwari, A. K., Ghosh, S., and Doğan, B. (2023). Dynamic effect of Bitcoin, fintech and artificial intelligence stocks on eco-friendly assets, Islamic stocks and conventional financial markets: Another look using quantile-based approaches. Technological Forecasting and Social Change, 192. https://doi.org/10.1016/j.techfore.2023.122566
• Adcock, R., and Gradojevic, N. (2019). Non-fundamental, non-parametric Bitcoin forecasting. Physica A: Statistical Mechanics and Its Applications, 531. https://doi.org/10.1016/j.physa.2019.121727
• Almeida, J., and Gonçalves, T. C. (2022). A Systematic Literature Review of Volatility and Risk Management on Cryptocurrency Investment: A Methodological Point of View. Risks, 10(5), 107. https://doi.org/10.3390/risks10050107
• Almeida, J., and Gonçalves, T. C. (2023). A Decade of Cryptocurrency Investment Literature: A Cluster-Based Systematic Analysis. International Journal of Financial Studies, 11(2), 71. https://doi.org/10.3390/ijfs11020071
• Altan, A., Karasu, S., and Bekiros, S. (2019). Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques. Chaos, Solitons and Fractals, 126, 325–336. https://doi.org/10.1016/j.chaos.2019.07.011
• Ante, L. (2020). A place next to Satoshi: Foundations of blockchain and cryptocurrency research in business and economics. Scientometrics, 124(2), 1305–1333. https://doi.org/10.1007/s11192-020-03492-8
• Aras, S. (2021). Stacking hybrid GARCH models for forecasting Bitcoin volatility. Expert Systems with Applications, 174, 114747. https://doi.org/10.1016/j.eswa.2021.114747
• Aria, M., and Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
• Atree, M. K., and Tripathy, N. (2025). Cryptocurrency research: Bibliometric review and content analysis. International Review of Economics and Finance, 98, 103940. https://doi.org/10.1016/j.iref.2025.103940
• Atsalakis, G. S., Atsalaki, L. G., Pasiouras, F., and Zopounidis, C. (2019). Bitcoin price forecasting with neuro-fuzzy techniques. European Journal of Operational Research, 276(2), 770–780. https://doi.org/10.1016/j.ejor.2019.01.040
• Aysan, A. F., Demirtaş, H. B., and Saraç, M. (2021). The Ascent of Bitcoin: Bibliometric Analysis of Bitcoin Research. Journal of Risk and Financial Management, 14(9), 427. https://doi.org/10.3390/jrfm14090427
• Bariviera, A. F., and Merediz-Solà, I. (2021). Where Do We Stand in Cryptocurrencies Economic Research? A Survey Based on Hybrid Analysis. Journal of Economic Surveys, 35(2), 377–407. https://doi.org/10.1111/joes.12412
• Bleher, J., and Dimpfl, T. (2019). Today I got a million, tomorrow, I don’t know: On the predictability of cryptocurrencies by means of Google search volume. International Review of Financial Analysis, 63, 147–159. https://doi.org/10.1016/j.irfa.2019.03.003
• Bouri, E., and Gupta, R. (2021). Predicting Bitcoin returns: Comparing the roles of newspaper- and internet search-based measures of uncertainty. Finance Research Letters, 38, 101398. https://doi.org/10.1016/j.frl.2019.101398
• Bouri, E., Molnar, P., Azzi, G., Roubaud, D., and Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? Finance Research Letters, 20, 192–198. https://doi.org/10.1016/j.fri.2016.09.025
• Brauneis, A., and Mestel, R. (2018). Price discovery of cryptocurrencies: Bitcoin and beyond. Economics Letters, 165, 58–61. https://doi.org/10.1016/j.econlet.2018.02.001
• Cai, C. W., Xue, R., and Zhou, B. (2023). Cryptocurrency puzzles: A comprehensive review and re-introduction. Journal of Accounting Literature, 46(1), 26–50. https://doi.org/10.1108/JAL-02-2023-0023
