Blockchain-based Cryptocurrency Price Prediction with Chaos Theory, Onchain Analysis, Sentiment Analysis and Fundamental-Technical Analysis

Abstract

Crypto assets succeeded in making their name known to large masses with Bitcoin, which emerged as a result of the creation of the first genesis block in 2008. Until 2010, the aforementioned recognition showed itself mostly in areas such as games, but over time it managed to enter the portfolios of individual investors. Especially as of end of 2017, the rapid increases in monetary value quickly attracted the attention of corporate companies and then the (Central Banks). These assets have created different alternatives (also know as altcoins) by working and have managed to become one of the important financial instruments today. This study has examined in detail the techniques (Chaos theory, Onchain analysis and Sentiment analysis) developed on the price predictions of crypto assets, which are very important in terms of the number and quality of investors. In the study, findings were obtained that new techniques such as onchain and sentiment are more prominent in estimating crypto asset prices compared to traditional asset price estimation methods of crypto assets and that these techniques can make consistent estimations.

Keywords

• Blockchain
• Cryptocurrency
• Chaos theory
• Onchain analysis
• Sentiment Analysis

References

1. Abraham, J., D. Higdon, J. Nelson, and J. Ibarra, 2018 Cryptocurrency price prediction using tweet volumes and sentiment analysis. SMU Data Science Review 1: 1.
2. AJ, H. S. S. W. M. and S. Vanstone, 1990 How to time-stamp a digital document. In Advances in Cryptology-CRYPT0, volume 1991.
3. Alpar, O. and E. Özge, 2016 Imkb100 endeks de˘ gi¸sim de˘ gerlerinde lyapunov üsteli metoduyla kaosun incelenmesi. ˙Istanbul Aydın Üniversitesi Dergisi 8: 151–174.
4. Biswas, H. R., M. M. Hasan, and S. K. Bala, 2018 Chaos theory and its applications in our real life. Barishal University Journal Part 1: 123–140.
5. Bouri, E., R. Gupta, and D. Roubaud, 2019 Herding behaviour in cryptocurrencies. Finance Research Letters 29: 216–221.
6. Chen, Z., C. Li, and W. Sun, 2020 Bitcoin price prediction using machine learning: An approach to sample dimension engineering. Journal of Computational and Applied Mathematics 365: 112395.
7. Ciftci, B. and M. S. Apaydin, 2018 A deep learning approach to sentiment analysis in turkish. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), pp. 1–5, IEEE.
8. Cortez, C. T., S. Saydam, J. Coulton, and C. Sammut, 2018 Alternative techniques for forecasting mineral commodity prices. International Journal of Mining Science and Technology 28: 309– 322.