• Catania, L., Grassi, S., and Ravazzolo, F. (2019). Forecasting cryptocurrencies under model and parameter instability. International Journal of Forecasting, 35(2), 485–501. https://doi.org/10.1016/j.ijforecast.2018.09.005
• Chen, W., Xu, H., Jia, L., and Gao, Y. (2021). Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants. International Journal of Forecasting, 37(1), 28–43. https://doi.org/10.1016/j.ijforecast.2020.02.008
• Chen, Z., Li, C., and Sun, W. (2020). Bitcoin price prediction using machine learning: An approach to sample dimension engineering. Journal of Computational and Applied Mathematics, 365, 112395. https://doi.org/10.1016/j.cam.2019.112395
• Coinmarketcap.com (22 August 2025). www.coinmarketcap.com. Access 22 August 2025, https://coinmarketcap.com/charts/
• Corbet, S., Lucey, B., Urquhart, A., and Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182–199. https://doi.org/10.1016/j.irfa.2018.09.003
• Detzel, A., Liu, H., Strauss, J., Zhou, G., and Zhu, Y. (2021). Learning and predictability via technical analysis: Evidence from Bitcoin and stocks with hard-to-value fundamentals. Financial Management, 50(1), 107–137. https://doi.org/10.1111/fima.12310
• Dotsika, F., and Watkins, A. (2017). Identifying potentially disruptive trends by means of keyword network analysis. Technological Forecasting and Social Change, 119, 114–127. https://doi.org/10.1016/j.techfore.2017.03.020
• Dutta, A., Kumar, S., and Basu, M. (2020). A Gated Recurrent Unit Approach to Bitcoin Price Prediction. Journal of Risk and Financial Management, 13(2), 23. https://doi.org/10.3390/jrfm13020023
• Eom, C., Kaizoji, T., Kang, S. H., and Pichl, L. (2019). Bitcoin and investor sentiment: Statistical characteristics and predictability. Physica A: Statistical Mechanics and Its Applications, 514, 511–521. https://doi.org/10.1016/j.physa.2018.09.063
• Faghih Mohammadi Jalali, M., and Heidari, H. (2020). Predicting changes in Bitcoin price using grey system theory. Financial Innovation, 6(1), 13. https://doi.org/10.1186/s40854-020-0174-9
• Garcia, D., and Schweitzer, F. (2015). Social signals and algorithmic trading of Bitcoin. Royal Society Open Science, 2(9), 150288. https://doi.org/10.1098/rsos.150288
• García-Corral, F. J., Cordero-García, J. A., de Pablo-Valenciano, J., and Uribe-Toril, J. (2022). A bibliometric review of cryptocurrencies: How have they grown? Financial Innovation, 8(1), 2. https://doi.org/10.1186/s40854-021-00306-5
• Giudici, P., and Raffinetti, E. (2021). Shapley-Lorenz eXplainable Artificial Intelligence. Expert Systems with Applications, 167, 114104. https://doi.org/10.1016/j.eswa.2020.114104
• Giudici, P., and Raffinetti, E. (2023). SAFE Artificial Intelligence in finance. Finance Research Letters, 56, 104088. https://doi.org/10.1016/j.frl.2023.104088
• Gradojevic, N., Kukolj, D., Adcock, R., and Djakovic, V. (2023). Forecasting Bitcoin with technical analysis: A not-so-random forest? International Journal of Forecasting, 39(1), 1–17. https://doi.org/10.1016/j.ijforecast.2021.08.001
• Hossain, M. S. (2021). What do we know about cryptocurrency? Past, present, future. China Finance Review International, 11(4), 552–572. https://doi.org/10.1108/CFRI-03-2020-0026
• Indera, N. I., Yassin, I. M., Zabidi, A., and 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. https://doi.org/10.4314/jfas.v9i3s.61
• Jalal, R. N.-U.-D., Alon, I., and Paltrinieri, A. (2025). A bibliometric review of cryptocurrencies as a financial asset. Technology Analysis & Strategic Management, 37(4), 432–447. https://doi.org/10.1080/09537325.2021.1939001
• Jang, H., and Lee, J. (2018). An Empirical Study on Modeling and Prediction of Bitcoin Prices with Bayesian Neural Networks Based on Blockchain Information. IEEE Access, 6, 5427–5437. https://doi.org/10.1109/ACCESS.2017.2779181
• Jeris, S. S., Chowdhury, A. S. M. N. U. R., Akter, M. T., Frances, S., and Roy, M. H. (2022). Cryptocurrency and stock market: Bibliometric and content analysis. Heliyon, 8(9). https://doi.org/10.1016/j.heliyon.2022.e10514
• Ji, Q., Zhang, D., and Zhao, Y. (2020). Searching for safe-haven assets during the COVID-19 pandemic. International Review of Financial Analysis, 71, 101526. https://doi.org/10.1016/j.irfa.2020.101526
• Ji, S., Kim, J., and Im, H. (2019). A Comparative Study of Bitcoin Price Prediction Using Deep Learning. Mathematics, 7(10), 898. https://doi.org/10.3390/math7100898
• Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, 3–6. https://doi.org/10.1016/j.econlet.2017.06.023
• Klein, T., Pham Thu, H., and 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. https://doi.org/10.1016/j.irfa.2018.07.010
• Lahmiri, S., and Bekiros, S. (2019). Cryptocurrency forecasting with deep learning chaotic neural networks. Chaos Solitons and Fractals, 118, 35–40. https://doi.org/10.1016/j.chaos.2018.11.014
• Liu, M., Li, G., Li, J., Zhu, X., and Yao, Y. (2021). Forecasting the price of Bitcoin using deep learning. Finance Research Letters, 40, 101755. https://doi.org/10.1016/j.frl.2020.101755
• Liu, Y., and Tsyvinski, A. (2021). Risks and Returns of Cryptocurrency. The Review of Financial Studies, 34(6), 2689–2727. https://doi.org/10.1093/rfs/hhaa113
• Mallqui, D. C. A., and Fernandes, R. A. S. (2019). Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques. Applied Soft Computing, 75, 596–606. https://doi.org/10.1016/j.asoc.2018.11.038
• Mohamed, S. D., Ismail, M. T., and Ali, M. K. B. M. (2025). Improving and evaluating GARCH-type models for Bitcoin volatility prediction. Eurasian Economic Review, 15, 1219-1260. https://doi.org/10.1007/s40822-025-00328-9
• Mongeon, P., and Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106(1), 213–228. https://doi.org/10.1007/s11192-015-1765-5
• Mudassir, M., Bennbaia, S., Ünal, D., and Hammoudeh, M. A. A. (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
• Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. Access 22 August 2025, https://bitcoin.org/bitcoin.pdf
• Nasir, A., Shaukat, K., Khan, K. I., Hameed, I. A., Alam, T. M., and Luo, S. (2021). What is Core and What Future Holds for Blockchain Technologies and Cryptocurrencies: A Bibliometric Analysis. IEEE Access, 9, 989–1004. https://doi.org/10.1109/ACCESS.2020.3046931
• Nasir, M. A., Huynh, T. L. D., Nguyen, S. P., and Duong, D. (2019). Forecasting cryptocurrency returns and volume using search engines. Financial Innovation, 5(1), 2. https://doi.org/10.1186/s40854-018-0119-8
• Neetu, and Symss, J. (2023). Can cryptocurrency solve the problem of financial constraint in corporates? A literature review and theoretical perspective. Qualitative Research in Financial Markets, 17(3), 453–472. https://doi.org/10.1108/QRFM-12-2021-0215
• Nosratabadi, S., Mosavi, A. H., Duan, P., Ghamisi, P., Filip, F., Band, S. S., Reuter, U., Gama, J. M. P., and Gandomi, A. H. (2020). Data science in economics: Comprehensive review of advanced machine learning and deep learning methods. Mathematics, 8(10), 1–25. https://doi.org/10.3390/math8101799
• Patel, M. M., Tanwar, S., Gupta, R., and Kumar, N. (2020). A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions. Journal of Information Security and Applications, 55, 102583. https://doi.org/10.1016/j.jisa.2020.102583