9. Diffie,W. and M. E. Hellman, 2022 New directions in cryptography. In Democratizing Cryptography: The Work of Whitfield Diffie and Martin Hellman, pp. 365–390.
10. El Montasser, G., L. Charfeddine, and A. Benhamed, 2022 Covid-19, cryptocurrencies bubbles and digital market efficiency: sensitivity and similarity analysis. Finance Research Letters 46: 102362.
11. Erdo˘gan, N. K., 2017 Finansal zaman serilerinin fraktal analizi. Aksaray üniversitesi iktisadi ve idari bilimler fakültesi dergisi 9: 49–54.
12. Faggini, M. and A. Parziale, 2012 The failure of economic theory. lessons from chaos theory .
13. Gözde, K., 2021 Bitcoin üzerine twitter verileri ile duygu analizi. Anadolu Üniversitesi ˙Iktisadi ve ˙Idari Bilimler Fakültesi Dergisi 22: 19–30.
14. Gu, Z., D. Lin, and J. Wu, 2022 On-chain analysis-based detection of abnormal transaction amount on cryptocurrency exchanges. Physica A: Statistical Mechanics and its Applications 604: 127799.
15. Gurrib, I. and F. Kamalov, 2021 Predicting bitcoin price movements using sentiment analysis: a machine learning approach. Studies in Economics and Finance .
16. Hacinliyan, A. and E. Kandiran, 2015 Türkiye’deki borsa endekslerinin fraktal analizi. AJIT-e: Bili¸sim Teknolojileri Online Dergisi 6: 7–19.
17. Holiachenko, A., L. Lyushenko, and O. Strutsynsky, 2022 Modified method of cryptocurrency exchange rate forecasting based on arima class models with data verification. In The International Conference on Artificial Intelligence and Logistics Engineering, pp. 123–136, Springer.
18. Hudson, R. and A. Urquhart, 2021 Technical trading and cryptocurrencies. Annals of Operations Research 297: 191–220.
19. Jagannath, N., T. Barbulescu, K. M. Sallam, I. Elgendi, B. Mc- Grath, et al., 2021 An on-chain analysis-based approach to predict ethereum prices. IEEE Access 9: 167972–167989.
20. Jain, A., S. Tripathi, H. D. Dwivedi, and P. Saxena, 2018 Forecasting price of cryptocurrencies using tweets sentiment analysis. In 2018 eleventh international conference on contemporary computing (IC3), pp. 1–7, IEEE.
21. Kang, K., E. Abdelfatah, and M. Pournik, 2019 Nanoparticles transport in heterogeneous porous media using continuous time random walk approach. Journal of Petroleum Science and Engineering 177: 544–557.
22. Klioutchnikov, I., M. Sigova, and N. Beizerov, 2017 Chaos theory in finance. Procedia computer science 119: 368–375.
23. Lahmiri, S. and S. Bekiros, 2018 Chaos, randomness and multifractality in bitcoin market. Chaos, solitons & fractals 106: 28–34.
24. Lookintobitcoin, 2022 (accessed November 7, 2022)a Hodl-wave. https://www.lookintobitcoin.com/charts/hodl-waves/.
25. Lookintobitcoin, 2022 (accessed November 7, 2022)b Mvrv z-score. https://www.lookintobitcoin.com/charts/mvrv-zscore/.
26. Lookintobitcoin, 2022 (accessed November 7, 2022)c Nupl graph. https://www.lookintobitcoin.com/charts/ relative-unrealized-profit--loss/.
27. Lv, Z., F. Sun, and C. Cai, 2022 A new spatiotemporal chaotic system based on two-dimensional discrete system. Nonlinear Dynamics 109: 3133–3144.
28. Lyashenko, V., M. Bril, and O. Shapran, 2021 Dynamics of world indices as a reflection of the development world financial market.
29. Malkiel, B. G., 2003 The efficient market hypothesis and its critics. Journal of economic perspectives 17: 59–82.
30. Medhat,W., A. Hassan, and H. Korashy, 2014 Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal 5: 1093–1113.
31. Nakamoto, S., 2008 Bitcoin: A peer-to-peer electronic cash system. Decentralized Business Review p. 21260.
32. Nie, C.-X., 2022 Analysis of critical events in the correlation dynamics of cryptocurrency market. Physica A: Statistical Mechanics and its Applications 586: 126462.
33. Pietrych, L., J. E. Sandubete, and L. Escot, 2021 Solving the chaos model-data paradox in the cryptocurrency market. Communications in Nonlinear Science and Numerical Simulation 102: 105901.
34. ¸Sahin, E. E., 2020 Kripto para fiyatlarında balon varlı ˘gının tespiti: Bitcoin, iota ve ripple örne˘ gi. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi pp. 62–69.
35. Sarmah, S. S., 2018 Understanding blockchain technology. Computer Science and Engineering 8: 23–29.
36. Sebastião, H. and P. Godinho, 2021 Forecasting and trading cryptocurrencies with machine learning under changing market conditions. Financial Innovation 7: 1–30.
37. Simsek, M., M. Samar, A. Oweida, A. F. Hersh, A. Alkı¸s, et al., 2020 Necmettin erbakan üniversitesi yayınları: 47 islami finans ve finansal teknolojiler (fintech) blokzincir-akıllı sözle¸smeler-kripto paralar editörler .
38. Stevens, L., 2002 Essential technical analysis: tools and techniques to spot market trends, volume 162. John Wiley & Sons.
39. Su, F., 2021 The chaos theory and its application. In Journal of Physics: Conference Series, volume 2012, p. 012118, IOP Publishing. Tosun, T., 2006 Türev Araçlar, Kaos Teorisi ve Fraktal Yapıların Vadeli ˙I¸slem Zaman Serilerinde Uygulanması. Ph.D. thesis, Marmara Universitesi (Turkey).
40. Trigg, R. and K. Yerci, 1996 Akılcılık ve bilim: bilim her ¸seyi açıklayabilir mi?. Sarmal Yayınevi.
41. Tschorsch, F. and B. Scheuermann, 2016 Bitcoin and beyond: A technical survey on decentralized digital currencies. IEEE Communications Surveys & Tutorials 18: 2084–2123.
42. Ural, M. and E. Demireli, 2009 Hurst üstel katsayisi aracili ˘giyla fraktal yapi analizi ve imkb’de bir uygulama. Atatürk üniversitesi iktisadi ve idari bilimler dergisi 23: 243–255.
43. Vo, A.-D., Q.-P. Nguyen, and C.-Y. Ock, 2019 Sentiment analysis of news for effective cryptocurrency price prediction. International Journal of Knowledge Engineering 5: 47–52.
44. Wang, S., W. Chen, S. M. Xie, G. Azzari, and D. B. Lobell, 2020 Weakly supervised deep learning for segmentation of remote sensing imagery. Remote Sensing 12: 207.
45. Wasiuzzaman, S., A. N. M. Azwan, and A. N. H. Nordin, 2022 Analysis of the performance of the islamic gold-backed cryptocurrency during the bear market of 2020. Emerging Markets Review p. 100920.
46. Yerlikaya, T., 2006 Yeni ¸sifreleme algoritmalarının analizi. trakya üniversitesi. Fen Bilimleri Enstitüsü, Bilgisayar Mühendisli˘ gi Anabilim Dalı, Doktora Tezi .
47. Yue, Y., X. Li, D. Zhang, and S. Wang, 2021 How cryptocurrency affects economy? a network analysis using bibliometric methods. International Review of Financial Analysis 77: 101869.