• Patrício, L. D., and Ferreira, J. J. (2020). Blockchain security research: Theorizing through bibliographic-coupling analysis. Journal of Advances in Management Research, 18(1), 1–35. https://doi.org/10.1108/JAMR-04-2020-0051
• Peng, Y., Melo Albuquerque, P. H., Camboim de Sa, J. M., Akaishi Padula, A. J., and Montenegro, M. R. (2018). The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression. Expert Systems with Applications, 97, 177–192. https://doi.org/10.1016/j.eswa.2017.12.004
• Ramona, O., Cristina, M. S., and Raluca, S. (2019). Bitcoin in the Scientific Literature – A Bibliometric Study. Studies in Business and Economics, 14(3), 160–174. https://doi.org/10.2478/sbe-2019-0051
• Ren, Y.-S., Ma, C.-Q., Kong, X.-L., Baltas, K., and Zureigat, Q. (2022). Past, present, and future of the application of machine learning in cryptocurrency research. Research in International Business and Finance, 63, 101799. https://doi.org/10.1016/j.ribaf.2022.101799
• Saad, M., Choi, J., Nyang, D., Kim, J., and Mohaisen, A. (2020). Toward Characterizing Blockchain-Based Cryptocurrencies for Highly Accurate Predictions. IEEE Systems Journal, 14(1), 321–332. https://doi.org/10.1109/JSYST.2019.2927707
• Sebastiao, H., and Godinho, P. (2021). Forecasting and trading cryptocurrencies with machine learning under changing market conditions. Financial Innovation, 7(1), 3. https://doi.org/10.1186/s40854-020-00217-x
• Shahzad, S. J. H., Bouri, E., Roubaud, D., Kristoufek, L., and Lucey, B. (2019). Is Bitcoin a better safe-haven investment than gold and commodities? International Review of Financial Analysis, 63, 322–330. https://doi.org/10.1016/j.irfa.2019.01.002
• Shen, D., Urquhart, A., and Wang, P. (2020). Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks. European Financial Management, 26(5), 1294–1323. https://doi.org/10.1111/eufm.12254
• Sousa, A., Calçada, E., Rodrigues, P., and Pinto Borges, A. (2022). Cryptocurrency adoption: A systematic literature review and bibliometric analysis. EuroMed Journal of Business, 17(3), 374–390. https://doi.org/10.1108/EMJB-01-2022-0003
• Sun, X., Liu, M., and Sima, Z. (2020). A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Research Letters, 32, 101084. https://doi.org/10.1016/j.frl.2018.12.032
• Tranfield, D., Denyer, D., and Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375
• Troster, V., Tiwari, A. K., Shahbaz, M., and Macedo, D. N. (2019). Bitcoin returns and risk: A general GARCH and GAS analysis. Finance Research Letters, 30, 187–193. https://doi.org/10.1016/j.frl.2018.09.014
• Urom, C., Abid, I., Guesmi, K., and Chevallier, J. (2020). Quantile spillovers and dependence between Bitcoin, equities and strategic commodities. Economic Modelling, 93, 230–258. https://doi.org/10.1016/j.econmod.2020.07.012
• Wu, C.-H., Lu, C.-C., Ma, Y.-F., and Lu, R.-S. (2018). A New Forecasting Framework for Bitcoin Price with LSTM. 2018 IEEE International Conference on Data Mining Workshops (ICDMW), 168–175. https://doi.org/10.1109/ICDMW.2018.00032
• Yi, S., Xu, Z., and Wang, G.-J. (2018). Volatility connectedness in the cryptocurrency market: Is Bitcoin a dominant cryptocurrency? International Review of Financial Analysis, 60, 98–114. https://doi.org/10.1016/j.irfa.2018.08.012
• Yue, Y., Li, X., Zhang, D., and Wang, S. (2021). How cryptocurrency affects economy? A network analysis using bibliometric methods. International Review of Financial Analysis, 77, 101869. https://doi.org/10.1016/j.irfa.2021.101869
• Zhang, W., Li, Y., Xiong, X., and Wang, P. (2021). Downside risk and the cross-section of cryptocurrency returns. Journal of Banking and Finance, 133, 106246. https://doi.org/10.1016/j.jbankfin.2021.106246